Measurement Error Correction Research Articles

Error Correction Method of Platform Displacement for Large Load Static Test

The test platform is an indispensable supporting bearing structure in static tests, which is mainly used to fix test products, simulate the real loading boundary, and provide better operation space for measurement. With the large-scale and complicated spacecraft structure, the static test platform tends to be large-scale, and the displacement measurement error of the static test platform under large load conditions cannot be ignored. The conventional error correction method only uses the displacement value of the monitoring point at the corresponding position of the platform to compensate, ignoring the comprehensive influence of the six degrees of freedom displacement for the test platform on the displacement measurement of the specimen. When it is used for the error correction of the static test of large spacecraft structures, there is a large measurement error affected by the structural size. In view of this, according to the static test requirements of a large spacecraft, a large bearing static test platform with a lateral load of 22 T and a longitudinal load of 52 T is designed, and a displacement measurement error correction method based on six degrees of freedom is proposed. Through the static test of a large spacecraft structure, the method can effectively reduce the displacement measurement error caused by the displacement of the test platform, and the error correction rate can reach 20%.

Spectroscopy‐based prediction of 73 wheat quality parameters and insights for practical applications

AbstractBackground and ObjectivesQuality assessment of bread wheat is time‐consuming and requires the determination of many complex characteristics. Because of its simplicity, protein content prediction using near‐infrared spectroscopy (NIRS) serves as the primary quality attribute in wheat trade. To enable the prediction of more complex traits, information from Raman and fluorescence spectra is added to the NIR spectra of whole grain and extracted flour. Model robustness is assessed by predictions across cultivars, locations, and years. The prediction error is corrected for the measurement error of the reference methods.FindingsSuccessful prediction, robustness testing, and measurement error correction were achieved for several parameters. Predicting loaf volume yielded a corrected prediction error RMSECV of 27.5 mL/100 g flour and an R² of 0.86. However, model robustness was limited due to data distribution, environmental factors, and temporal influences.ConclusionsThe proposed method was proven to be suitable for applications in the wheat value chain. Furthermore, the study provides valuable insights for practical implementations.Significance and NoveltyWith up to 1200 wheat samples, this is the largest study on predicting complex characteristics comprising agronomic traits; dough rheological parameters measured by Extensograph, micro‐doughLAB, and GlutoPeak; baking trial parameters like loaf volume; and specific ingredients, such as grain protein content, sugars, and minerals.

Evaluation of precipitation measurements using a standard rain gauge in relation to data from a precision lysimeter

Abstract The construction of modern lysimeters with a precise weighing system made it possible to achieve an unprecedented accuracy of precipitation measurement. This study compares two methods of measuring precipitation in the conditions of the humid continental climate of the Eastern Slovakian Lowland (Slovakia): measurement using a standard tipping-bucket rain gauge vs. precision weighable lysimeter. Data from the lysimeter were used as a reference measurement. The comparison period lasted four years (2019–2022). Only liquid rainfall was compared. The rain gauge was found to underestimate precipitation compared to the lysimeter. Cumulative precipitation for the entire monitored period captured by the rain gauge was 2.8% lower compared to lysimeter measurements. When comparing hourly and daily totals of precipitation and precipitation events, a very high degree of agreement was detected (r 2 > 0.99; RMSE from 0.22 to 0.51 mm h–1). A comparison based on precipitation intensity showed a decreasing trend in measurement accuracy with increasing precipitation intensity. This tendency has an exponential course. With increasing intensity of precipitation, increasing intensity of wind was also recorded. In order to correct measurement errors, simple correction method was proposed, which helped to partially eliminate the inaccuracies of the rain gauge measurement.

Effect of flame thickness on the temperature measurement error for bare-bead thermocouple in open pool fires with different fuels

Accurately obtaining the temperature distribution of the flame is crucial for a comprehensive understanding of the combustion behavior of the pool fire. The correction method is needed to compensate the measurement error when the temperature is measured by bare-bead thermocouple in the open pool experiment, but the effect of flame thickness is rarely considered in the current method. Thus, this work develops a coupled heat transfer model of bare-bead thermocouples in open pool fires and modifies the flame radiation characteristics based on the fuel type and flame thickness. The control mechanism of measurement error in the model is identified, and then the effects of main parameters such as flame thickness and flame temperature on measurement error are analyzed. The results show that the flame emissivity decreases exponentially with the decrease of flame thickness, the minimum emissivity of gasoline flame is 0.26 when flame thickness is 0.1 m. The decrease of flame thickness leads to a sharp increase of temperature measurement error. The maximum increment of error can reach 227.3 K. On this basis, the flame thickness limit value, considering the correction of temperature measurement error in different hydrocarbon pool fire experiments is proposed.

