Optical spectrum based measurement of flotation slurry contents

Monitoring and control of a flotation circuit is mainly based on the information gained by assaying the process slurry lines. In flotation plants, usually an X-ray fluorescence analyzer is used to obtain the on-line assays. This article introduces the visual and near-infrared reflectance spectroscopic analysis of the process slurries as a supplementary method which complements the on-line assay information available from an X-ray fluorescence analyzer. It is shown that the spectral information can be used to accurately predict the element contents in the slurry in between successive XRF analyses. Since the spectral measurements can be taken with high frequency as opposed to the sparse X-ray fluorescence analysis, a practically continuous on-line estimate of the slurry contents is reached. These estimates can be used in the plant control to improve the overall performance of the circuits and to yield also economical savings. Additionally, the frequent monitoring of the grades can provide means to eliminate rapid disturbances in the circuits, thus improving the stability of the process.     

Stokes波的谱特性分析

应用理论推导及数值计算方法，对Stokes随机波的谱特性进行了分析。首先将波面方程，海水质点水平速度用一阶波面分量的非线性组合表示，应用平稳随机高阶短的降阶计算法则，得到了波面方程及海水质点水平速度与一阶波面分量的自相关函数之间的关系，从而确定了Stokes随机波浪的波浪谱密度及海水质点水平速度和加速度谱密度，进而求得有关波浪要素的均方根值。文章还应有数值计算方法，分析了波浪基本参数对均方根值的影响。     

PLS-ANN分析血浆自体荧光光谱的二阶导数光谱识别胃癌

探讨偏最小二乘法结合神经网络法(简称PLS-ANN)分析血浆自体荧光光谱的二阶导数光谱识别胃癌的优势,对20例胃癌病人和23例健康人血浆进行以405nm为激发光的自体荧光光谱检测,采用PLS-ANN法分别对血浆自体荧光光谱和二阶导数光谱进行判别分析。PLS-ANN分析血浆的自体荧光光谱法诊断胃癌的灵敏度为75%,特异度为83%,准确率为79%,PLS-ANN分析血浆的二阶导数光谱法诊断胃癌的灵敏度为90%,特异度为96%,准确率为93%。结果表明PLS-ANN分析血浆的二阶导数光谱法识别胃癌,优于PLS-ANN分析血浆的自体荧光光谱法,有望成为快速识别胃癌的较理想方法。     

基于最小二乘配置的三维空间坐标转换

在求解未知转换参数时,鉴于公共点坐标本身的误差,提出利用最小二乘配置法进行空间坐标转换,即将公共点本身坐标作为随机参数,把转换参数作为非随机参数进行处理,最后通过实验对比分析表明,该方法明显优于一般坐标转换方法。     

区域似大地水准面确定的最小二乘支持向量机方法

支持向量机(SVM)是近年来发展起来的机器学习的新方法,它较好地解决小样本、非线性、高维数、局部极小点等实际问题.文中研究支持向量机的拓展算法--最小二乘支持向量机(LSSVM),并将其应用于确定大面积复杂似大地水准面.通过工程实例并与神经网络模型和二次曲面多项式拟合模型相比较,验证确定区域似大地水准面的LSSVM方法的有效性.     

Using Topological Relationships to Inform a Data Integration Process

When spatial datasets are overlaid, corresponding features do not always coincide. This may be a result of the datasets having differing quality characteristics, being captured at different scales or perhaps being in different projections or datums. Data integration methods have been developed to bring such datasets into alignment. Although these methods attempt to maintain topological relationships within each dataset, spatial relationships between features in different datasets are generally not considered. The preservation of inter‐dataset topology is a research area of considerable current interest. This research addresses the preservation of topology within a data integration process. It describes the functional models established to represent a number of spatial relationships as observation equations. These are used to provide additional information concerning the relative positions of features. Since many topological relationships are best modelled as inequalities, an algorithm is developed to accommodate such relationships. The method, based on least squares with inequalities (LSI), is tested on simulated and real datasets. Results are presented to illustrate the optimal positioning solutions determined using all of the available information. In addition, updated quality parameters are provided at the level of the individual coordinate, enabling communication of local variation in the resultant quality of the integrated datasets.     

多源遥感影像误匹配点剔除的粗糙模糊C-均值方法

简要概述了遥感影像匹配中,误匹配点剔除的常用方法.对SIFT特征描述子生成及其匹配、粗糙模糊C-均值方法聚类分析的原理方法进行了详细论述.针对SIFT方法匹配出的大量点对,将不同时相,不同传感器、不同分辨率影像广义划分为同分辨率和不同分辨率.基于同分辨率影像匹配点对问连线具有相同的距离及斜率、不同分辨率影像匹配点对间连线的延长线交于一点的几何约束条件,利用粗糙模糊C-均值模型聚类分析方法对点对快速剔除误匹配点后,对剔除的结果利用最小二乘方法进行精匹配,结果达到亚像素.最后,通过对不同时相、不同分辨率、不同传感器的多源遥感影像实验分析验证了该方法的适用性、可靠性及精确性.     

抗差最小二乘配置在数字化地图几何纠正中的应用讨论

抗差估计具有较好的抗拒异常观测值及粗差的能力,而最小二乘配置又能较好地处理系统误差,本文结合两者的优点,利用抗差最小二乘配置对数字化地图进行几何纠正,其中对协方差函数采用抗差拟合,得到了较好的结果。实验证明在GIS数据处理的扫描数字化地图几何纠正中,抗差最小二乘配置在抗拒异常值和处理系统误差方面优于单纯的最小二乘估计和单纯的最小二乘配置方法。     

Comparison of structural and least-squares lines for estimating geologic relations

Two different goals in fitting straight lines to data are to estimate a true linear relation (physical law) and to predict values of the dependent variable with the smallest possible error. Regarding the first goal, a Monte Carlo study indicated that the structural-analysis (SA) method of fitting straight lines to data is superior to the ordinary least-squares (OLS) method for estimating true straight-line relations. Number of data points, slope and intercept of the true relation, and variances of the errors associated with the independent (X) and dependent (Y) variables influence the degree of agreement. For example, differences between the two line-fitting methods decrease as error in X becomes small relative to error in Y. Regarding the second goal—predicting the dependent variable—OLS is better than SA. Again, the difference diminishes as X takes on less error relative to Y. With respect to estimation of slope and intercept and prediction of Y, agreement between Monte Carlo results and large-sample theory was very good for sample sizes of 100, and fair to good for sample sizes of 20. The procedures and error measures are illustrated with two geologic examples.