基于中药血竭光谱指纹图谱的模式识别研究

建立了10种药血竭的红外光谱、荧光光谱指纹图谱,并把图谱信息进行数据化及数据标准化处理;利用相关系数定量地对10种血竭的光谱指纹图谱进行了相似性评价;在此基础上用系统聚类分析法定性地对这10种样品进行了分类和鉴别,从而建立了一种基于中药血竭光谱指纹图谱的模式识别方法。为中药血竭的质量评价和分类鉴别提供了一个很好的方法和思路。     

基于红外光谱和随机森林的枸杞产地鉴别

采用傅里叶变换红外光谱,测定了45个来自青海省不同产地的枸杞样品的红外光谱。小波变换对红外光谱原始数据进行了预处理。红外光谱数据压缩到原来的1/8,其分析精度与原始光谱数据基本相当。将45个样本数据分为30个训练集和15个测试集,建立随机森林(RF)预测枸杞产地模型,使用内部交叉验证和外部数据进行验证。采用R语言实现随机森林算法,并对模型的参数进行了优化。结果,所建立的判别模型中训练样本判别正确率为100%,测试样本判别正确率为100%。研究结果表明,建立的模型能够正确地对枸杞样品快速地进行产地鉴别,红外光谱法结合随机森林可作为中药材产域分类鉴别的一种新的现代化方法。     

中药吴茱萸中脂肪酸气相色谱数据的化学模式识别

采用气相色谱法对中药吴茱萸脂肪酸进行了分析测定,根据测定的数据用主成分分析法进行特征压缩、提取,将代表诸样品特征的点即“星”显示在半圆形极坐标上构成星座图,根据“星”所属的星座和所走的路径,对１８种不同品种、不同产地的吴茱萸进行自然分类,为中药吴茱萸的品种鉴别和质量优选提供了依据。本法可望推广应用于其它中药的质量评价。     

径向基函数网络用于细菌的MALDI-TOF-MS分类

径向基函数（RBF）网络被用于根据基质辅助激光解吸／电离飞行时间质谱（MALDI－TOF－MS）对细菌的分类辨识。为了加速网络训练和减少网络的复杂性，本文采用小波变换对原始质谱数据进行压缩，将原来的13828个数据点压缩至328个，且保持了原来的特征谱峰。本文研究了在不同培养时间（24、48和72小时）的5种细菌分类，并对RBF网络参数的影响做了详细地研究，为生物学研究提供了有用的信息。结果表明，约60％以上的细菌样本能够被正确地分类辨识。由于细菌培养的生物学影响因素复杂，因而进一步严格控制细菌的培养条件是改善细菌分类正确率的关键。     

基于小波神经网络的光谱数据压缩与分类研究

文中介绍了一种基于小波分析而构造的神经网络模型－小波神经网络,利用它并适当选取网络结构和小波基。实现了对化学物质红外光谱数据的压缩表达和分类,实验表明,网络在大幅度压缩数据的同时能很好地恢复原始光谱,较准确地反映吸收峰的和强度。在分类方面它比其它网络具有更高的分辨率和特征提取能力。     

青海枸杞的红外光谱鉴别及相似度分析

利用傅里叶变换红外光谱,测定了35个来自青海省不同产地的枸杞样品的红外光谱。常规和小波变换的方法对红外光谱原始数据进行了预处理。对不同产地枸杞药材进行红外光谱指纹鉴别,并以质量最好的S1样品作为参考,建立枸杞的对照红外指纹图谱,采用相关系数法和夹角余弦法计算样品红外指纹图谱与对照指纹图谱的相似度。7批样品中,质量较好的红外指纹图谱与对照指纹图谱的相似度(夹角余弦法)均在0.99以上,其它的相似度均在0.99以下。进一步,聚类分析将7份样品聚为枸杞主产地与非主产地两大类及枸杞制品类,验证了相似度分析的结果。不同产地枸杞的红外指纹图谱可以用于药材的质量评价,其相似度值的大小可作为枸杞药材产地鉴别和质量评价。     