Application of Artificial Neural Networks and Adaptive Neuro‐Fuzzy Inference Systems for Attenuating Effects of Temperature and Humidity on Sensor Accuracy

AbstractThe novelty of this work is the correction of measurement errors in a galvanic cell sensor, which might occur due to temperature and humidity variations, using artificial neural networks (ANN) and adaptive neuro‐fuzzy inference systems (ANFIS). The training process for the ANN and ANFIS employed 540 data sets consisting of voltage responses of the galvanic cell to known oxygen concentrations, temperatures, and relative humidities. The trained ANFIS was embedded within a central processing unit (CPU) and tested on another 144 data sets with various oxygen concentrations, temperatures, and relative humidities. The deviations for the sensor with ANFIS were less than 0.5 % for 109 data points (75 %), and there was no deviation greater than 2.5 %.

A study of error correction method based on selective focus for curved rail profile measurement

Rail profile detection is an important means of railway operation and maintenance. Currently, structured light profile measurement technology is mainly used for vehicle-mounted rail profile measurement. The body attitude of the vehicle will change as it passes by a curve, and the laser plane of the rail profile measurement device would be non-perpendicular to the rail in the local longitudinal direction, resulting in errors in rail profile measurement. To address this issue, an error correction method based on selective focus for curved rail profile measurement is proposed. The virtual 3D rail is generated with the section image of a single-beam laser and projected onto the image plane, which is then combined with the projection data of virtual 3D rail and the actual rail from the rail image to build the optimization function. The optimal auxiliary plane perpendicular to the rail’s longitudinal direction is then determined. Finally, error correction is achieved by projecting the measured data onto the optimal auxiliary plane. The effectiveness and accuracy of the proposed method are validated through experiments, demonstrating that regardless of the laser plane’s relative pitch or yaw motion with respect to the rail, the corrected profile wear measurement error was under 0.10 mm, satisfying the requirement for rail wear measurement accuracy. In this study, the plane perpendicular to the longitudinal direction of the rail was obtained through selective focus, thus providing a new insight into future work on rail profile measurement error correction.

Multivariate longitudinal analysis for the association between brain atrophy and cognitive impairment in prodromal Huntington's disease subjects.

Cognitive impairment has been widely accepted as a disease progression measure prior to the onset of Huntington's disease. We propose a sophisticated measurement error correction method that can handle potentially correlated measurement errors in longitudinally collected exposures and multiple outcomes. The asymptotic theory for the proposed method is developed. A simulation study is conducted to demonstrate the satisfactory performance of the proposed two-stage fitting method and shows that the independent working correlation structure outperforms other alternatives. We conduct a comprehensive longitudinal analysis to assess how brain striatal atrophy affects impairment in various cognitive domains for Huntington's disease.

Development of a semi-open chamber system for the gas exchange measurement of whole-canopy under steady and unsteady states in cucumber seedlings

BackgroundLarge-scale data on the photosynthetic characteristics of whole crop canopy is crucial for improving yield. However, current data collection methods remain challenging, and the time constraints associated with photosynthetic data collection further complicate matters. Developing a practical yet easy-to-use tool for collecting whole-canopy data is essential to address these challenges. Furthermore, it is necessary to obtain instantaneous measurements of photosynthetic rate over a wide range of CO2 concentrations under an unsteady state to enable faster data collection and obtain reliable biochemical limits of carbon assimilation. This study developed a semi-open chamber system with steady and unsteady state measurement techniques to collect biochemical photosynthetic data from an entire cucumber canopy, emphasizing the correction procedures for CO2 concentration of unsteady state measurements applicable regardless of chamber scale.ResultsAfter constructing a semi-open chamber system, we described how to correct measurement errors according to chamber volume. In order to assess the accuracy of the newly developed system, an analysis was conducted to determine the overall measurement error resulting from variations in the reference, sample CO2 concentration, and leakage flow rate. The total measurement error was accurate to no more than 10%. Furthermore, the difference between the photosynthetic rate of the single leaf and that of the whole-canopy was not significant in Rubisco activity-limited carboxylation range. In addition, the Farquhar–von Caemmerer–Berry (FvCB) model parameters and the photosynthetic rate estimation values were compared to evaluate the steady- and unsteady state measurement methods between the cucumber seedlings' single-leaf and whole-canopy. The average root mean square error of the FvCB model in the steady (standard A-Ci response) and unsteady states (800 to 400 ramp) of the chambers was 1.4 and 2.3, respectively. Results show that the developed system is suitable for measuring the gas exchange rate of the cucumber canopy.ConclusionsWe demonstrate the correction method for measurement errors to enable the gas exchange rate of the whole-canopy even in an unsteady state. The correction method of the measurement system of the gas exchange rate for the whole- canopy can be applied regardless of the volume of the chamber, and it can be applied simply to other chamber systems. In addition, an unsteady state measurement method for fast data collection was also applicable. However, it was deemed necessary to identify a more optimal measurement range by conducting measurements across a broader range of values.

