Neural-network-based method of calibration and measurandreconstruction for a high-pressure measuring system

The problem of applying the neural networks for static calibration of measuring systems and for measurand reconstruction is addressed. A multilayered neural network based method for the static calibration of this system is proposed. The functioning of the calibrated measuring system is based on three fiber-optic transducers whose static characteristics are nonmonotonic and significantly influenced by temperature. The applicability of the proposed calibration method is demonstrated in the case under consideration using synthetic and real data. The neural network is designed and implemented in a general purpose microcontroller. In comparison with the spline-based method of calibration, for the same reference data, the proposed method allows obtention of a better quality of calibration and, most important, when calibrated, the multilayered neural network does not require the measurement of temperature for pressure reconstruction     

Calibration modeling for mobile vision based laser imaging and approximation networks

A mobile calibration technique for three-dimensional vision is presented. In this technique, the parameters of mobile vision are computed automatically by approximation networks and image processing of a laser line. Also, the three-dimensional vision is performed by the networks. The proposed vision system provides online geometric modifications from the initial configuration. Here, an online re-calibration is performed via network data to determine the setup modifications. Thus, vision limitations caused by the geometric modifications are overcome. The network is built based on the behavior of the laser line and the camera position. In this manner, the network provides the data for the automatic re-calibration. This system avoids calibrated references and external procedures for re-calibration. Therefore, the calibration model improves the accuracy and performance of the mobile vision. It is because the external data are not passed to the procedure of the three-dimensional vision. This kind of modeling represents a contribution for calibration of mobile vision. To elucidate this contribution an evaluation is carried out based on the calibration via perspective projection model. The time processing is also described.     

Intercomparison exercise within a distributed-dosimetry network

The results of an intercomparison exercise within the US Navy dosimetric network (USN-DN) are presented and discussed. The USN-DN uses a commercially available LiF:Mg,Cu,P thermoluminescent dosemeter (TLD) model Harshaw 8840/8841 and TLD reader model Harshaw 8800 manufactured by Thermo Fisher Scientific. The USN-DN consists of a single calibration facility and 16 satellite dosimetry reading facilities throughout the world with ～ 40 model 8800 TLD readers and in excess of 350 000 TLD cards in circulation. The Naval Dosimetry Center (NDC) is the primary calibration site responsible for the distribution and calibration of all TLD cards and their associated holders. In turn, each satellite facility is assigned a subpopulation of cards, which are utilised for servicing their local customers. Consistency of the NDC calibration of 150 dosemeters (calibrated at NDC) and 27 locally calibrated remote readers was evaluated in the framework of this intercomparison. Accuracy of TLDs' calibration, performed at the NDC, was found to be <3 % throughout the entire network. Accuracy of the readers' calibration, performed with the NDC issued calibration dosemeters at remote sites, was found to be better than 4 % for most readers. The worst performance was found for reader Channel 3, which is calibrated using the thinnest chip of the Harshaw 8840/8841 dosemeter. The loss of sensitivity of this chip may be caused by time-temperature profile that has been designed for all four chips without consideration of chip thickness.     

15-term self-calibration methods for the error-correction ofon-wafer measurements

An improved method of network analyzer calibration is described using the 15-term full model which includes all leakage errors between on-wafer probe tips. This model is well suited to eliminate measurement errors of network analyzer measurements on the wafer. All procedures presented are so-called self-calibration methods, allowing for standards that are not completely known. This allows one to create calibration standards in an easy way and to monitor the calibration process. Simple and robust closed-form equations are presented for all procedures. All procedures can be derived from the general method MURN (match, unknown, reflect, network). The MORN (match, open, reflect, network) is presented, which is particular interesting for on-wafer-measurements. Furthermore, the TMRN (through, match, reflect, network) procedure presented is especially designed for coaxial measurement problems. Experimental results of the TMRN method attest to the very good accuracy and viability of the 15-term self-calibration procedures and can be compared with other 15-term procedures     

ANA Calibration Method for Measurements of Dielectric Properties

Routine network analyzer calibration procedures in measurements of the dielectric properties of materials using an open-ended coaxial line probe are frequently inadequate and limit the accuracy of measurements. A calibration method, which makes use of liquids whose properties are well known, is proposed to alleviate this limitation. It is shown that even one liquid used as a standard in place of a matched load greatly improves the accuracy of measurements. Theoretical relationships and experimental results as well as some practical suggestions related to the application of this method are given.     

