用“消隐点”法标定线结构光三维视觉传感器

提出了一种以线结构光为基础的三维视觉传感器，并根据所建立的数学模型，结合传感器的实际使用情况，提出用“消隐点”法对该传感器结构参数进行标定，该标定方法装置简单，速度快，在保证精度的同时简化标定的过程实验证明“消隐点”法是一种高效，实用的视觉传感器标定方法。     

标定十字结构光传感器的新方法

建立了基于十字结构光的视觉传感器的数学模型,提出了一种基于自由移动平面靶标的十字结构光传感器参数的简易标定方法。在自由移动的平面靶标上建立局部世界坐标系,将通过交比不变方法获得的两个光平面上特征点的局部世界坐标,变换到摄像机坐标系,从而获得已知三维的标定特征点。利用构建的分别位于两个光平面上标定特征点,可以实现工作状态的十字结构光传感器参数的优化估计。该标定方法降低了标定设备的成本,简化了标定过程,为十字结构光传感器的工程化应用奠定了基础。通过对圆的直径和中心坐标的测量实验,结果表明,该方法切实可行。     

氢氧化钾溶液标定的不确定度评定

本文依据GB6 0 1- 88对氢氧化钾溶液进行标定 ,系统分析了该法标定过程中的不确定度来源 ,采用代数法计算滴定终点的不确定度 ,介绍了采用该法标定氢氧化钾溶液的不确定度评定方法。     

利用齿形靶标标定线结构光传感器结构参数的新方法

本文建立了线结构光传感器数学模型，并在基础上提出了一种新的、高准确度的线结构光传感器结构参数的标定方法，该方法利用简单的齿形靶标的一维移动，以确定交平面与摄像机间的位置关系。     

线结构光视觉传感器机器人手眼关系标定

为实现对工业机器人手眼关系的标定,提出一种基于线结构光视觉传感器的手眼关系标定方法。该方法在标定时,将一个平面靶标作为参考物固定在工业机器人工作空间内,控制工业机器人末端运动以带动线结构光视觉传感器作多组变位姿运动,获取在不同位姿状态下的平面靶标图像并对其进行图像处理。通过对图像上固定特征点的测量,以及建立线结构光视觉传感器模型和手眼关系模型实现对线结构光内参数和手眼关系的标定。用棋盘格标定板进行测量实验验证,实验结果表明该方法准确度为0.036 mm,即优于40μm,可用于工业机器人的测量应用。     

硫酸标准溶液标定的不确定度评定

通过对硫酸标准溶液标定过程的分析,找出影响标定结果的各个分量,从而对硫酸标准溶液标定的不确定度进行评定.     

线位移传感器检定装置的不确定度分析

本文通过详细分析线位移传感器检定装置误差来源,最终确定了该检定装置的合成标准不确定度及扩展不确定度,并给出了最终结果表达式。     

液氢电容式密度传感器结构系数的不确定度评定

本文通过全面分析电容式密度传感器在液氢环境下结构系数的不确定度来源,并依据测量不确定度评定方法,得到了密度传感器结构系数的不确定度,为液氢密度测量结果的准确性提供了重要的评判依据。     

Data assimilation and uncertainty analysis of environmental assessment problems—an application of Stochastic Transfer Function and Generalised Likelihood Uncertainty Estimation techniques

Stochastic Transfer Function (STF) and Generalised Likelihood Uncertainty Estimation (GLUE) techniques are outlined and applied to an environmental problem concerned with marine dose assessment. The goal of both methods in this application is the estimation and prediction of the environmental variables, together with their associated probability distributions. In particular, they are used to estimate the amount of radionuclides transferred to marine biota from a given source: the British Nuclear Fuel Ltd (BNFL) repository plant in Sellafield, UK. The complexity of the processes involved, together with the large dispersion and scarcity of observations regarding radionuclide concentrations in the marine environment, require efficient data assimilation techniques. In this regard, the basic STF methods search for identifiable, linear model structures that capture the maximum amount of information contained in the data with a minimal parameterisation. They can be extended for on-line use, based on recursively updated Bayesian estimation and, although applicable to only constant or time-variable parameter (non-stationary) linear systems in the form used in this paper, they have the potential for application to non-linear systems using recently developed State Dependent Parameter (SDP) non-linear STF models. The GLUE based-methods, on the other hand, formulate the problem of estimation using a more general Bayesian approach, usually without prior statistical identification of the model structure. As a result, they are applicable to almost any linear or non-linear stochastic model, although they are much less efficient both computationally and in their use of the information contained in the observations. As expected in this particular environmental application, it is shown that the STF methods give much narrower confidence limits for the estimates due to their more efficient use of the information contained in the data. Exploiting Monte Carlo Simulation (MCS) analysis, the GLUE technique is used to estimate how the errors involved in the STF model structure and observations influence the model outputs and errors. The discussion on updating information originating from different locations using GLUE procedure is also given. A final aim of the paper is to use the results obtained in this particular example to explore the differences between the GLUE and STF approaches.     

