开放水热法制备莫来石复相纳米晶体的影响因素

以天然高岭土为原料,经精细化处理煅烧制备莫来石.用常压、开放水热技术制备出莫来石复相纳米晶体材料.实验表明:常压、开放水热条件下制备莫来石复相纳米晶粉体的水热反应中,采用质量比为m(Na2O)/m(SiO2)=2.8,m(H2O)/m(Na2O)=35～30,0～80℃控温升温1 h,在80℃保温2 h,可制备出尺寸为80～100 nm莫来石复相纳米晶体.所制备的莫来石纳米晶体的水热溶液相变驱动力△μ为(3.609～4.672)×10-20 J/mol;水热体系溶解度控制在3.891×10-6 mol/L;熔解热△E为18.846 kJ/mol;晶体生长速率常数选择0.85～0.92,有利于快速成核和晶体生长,并且可以很好地控制晶体尺寸和分散度.     

气固相氢氟化催化剂的研究

对于气固相氢氟化反应制备七氟丙烷的催化剂,进行了系统的研究。采用浸渍法制备催化剂,当催化活性组分Ａ为载体的８．５％～１０％,组分Ｃ２为１．０％～２．５％、二者之和为１１％时,达到了较佳的催化性能,将其应用于由六氟丙烯制备七氟丙烷时,在反应温度≥２１０℃时,粗品气中的Ｆ－２２７含量≥９８．５％。     

基于重叠片排列的流形学习算法

针对线性数据降维算法对处理非线性结构数据的降维效果不是很好,提出一种基于重叠片排列的流形学习算法,该算法根据局部的线性贴片处在非线性流形中的特性,将流形划分为线性互相重叠的局部区域贴片,且利用主成分分析方法得到局部区域贴片的低维表示,然后排列且对齐其低维坐标,以获得整体数据的低维坐标.通过仿真结果证明,基于重叠片排列的流形学习算法在应用于人脸识别和分类问题时以及在识别准确率方面要优于其他经典的流形学习算法.     

Shear rate dependence of viscosity and first normal stress difference of LCP/PET blends at solid and molten states of LCP

The liquid crystalline polymer (LCP) and polyethylene terephthalate (PET) were blended in an elastic melt extruder to make samples having 20, 40, 60, 80, and 100 wt % of LCP. Morphology of these samples was studied using scanning electron microscopy. The steady state shear viscosity (η), dynamic complex viscosity (η*) and first normal stress difference (N₁) were evaluated and compared at two temperatures: 265°C, at which LCP was in solid state, and 285°C, at which LCP was in molten state. The PET was in molten state at both the temperatures. The shear viscosity of the studied blends displayed its dependence on composition and shear rate. A maxima was observed in viscosity versus composition plot corresponding to 80/20 LCP/PET blend. The N₁ increased with LCP loading in PET and with the increased asymmetry of LCP droplets. The N₁ also varied with the shear stress in two stages; the first stage demonstrated elastic deformation, whereas second stage displayed dominant plastic deformation of LCP droplets. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 2212–2218, 2007     

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.