利用矢量网络分析仪单端口校准误差项提取二端口网络的S参数

提出了一种利用矢量网络分析仪单端口校准误差项提取互易二端口网络S参数的方法。该方法要求在不同的参考平面上分别进行两次单端口校准,然后利用两次单端口校准的误差项求解二端口网络的S参数,通过实验验证该方法的正确性。该方法可应用于负载牵引和噪声参数测量过程中阻抗调配网络的S参数提取。     

Permittivity determination of liquid materials using waveguide measurements for industrial applications

There is a need for a simple and relatively inexpensive microwave method for complex permittivity, ?, determination of liquid materials in industrial applications. Measurement cables that connect the measurement cell to the measuring instrument (usually a vector network analyser) may be either subject to severe phase instability particularly if they are long or metrology-grade cables are not available, or subject to thermal variation. In these instances, calibration techniques based on complex scattering parameter measurements may drastically suffer from extension of the reference plane to the boundaries of the measurement cell or sample end surfaces. In such situations, amplitude-only methods based on simplified calibration techniques such as response calibration are appropriate and very feasible. These methods have applications in industry such as thickness measurement and disbonding and delamination detection. The motivation of this study is to investigate a suitable method for ? determination of liquid materials using amplitude-only waveguide measurements. The analysis is restricted to lossy dielectric materials, which possess large enough attenuation such that the multiple reflections between their two end surfaces can be neglected. We derived an equation in terms of the loss tangent of these materials for selecting a suitable material thickness. We presented two approximations (frequency-independent and power-series representation) for ? determination of these materials. For validation of the method, we measured the ? of methanol and commercially available antifreeze solution by our method using a simple (frequency response) calibration and by another waveguide method using the thru-reflect-line calibration.     

Reflectometer calibration without an open circuit

This paper presents a convenient calibration technique of a six-port reflectometer requiring a set of loads including two standards and avoiding the use of an open circuit. This method has been applied for different reflectometers at three frequencies: 2, 33, and 94 GHz. Reflection coefficient measurement results obtained by this method and other calibration techniques prove its efficiency. Transmission coefficient measurement has also been performed at 94 GHz. Characterizations of some coaxial components by the heterodyne network analyzer using this technique and other calibration methods are presented at frequencies between 0.5 and 10 GHz.     

A calibration and measurement method of a tri-six-port networkanalyzer suitable for on-wafer characterization of three-port devices

A calibration and measurement method using a wideband tri-six-port network and analyzer (TSPNA) is proposed. The scattering parameters of a three-port device under test are found to be the unknowns of a set of nine simultaneous complex linear equations. These equations are established via a group of three calibrated linearly independent excitations. No isolators are required for calibration and measurement procedures. Three unknown reciprocal two-port standards are needed for the calibration of the TSPNA. The proposed method is rigorous, explicit, and suitable for wideband on-wafer three-port device S-parameter measurements. It can easily be adapted for heterodyne automated network analyzers equipped with three reflection test-set units     

Automated Characterization of Bolometric and Electrothermic Mounts

The greatest inaccuracy in making a microwave power measurement is usually the uncertainty of the calibration factor or the effective efficiency. These terms account for the RF losses and substitution errors in the bolometric or electrothermic mount. A new method for transferring calibration from a standard mount to a mount to be calibrated has production line speed and simplicity; yet its accuracy rivals standards laboratory techniques. This method uses an automatic network analyzer to measure the quantities required and to solve the mismatch equation in transferring calibration from the standard mount to the one to be calibrated. A technique is also described to evaluate the errors of the transfer measurement. An accurate method of measuring the complex reflection coefficient of a stabilized source is another result of this work. The present implementation measures effective efficiency, calibration factor, and the magnitude and phase of the reflection coefficient of a mount at six calibration frequencies in 60 seconds. The uncertainty in transferring calibration factor and effective efficiency is about 0.5 percent.     

