System modeling based measurement error analysis of digital sun sensors

Stringent attitude determination accuracy is required for the development of the advanced space technologies and thus the accuracy improvement of digital sun sensors is necessary.In this paper,we presented a proposal for measurement error analysis of a digital sun sensor.A system modeling including three different error sources was built and employed for system error analysis.Numerical simulations were also conducted to study the measurement error introduced by different sources of error.Based on our model and study,the system errors from different error sources are coupled and the system calibration should be elaborately designed to realize a digital sun sensor with extra-high accuracy.

SOME MATHEMATICAL MODELS OF MEASUREMENT OF CYLINDRICITY ERROR WITH MULTI-POINT METHOD

Through the analyses and researches on some related references of error separation techniques at home and abroad, this paper has built-up some mathematical models to measure and evaluate workpiece cylindricity error with multipoint method as well as unconstrained optimization methods. A few shortcomings of the technique to solve rotational error and cylindricity error are found, and some precise formulas are given. It is feasible by computer simulation tests.

Simulation of Current Measurement Using Magnetic Sensor Arrays and Its Error Model

Magnetic sensor arrays are proposed to measure electric current in a non-contac tway. In order to achieve higher accuracy, signal processing techniques for magnetic sensor arrays are utilized. Simulation techniques are necessary to study the factors influencing the accuracy of current measurement. This paper presents a simulation method to estimate the impact of sensing area and position of sensors on the accuracy of current measurement. Several error models are built up to support computer-aided design of magnetic sensor arrays.

Installation error calibration for MEMS angular measurement system in hypersonic wind tunnel

The accuracy of model attitude measurement has an important impact on wind tunnel test results. Microelectromechanical System Inertial Measurement Unit(MEMS IMU) provides a feasible way to measure model attitudes with high accuracy. However, the installation error between MEMS IMU coordinate system and the body coordinate system of test models can make the accuracy of the model attitude measurement decrease. In wind tunnel tests, the installation error depends on the relationship between the IMU and the model mechanism before tests. Therefore, infield calibration in wind tunnel tests is necessary to reduce installation errors. To improve attitude measurement accuracy, the least squares quaternion calibration method based on MEMS IMU and six-position calibration procedure are proposed. High-precision three-axis turntable tests are performed. The pitch accuracy after calibration is higher than that before calibration in the angle of attack sweeping tests. The Root-Mean-Square Errors(RMSE) in the roll and yaw are within0.01°, which are smaller than those before calibration. In the roll sweeping tests, RMSE of three attitude angles decrease significantly. In hypersonic wind tunnel tests, the pitch errors before and after calibration are within 0.05° and 0.02° in the angle of attack sweeping tests without wind. In five angle of attack sweeping tests with wind, the deviation between the mean of the pitch and the pitch after the elastic angle correction is within 0.03° and the standard deviation of five tests is within 0.01°. The proposed method is confirmed to enhance the accuracy of attitude measurement effectively, which is convenient for engineering applications.

Positional Error Model of Line Segments with Modeling and Measuring Errors Using Brownian Bridge

Spatial linear features are often represented as a series of line segments joined by measured endpoints in surveying and geographic information science.There are not only the measuring errors of the endpoints but also the modeling errors between the line segments and the actual geographical features.This paper presents a Brownian bridge error model for line segments combining both the modeling and measuring errors.First,the Brownian bridge is used to establish the position distribution of the actual geographic feature represented by the line segment.Second,an error propagation model with the constraints of the measuring error distribution of the endpoints is proposed.Third,a comprehensive error band of the line segment is constructed,wherein both the modeling and measuring errors are contained.The proposed error model can be used to evaluate line segments’overall accuracy and trustability influenced by modeling and measuring errors,and provides a comprehensive quality indicator for the geospatial data.

