基于小波变换的图像融合新算法

针对现有图像融合技术中的尺度系数卷积法存在的边缘细节损失和对比度下降问题,提出了将Canny边缘检测算子等价于提取图像的小波变换模极大值点这一思想引入图像的低频分量融合规则以及用于高频子带融合的局部对比度结合方向方差方法.最后通过仿真试验以及熵值数据证明该算法有效改善了边缘细节的准确度,提高了分辨率,并使局部对比度信息得以保留,从而更加符合人类的视觉特性.     

NiO/La2O3催化剂上甲烷催化部分氧化制合成气的研究

采用浸渍法制备了一系列不同N i含量的N iO/La2O3催化剂,并利用XRD、BET对催化剂结构进行了表征,采用常压下固定床石英管反应器考察了催化剂对甲烷部分氧化制合成气的反应性能和催化剂的稳定性,其甲烷转化率与反应温度、N i含量以及空速有关.结果表明,N i含量为30%的N iO/La2O3催化剂具有良好的催化活性,800℃时甲烷的转化率为88%,CO选择性可达83%,100 h的连续测试显示N iO/La2O3具有良好的稳定性.     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

近红外光谱-径向基神经网络在异烟肼片无损定量分析中的应用

应用异烟肼片粉末的近红外漫反射光谱数据分别结合偏最小二乘法(PLS)和径向基神经网络(RBFNN)建立定量分析模型,并用所建模型对预测集样品进行了预测,结果表明:应用RBFNN所建立的定量分析模型优于PLS模型,相关系数(r)值由0.99593提高到0.99734,交互验证均方根误差(RMSECV)值由0.00523下降到0.00423,预测均方根误差(RMSEP)值由0.00614下降到0.00501。     

偏最小二乘-同步荧光法测定人尿中黄蝶呤

建立了人尿中黄蝶呤含量测定的同步荧光分析方法。在pH 7.8 KH2PO4-NaOH缓冲溶液中,于Δλ为70 nm的条件下对黄蝶呤及其它蝶呤类化合物进行同步荧光扫描,所得的重叠波谱数据用主成分回归法(PCR)、偏最小二乘法(PLS)、经典最小二乘法(CLS)和径向基人工神经网络(RBF-ANN)等多元校正法进行处理,结果表明偏最小二乘法(PLS)的分析结果最好,其标准偏差为4.29%。该方法简便、快速、准确,避免了较繁琐的样品前处理过程,应用于人尿中黄蝶呤分析,结果令人满意。     

偏最小二乘法用于同步荧光法同时测定维生素B1,维生素B2和维生素B6

本文将偏最小二乘法结合同步荧光扫描技术对含维生素Ｂ１，Ｂ２和Ｂ６的混合物进行了同时测定。对同步荧光法的测定条件及△λ的选择进行了试验和讨论，比较了△λ分别为６５ｎｍ和３０ｎｍ时的计算结果。所建立的方法用于复合维生素Ｂ片等药片叶Ｂ１，Ｂ２和Ｂ６孤同时测定，获较满意的结果。     

Coal analysis by diffuse reflectance near-infrared spectroscopy: Hierarchical cluster and linear discriminant analysis

An extensive study was carried out in coal samples coming from several origins trying to establish a relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding near-infrared spectral data. This research was developed by applying both quantitative (partial least squares regression, PLS) and qualitative multivariate analysis techniques (hierarchical cluster analysis, HCA; linear discriminant analysis, LDA), to determine a methodology able to estimate property values for a new coal sample. For that, it was necessary to define homogeneous clusters, whose calibration equations could be obtained with accuracy and precision levels comparable to those provided by commercial online analysers and, study the discrimination level between these groups of samples attending only to the instrumental variables. These two steps were performed in three different situations depending on the variables used for the pattern recognition: property values, spectral data (principal component analysis, PCA) or a combination of both. The results indicated that it was the last situation what offered the best results in both two steps previously described, with the added benefit of outlier detection and removal.     

Short-wave near-infrared spectroscopy analysis of major compounds in milk powder and wavelength assignment

In this study, short-wave near-infrared (NIR) spectroscopy at 800–1050 nm region was investigated for the analysis of main compounds in milk powder. Through quantitative analysis, the feasibility is further demonstrated for the simultaneous measurement of fat, proteins and carbohydrate in milk powder. Two models, partial least-squares and least-squares support vector machine, were compared and utilized for regression coefficients and loading weights. The affect of standard normal variate spectral pretreatment to model performance was evaluated. Based on the resulted coefficients and loading weights, interesting wavelength regions of nutrition in milk powder are screened and the assignment of all specific wavelengths is firstly proposed in the details associated with chemical base. Instead of the whole short-wave NIR spectral data, these assigned wavelengths which can be reliably exploited were used for the content determination. Compared with other spectroscopy technique, assigned short-wave NIR spectral wavelengths did a good work. Determination coefficients for prediction are 0.981, 0.984, and 0.982, respectively for three components. The proposed wavelength assignment in the short-wave NIR region could be used for the component contents determination of milk powder, and could be as a guidance to interpret the spectra of milk powder.     

甲烷部分氧化过程中强制振荡的动力学Monte Carlo模拟

为了有效控制动力学振荡行为以提高反应的转化率, 利用Monte Carlo 方法研究了甲烷催化部分氧化过程中的强制振荡行为, 探讨了原料气周期性变化对振荡动力学和转化率的影响. 研究表明原料气周期性变化不仅可以有效调控振荡的动力学行为, 产生如短周期振荡和双峰振荡等特殊动力学过程, 而且还可以提高反应的转化率. 当强制过程的周期从T/3 增大到2T(T为自发振荡过程的平均周期), 振荡过程从短周期振荡变化为双峰振荡. 对反应过程中CO的转化率进行了计算, 结果表明原料气周期性变化可以有效提高反应的转化率.振荡动力学的改变和转化率的提高主要是因为强制振荡过程使得催化剂表面发生了从氧化态向还原态的转变.     

近红外光谱法分析格列齐特片

应用化学计量法处理光谱数据,用偏最小二乘法建立格列齐特片的近红外分析模型。通过光谱预处理和模型的逐步优化最终确定定量分析模型的相关系数为0.991,交叉验证均方差(RMSECV)为0.641,预测均方根误差(RMSEP)为0.980,主因子数为4。选取15个验证样品对模型进行检验,检测结果相对误差在-2.04%～3.52%之间。