大量程杠杆式光学轮廓仪的非线性分析与补偿

介绍了以角锥棱镜为目标镜的杠杆式光学轮廓仪的测量方法。应用矢量坐标变换的方法理论推导了在大量程范围内测量位移与光程差的非线性关系,给出了解析解与数值解。通过理论数值解曲线图的分析,提出了采用二阶拟合的补偿方法,求出了补偿后理论误差结果。以微动工作台为位移发生器,在5.2mm范围内,实验给出了轮廓仪的测量结果,并分别计算了测量结果的线性度误差与二阶拟合补偿后的误差。     

压阻式复合加速度传感器的研究

对压阻式复合惯性加速度传感器的惯性变换系统的结构、特性、工作原理进行了分析讨论,建立了数学模型,所设计的惯性式弹性敏感元件,能分别将线加速度产生的惯性力及角加速度产生的惯性力矩转换成弹性梁的变形,通过应变计及电路将此变形转换成为可测的电学量。并能通过合理组合的测量电桥,消除线加速度及角加速度之间交叉耦合影响。试验证明:该惯性系统设计合理,它可以作为线加速度及角加速度复合测量用。     

一种电加热式液位计设计

提出一种电加热式液位计的设计方案,通过设计一测量杆,利用气体,液体导热系数的差异.将液位测量问题巧妙地转化为温度测量问题,实现了液位的连续测量.仪表具有较高的灵敏度和准确性.在传统液位测量方法可靠性低、误差大的高温、高压、处于沸腾状态工质液位测量场合,具有独特优势.     

基于Allan方差的MEMS陀螺随机误差建模

针对由于MEMS陀螺随机误差较大而影响MEMS惯性测量系统测量精度的问题,提出一种利用Allan方差分析随机误差并建模的方法。在分析Allan方差原理的基础上,通过Allan方差分析法分离和辨识了MEMS陀螺仪的各项随机误差以及误差系数,并利用随机误差系数进行了数学建模。通过与ARMA模型比较,表明利用Allan方差建立的模型更加精确。该方法为MEMS惯性导航系统中姿态测量的误差补偿和滤波提供了新的思路,对提高MEMS惯性测量系统的测量精度具有一定的实际应用价值。     

一种机械量测量新方法

介绍一种用于调整测量摆动轴辊与定轴辊轧距的装置,它由调整测量杆、导套、手轮及复位弹簧组成.用调整杆的位移变化量来替代难以测量的轧距.     

基于光学杠杆法的微纳位移测量系统

采用自行设计搭建的光学杠杆光路系统,使用位置敏感度探测元件(PSD)对悬臂梁的微小挠度变化进行测量.介绍了实验原理及系统搭建.实验测量与分析表明:系统分辨力可达0.06μm.对自制的悬臂梁挠度变形进行测量,并与微动平台的实际位移量对比,结果基本一致,验证了系统的可靠性.     

光学杠杆法测量电致伸缩材料应变研究

提出利用光学杠杆测量电致伸缩材料性能的新方法,通过触点短臂传感微形变,并通过光学杠杆将微形变放大后形成光斑,利用CCD摄像头采集放大前后的光斑,通过对光斑图像进一步的处理可以得到微位移量,从而实现电致伸缩材料应变测量.利用该方法构建的测试系统对掺杂了钛酸钡的聚氨酯高分子材料的电致伸缩性能进行了测量,结果表明:在高场强作用下,掺杂聚氨酯高分子材料表现出了明显的电致伸缩特性;光学杠杆法的测试系统能有效地测量包括电致伸缩微形变在内的微形变,同时为电致伸缩材料性能研究提供了一种方法.     

基于FBG传感器皮革弹性测量设计与实现

提出一种基于FBG(Fiber Bragg Grating)光纤光栅传感器实现皮革弹性测量中温度、因温度变化引起的皮革纤维受力变形长度变化量和拉伸力引起的皮革纤维受力变形长度变化量三个参数被测量。采用用宽夹子把皮革样品两端固定好,并把一个FBG1传感器固定在夹子上,并在沿皮革样品发生形变方向放置FBG2传感器来解决皮革弹性变形和温度相互交叉感染问题;同时采用镀层FBG来提高FBG传感器的灵敏度。实验表明,三个参数能被精确和同时测量;并且FBG传感器灵敏度也被提高了8～13倍。     

