智能化钾,钠,氯,钙,pH多参数电解质分析仪

用电子技术实现血液中钾、钠、氯、钙、pH 分析自动化和智能化。根据离子选择电极量原理,自动完成电极的清洗、标定、测量、及结果的校正。该测量接近人体的生理状态,具有很大的临床意义。本文还阐述了系统的组成、软件及硬件配置的设计思想。     

治学严谨,教风端正,善待学生,诚信育人

<正>1教书育人,为人师表(1)治学严谨,教风端正。姜波教授治学严谨,教风端正,对待教学始终坚持"每课必备、每课必新"的原则。作为一名老教师,虽然对教学内容已经非常熟悉,但她从不满足,而是多方搜集资料,认真备课,做到精心准备、合理安排、深入浅出、     

蒙文,汉字,西文操作系统

本文介绍了在IBM-PC/XT机上研制成功的蒙文、汉字、西文操作系统。该系统可以使用内蒙古自治区现行使用的蒙文,蒙古人民共和国使用的斯拉夫蒙文,苏联境内使用的布利亚特蒙文,克尔梅克蒙文,国际一、二级汉字及英(日、俄)文等多种文字的输入、显示、打印、存储及全屏幕编辑;该系统在汉字级别上与CC-DOS兼容,可支持BASIC,dBASE等高级语言。本文还着重讨论了蒙文的特殊性及设计时处理的办法。     

夯实基础,强化特色,专业学科,持续发展

介绍北京信息科技大学智能科学与技术专业"重视基础、重视构架、重视发展"的建设特色,分析专业建设与学科建设、团队构架、学科竞赛等内容的关系,探讨以专业建设为基础和以学科建设为龙头的思路,阐述可持续发展的专业建设之路。     

网络,网络,网……

<正> 互联网,这个魅力四射的“天使”,悄悄飞进千家万户,占领了我们的视听空间,成为我们生活不可或缺的一部分。网络时代逐步走来,处处都有它的踪迹地铁站上下、火车厢内外、公交车里、公告牌上,路上的传单,“家庭”的住址,办公室的网站,曾经的传统产业的广告圣地也成了网络广告瞬息万变的舞台……我们沐浴在网络的光芒里,接受网络文明的洗礼,感念网络文化带来的兴奋、喜悦、失望或沮丧。曾几何时,互联网甚至网络了我们高贵的意志。     

更高,更快,更好

当今社会已步人信息社会,人们对电子商务、电子邮件的认识已达到“日益临近并不可离弃的”程度。信息通信被公认为是新的经济增长点和企业生存发展的生     

明确目标,积极主动,面向未来,研究为本

伴随着计算机专业本科教育的迅速增长,研究生的教育规模也相继扩大。据统计,我国现有128个单位具有计算机科学与技术学科研究生的培养资格,拥有该一级学科博士学位授权的单位就有20个,每年的招收量也处于各学科的前列。一方面,一大批的人渴望拥有更多的机会接受高层次的教育,另一方面,我们还渴望保持曾经拥有的教育水平。如何在现有条件下保证并不断提高教育质量,以满足社会不断增长的受教育需要和人才需求,是一个涉及到教、学、管三方面的问题。考虑到研究生在教育中的自主要求与自主能力基础,本文仅讨论作为一名研究生,如何明确目标,以研…     

无线,无线,再无线

越来越快的计算 尽管数字信息技术在基础研究上会不断突破,但各种产品和技术在计算速度、存储容量等方面已达到的成就使人们对这些性能的要求经常不是很迫切,而产品和技术的排列组合(解决方案)、进一步挖掘和优化等工作显得越来越重要。 网络将计算发挥得更好 网络的发展将数字信息技术的应用推向了一个新高度——的确,惟有将现有的各类计算设备通过各种媒介和机制通畅地连接起来,才能让它们联合起来发挥各自强大的计算、存储和通信等信息处理能力。 移动需要无线网络 然而,光是传统的线缆连网方式还远远不能满足人们的要求,因为线缆把计算设备固定在某个地方     

寒假,寒假,不要虚度

大学没有寒假作业真是完美,自由自在!如何度过呢?睡觉、吃饭、长肉?我们是有志青年,当然要有所追求,做一些有意义的事情!     

