八种蜂蜜挥发性成分分析

用同时蒸馏萃取方法分别收集了洋槐蜜、霍香蜜、紫云英蜜、瑞苓草蜜、苜蓿蜜、油菜蜜、枸杞蜜和龙眼蜜的挥发性成分,所得到的8种蜂蜜挥发性成分的二氯甲烷浓缩液,用气相色谱-质谱(GC-MS)法分离并分析鉴定了其组成及质量分数,分别鉴定出19、32、17、20、22、64、21、27个化合物,各占其总峰面积的93.35%、76.93%、38.58%、93.18%、93.92%、85.66%、46.37%、75.01%。     

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.     

Scheduling component replacement for timely execution in dynamic systems

Timely run‐time software replacement techniques are a corner stone for reconciling real‐time systems development and dynamic behavior. Typical real‐time systems do not consider dynamic behavior because it deeply challenges predictability and timeliness. Current efforts are starting to merge the safe and predictable execution with a controllable level of dynamicity by imposing a set of bounds and limitations to the system dynamic behavior. One of the obstacles for this is how to time‐bound the different operations required to effectively implement component replacement. In this paper, the main challenges for this problem are identified, and a model to ensure that components can be replaced at run time preserving the temporal properties of the system is provided that also avoids failures in replacements. A real example and simulations of our replacement model are provided that validate the presented ideas. Copyright © 2013 John Wiley & Sons, Ltd.     

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。     

协同仿真平台中仿真组件的封装技术研究

在多学科多领域协同仿真平台中,仿真组件对应的仿真软件种类繁多且异构性较大。针对不同类型仿真组件之间数据庞杂和数据交换困难的问题,提出可对仿真组件进行一致访问与控制的封装技术。介绍仿真组件的构成及封装对象,研究仿真组件的封装机制、数据变量封装以及封装变量的映射与传递等关键封装技术,阐述封装组件的检测方法。通过仿真组件封装工具与自研协同仿真平台的集成,验证仿真组件封装技术的可行性,并结合具体的仿真组件封装实例,说明仿真组件的封装技术能有效提高模型和数据的重用性,降低对复杂仿真流程的管理难度。