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2009中国控制与决策会议

作者： Jiaqi Li,Wen Tan,Caifen Fu Department of Automation,North China Electric Power University,Zhuxinzhuang,Dewai,Beijing,China

＜正＞In order to check whether a linear controller is sufficient for a nonlinear process,a control-relevant nonlin-earity measure is defined in this *** measure is based on the variation of the disturbance rejection performance at the whole operating range for a fixed linear *** is simple to compute and can show where the process is most nonlinear or *** show that the measure can obtain more accurate process nonlinearity information than that given by the gap metric.

关键词： nonlinearity measure Nonlinear Process Disturbance Rejection

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nonlinearity for a parallel kinematic machine tool and its application to interpolation accuracy analysis

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Science China(Technological Sciences) 2002年第1期45卷 97-105页

作者：汪劲松王忠华黄田 D.J.Whitehouse Department of Engineering University of WarwickCoventry CV4 7ALW Midlands UK

This paper is concerned with the kinematic nonlinearity measure of parallel kinematic machine tool (PKM), which depends upon differential geometry curvalure. The nonlinearity can be described by the curve of the solution locus and the equal interval input of joints mapping into inequable interval output of the end-effectors. Such curing and inequation can be measured by BW curvature. So the curvature can measure the nonlinearity of PKM indirectly. Then the distribution of BW curvature in the local area and the whole workspace are also discussed. An example of application to the interpolation accuracy analysis of PKM is given to illustrate the effectiveness of this approach.

关键词： parallel kinematic machine tool nonlinearity measure curvature interpolation accuracy analysis

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维普期刊数据库

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基于数据降维技术的全基因组区域化关联分析统计推断方法研究

基于数据降维技术的全基因组区域化关联分析统计推断方法研究

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作者：高青松山东大学

学位级别：硕士

Many common human diseases, such as cancer, schizophrenia, essential hypertension, type 2 diabetes, and cardiovascular disease, are known to be complex diseases. Complex diseases, also known as multifactorial diseases, are controlled by multiple genetic and environmental factors. Although they often show a tendency for family aggregation, complex diseases do not have a clear-cut pattern of inheritance. This makes it difficult to determine one's risk of inheriting or passing on these disorders. Recently with rapid improvements in high-throughout genotyping techniques and the growing number of available markers, genome-wide association studies (GWAS), which genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) on thousands of participants, are emerging as promising approaches for the identification of SNPs that are marginally associated with complex diseases. On the other hand, researches on gene-gene interactions (epistasis) in GWAS have shed light on some disease-associated pathways and networks to some extent, and improved our understanding of the genetic basis of complex diseases despite the computational challenge. However, there are still many analytic and interpretation challenges in GWAS. It is customary to run SNP-based association or interaction tests in the whole genome to identify causal or associated SNPs with strong marginal or jointly epistasis effects on disease or *** other words, the unit of association is the SNP. However, such a SNP-based analysis usually leads to computational burden and the well-known multiplicity problem, with a highly inflated risk of type I error and decreased ability to detect modest effects. In the present study, higher units, such as gene or genome regions, were considered to deal with these and related challenges. Under the framework, we proposed four methods to detect disease-associated genes or gene-gene interactions in the genome, presented in four chapters as follows:Chapter 1 A new method

关键词： nonlinearity measure gene- or region-based kernel principal component analysis exhaustive sliding-window kernel canonical correlation analysis

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