比例尺,分辨率,Ft指数,制图学,地理信息系统和遥感的一些观点

1990年8月,在北京召开的第二届地理信息系统国际讨论会上,美国路易斯安那州立大学地理系的林绍颜副教授特别强调了“fractals”一词的重要性,引起了国内有关专家和学者的重视。自1975年曼德布罗特(Mandelbrot)造出该词以来,对它的研究愈益广泛、深化。据不完全统计,1975年只有2篇与该词有关的报导,而截至1986年却增加到约240篇。贝特     

面向提高图像分辨率的遥感数据融合新算法

在遥感应用研究中,数据融合技术有着非常广泛的应用.主分量分析方法(principalcomponentanalysis,简称PCA)是一种经典的遥感数据融合技术,在主分量分析方法的基础上,将小波变换与其结合起来,提出了一种新的基于小波叠加的PCA融合算法(addingwaveletcoefficientsprincipalcomponentanalysis,简称AWPCA).实验证明,与原来的PCA和IHS方法相比,基于小波叠加的PCA融合算法进一步提高了融合信息的质量,并能应用于其他需要高分辨率图像的场合中.     

From image vector to matrix: a straightforward image projection technique—IMPCA vs. PCA

The conventional principal component analysis (PCA) and Fisher linear discriminant analysis (FLD) are both based on vectors. Rather, in this paper, a novel PCA technique directly based on original image matrices is developed for image feature extraction. Experimental results on ORL face database show that the proposed IMPCA are more powerful and efficient than conventional PCA and FLD.     

A novel hybrid approach based on sub-pattern technique and whitened PCA for face recognition

Recently, in a task of face recognition, some researchers presented that independent component analysis (ICA) Architecture I involves a vertically centered principal component analysis (PCA) process (PCA I) and ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). They also concluded that the performance of ICA strongly depends on its involved PCA process. This means that the computationally expensive ICA projection is unnecessary for further process and involved PCA process of ICA, whether PCA I or II, can be used directly for face recognition. But these approaches only consider the global information of face images. Some local information may be ignored. Therefore, in this paper, the sub-pattern technique was combined with PCA I and PCA II, respectively, for face recognition. In other words, two new different sub-pattern based whitened PCA approaches (which are called Sp-PCA I and Sp-PCA II, respectively) were performed and compared with PCA I, PCA II, PCA, and sub-pattern based PCA (SpPCA). Then, we find that sub-pattern technique is useful to PCA I but not to PCA II and PCA. Simultaneously, we also discussed what causes this result in this paper. At last, by simultaneously considering global and local information of face images, we developed a novel hybrid approach which combines PCA II and Sp-PCA I for face recognition. The experimental results reveal that the proposed novel hybrid approach has better recognition performance than that obtained using other traditional methods.     

三种判别分析方法在元音库上的分类

分别用降秩线性判别分析(RRLDA)、降秩二次判别分析(RRQDA)和主成分分析+线性判别分析(PCA+LDA)三种模型对数据进行了分析,并在元音测试数据集上进行了测试.分别画出了这三种模型的误分类率曲线,画出了RRLDA和PCA+LDA分别降至二维后的最优分类面.从实验结果中可以发现,RRLDA模型的实验结果优于PC...     

Improved discriminate analysis for high-dimensional data and its application to face recognition

Many pattern recognition applications involve the treatment of high-dimensional data and the small sample size problem. Principal component analysis (PCA) is a common used dimension reduction technique. Linear discriminate analysis (LDA) is often employed for classification. PCA plus LDA is a famous framework for discriminant analysis in high-dimensional space and singular cases. In this paper, we examine the theory of this framework and find out that even if there is no small sample size problem the PCA dimension reduction cannot guarantee the subsequent successful application of LDA. We thus develop an improved discriminate analysis method by introducing an inverse Fisher criterion and adding a constrain in PCA procedure so that the singularity phenomenon will not occur. Experiment results on face recognition suggest that this new approach works well and can be applied even when the number of training samples is one per class.     