重载并联六维力传感器及静态标定

针对传感器重载小尺寸需求,提出一种具有混合分支的重载并联六维力传感器,分析了其结构特点和测量原理。搭建了重载并联六维力传感器标定系统,为改善维间耦合及制造误差等对测量精度产生的影响,从标定算法及模型优化方面对其进行了研究。分别利用最小二乘法和BP神经网络算法对加载实验数据进行了处理,分析结果表明BP神经网络算法要明显优于最小二乘法,并通过数据随机分组测试验证了结果的正确性。基于BP神经网络,提出了一种基于人工鱼群算法的BP神经网络算法,并采用优化后的BP神经网络标定算法对实验数据进行了计算分析,结果表明优化后的BP神经网络计算结果较好且稳定,不易陷入局部极值。     

Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction

Six popular approaches of «NIR spectrum–property» calibration model building are compared in this work on the basis of a gasoline spectral data. These approaches are: multiple linear regression (MLR), principal component regression (PCR), linear partial least squares regression (PLS), polynomial partial least squares regression (Poly-PLS), spline partial least squares regression (Spline-PLS) and artificial neural networks (ANN). The best preprocessing technique is found for each method. Optimal calibration parameters (number of principal components, ANN structure, etc.) are also found. Accuracy, computational complexity and application simplicity of different methods are compared on an example of prediction of six important gasoline properties (density and fractional composition). Errors of calibration using different approaches are found. An advantage of neural network approach to solution of «NIR spectrum–gasoline property» problem is illustrated. An effective model for gasoline properties prediction based on NIR data is built.     

Robust algorithms for Txx network analyzer self-calibrationprocedures

A closed-form theory for performing a network analyzer calibration using different Txx procedures is presented. This theory makes it possible to describe all known self-calibration procedures with the same algorithm. The improved properties of this general TAN method in contrast to the conventional TAN procedure are shown by numerical and experimental results     

Real-time inference of stochastic damage in composite materials

This study describes a control system designed for real-time monitoring of damage in materials that employs methods and models that account for uncertainties in experimental data and parameters in continuum damage mechanics models. The methodology involves (1) developing an experimental set-up for direct and indirect measurements of damage in materials; (2) modeling damage mechanics based constitutive equations for continuum models; and (3) implementation of a Bayesian framework for statistical calibration of model with quantification of uncertainties. To provide information for real-time monitoring of damage, indirect measurement of damage is made feasible using an embedded carbon nanotube (CNT) network to perform as sensor for detecting the local damage. A software infrastructure is developed and implemented in order to integrate the various constituents, such as finite element approximation of the continuum damage models, generated experimental data, and Bayesian-based methods for model calibration and validation. The outcomes of the statistical calibration and dynamic validation of damage models are presented. The experimental program designed to provide observational data is discussed.     

脉冲信号发生器自动检定/校准系统

脉冲信号发生器被广泛应用在电子测量和数据通信领域。利用Visual Basic高级语言，开发基于GPIB接口的脉；中信号发生器自动检定／校准系统。该系统能够完成对脉冲信号发生器的自动检定／校准，测量数据自动保存在数据库，实现检定／校准证书自动生成，利用网络打印检定／校准证书。     

Bond calibration method for Young’s modulus determination in the discrete element method framework