智能仪器与应用

<正> 一、前言使用仪器进行测量总是存在测量误差。测定值与实际值之差称为测量结果的绝对误差。在相同条件下,对一个不变的量重复测量,常常发现测量结果并不完全一致,这些误差称为随机误差。它类似于随机噪音,具有大小与方向均随机变化这一特征。随机误差主要是由于各种影响量,例如外界电磁场的变化、热状态的波动、空气的扰动、大地的微震等一些互不相关的独立因素对测量过程的综合影响所造成的。一次测量的随机误差没有规律,不可预见,不可控制,也不能用实验方法加以消除。但是多次重复测量的随机误差服从统计规律,因此对于随机误差的处理必须按照统计规律进行。在一定的条件下误差的数值保持恒定或服从于某种确定的函数规律,这种误差称为系统误差,例如测量放大器的失调电压或失调电流引起的误差、开关接触电势引起的误差均为系统误差。系统误差的大小常     

CFan新视野

入门触控优派TD2420显示器优派TD2420是一款23.6英寸Full HD 1080P的全高清显示器,能卖到1999元的价格是因为其引入了在早期一体电脑上常见的光学式触控技术:利用屏幕对角线位置的感应器捕捉手指动作,可以实现两点触控应用。为了避免屏幕受损,TD2420采用了8H高硬度的强化玻璃,而且还内置2WSRS音效喇叭提升音质,堪称目前最超值的触控显示器之一。     

基于加性核SVM的二尖瓣瓣根识别

摘要：心脏二尖瓣（mitral annulus,MA）瓣根位置对于心室的分割、心脏建模以及多模态配准很重要。本文识别超声心动图中的二尖瓣瓣根位置。但是超声心动图中图像噪声严重、分辨率低以及成像范围有限等缺点,导致二尖瓣瓣根的识别非常困难。本文提出将加性核支撑向量机（Support Vector Machines ,SVM）分类算法并结合一个局部的上下文特征用于二尖瓣瓣根的识别。本文的主要创新点有：利用图像中的上下文特征提取二尖瓣瓣根部特征;应用最小加性核的SVM分类器快速识别二尖瓣瓣根的候选点;对于候选点应用加权模板,计算候选点的加权密度;在加权密度场中,采用二分查找算法,自适应确定一个阈值,剔除二尖瓣瓣根的错分点,确定二尖瓣瓣根的位置。我们的算法在10个儿科病人的超声四腔心动图上测试,和手动选出的二尖瓣瓣根点相比,平均误差控制在1.52±2.25个像素。     

Non-stationary stochastic embedding for transfer function estimation

This paper presents a consistent framework for the quantification of noise and undermodelling errors in transfer function model estimation. We use the, so-called, “stochastic embedding” approach, in which both noise and undermodelling errors are treated as stochastic processes. In contrast to previous applications of stochastic embedding, in this paper we represent the undermodelling as a multiplicative error characterised by random walk processes in the frequency domain. The benefit of the present formulation is that it significantly simplifies the estimation of the parameters of the embedded process yielding a closed-form expression for the model error quantification. Simulation and experimental examples illustrate how the random walks effectively capture typical cases of undermodelling found in practice, including underdamped modes. The examples also show how to use the method as a tool in the determination of model order and pole location in fixed denominator model structures.     

采用加性核SVM的二尖瓣瓣根识别

目的 超声心动图中图像噪声严重、分辨率低以及成像范围有限等缺点,导致二尖瓣（MA）瓣根的识别非常困难,采用加性核函数的支持向量机（SVM）分类器识别超声心动图中的二尖瓣瓣根位置。方法 心脏二尖瓣瓣根位置对于心室的分割、心脏建模以及多模态配准很重要。本文提出将加性核支撑向量机分类算法并结合一个局部的上下文特征用于二尖瓣瓣根的识别。主要创新点有：1）利用图像中的上下文特征提取二尖瓣瓣根部特征;2）应用最小加性核的SVM分类器快速识别二尖瓣瓣根的候选点;3）对于候选点应用加权模板,计算候选点的加权密度;4）在加权密度场中,采用二分查找算法,自适应确定一个阈值,剔除二尖瓣瓣根的错分点,确定二尖瓣瓣根的位置。结果 本文算法在10个儿科病人的超声四腔心动图上测试,和手动选出的二尖瓣瓣根点相比,平均误差控制在1.52±2.25个像素。结论 采用加性核函数的SVM分类器能够快速、准确地识别二尖瓣瓣根点。     

Robust design of pole placement controllers in the presence of structured uncertainty

This paper addresses the robust design of pole placement controllers in the presence of structured parametric uncertainty in a plant. A design algorithm is proposed to minimize pole placement error so as to achieve the desired closed-loop performances better. It is shown that the proposed design algorithm not only has a fast rate convergence in the search of maximum tolerance with regard to pole placement error but also is easy to understand and convenient to implement. A numerical example is given to illustrate the application of the design algorithm.     

