多类型分类器融合的文本分类方法研究

传统的文本分类方法大多数使用单一的分类器,而不同的分类器对分类任务的侧重点不同,就使得单一的分类方法有一定的局限性,同时每个特征提取方法对特征词的考虑角度不同。针对以上问题,提出了多类型分类器融合的文本分类方法。该模型使用了word2vec、主成分分析、潜在语义索引以及TFIDF特征提取方法作为多类型分类器融合的特征提取方法。并在多类型分类器加权投票方法中忽略了类别信息的问题,提出了类别加权的分类器权重计算方法。通过实验结果表明,多类型分类器融合方法在二元语料库、多元语料库以及特定语料库上都取得了很好的性能,类别加权的分类器权重计算方法比多类型分类器融合方法在分类性能方面提高了1.19%。     

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.     

热重法研究环氧树脂胶粘剂

用热重法研究环氧树脂胶粘剂，研究结果表明热重法能较好地研究环氧树脂胶粘剂的组成与性能，研究结果还表明固化剂中过量的游离胺与惰性稀释剂的存在不利于固化胶粘剂的力学性能，有利于其热稳定性。     

基于KPCA-PSO-SVM的径流预测研究

为提高径流预测模型的准确性与稳定性,对KPCA-PSO-SVM的径流预测方法进行了研究。在分析径流影响因素的基础上,利用核主成分分析(KPCA)法对径流影响因子进行非线性特征提取,获得主成分作为支持向量机(SVM)的输入变量,建立了径流预测SVM模型,其中模型参数通过粒子群算法(PSO)进行优化。模型建立后,以新疆伊犁河雅马渡站中长期径流预测为例进行分析。预测分析结果表明,在拟合和检验阶段模型的平均相对误差分别为0.77%和7.64%,与其他预测模型比较,基于KPCA-PSO-SVM方法建立的径流预测模型有较好的预测和泛化能力,是一种行之有效的中长期径流预测方法。     

一种改进的动态核主元分析故障检测方法

针对复杂工业系统动态非线性故障检测过程精度低和计算量大的问题,提出了一种改进的动态核主元分析故障检测方法，该方法首先利用不可区分度剔除相关程度较小或者不相关变量,减少数据量，然后通过观测值扩展对筛选后的新数据构建增广矩阵，并对矩阵使用核主元分析提取变量数据的非线性空间相关特征，最后通过监测T ²和SPE 两种统计量诊断出系统发生故障及识别故障变量。仿真实验证明，该方法能对风力发电机故障进行有效监测和诊断，与KPCA方法相比，改进的动态核主元分析方法对微小故障更为敏感。     

GLCM based extraction of flame image texture features and KPCA-GLVQ recognition method for rotary kiln combustion working conditions

According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GLVQ) neural network is proposed.Firstly,the numerical flame image is analyzed to extract texture features,such as energy,entropy and inertia,based on grey-level co-occurrence matrix(GLCM) to provide qualitative information on the changes in the visual appearance of the flame.Then the kernel principal component analysis(KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly.Finally,the GLVQ neural network is trained by using the normalized texture feature data.The test results show that the proposed KPCA-GLVQ classifer has an excellent performance on training speed and correct recognition rate,and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.     

面向能力组件的项目管理系统研究

当前企业面临的多项目管理环境日益突出,如何结合管理创新,搭建有效的项目管理平台,支撑多项目目标实现是众多企业面临的突出问题。文章结合装备研制,分析了多项目环境下传统管理方式存在的不足,提出了能力组件和计划视图的概念,构建了一种基于能力组件的项目管理方法和分级计划管理模型,设计并开发了面向能力组件的项目管理系统。详细说明了系统的技术架构、功能组成和关键技术,并结合应用实践,介绍了一种基于能力组件的项目管理组织模式和计划制定过程。     

行政监督量化分级指标的应用数据分析

行政监督量化考核指标虽然实施数年,对这些指标量之间的关系和作用的分析却很少涉及,为了提高行政监督考核量化指标的设置,分析指标间的相互关系,利用行政监督管信息管理系统的数据,应用主成分分析方法对不同业务的量化分级管理数据进行处理,依照不同的贡献率从数据中提炼出主信息,可去掉变量之间的多重相关性,简化考核指标项,指导优化行政监督过程,提高效率、降低成本。     

基于荧光卟啉传感器阵列系统的肺癌标志物检测研究

针对肺癌呼出挥发性有机气体（VOCs）中的特定标志物,提出了一种新型的基于荧光卟啉传感器阵列检测系统,并对4种肺癌呼出标志物进行检测研究。通过小波分析等数学工具对测得的荧光光谱数据进行特征提取,然后采用层次聚类、主成分分析等统计学方法对特征向量进行分析。不同体积分数的各类标志物在聚类分析中能够完全正确的聚到一起。通过主成分分析得到的前3个主成分包含了标志物的88%的信息,便能对不同类别的标志物进行识别。研究表明：该荧光卟啉传感器阵列系统能够快速有效地对不同肺癌标志物进行识别,有望在临床中得到应用。     

基于贪心核特征提取方法的中期峰值负荷预测

针对中期电力负荷预测, 提出基于贪心核主元回归(GKPCR)、贪心核岭回归(GKRR) 的特征提取建模方法. 通过对核矩阵的稀疏逼近, GKPCR和GKRR两种贪心核特征提取方法旨在寻找特征空间中数据的低维表示, 计算需求低, 适用于大数据集的在线学习. 将所提出的方法应用于不同地区的电力负荷中期峰值预测, 并与现有预测方法进行了比较. 实验结果表明, 在同等条件下, 所提出的方法能有效地改进预测精度, 而且性能更好, 显示了其有效性和应用潜力.