用于内表面测量的圆结构光视觉图像处理方法

简要介绍了用于内表面测量的圆结构光视觉系统,分析了内表面圆结构光视觉图像特征,并给出了干扰光条的产生原因。在此基础上,提出了干扰光条的剔除方法与有用光条图像中心的子像素级提取方法。介绍了基于阶跃边缘轮廓模型的边缘提取方法,该方法采用非线性优化方法进行模型参数估计,具有速度快、鲁棒性强、精度高的特点。     

基于光学小波变换的机器人视觉传感器设计

针对视觉系统处理速度慢的问题,利用光电信息技术设计出基于光学小波变换的机器人视觉传感器。该传感器用4 f光学处理系统实现光学小波变换滤波,以计算机编程加载滤波器,因此兼有光学信息处理的二维、高速、并行和大容量的优点,以及计算机控制灵活的优点。图像特征提取仿真实验结果表明:效果理想,处理速度快,方案可行。     

Data-driven modal surrogate model for frequency response uncertainty propagation

A method is developed for propagation of model parameter uncertainties into frequency response functions based on a modal representation of the equations of motion. Individual local surrogate models of the eigenfrequencies and residue matrix elements for each mode are trained to build a global surrogate model. The computational cost of the global surrogate model is reduced in three steps. First, modes outside the range of interest, necessary to describe the in-band frequency response, are approximated with few residual modes. Secondly, the dimension of the residue matrices for each mode is reduced using principal component analysis. Lastly, multiple surrogate model structures are employed in a mixture. Cheap second-order multivariate polynomial models and more expensive Gaussian process models with different kernels are used to model the modal data. Leave-one-out cross-validation is used for model selection of the local surrogate models. The approximations introduced allow the method to be used for modally dense models at a small computational cost, without sacrificing the global surrogate model’s ability to capture mode veering and crossing phenomena. The method is compared to a Monte Carlo based approach and verified on one industrial-sized component and on one assembly of two car components.     

Multi-objective design optimization of electrostatically actuated microbeam resonators with and without parameter uncertainty

Electrostatically actuated microbeam resonators are widely used components in microelectromechanical systems for sensing and signal filtering purposes. Due to the uncertainties resulting from manufacturing processes, material properties, and modeling assumptions, microbeam resonators may exhibit significant variations in their performance compared to nominal designs. There has been limited research on the performance prediction and the design optimization of such microsystems while accounting for relevant uncertainties. In this study, such uncertainties are considered in terms of the variability of parameters that define the dimensions, the material properties, and the operating conditions of the device. In addition, uncertainties with respect to a two-dimensional model of a microbeam resonator subject to electrostatic actuation are considered. A finite element model consisting of both the microbeam and the substrate is developed. The actuation forces are predicted by a reduced order electrostatic model, which accounts for the electromechanical interaction. A computationally efficient procedure is presented for simulating the steady-state dynamic response under electrostatic forces. The probabilistic performance of the microresonator is investigated using Monte Carlo simulation. A genetic algorithm is used to optimize the stochastic behavior of the microbeam resonator. The design is posed as combinatorial multi-objective optimization problem. Two design criteria describing the filter performance in terms of the shape of the frequency–response curve are simultaneously considered. The numerical results demonstrate the effectiveness of this procedure for the multi-objective optimization design of microbeam resonators and the importance of considering parameter uncertainty in the design of these devices.     

Monte Carlo simulation of Stokes vectors of polarized light scattering from two-dimensional random rough surfaces

A Monte Carlo model was established to simulate polarized scattering fields of two-dimensional rough surfaces based on the Kirchhoff approximation. Based on this model, numerical studies of the hemispherical distribution of Stokes vectors of scattered light from dielectric and metal rough surfaces were carried out. These surfaces have Gaussian distributions with correlation length of 3.1?µm and standard deviation varying between 0.1 and 0.6?µm. The results reveal that the V component of metal surfaces has peaks antisymmetric with the incident plane, whereas the V component of dielectric surfaces is almost zero. We consider that this property of the V component would provide a new method which could be used to distinguish the target material.     

An object-oriented modeling framework for representing uncertainty in early variant design

This article presents a framework for the representation of uncertainty in the early design of complex adaptive products such as automobiles. The core of the framework is an object-oriented approach in which design objects and their inter-relationships may be modeled, and in which both the design attributes and the product structure may be uncertain. Relationship objects allow product variants and design alternatives to be represented. In addition to the design model, derivation methods for design attributes may be modeled, and methods may be incorporated to allow the deterministic or probabilistic computation of attributes. The modeling framework is the basis of a risk modeling tool, RiTo, in which Monte Carlo simulation is used to compute estimates for costs and other design attributes together with their probability of achievement in the final design. Uncertainties may be aggregated and levels of uncertainty in different parts of the model may be continually analysed and assessed. The framework also provides a mechanism for accumulating product knowledge, in particular knowledge concerning relationships between elements of part and assembly models, product volumes and manufacturing considerations.

Optimization of laminated composite structure considering uncertainty effects

In this paper, the most conservative Tsai–Wu failure envelopes are obtained for laminated composite considering material as well as ply angle uncertainty. The uncertainty analysis is performed using Monte Carlo simulation (MCS). The obtained failure envelopes are then used as the constraint functions to perform the minimum weight design optimization problem using particle swarm optimization (PSO). Results show increase in weight of the laminate from the deterministic results and it varies from 4% to 50% depending upon the stacking sequence and loading condition. Substantial effects of uncertainty on the failure envelope and optimal design are quantified.     

Distribution propagation in estimating measurement uncertainty

Arguments are presented that show that it is ineffective to replace distribution propagation by uncertainty propagation in estimating the uncertainty of measurements, which has a bearing on the accuracy and difficulty of estimating indirect-measurement uncertainties with nonlinear transformations from input data. __________ Translated from Izmeritel’naya Tekhnika, No. 2, pp. 15–18, February, 2008.