Objective Bayesian analysis for the accelerated degradation model usingWiener process with measurement errors

The Wiener process as a degradation model plays an important role in the degradation analysis.In this paper, we propose an objective Bayesian analysis for an acceleration degradation Wienermodel which is subjected to measurement errors. The Jeffreys prior and reference priors underdifferent group orderings are first derived, the propriety of the posteriors is then validated. It isshown that two of the reference priors can yield proper posteriors while the others cannot. A simulation study is carried out to investigate the frequentist performance of the approach comparedto the maximum likelihood method. Finally, the approach is applied to analyse a real data.

Corrected empirical likelihood for a class of generalized linear measurement error models

Generalized linear measurement error models, such as Gaussian regression, Poisson regression and logistic regression, are considered. To eliminate the effects of measurement error on parameter estimation, a corrected empirical likelihood method is proposed to make statistical inference for a class of generalized linear measurement error models based on the moment identities of the corrected score function. The asymptotic distribution of the empirical log-likelihood ratio for the regression parameter is proved to be a Chi-squared distribution under some regularity conditions. The corresponding maximum empirical likelihood estimator of the regression parameter π is derived, and the asymptotic normality is shown. Furthermore, we consider the construction of the confidence intervals for one component of the regression parameter by using the partial profile empirical likelihood. Simulation studies are conducted to assess the finite sample performance. A real data set from the ACTG 175 study is used for illustrating the proposed method.

A three-step system calibration procedure with error compensation for 3D shape measurement

System calibration,which usually involves complicated and time-consuming procedures,is crucial for any three-dimensional(3D) shape measurement system based on vision.A novel improved method is proposed for accurate calibration of such a measurement system.The system accuracy is improved with considering the nonlinear measurement error created by the difference between the system model and real measurement environment.We use Levenberg-Marquardt optimization algorithm to compensate the error and get a good result.The improved method has a 50%improvement of re-projection accuracy compared with our previous method.The measurement accuracy is maintained well within 1.5%of the overall measurement depth range.

Statistical Inference for Partially Linear Regression Models with Measurement Errors

在这篇论文，作者为某 covariates 与错误被测量的部分线性的回归模型调查统计推理的三个方面。第一，一个带宽选择过程被建议，它是基于差别的技术的联合；GCV 方法。第二，一个 goodness-of-fit 测试过程被建议，它是概括可能性技术的延期。第三，为参量的部分的一个可变选择过程被提供基于非凹面 penalization；改正的侧面最不摆平。作为一样“可变选择经由非，凹面惩罚了可能性；它的神谕性质“(J。Amer。统计员。Assoc， 96， 2001， 1348 1360 ) ，产生评估者与规则化参数的一种合适的选择有一个神谕性质，这被显示出；惩罚功能。模拟研究被进行说明建议过程的有限样品表演。

Partially Linear Single-Index Model in the Presence of Measurement Error

The partially linear single-index model(PLSIM) is a flexible and powerful model for analyzing the relationship between the response and the multivariate covariates. This paper considers the PLSIM with measurement error possibly in all the variables. The authors propose a new efficient estimation procedure based on the local linear smoothing and the simulation-extrapolation method,and further establish the asymptotic normality of the proposed estimators for both the index parameter and nonparametric link function. The authors also carry out extensive Monte Carlo simulation studies to evaluate the finite sample performance of the new method, and apply it to analyze the osteoporosis prevention data.

Dynamic Factor Method of Computing Dynamic Mathematical Model for System Simulation

The computational methods of a typical dynamic mathematical model that can describe the differential element and the inertial element for the system simulation are researched. The stability of numerical solutions of the dynamic mathematical model is researched. By means of theoretical analysis, the error formulas, the error sign criteria and the error relationship criterion of the implicit Euler method and the trapezoidal method are given, the dynamic factor affecting the computational accuracy has been found, the formula and the methods of computing the dynamic factor are given. The computational accuracy of the dynamic mathematical model like this can be improved by use of the dynamic factor.