Prediction method of peak carbon emission in power grid based on energy consumption elasticity coefficient

With the implementation of China’s “two-carbon” target strategy, the status of renewable energy in the power system is gradually improving. In order to improve the carbon emission control level of power grid, it is necessary to predict the peak carbon emission of power grid. A prediction method of grid carbon emission peak based on energy elasticity coefficient is proposed. A time series model of grid carbon emission samples was constructed, and a combination of combinatorial sorting and machine learning was used to reconstruct the time series of grid carbon emission peaks. Based on the results of time series reconstruction, feature extraction and classification training of grid carbon emission peak are carried out, and the measurement error correction of grid carbon emission under the constraint of energy consumption elasticity is realized. Combined with the association rule mining method, the fusion processing of grid carbon emission peak samples under the elastic constraint of energy consumption is realized. Through characteristic detection and fusion processing of energy consumption elasticity coefficient, the reconstituted carbon emission samples of power grid constrained by energy consumption elasticity were reconstructed, and the peak carbon emission of power grid was predicted. The simulation results show that this method has high accuracy and convergence in predicting the peak carbon emission of power grid, and improves the error correction ability in the prediction process.

ETAM: Next Generation Event Timer for Picosecond-Precision Time and Amplitude Measurements.

Riga Event Timers have the ability to measure the interval between events with high resolution, on the order of picoseconds. However, they have several drawbacks, such as sensitivity to environmental temperature changes and an inability to capture the amplitude of the events. In this work, we present the ETAM: a next generation Event Timer. Its innovative features include adaptive correction of measurement errors based on an internal temperature sensor, and integrated peak-detector circuit to determine the amplitude of nanosecond-duration pulses. Evaluation shows that the ETAM has high thermal stability with a root mean square error (RMSE) of <3 ps in a temperature range between 0 and +40 °C, and accurate event amplitude measurement capability, with <2.3 mV RMSE in the 100-1000 mV range. These improvements allow the ETAM to be used in satellite laser ranging, optical time-domain reflectometry, and other field applications that require temperature- and amplitude-based time correction in addition to high robustness, performance, and stability.

Effects of Gas and Steam Humidity on Particulate Matter Measurements Obtained Using Light-Scattering Sensors.

With the increasing need for particulate matter (PM) monitoring, the demand for light-scattering sensors that allow for real-time measurements of PM is increasing. This light-scattering method involves irradiating light to the aerosols in the atmosphere to analyze the scattered light and measure mass concentrations. Humidity affects the measurement results. The humidity in an outdoor environment may exist as gas or steam, such as fog. While the impact of humidity on the light-scattering measurement remains unclear, an accurate estimation of ambient PM concentration is a practical challenge. Therefore, this study investigated the effects of humidity on light-scattering measurements by analyzing the variation in the PM concentration measured by the sensor when relative humidity was due to gaseous and steam vapor. The gaseous humidity did not cause errors in the PM measurements via the light-scattering method. In contrast, steam humidity, such as that caused by fog, resulted in errors in the PM measurement. The results help determine the factors to be considered before applying a light-scattering sensor in an outdoor environment. Based on these factors, directions for technological development can be presented regarding the correction of measurement errors induced by vapor in outdoor environments.

Effect of deformation on crack extension measurement for compact tension specimen with the DCPD technique

Direct current potential drop (DCPD) technique is a crack growth monitoring technique widely used in the crack growth test of specimens in laboratory environment. Deformation of the specimen during test is one of the important factors that affect the accuracy and resolution. Because the effect of deformation on measurement is usually mixed with other factors, quantitative evaluation is difficult through experiments method only. In this work a novel quantitative estimation method combining experiment and finite element analysis has been proposed, and the effect of deformation on measurement for 316 L stainless steel compact tension specimen has been quantitatively evaluated. Through the tensile test and DCPD monitoring test of the non-cracked 316 L stainless steel plate-shaped specimen, the material mechanical property parameters and the change of potential drop caused by deformation were obtained. Then the slow tensile tests have been performed on compact tension specimens. Combined with the FE simulation of the experimental process, the effect of deformation on the potential drop under different crack length has been evaluated. The results show that the influence of deformation on the potential drop is a factor that must be considered for the crack growth monitoring experiment with high crack growth resolution. The proposed method can be used to quantitatively evaluate the influence of deformation, a factor that was usually ignored in the past, on the measurement of crack growth, which provides a method and basis for correction of crack measurement errors caused by deformation. In addition, the proposed method can be used to obtain a more accurate calibration function, which is of great significance to the improvement of the accuracy and resolution of crack growth monitoring.

