Measurement of soybean fatty acids by near-infrared spectroscopy: Linear and nonlinear calibration methods

A key element of successful development of new soybean cultivars is availability of inexpensive and rapid methods for measurement of FA in seeds. Published research demonstrated applicability of NIR spectroscopy for FA profiling in oilseeds. The objectives of this study were to investigate the applicability of NIR spectroscopy for measurement of FA in whole soybeans and compare performance of calibration methods. Equations were developed using partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM) regression methods. Validation results demonstrated that (i) equations for total saturates had the highest predictive ability (r ²=0.91–0.94) and were usable for quality assurance applications, (ii) palmitic acid models (r ²=0.80–0.84) were usable for certain research applications, and (iii) equations for stearic (r ²=0.49–0.68), oleic (r ²=0.76–0.81), linoleic (r ²=0.73–0.76), and linolenic (r ²=0.67–0.74) acids could be used for sample screening. The SVM models produced significantly more accurate predictions than those developed with PLS. ANN calibrations were not different from the other two methods. Reduction in the number of calibration samples reduced predictive ability of all equations. The rate of performance degradation of SVM models with sample reduction was the lowest.     

用GA、PCA和改进SVM相结合进行车辆实时分类

用GA、PCA和改进SVM结合进行车辆实时分类研究对公路管理和控制具有实际应用意义和社会效益。在某公路的匝道口两侧设置了8个测试点,对通过匝道口的车辆进行测试,提取特征向量,采用声波和振地波信号在匝道口进行了实时分类测试研究。由于特征向量的维数太高,用GA和PCA降低特征向量的维数,再用SVM和改进的SVM对特征向量进行分类,大大提高了分类精度。通过实验及分类得到声波和振地波的测试集分类精度最高分别是92.0%和76.1%,同时声波和振地波特征向量的维数降低至26和21,其相应的比率分别为95%和99%,独立集的分类精度分别为87.5%和71.3%。实验表明:用改进的SVM和GA、PCA结合的方法进行分类,其效果要优于单独使用主成分分析(PCA)、遗传算法(GA)以及它俩的结合使用的方法。     

利用密度聚类支持向量机的气象云图云检测

为了提高气象云图云检测的判识精度和计算效率,提出一种基于密度聚类支持向量机(DC-SVM)的云检测方法。分析了MTSAT气象云图的特征提取和选择方案,建立了云和下垫面的分类样本集;在SVM学习中,通过引入样本集的纯度及充足度,选择关键样本,减少了噪声和异常样本的干扰,从而降低了计算复杂度,提高了分类精度。实验表明,该算法的分类正确率较BP神经网络及传统SVM的方法分别提高了2.54%和0.21%,训练时间及测试时间也明显减少;而且,该方法还克服了传统云检测方法需要根据先验知识确定阈值的缺点,检测结果与人工解译结果基本吻合。     

基于SVM的汉语句子片段划分

针对长句子引起句法分析性能下降的问题,本文提出了一种基于SVM的句子片段划分方法:先根据语法结构将句子划分为多个片段,识别出每个片段的类别;然后根据片段的类别将句子分割为几个部分,每个部分作为句法分析的基本单元;最后将句法分析之后的各个部分进行合并,形成完整的分析结果.该方法减小了句法分析的复杂度,提高了分析的准确率.     

粗支持向量机分类建模方法

为了克服样本模式的复杂性、噪声的影响以及信息的不完整性问题,利用粗糙集和支持向量机(SVM)的优点,把粗糙集理论用于二分类球形SVM,提出一种称为粗支持向量机分类建模方法.粗糙集具有刻画不确定、不完整数据和复杂模式的能力,分类结果能够体现出数据的不确定性,但是它不仅不具备良好的学习能力,而且也不能保证分类模型具有良好的推广能力;SVM具有良好的推广性能,但是对不确定数据的建模能力较差.本文把分类结果分为正域、边界域和负域,由此来判断不确定数据样本的分类结果的不确定性程度.通过调整参数来调节边界的宽度和允许建模的在野点样本的比例,提高分类模型的灵活性.仿真结果说明了算法的有效性.     

Support vector machine forecasting method improved by chaotic particle swarm optimization and its application

By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects. Foundation item: Project(70572090) supported by the National Natural Science Foundation of China     

一种红细胞特征提取与分类识别的研究

为解决血涂片细胞形态学检查中存在的一些问题,提高贫血检验的效率和准确率,运用图像特征提取与分类识别技术,对12类形态产生变异的红细胞进行图像特征提取研究,选择了5类几何特征和2类纹理特征,同时改进了圆形度的表达公式,并将多级SVM分类器结构应用到分类识别中.实验证明,改进的圆形度表达式能提高分类的准确率,树形结构的分类器预测分类效果理想,为计算机自动分析血细胞提供了理论和方法,对于贫血的诊断有着重要的意义.     

基于SKO—KPCA和SVM的雷达目标HRRP特征提取与识别

将依赖数据变化的自适应SKO技术和迭代算法引入KPCA算法中,结合SVM分类技术,提出了基于SKO—IKPCA和SVM的雷达目标HRRP特征提取与识别方法。讨论了HRRP预处理、迭代KPCA算法和SKO／FKO技术的实现方法,将SKO／FKO技术应用到KPCA的核优化中,并用人工合成数据对其优化性能进行了测试与比较,应用该方法对su-27、F-16及M2000等三类飞机目标的实测HRRP数据进行了特征提取与识别实验。结果表明,所提方法具有较好的稳定性和可靠性,能有效地优化雷达目标主元特征的提取,提高目标的识别性能。     

基于SVM的GPS/DR组合定位算法研究

为解决GPS失效时系统无法较长时间高精度定位的问题,文章给出了一种基于SVM的数据融合算法并将其应用于GPS/DR组合定位系统的数据融合中。实验结果表明：当GPS失效时,使用该算法的定位系统可保持更长时间的精确定位。     

基于决策树支持向量机模型的变压器故障诊断法

根据对变压器常见故障原因和危害程度的分析,构造了电力变压器故障诊断决策树。其判定级别为自上而下,而决策树的每一叶都对应着一种具体的故障模式,并选用不同的支持向量机单元模块作为决策树不同分支中的基本分类器,建立组合支持向量机模型,实现了对故障的多分辨识别,该方法提高了故障分析及预测的准确度。应用结果表明该系统模型是富有成效的。