BLUP Estimation of Linear Mixed-effects Models with Measurement Errors and Its Applications to the Estimation of Small Areas

The linear mixed-effects model(LMM) is a very useful tool for analyzing cluster data.In practice,however,the exact values of the variables are often difficult to observe.In this paper,we consider the LMM with measurement errors in the covariates.The empirical BLUP estimator of the linear combination of the fixed and random effects and its approximate conditional MSE are derived.The application to the estimation of smaE area is provided.Simulation study shows good performance of the proposed estimators.

Generalized Profile LSE in Varying-Coefficient Partially Linear Models with Measurement Errors

This paper is concerned with the estimating problem of a semiparametric varying-coefficient partially linear errors-in-variables model Yi=Xτiβ+Zτiα(Ui)+εi , Wi=Xi+ξi,i=1, ··· , n. Due to measurement errors, the usual profile least square estimator of the parametric component, local polynomial estimator of the nonparametric component and profile least squares based estimator of the error variance are biased and inconsistent. By taking the measurement errors into account we propose a generalized profile least squares estimator for the parametric component and show it is consistent and asymptotically normal. Correspondingly, the consistent estimation of the nonparametric component and error variance are proposed as well. These results may be used to make asymptotically valid statistical inferences. Some simulation studies are conducted to illustrate the finite sample performance of these proposed estimations.

Derivation and conformity measurement of a popular explicit analytic Borowy 2C PV module model

A popular explicit analytic Borowy 2C PV module model is proposed for power generation prediction.The maximum power point and the open-circuit point which are calculated in this model cannot be equal to the data given by manufacturers under standard test condition(STC).The derivation of this model has never been mentioned in any literatures.The parameter forms of 2C model in this paper are more simplified,and the model is decomposed into a STC sub-model and an incremental sub-model.The STC model is derived successfully from an ideal single-diode circuit model.Relative error estimations are developed to do the conformity error measurements.The analysis results showed that though the biases at those critical points are very small,the conformity will depend on both of the two ratio values I_(m)/I_(sc) and V_(m)/V_(oc),which can be used to verify whether 2C model is applicable for the PV module produced by a particular manufacturer.

Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer

The state of charge(SOC)estimation of lithium-ion battery is an important function in the battery management system(BMS)of electric vehicles.The long short term memory(LSTM)model can be employed for SOC estimation,which is capable of estimating the future changing states of a nonlinear system.Since the BMS usually works under complicated operating conditions,i.e the real measurement data used for model training may be corrupted by non-Gaussian noise,and thus the performance of the original LSTM with the mean square error(MSE)loss may deteriorate.Therefore,a novel LSTM with mixture kernel mean p-power error(MKMPE)loss,called MKMPE-LSTM,is developed by using the MKMPE loss to replace the MSE as the learning criterion in LSTM framework,which can achieve robust SOC estimation under the measurement data contaminated with non-Gaussian noises(or outliers)because of the MKMPE containing the p-order moments of the error distribution.In addition,a meta-heuristic algorithm,called heap-based-optimizer(HBO),is employed to optimize the hyper-parameters(mainly including learning rate,number of hidden layer neuron and value of p in MKMPE)of the proposed MKMPE-LSTM model to further improve its flexibility and generalization performance,and a novel hybrid model(HBO-MKMPE-LSTM)is established for SOC estimation under non-Gaussian noise cases.Finally,several tests are performed under various cases through a benchmark to evaluate the performance of the proposed HBO-MKMPE-LSTM model,and the results demonstrate that the proposed hybrid method can provide a good robustness and accuracy under different non-Gaussian measurement noises,and the SOC estimation results in terms of mean square error(MSE),root MSE(RMSE),mean absolute relative error(MARE),and determination coefficient R2are less than 0.05%,3%,3%,and above 99.8%at 25℃,respectively.

