从MC9S12D到MC9S12XS

从第四届全国大学生"飞思卡尔"杯智能车竞赛开始,竞赛组委会推荐使用MC9S12XS128单片机替代前三届竞赛中使用的MC9S12DG128。S12X是新一代双核单片机,制造工艺从0.25μm进步到0.18μm;总线频率从25MHz提高到40MHz;S12XS单片机保留了S12X CPU,去掉了另一个CPU-XGate,保留了技术进步,提高了性能,简化了操作。     

提高测厚仪系统安全性和控制精度

为了提高钢板的头部精度,从测量原理、组成等方面,介绍了安钢炉卷生产线X射线测厚仪系统安全性和控制精度的提高.     

基于S7-300／400PLC的冗余控制系统在变压吸附（PSA）制氢装置中的应用

本文介绍了分别基于西门子S7—300PLC和S7—400PLC的两种冗余控制系统在荆门石化和鞍钢变压吸附制氢装置中的应用。概述了工艺过程和工艺对控制系统的要求,着重对上述两套系统的硬件配置和软件编程组态实施方案进行了描述。运行结果表明,上述系统稳定可靠,控制效果良好,能满足工艺要求。     

Geometric Mean for Subspace Selection

Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.     

A kernel‐based core growing clustering method

In this paper, a novel clustering method in the kernel space is proposed. It effectively integrates several existing algorithms to become an iterative clustering scheme, which can handle clusters with arbitrary shapes. In our proposed approach, a reasonable initial core for each of the cluster is estimated. This allows us to adopt a cluster growing technique, and the growing cores offer partial hints on the cluster association. Consequently, the methods used for classification, such as support vector machines (SVMs), can be useful in our approach. To obtain initial clusters effectively, the notion of the incomplete Cholesky decomposition is adopted so that the fuzzy c‐means (FCM) can be used to partition the data in a kernel defined‐like space. Then a one‐class and a multiclass soft margin SVMs are adopted to detect the data within the main distributions (the cores) of the clusters and to repartition the data into new clusters iteratively. The structure of the data set is explored by pruning the data in the low‐density region of the clusters. Then data are gradually added back to the main distributions to assure exact cluster boundaries. Unlike the ordinary SVM algorithm, whose performance relies heavily on the kernel parameters given by the user, the parameters are estimated from the data set naturally in our approach. The experimental evaluations on two synthetic data sets and four University of California Irvine real data benchmarks indicate that the proposed algorithms outperform several popular clustering algorithms, such as FCM, support vector clustering (SVC), hierarchical clustering (HC), self‐organizing maps (SOM), and non‐Euclidean norm fuzzy c‐means (NEFCM). © 2009 Wiley Periodicals, Inc.4     

Median MSD-based method for face recognition

An improved maximum scatter difference (MSD) criterion is proposed in this paper. A weakness of existing MSD model is that the class mean vector in the expressions of within-class scatter matrix and between-class scatter matrix is estimated by class sample average. Under the non-ideal conditions such as variations of expression, illumination, pose, and so on, there will be some outliers in the sample set, so the class sample average is not sufficient to provide an accurate estimate of the class mean using a few of given samples. As a result, the recognition performance of traditional MSD model will decrease. To address this problem, also to render MSD model rather robust, within-class median vector rather than within-class mean vector is used in the original MSD method. The results of experiments conducted on CAS-PEAL and FERET face database indicate the effectiveness of the proposed approach.     

Micromachining of passive planar micromixer on poly (methyl methacrylate) substrate with excimer laser ablation

Excimer laser ablation technique was introduced into this work to fabricate a passive planar micromixer on the PMMA substrate. T-junction shaped and width-changed S-shaped microchannels were both designed in this micromixer to enhance mixing effect. The mixing experiment of distilled water and Rhodamine B with injection flow rate of 500 and 1,500 μm/s validates the mixing effectivity of this micromixer, and indicates the feasibility of excimer laser ablation in the microfabrication of μ-TAS device.     

采用V1200气动阀实现高压注水泵自动启泵

针对吐哈油田注水系统运行现状及其潜在的安全隐患,对鄯善采油厂高压注水系统进行了全面的分析与研究.从安全、经济及可行性角度人手,充分应用成熟可靠的测控技术,设计采用了V1200气动调节阀,从而实现了自动启泵.该系统不仅解决了由于人工手动启泵给操作人员带来的人身安全问题,同时为数字化油田的建设提供了重要的现场设备运行参数,取得了较好的现场使用效果,并且也为今后类似注水系统地面工程建设提供设计指导思路和技术借鉴.     

CBERS-02星CCD数据土地利用分类方法研究——以江苏省宜兴地区为例

以江苏省宜兴地区为试验区,首先进行了CBERS-02星CCD数据的图像预处理,然后利用ERDASI-MAGINE和Definiens7.0等软件,采用非监督分类、监督分类、混合监督分类、面向对象四种分类方法进行了土地利用分类,并将影像分类结果进行了精度评价分析。结果表明：在一级地类的提取中,面向对象的最邻近分类器的分类精度最低,混合监督分类的精度最高;在二级地类的提取中,面向对象的特征阈值法得到的分类结果较好,能够轻松提取传统分类方法难于提取的地类。     

基于权重信息挖掘社会网络中的隐含社团

社团结构是一种普遍存在于各类真实网络中的结构特性.挖掘网络的社团结构对于理解网络的功能与行为有着重要作用.然而,现有的各种社团挖掘算法仅仅基于网络拓扑结构信息,而忽视了蕴涵于真实社会网络边权信息中丰富的语义信息.目前普遍使用的基于模块性最大化的社团挖掘算法倾向于将小社团合并,这使得语义上丰富的小社团容易湮灭于基于拓扑结构信息所挖掘出的大社团中.而挖掘出这些隐含于大社团中的有着丰富语义内涵的小社团对于加深社会网络语义层面的理解有着重要作用.为此,提出一个接近线性复杂度的有权网络社团挖掘算法.通过充分利用权重信息,算法可以将社会网络划分为富含语义信息的粒度较细且相对较小的隐含社团.通过对基于DBLP作者合作网络的实证分析,证实了新算法的有效性和高效性.