正交几何椭圆拟合与代数拟合及椭圆定义的迭代拟合的比较分析

通过把正交几何椭圆拟合与代数拟合和椭圆定义的迭代拟合进行比较分析,考虑到原有拟合存在的缺陷,充分应用正交的概念及最小二乘法(LS)原理和方法的优点,对椭圆进行正交的几何拟合。实验表明,正交几何拟合很好地弥补了其它拟合方法的缺陷,取得良好的效果。     

一种用于目标定位与跟踪的椭圆拟合方法

给出了一种适用于数字闭合曲线的椭圆拟合方法。该方法利用闭合曲线的傅立叶级数表示的直流分量和一次分量实现对闭合曲线的椭圆拟合，可以避免通常椭圆拟合方法中的矩阵求逆问题。在对目标轮廓无先验知识的情况下，一次便可得到其拟会椭圆方程的所有参数，从而确定其方位。     

八心圆弧拟合椭圆误差的理论解析及最优解及最优解

在数控加工领域,由于加工刀具一般采用的是球刀,因此在加工非圆的曲面时采 用的是用圆弧拟合的方法转换成圆弧加工,理论拟合精度决定了加工误差;圆弧拟合椭圆有无 数解,针对八心圆弧拟合椭圆没有准确的误差算法导致拟合椭圆的精度较模糊这一问题,根据 图形学理论提出了等差拟合弧的概念,确定了八心圆弧拟合椭圆的定解区间,导出了拟合椭圆 的八心圆弧法向误差超越方程解析式,并用二分法求解,在AutoCAD 环境下应用Visual LISP 语言编程,求解出根据法向误差确定八心圆弧拟合椭圆的最小误差带,从而确定八心圆弧拟合 椭圆的最优解,使八心圆弧是否可以拟合给定形状公差的椭圆有了准确的判断依据。     

Reducing Algorithm Complexity for Computing an Aggregate Uncertainty Measure

In the theory of evidence, two kinds of uncertainty coexist, nonspecificity and discord. An aggregate uncertainty (AU) measure has been defined to include these two kinds of uncertainty, in an aggregate fashion. Meyerowitz et al. proposed an algorithm for calculating AU and validated its practical usage. Although this algorithm was proven to be absolutely correct by Klir and Wierman, in some cases, it remains too complex. In fact, when the cardinality of the frame of discernment is very large, it can be impossible to calculate AU. Therefore, based on Klir's and Harmanec's seminal work, we give some justifications for restricting the computation of AU(Bel) to the core of the corresponding belief function, and we also propose an algorithm to calculate AU(Bel), the F-algorithm, which reduces the computational complexity of the original algorithm of Meyerowitz et al. We prove that this algorithm gives the same results as Meyerowitz's algorithm, and we outline conditions under which it reduces the computational complexity significantly. Moreover, we illustrate the use of the F-algorithm in computing AU in a practical scenario of target identification.     

参数不确定离散广义大系统的保性能控制

The problem of optimal guaranteed cost control for discrete-time singular large-scale systems with a quadratic cost function is considered in this paper. The system under discussion is subject to norm bounded time-invariant parameter uncertainty in all the matrices of model. The problem we address is to design a state feedback controller such that the closed-loop system not only is robustly stable but also guarantees an adequate level of performance for all admissible uncertainties. A sufficient condition for the existence of guaranteed cost controllers is presented in terms of linear matrix inequalities (LMIs), and a desired state feedback controller is obtained via convex optimization. An illustrative example is given to demonstrate the effectiveness of the proposed approach.     

Guaranteed Cost Control for Discrete-time Singular Large-scale Systems with Parameter Uncertainty

The problem of optimal guaranteed cost control for discrete-time singular large-scale systems with a quadratic cost function is considered in this paper.The system under discussion is subject to norm bounded time-invariant parameter uncertainty in all the matrices of model.The problem we address is to design a state feedback controller such that the closed-loop system not only is robustly stable but also guarantees an adequate level of performance for all admissible uncer- tainties.A sufficient condition for the existence of guaranteed cost controllers is presented in terms of linear matrix inequalities(LMIs),and a desired state feedback controller is obtained via con- vex optimization.An illustrative example is given to demonstrate the effectiveness of the proposed approach.     

A Confidence Region for Regression Parameters with Inequality Constraints

A confidence region for linear regression parameters is determined for the case where the parameters are estimated with regard for linear constraints. An example of application of the results obtained is described.     

结合方向信息测度和NSCT分解的图像融合算法

针对传统图像融合算法容易造成边缘细节信息丢失且抗噪声能力不强的问题,提出一种结合方向信息测度和非下采样轮廓波变换分解的图像融合算法,先利用方向信息测度对图像进行复原操作,再对低频部分采用方向信息测度加权融合规则进行融合,对高频部分采用改进区域能量加权融合规则进行融合.该算法较好地结合了非下采样轮廓波变换良好的信息保持能力和方向信息测度良好的抗噪性能,使得融合图像的效果良好.实验证明,该算法能较好的保持边缘信息且抗噪性强,能广泛地应用于多聚焦图像的融合.     

平面椭圆与二次曲线间位置关系的判别式

平面实体间的碰撞检测中,经常需要讨论二次曲线间的位置关系．根据切点既是交点,又是最近／最远距离点的性质,结合结式理论推导出若干代数多项式,并用来判定平面椭圆与二次曲线间的位置关系．该方法直观简单,仅需要简单四则运算,可以直接应用于判别运动曲线间的位置关系．最后的实例及其比较表明,该方法应用范围广,结果也稳定．     

Confidence intervals for a common mean with missing data with applications in an AIDS study

In practical data analysis, nonresponse phenomenon frequently occurs. In this paper, we propose an empirical likelihood based confidence interval for a common mean by combining the imputed data, assuming that data are missing completely at random. Simulation studies show that such confidence intervals perform well, even when the missing proportion is high. Our method is applied to an analysis of a real data set from an AIDS clinic trial study.     