Computer-aided optimal design of a polar-based template digitizer-NC contouring system—II. Dynamic analysis

This paper presents the computer-aided optimal design of a template digitizer-NC contouring system which utilizes a set of polar coordinates. The system can operate both as a copying machine or as a numerical control (NC) machine. Part I of the paper describes the proposed system and its salient features. It also analyzes the contouring error due to the incremental approximation of the contour. This error is dependent on the relative location of the contour pole from the machine pole. An objective function relating the area error to the position of the contour is derived and an optimization procedure to minimize this error is outlined. Test results of an attempt to locate the poles of three test contours for minimum error are then presented.     

Linear classifiers are nearly optimal when hidden variables have diverse effects

We analyze classification problems in which data is generated by a two-tiered random process. The class is generated first, then a layer of conditionally independent hidden variables, and finally the observed variables. For sources like this, the Bayes-optimal rule for predicting the class given the values of the observed variables is a two-layer neural network. We show that, if the hidden variables have non-negligible effects on many observed variables, a linear classifier approximates the error rate of the Bayes optimal classifier up to lower order terms. We also show that the hinge loss of a linear classifier is not much more than the Bayes error rate, which implies that an accurate linear classifier can be found efficiently.     

基于机器学习的肺部CT图像非刚性配准误差预测方法

配准误差评估通常由人工完成,耗时费力;常用的Dice测度只关注组织边缘的配准误差,难以评估组织内部配准结果。针对以上问题,提出一种基于机器学习的肺部CT图像非刚性配准误差预测方法(PREML)。该方法首先构建形变场统计特征、形变场物理保真度特征和图像相似性特征三类特征,然后通过池化方法扩充特征数量,最后使用随机森林回归方法预测非刚性配准误差,并且使用自适应随机扰动方法模拟肺部配准误差空间分布,进一步提升形变场统计特征的配准误差表征能力。在三个肺部CT图像数据集上进行训练与测试,其配准误差预测结果与金标准之间的平均绝对差异为1.245±2.500 mm,预测性能优于基线方法。结果表明,PREML方法具有预测精度高、鲁棒性强的特点,可提升配准算法在临床应用的有效性和安全性。     

A parametrization for model reference adaptive pole placement

A new parametrization for a linear system is presented. This parametrization is the basis for two approaches to design an adaptive controller for pole placement. One approach is based on parameter estimation and requires sufficient excitation, while the other, the model reference adaptive pole placement, uses a reference model and an augmented error. It is shown that the two distinct approaches result in identical error terms.     

三铰扑翼建模及数值计算

为研究鸟类翅膀展向变形对扑翼气动特性的影响,基于对鸟类翅膀变形的观察,分别用动静法和拉格朗日方程建立三铰扑翼的数学模型,并利用数值计算研究该模型.结果表明：使用NACA0004作为截面的三铰扑翼在对称扑动时能够产生净升力;随着中翼弹性系数比的增大,周期平均升力因数增大,但周期平均阻力因数不存在单调性;三铰扑翼比双铰翼的周期平均升力因数明显小;随着外翼弹性系数比的增大,周期平均升力因数和周期平均阻力因数也不存在单调特性.     

Intrinsic Random Fields and Image Deformations

The stochastic structure of images, especially individual medical images as they are reconstructed nowadays from arrays of medical imaging sensors, is becoming steadily better understood. Less attention has been paid to the parallel notion of estimation error for the deformations that convey relations among these images, such as localized abnormality or growth prediction. The dominant current formalism for the biostatistics of deformations deals solely with the shape of a set of landmarks parameterizing the deformation, not otherwise with its behaviour inbetween the landmarks. This paper attempts to fit a rigorous stochastic model for a deformation between landmarks and to assess the error of the fitted deformation. The relation between two images is modelled as a stochastic deformation, i.e. as an identity map plus a stochastic process whose value at every point is a vector-valued displacement. There are two common strategies for fitting deformations given information at a set of landmarks. One involves minimizing a roughness penalty, e.g. for a thin-plate spline, and another involves prediction for a stochastic process, e.g. for a self-similar intrinsic random field. The stochastic approach allows parameter estimation and confidence limits for the predicted deformation. An application is presented from a study of breast images and how they deform as a function of the imaging procedure.