基于交叉熵的图像过渡区提取与压缩研究*

改进过渡区提取算法,将最小交叉熵应用于红外图像过渡区提取中,提出基于交叉熵的图像过渡区算法,并将该过渡区分割法应用于红外图像感兴趣区域的自动提取,最后提出了一种基于JPEG2000框架红外图像感兴趣区压缩方案进行分类压缩。经图像实验充分验证了该方法的有效性、实时性,其具有重要的应用价值。     

近红外漫反射光谱在中药分类及真伪鉴别中的应用

应用近红外漫反射光谱分析技术对几种常见中药建立了一种简单、快速、有效的分类和真伪鉴别方法.通过渐进窗口式相关系数分析方法得到的相关系数表征了不同波长下样本近红外光谱的相似程度,从而选择出能区分不同种类中药的特征波长范围,利用PEA投影对白芷、葛根、当归、白术等几种外观相似的中药成功地进行了分类,而且对白芷及混有淀粉的模拟伪劣样品也能有效地鉴别.该方法可以作为一种区分药材种类、判断中药真伪的参考方法,在中药质量控制方面具有一定的应用前景.     

田基黄薄层色谱分类的图像分析方法研究

薄层中药色谱与计算机图像处理技术结合可以用来对中药进行种类鉴别和产地鉴别。本文介绍了一种用图像处理与分析技术对田基黄进行产地分类的方法 ,采用灰度映射的方法辨别出田基黄产地的相同与否进而对田基黄进行产地归类 ,实验结果表明了该法有较好的效果     

基于压缩感知的红外人脸识别

压缩感知理论是一种全新的数据采集技术,其采用非自适应线性投影来保持信号的原始结构,通过数值最优化问题准确重构原始信号。本文利用压缩感知的优秀特性,采用基于稀疏表示的模式分类方法,通过提取红外人脸图像的全部信息作为特征并建立特征矩阵,将待识别人脸作为压缩感知测量值,并通过正交匹配追踪算法进行重构,根据重构的稀疏系数所属类别进行红外人脸识别。实验表明,基于压缩感知的红外人脸识别结果准确率高。实验验证了本算法的有效性。     

基于神经网络的丙酮产品质量分类挖掘

针对丙酮精制过程的特点，提出一种基于神经网络的丙酮产品质最分类挖掘方法。首先，讨论了数据挖掘中自变量筛选的方法，包括相关性分析、Fisher指数分析、主成分回归分析以及偏最小二乘回归分析等，综合各种疗法分析的结果，对丙酮精制过程中众多的工艺影响因素进行了重要性排序并据此筛选出重要的自变量；以选入的变量作为输入变量，构造基于神经网络的产品质量分类器。实验结果表明，训练后的神经网络分类器在丙酮产品质量分类挖掘中取得了良好的效果。     

A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters for hyperspectral data

A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters is proposed to avoid the error that is caused by the difference between the real data distribution and the hypothetic Gaussian distribution and avoid the computational burden working in the logistic regression classification directly for hyperspectral data. The multi-fractal spectra and parameters are calculated firstly with training samples along the spectral dimension of hyperspectral data. Secondly, the logistic regression model is employed in our work because the logistic regression classification model is a distribution-free nonlinear model which is based on the conditional probability without the Gaussian distribution assumption of the random variables, and the obtained multi-fractal parameters are applied to establish the multi-class logistic regression classification model. Finally, the Newton–Raphson method is applied to estimate the model parameters via the maximum likelihood algorithm. The classification results of the proposed model are compared with the logistic regression classification model based on an adaptive bands selection method by using the Airborne Visible/Infrared Imaging Spectrometer and airborne Push Hyperspectral Imager data. The results illuminate that the proposed approach achieves better accuracy with lower computational cost simultaneously.     