Confidence intervals for a common mean with missing data with applications in an AIDS study

In practical data analysis, nonresponse phenomenon frequently occurs. In this paper, we propose an empirical likelihood based confidence interval for a common mean by combining the imputed data, assuming that data are missing completely at random. Simulation studies show that such confidence intervals perform well, even when the missing proportion is high. Our method is applied to an analysis of a real data set from an AIDS clinic trial study.     

局部表面拟合的点云模型法向估计及重定向算法

为了提高对点云模型处理的有效性,提出一种对点云模型的法向估计和重定向方法.首先利用基于局部平面拟合的主元分析方法得到初步法向估计;然后改进移动最小二乘曲面实现局部曲面拟合,进一步得到更加准确的法向,实现了点云模型的去噪光顺;最后通过增加切向约束规则来修正法向重定向中的法向传播方向.实验结果表明,对于具有复杂细节(如紧邻面、尖角形状等)的点云模型,该方法可以提高法向计算的准确度,并得到光顺的点云模型.在实际应用中,该方法可以很好地应用于点云模型的预处理,为后续的模型处理和分析提供良好的数据基础.     

Guaranteed search of a target object by an electromechanical system with minimal light power inputs

An optimal guaranteed control problem for electromechanical manipulator with the goal of finding an immovable or mobile target objects is considered. Unlike [1–6], as the optimality criterion, a functional taking into account power inputs of the light source on the gripper of the manipulator is taken. The sought object is assumed to be detected if it falls into the light circle with given illumination. The control algorithms of the gripper motion and corresponding laws of electric current variation in the light source circuit providing the detection of both immovable and mobile sought object in a guaranteed time for minimal light power inputs during the search are developed. The gain in light power inputs achieved for the proposed search algorithms is estimated.     

Vision Based Neuro-Fuzzy Controller for a Two Axes Gimbal System with Small UAV

This paper presents the development of a vision-based neuro-fuzzy controller for a two axes gimbal system mounted on a small Unmanned Aerial Vehicle (UAV). The controller uses vision-based object detection as input and generates pan and tilt motion and velocity commands for the gimbal in order to keep the interest object at the center of the image frame. A readial basis function based neuro-fuzzy system and a learning algorithm is developed for the controller to address the dynamic and non-linear characteristics of the gimbal movement. The controller uses two separate, but identical radial basis function networks, one for pan and one for tilt motion of the gimbal. Each system is initialized with a fixed number of neurons that act as rules basis for the fuzzy inference system. The membership functions and rule strengths are then adjusted with the feedback from the visual tracking system. The controller is trained off-line until a desired error level is achieved. Training is then continued on-line to allow the system to accommodate air speed changes. The algorithm learns from the error computed from the detected position of the object in image frame and generates position and velocity commands for the gimbal movement. Several tests including lab tests and actual flight tests of the UAV have been carried out to demonstrate the effectiveness of the controller. Test results show that the controller is able to converge effectively and generate accurate position and velocity commands to keep the object at the center of the image frame.     

Measure theoretic imaging,with an example employing magnetic resonance input

We present a new medical imaging principle which allows reconstruction of images (from the output of a general digital imaging technology) whose contrast is based on a fundamentally different mathematical mechanism than that of standard images. These images have the useful property that they are capable of exhibiting high contrast between tissues which in currently produced images necessarily have low contrast. The meaning of these images, and their general place in the context of present image generation techniques, is most naturally expressed in the formalism of measure theory. The property actually imaged is derived from a probability measure associated with the mapping which expresses the output of the imaging technology. It also has a nonprobabilistic interpretation as a generalization of the Jacobian, specifically, the Radon-Nikodym derivative. In particular, unlike standard images, contrast is independent of the metric in the space of physical signals that the imaging technology associates with points of the region to be imaged. Images based on this approach using magnetic resonance input are presented.     

Fitting semiparametric regressions for panel count survival data with an R package spef

Observing recurrent event processes at discrete, possibly random times produces panel count data. Modeling panel count data is challenging because the event process may be associated with the observation pattern and censoring time. Various methods have been proposed to fit flexible semiparametric regression models, but no software is available to practitioners. We develop an R package spef that fits semiparametric regression models for panel count data. Existing methods in the literature are implemented as well as our recently developed estimating equations approach. Some of the implemented methods allow informative observation and censoring scheme. The package usage is illustrated with a well-known bladder tumor data set.     

Guaranteed cost LQG control for uncertain systems with a normalized coprime factor uncertainty structure

The paper extends the robust minimax LQG control design methodology to stochastic uncertain systems with a general uncertainty structure, which includes normalized coprime factor uncertainty, passive uncertainty and sector norm-bounded uncertainty as special cases. The derivation of the result uses a special parameter-dependent Girsanov measure transformation. The efficacy of the proposed methodology is illustrated using the problem of frequency locking of an optical cavity which occurs in the area of experimental quantum optics.     

Trajectory Planning for Systems with a Multiplicative Stochastic Uncertainty

A trajectory planning problem for linear stochastic differential equations is considered in this paper. The aim is to control the system such that the expected value of the output interpolates given points at given times while the variance and an integral cost of the control effort are minimized. The solution is obtained by using the dynamic programming technique, which allows for several possible generalizations. The results of this paper can be used for control of systems with a multiplicative stochastic disturbance on the state vector and systems with a stochastic growth rate. This is frequently the case in investment problems, biomathematics and control theory.