On the bounded-real and positive-real conditions for affine nonlinear state-delayed systems and applications to stability

In this paper, bounded-real conditions for affine nonlinear state-delayed systems are derived using the concept of dissipativeness. Necessary and sufficient conditions for the system to be dissipative and to have finite L₂-gain also referred to as the bounded-real condition are given. The implications on the stability of the system and feedback interconnections of such systems are also considered. Finally, an equivalent of the positive-real lemma is derived and its implications on stability of the system and feedback interconnections of such systems are similarly discussed.     

A method of predicting the number of clusters using Rand's statistic

Distributional and asymptotic results on the moment of Rand's C_k statistic were derived by DuBien and Warde [1981. Some distributional results concerning a comparative statistic used in cluster analysis. ASA Proceedings of the Social Statistics Section, 309–313.]. Based on those results, a method to predict the number of clusters is suggested by applying various agglomerative clustering algorithms. In the procedure, the methods using different indexes are examined and compared based on the concept of agreement (or, disagreement) between clusterings generated by different clustering algorithms on the set of data. Our method having practical generality works better than the other methods and assigns statistical meaning to C_k values in determining the number of clusters from the comparison.     

使数据库能够存取ADT数据

阐述了ADT类型的数据在数据库应用过程中的重要性,提供了为数据库增加存取ADT数据功能的方法和手段,介绍了二进制流和二进制字段在存取ADT数据时的作用。     

OMR抽象模型

在计算机领域反映现实世界中的事物,离不开有效模型的支持。这个模型一方面是现实世界中具体事物的抽象,另一方面又便于计算机实现。提出的OMR模型反映了现实世界的运行规律,用对象、消息和关系三要素对现实事物进行抽象和描述,是辅助计算机实现的有效工具。为了从形式化和数学化的角度描述OMR模型,又探讨了对象内Petri网和对象间Petri网,从而使OMR模型进一步丰满。     

区域自识别医用观片灯控制系统设计

基于嵌入式控制技术,提出一种具有区域自识别功能的新型医用观片灯系统结构。该系统以超高亮白光LED构成分布均匀的LED阵列,通过嵌入式控制器识别X光片的规格及覆盖区域,并控制该区域的LED灯阵列发光。本系统所采用的分区LED灯光控制技术不仅可以有效节约电能,大幅延长观片灯寿命,而且还能改善医务工作者观察的舒适度。     

Discovering invariants via simple component analysis

We propose a new technique combining dynamic and static analysis of programs to find linear invariants. We use a statistical tool, called simple component analysis, to analyze partial execution traces of a given program. We get a new coordinate system in the vector space of program variables, which is used to specialize numerical abstract domains. As an application, we instantiate our technique to interval analysis of simple imperative programs and show some experimental evaluations.     

Geoadditive expectile regression

Quantile regression has emerged as one of the standard tools for regression analysis that enables a proper assessment of the complete conditional distribution of responses even in the presence of heteroscedastic errors. Quantile regression estimates are obtained by minimising an asymmetrically weighted sum of absolute deviations from the regression line, a decision theoretic formulation of the estimation problem that avoids a full specification of the error term distribution. Recent advances in mean regression have concentrated on making the regression structure more flexible by including nonlinear effects of continuous covariates, random effects or spatial effects. These extensions often rely on penalised least squares or penalised likelihood estimation with quadratic penalties and may therefore be difficult to combine with the linear programming approaches often considered in quantile regression. As a consequence, geoadditive expectile regression based on minimising an asymmetrically weighted sum of squared residuals is introduced. Different estimation procedures are presented including least asymmetrically weighted squares, boosting and restricted expectile regression. The properties of these procedures are investigated in a simulation study and an analysis on rental fees in Munich is provided where the geoadditive specification allows for an analysis of nonlinear effects of the size of flats or the year of construction and the spatial distribution of rents simultaneously.     

Functional linear regression after spline transformation

Functional linear regression has been widely used to model the relationship between a scalar response and functional predictors. If the original data do not satisfy the linear assumption, an intuitive solution is to perform some transformation such that transformed data will be linearly related. The problem of finding such transformations has been rather neglected in the development of functional data analysis tools. In this paper, we consider transformation on the response variable in functional linear regression and propose a nonparametric transformation model in which we use spline functions to construct the transformation function. The functional regression coefficients are then estimated by an innovative procedure called mixed data canonical correlation analysis (MDCCA). MDCCA is analogous to the canonical correlation analysis between two multivariate samples, but is between a multivariate sample and a set of functional data. Here, we apply the MDCCA to the projection of the transformation function on the B-spline space and the functional predictors. We then show that our estimates agree with the regularized functional least squares estimate for the transformation model subject to a scale multiplication. The dimension of the space of spline transformations can be determined by a model selection principle. Typically, a very small number of B-spline knots is needed. Real and simulation data examples are further presented to demonstrate the value of this approach.     

Novel algorithm for pose-invariant face recognition

By combining the AdaBoost modular locality preserving projection(AMLPP) algorithm and the locally linear regression(LLR) algorithm,a novel pose-invariant algorithm is proposed to realize high-accuracy face recognition under different poses.In the training stage of this algorithm,the AMLPP is employed to select the crucial frontal blocks and construct effective strong classifier.According to the selected frontal blocks and the corresponding non-frontal blocks,LLR is then applied to learn the linear mappings which will be used to convert the non-frontal blocks to visual frontal blocks.During the testing of the learned linear mappings,when a non-frontal face image is inputted,the non-frontal blocks corresponding to the selected frontal blocks are extracted and converted to the visual frontal blocks.The generated virtual frontal blocks are finally fed into the strong classifier constructed by AMLPP to realize accurate and efficient face recognition.Our algorithm is experimentally compared with other pose-invariant face recognition algorithms based on the Bosphorus database.The results show a significant improvement with our proposed algorithm.