A new methodology for the calibration of bond microparameters in rocks represented by a package of joined random spherical particles in the discrete element method (DEM) framework is presented. Typically, calibration is achieved through a trial-and-error procedure using several DEM simulations of uniaxial compressive tests (UCTs). The bond calibration model (BCM) does not need a time-dependent UCT-DEM simulation to establish the relation between the microproperties of the bond and the macroproperties of the rock specimen. The BCM uses matrices to describe the interaction forces exerted by bonds and, by means of an assembly process similar to the finite element method, it can describe the complex network of bonds, enabling the model to capture small variations in particle size and bond distribution as demonstrated in this work. In this work, the BCM is presented and compared with UCT simulations performed using Esys Particle software. Multiple simulations are done with constant bond properties and different particle size ratios (\(D_{MAX}/D_{MIN})\) that cause small variations in the specimen’s Young’s modulus; these variations are well captured by the BCM with an error of <10%.     

Applicability of multilayer feed-forward neural networks to model the onset of river breakup

Predicting the onset of breakup is an essential component of any ice jam flood forecasting system, yet it presents a difficult challenge due to the complex nature of the relationship between meteorological conditions, streamflow hydraulics and ice mechanics. For this research, data extracted from historical hydrometric and meteorological records were used to develop and assess a three-layer feed-forward artificial neural network (ANN) model for predicting the onset of breakup, using the Hay River in northern Canada as the demonstration site. The calibration results illustrate the potential of the ANN model for successful forecasting of the onset of river ice breakup, i.e. the first transverse cracking of the ice cover. However, rigorous validation also indicates that the accuracy of such ANN models can be optimistically overestimated by their performance during the calibration phase. The possible reasons for this poor predictive capability of the ANN model are also discussed. Despite this caveat, the proposed model shows improved performance as compared to the more conventional multiple linear regression (MLR) techniques typically applied to this problem.     

基于LS-SVM的立体视觉摄像机标定

利用最小二乘支持向量机来直接学习图像信息与三维信息之间的关系,不需确定摄像机具体的内部参数和外部参数.在双目视觉的情况下,两摄像机的位置关系不需具体求出,而是隐含在映射关系中.根据最小二乘支持向量机与摄像机标定的特点,提出了基于最小二乘支持向量机的双目立体摄像机标定方法.将摄像头采集到的图像的像素坐标作为输入,将世界坐标作为输出,用最小二乘支持向量机使网络实现给定的输入输出映射关系.该方法同BP神经网络预测结果对比表明:基于最小二乘支持向量机的双目视觉标定方法速度快,实时性好,能有效提高标定精度.     

Results of network analyzer measurements with leakageerrors-corrected with direct calibration techniques

Exact calibration procedures to correct two-port measurements with leakage errors are developed for a network analyzer having three or four measurement channels. Many measurement systems including open air devices, such as MMIC wafer probes, contain numerous crosstalk paths which are included in the full models of the direct calibration procedures. These novel 15-term and 22-term procedures are based on closed solutions and need five and six successive measurements of completely known calibration standards. First, both procedures solve these general error models exactly. The reduction of the common 22-term model to a 10-term model describes a network analyzer with three measurement channels in a simple and common manner. In this way one may create a great number of new calibration procedures. The sensitivity and accuracy of a direct 15-term and a direct 22-term error correction procedure is demonstrated by experimental results     

一种双目立体视觉系统的校准方法

基于双目立体视觉系统的图像分析以及人工神经网络的三维空间建模算法,设计了一种针对双目立体视觉相机的校准方法,并可应用于运动目标点的轨迹追踪。将均匀分布目标点的校准平面放置在有效视野内的不同位置,通过双目立体视觉系统来捕获处于不同位置的校准平面图像。在图像处理之后,使用校准点中心的二维坐标作为人工神经网络训练的输入样本集,通过建立人工神经网络模型结构,实现目标点二维平面坐标到三维空间坐标的映射关系。采用这种具有通用性的方法,可以有效修正系统中存在的失真因子,获得目标三维位置信息,而无需进行复杂的相机校准操作。实验表明,提出的方案具有良好的可行性和鲁棒性。