Evaluation and classification of otoneurological data with new data analysis methods based on machine learning

We improved the classification ability of multilayer perceptron networks by constructing a set of networks of as many as output classes and investigated the influence of different input variables on the classification. We have developed methods named scattering, spectrum and response analysis to express the classification complexity, especially the overlap of output classes, to disentangle the relation between the input variables and output classes of perceptron neural networks, and to establish the importance of input variables. The methods were tested by exploring complicated otoneurological data. In contrast to the variable selection problem, our methods characterize the importance of variables for classification and also describe the importance of the different values of each variable for output (disease) classes. When complex data is distributed in a biased manner between disease classes, we improved classification accuracy by developing a network set called NetSet, which increased average sensitivity and positive predictive value for at least 10% up to 85% and 83% respectively, compared to our earlier neural network classifications with the same data, which clarified class distribution effects and supported our comprehension of the significance of input.     

Results of spectrophotometric measurements of natural formations from the spacecraft “Soyuz-9” and investigations of environment from space

During the “Soyuz-9” flight, the reflection spectra of various natural formations have been obtained by means of the handheld spectrograph RSS-2. Making use of the literature data on the surface reflection spectra for sand and water, the parameters of the atmospheric transfer operator were computed. The spectra obtained from the high altitude observations was corrected to spectral reflectance values at the earth's surface and compared with the curves for spectral radiance coefficients of different types of natural formations according to E. L. Krinov's classification. Using the parameters of the atmospheric transfer operator, the curves for spectral radiance coefficients of different types of natural formations as expected from space observations (Krinov's classification) have been computed.     

改进的弹性网模型在深度神经网络中的应用

由于具有较高的模型复杂度，深层神经网络容易产生过拟合问题，为了减少该问题对网络性能的不利影响，提出一种基于改进的弹性网模型的深度学习优化方法。首先，考虑到变量之间的相关性，对弹性网模型中的L1范数的不同变量进行自适应加权，从而得到L2范数与自适应加权的L1范数的线性组合。其次，将改进的弹性网络模型与深度学习的优化模型相结合，给出在这种新正则项约束下求解神经网络参数的过程。然后，推导出改进的弹性网模型在神经网络优化中具有群组选择能力和Oracle性质，进而从理论上保证该模型是一种更加鲁棒的正则化方法。最后，在多个回归问题和分类问题的实验中，相对于L1、L2和弹性网正则项，该方法的回归测试误差可分别平均降低87.09、88.54和47.02，分类测试准确度可分别平均提高3.98、2.92和3.58个百分点。由此，在理论和实验两方面验证了改进的弹性网模型可以有效地增强深层神经网络的泛化能力，提升优化算法的性能，解决深度学习的过拟合问题。     

Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain

Practical and financial constraints associated with traditional field-based lithological mapping are often responsible for the generation of maps with insufficient detail and inaccurately located contacts. In arid areas with well exposed rocks and soils, high-resolution multi- and hyperspectral imagery is a valuable mapping aid as lithological units can be readily discriminated and mapped by automatically matching image pixel spectra to a set of reference spectra. However, the use of spectral imagery in all but the most barren terrain is problematic because just small amounts of vegetation cover can obscure or mask the spectra of underlying geological substrates. The use of ancillary information may help to improve lithological discrimination, especially where geobotanical relationships are absent or where distinct lithologies exhibit inherent spectral similarity. This study assesses the efficacy of airborne multispectral imagery for detailed lithological mapping in a vegetated section of the Troodos ophiolite (Cyprus), and investigates whether the mapping performance can be enhanced through the integration of LiDAR-derived topographic data. In each case, a number of algorithms involving different combinations of input variables and classification routine were employed to maximise the mapping performance. Despite the potential problems posed by vegetation cover, geobotanical associations aided the generation of a lithological map - with a satisfactory overall accuracy of 65.5% and Kappa of 0.54 - using only spectral information. Moreover, owing to the correlation between topography and lithology in the study area, the integration of LiDAR-derived topographic variables led to significant improvements of up to 22.5% in the overall mapping accuracy compared to spectral-only approaches. The improvements were found to be considerably greater for algorithms involving classification with an artificial neural network (the Kohonen Self-Organizing Map) than the parametric Maximum Likelihood Classifier. The results of this study demonstrate the enhanced capability of data integration for detailed lithological mapping in areas where spectral discrimination is complicated by the presence of vegetation or inherent spectral similarities.     