Estimation in Linear Regression with Laplace Measurement Error Using Tweedie-Type Formula

Based on a Tweedie-type formula developed under the Laplace distribution,this paper proposes a new bias-corrected estimator of the regression parameters in a simple linear model when the measurement error follows a Laplace distribution.Large sample properties,including the consistency and the asymptotic normality,are investigated.The finite sample performance of the proposed estimators are evaluated via simulation studies,as well as comparison studies with some existing estimation procedures.

Research on error compensation of robot based on multi-hole measurement technology and particle swarm optimization

Robot error compensation is a technique for enhancing the positioning accuracy of the system. This paper presented an error measuring technique for serial robots based on the multi-hole measuring method, combined with the intelligent particle swarm optimisation(PSO) to obtain the optimal solution of the robot’s error compensation values, thereby improving the positioning accuracy of the robot. In the experiment, the robot error was measured using self-made multi-hole measuring plates and probes, and the experimental data were combined with PSO for the error comprehensive analysis. The results showed that on this type of serial robot, the multi-hole measuring method and PSO algorithm had obvious error compensation effects, which effectively improved the positioning accuracy of the robot, with the error reduced by 35% after compensation.

Selection of the Relevant Turbulence Model in a CFD Simulation of a Flow Disturbed by Hydraulic Elbow--Comparative Analysis of the Simulation with Measurements Results Obtained by the Ultrasonic Flowmeter

The article is an attempt to compile the results of CFD liquid flow simulation through pipeline section containing hydraulic elbow with the results of ultrasonic flow measurements. To carry out the measurements behind the flow disturbing element(hydraulic elbow), an ultrasonic flowmeter with applied head set in accordance with the Z-type system was used. For comparative purposes, a flow simulation for 3 different turbulence models(k-epsilon, SST and SSG) was performed. It was found that with a proper ultrasonic flowmeter heads configurations, it is possible to measure the flow rate disturbed by the hydraulic elbow at any distance from the source of the disturbance. It has to use appropriate correction factor that can be determined by knowing the flow velocity profile equation. Based on comparison of CFD simulation results with experimental data, the accuracy/purposefulness of using individual turbulence models in the case of discussed hydraulic installation was evaluated.

Modeling, identification and compensation for geometric errors of laser annealing table

In order to improve the process precision of an XY laser annealing table, a geometric error modeling, and an identification and compensation method were proposed. Based on multi-body system theory, a geometric error model for the laser annealing table was established. It supports the identification of 7 geometric errors affecting the annealing accuracy. An original identification method was presented to recognize these geometric errors. Positioning errors of 5 lines in the workspace were measured by a laser interferometer, and the 7 geometric errors were identified by the proposed algorithm. Finally, a software-based error compensation method was adopted, and a compensation mechanism was developed in a postprocessor based on LabVIEW. The identified geometric errors can be compensated by converting ideal NC codes to actual NC codes. A validation experiment has been conducted on the laser annealing table, and the results indicate that positioning errors of two validation lines decreased from ±37 μm and ±33 μm to ±5 μm and ±4.5 μm, respectively. The geometric error modeling, identification and compensation method presented in this work can be straightforwardly extended to any configurations of 2-dimensional worktable.

Measurement Error for Age of Onset in Prevalent Cohort Studies

Prevalent cohort studies involve screening a sample of individuals from a population for disease, recruiting affected individuals, and prospectively following the cohort of individuals to record the occurrence of disease-related complications or death. This design features a response-biased sampling scheme since individuals living a long time with the disease are preferentially sampled, so naive analysis of the time from disease onset to death will over-estimate survival probabilities. Unconditional and conditional analyses of the resulting data can yield consistent estimates of the survival distribution subject to the validity of their respective model assumptions. The time of disease onset is retrospectively reported by sampled individuals, however, this is often associated with measurement error. In this article we present a framework for studying the effect of measurement error in disease onset times in prevalent cohort studies, report on empirical studies of the effect in each framework of analysis, and describe likelihood-based methods to address such a measurement error.