改进的PCA-LDA人脸识别算法的研究

主成分分析算法(PCA)和线性鉴别分析算法(LDA)被广泛用于人脸识别技术中,但是PCA由于其计算复杂度高,致使人脸识别的实时性达不到要求。线性鉴别分析算法存在“小样本”和“边缘类”问题,降低了人脸识别的准确性。针对上述问题,提出使用二维主成分分析法(2DPCA)与改进的线性鉴别分析法相融合的方法。二维主成分分析法提取的特征比一维主成分分析法更丰富,并且降低了计算复杂度。改进的线性鉴别分析算法重新定义了样本类间离散度矩阵和Fisher准则,克服了传统线性鉴别分析算法存在的问题,保留了最有辨别力的信息,提高了算法的识别率。实验结果表明,该算法比主成分分析算法和线性鉴别分析算法具有更高的识别率,可以较好地用于人脸识别任务。     

基于主元和判别集成分析的模拟电路故障诊断

提出了主元和线性判别的集成分析算法以实施模拟故障数据的特征提取过程和方法。该集成分析方法首先对模拟故障数据进行主元分析,然后在主元变换空间实行线性判别分析,最后将所获得的最优判别特征模式应用于模式分类器进行故障诊断。仿真结果表明,所提出的方法能够充分利用线性方法的计算简便优势,增强单一主元分析或线性判别分析的特征提取性能,获取故障数据集的本质特征,简化模式分类器的结构,降低系统运行的计算成本。     

Consistent dynamic PCA based on errors-in-variables subspace identification

In this paper, we make a comparison between dynamic principal component analysis (PCA) and errors-in-variables (EIV) subspace model identification (SMI) and establish consistency conditions for the two approaches. We first demonstrate the relationship between dynamic PCA and SMI. Then we show that when process variables are corrupted by measurement noise dynamic PCA fails to give a consistent estimate of the process model in general whether or not process noise is present. We then propose an indirect dynamic PCA approach for the consistent estimate of the process model resorting to EIV SMI algorithms. Consistent dynamic PCA models are obtained with and without process disturbances. Additional features of the indirect approach include (i) easy determination of the number of lagged variables in the model; (ii) determination of the number of significant process disturbances; and (iii) consistent estimate of the dynamic PCA models with and without process disturbances. We conduct two simulation examples and an industrial case study to support our theoretical results, where the relationship between dynamic PCA and EIV SMI is numerically verified.     

一种基于Gabor小波和2DPCA的掌纹识别改进算法

提出一种改进的基于Gabor小波变换和二维主分量分析2DPCA(2-Dimensional Principal component analysis)的掌纹识别。2DPCA克服了传统Gabor小波变换后直接进行主分量分析PCA(Principal component analysis)遇到的维数灾难问题,并且将PCA与Fisher线性判别FLD(Fisher Linear Discriminate)结合起来,利用了以前仅用于降维的PCA特征和FLD特征相融合进行掌纹识别。基于PolyU掌纹库的实验结果表明,该方法不仅有更高的识别率,而且维数更低。     

A general theory of a class of linear neural nets for principal and minor component analysis

This paper presents a unified theory of a class of learning neural nets for principal component analysis (PCA) and minor component analysis (MCA). First, some fundamental properties are addressed which all neural nets in the class have in common. Second, a subclass called the generalized asymmetric learning algorithm is investigated, and the kind of asymmetric structure which is required in general to obtain the individual eigenvectors of the correlation matrix of a data sequence is clarified. Third, focusing on a single-neuron model, a systematic way of deriving both PCA and MCA learning algorithms is shown, through which a relation between the normalization in PCA algorithms and that in MCA algorithms is revealed. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998