Broadleaf species recognition with in situ hyperspectral data

Timely and accurate identification of tree species by spectral methods is crucial for forest and urban ecological management. In this study, a total of 394 reflectance spectra (between 350 and 2500 nm) from foliage branches or canopy of 11 important urban forest broadleaf species were measured in the City of Tampa, Florida, USA with a spectrometer. The 11 species include American elm (Ulmus americana), bluejack oak (Quercus incana), crape myrtle (Lagerstroemia indica), laurel oak (Q. laurifolia), live oak (Q. virginiana), southern magnolia (Magnolia grandiflora), persimmon (Diospyros virginiana), red maple (Acer rubrum), sand live oak (Q. geminata), American sycamore (Platanus occidentalis), and turkey oak (Q. laevis). A total of 46 spectral variables, including normalized spectra, derivative spectra, spectral vegetation indices, spectral position variables, and spectral absorption features were extracted and analysed from the in situ hyperspectral measurements. Two classification algorithms were used to identify the 11 broadleaf species: a nonlinear artificial neural network (ANN) and a linear discriminant analysis (LDA). An analysis of variance (ANOVA) indicates that the 30 selected spectral variables are effective to differentiate the 11 species. The 30 selected spectral variables account for water absorption features at 970, 1200, and 1750 nm and reflect characteristics of pigments and other biochemicals in tree leaves, especially variability of chlorophyll content in leaves. The experimental results indicate that both classification algorithms (ANN and LDA) have produced acceptable accuracies (overall accuracy from 86.3% to 87.8%, kappa from 0.83 to 0.87) and have a similar performance for classifying the 11 broadleaf species with input of the 30 selected spectral variables. The preliminary results of identifying the 11 species with the in situ hyperspectral data imply that with current remote sensing techniques, including high spatial and spectral resolution data, it is still difficult but possible to identify similar species to such 11 broadleaf species with an acceptable accuracy.     

基于卷积神经网络及改进支持向量机的行人检测

针对自动驾驶实际道路场景复杂导致行人误检率高的问题,提出一种基于卷积神经网络及改进支持向量机的行人检测方法。利用聚合通道特征快速获取图像候选区域,将归一化后的候选区域图像输入卷积神经网络对其进行深度特征提取;利用主成分分析法将卷积神经网络末端所得到的特征向量进行降维处理,减少其冗余特征信息以获得精确的行人特征描述;将行人特征送至优化后的支持向量机完成分类。考虑支持向量机在分类过程中存在核函数参数选择困难的问题,利用改进后的蚁群算法对其进行优化选择,获得最优支持向量机参数以提高分类精度。实验结果表明,不同场景下的行人平均检测精确度达到92%,误检率大幅下降且具有较好的实时性。     

Applying neural networks in rare vegetation communities classification of remotely sensed images

Artificial neural networks (ANNs) are used for rare vegetation communities’ classification using remotely sensed data. Training of a neural network requires that the user specifies the network structure and sets the learning parameters. Heuristics proposed by a number of researchers to determine the optimum values of network parameters are compared using datasets. Training and test samples were collected for each class type (12 classes). After preliminary statistical tests for training samples, two modification algorithms of the classification scheme were defined: the first one led to creating a scheme which consisted of 7 classes, and the second one led us to creating of 5 class’s scheme. Testing results show that the use of ANNs on the based of 5 class’s scheme can produce higher classification accuracies than either alternative. The visual analysis of the results of the classification is described using Geoinformation Technologies in details. The text was submitted by the authors in English.     

Restricted Bayesian classification networks

Bayesian networks are graphical models that describe dependency relationships between variables, and are powerful tools for studying probability classifiers. At present, the causal Bayesian network learning method is used in constructing Bayesian network classifiers while the contribution of attribute to class is over-looked. In this paper, a Bayesian network specifically for classification-restricted Bayesian classification networks is proposed. Combining dependency analysis between variables, classification accuracy evaluation criteria and a search algorithm, a learning method for restricted Bayesian classification networks is presented. Experiments and analysis are done using data sets from UCI machine learning repository. The results show that the restricted Bayesian classification network is more accurate than other well-known classifiers.