Exploring regression dilution bias using repeat measurements of 2858 variables in ≤49 000 UK Biobank participants.

Measurement error in exposures and confounders can bias exposure-outcome associations but is rarely considered. We aimed to assess random measurement error of all continuous variables in UK Biobank and explore approaches to mitigate its impact on exposure-outcome associations. Random measurement error was assessed using intraclass correlation coefficients (ICCs) for all continuous variables with repeat measures. Regression calibration was used to correct for random error in exposures and confounders, using the associations of red blood cell distribution width (RDW), C-reactive protein (CRP) and 25-hydroxyvitamin D [25(OH)D] with mortality as illustrative examples. The 2858 continuous variables with repeat measures varied in sample size from 109 to 49 121. They fell into three groups: (i) baseline visit [529 variables; median (interquartile range) ICC = 0.64 (0.57, 0.83)]; (ii) online diet by 24-h recall [22 variables; 0.35 (0.30, 0.40)] and (iii) imaging measures [2307 variables; 0.85 (0.73, 0.94)]. Highest ICCs were for anthropometric and medical history measures, and lowest for dietary and heart magnetic resonance imaging.The ICCs (95% confidence interval) for RDW, CRP and 25(OH)D were 0.52 (0.51, 0.53), 0.29 (0.27, 0.30) and 0.55 (0.54, 0.56), respectively. Higher RDW and levels of CRP were associated with higher risk of all-cause mortality, and higher concentration of 25(OH)D with lower risk. After correction for random measurement error in the main exposure, the associations all strengthened. Confounder correction did not influence estimates. Random measurement error varies widely and is often non-negligible. For UK Biobank we provide relevant statistics and adaptable code to help other researchers explore and correct for this.

Fiducial Markers Aided Position Estimation for Vertical Landing

Autonomous landing is a critical step in unmanned aerial vehicles (UAVs) and requires accurate position information. In cases where GPS signals are unavailable or obstructed, vision-based approaches can provide support for landing capabilities. In this study, a vision-based position estimation algorithm is being developed in conjunction with markers used in vertical take-off and landing (VTOL) systems of UAVs. The developed framework is designed to be compatible with various types of visual markers. The Kalman Filter is used to the calculated position to correct measurement errors and reduce the uncertainty of the position estimation. The developed algorithm is extensively tested in a simulation environment. The positions of a quadrotor aircraft are compared with real measurements to analyze the performance of the proposed vision-based position estimation algorithm. The results demonstrate an acceptable level of accuracy for the algorithm. This study discusses the potential of using visual markers and integrating Kalman filtering to improve the accuracy of positioning in vertical takeoff and landing systems of the UAVs. The development of a vision-based position estimation algorithm can enhance the reliability and precision of autonomous landing capabilities and enable successful landings in situations where GPS signals are limited or unavailable.

Multiple imputation of more than one environmental exposure with nondifferential measurement error.

Measurement error is common in environmental epidemiologic studies, but methods for correcting measurement error in regression models with multiple environmental exposures as covariates have not been well investigated. We consider a multiple imputation approach, combining external or internal calibration samples that contain information on both true and error-prone exposures with the main study data of multiple exposures measured with error. We propose a constrained chained equations multiple imputation (CEMI) algorithm that places constraints on the imputation model parameters in the chained equations imputation based on the assumptions of strong nondifferential measurement error. We also extend the constrained CEMI method to accommodate nondetects in the error-prone exposures in the main study data. We estimate the variance of the regression coefficients using the bootstrap with two imputations of each bootstrapped sample. The constrained CEMI method is shown by simulations to outperform existing methods, namely the method that ignores measurement error, classical calibration, and regression prediction, yielding estimated regression coefficients with smaller bias and confidence intervals with coverage close to the nominal level. We apply the proposed method to the Neighborhood Asthma and Allergy Study to investigate the associations between the concentrations of multiple indoor allergens and the fractional exhaled nitric oxide level among asthmatic children in New York City. The constrained CEMI method can be implemented by imposing constraints on the imputation matrix using the mice and bootImpute packages in R.

A true score imputation method to account for psychometric measurement error.

Scores on self-report questionnaires are often used in statistical models without accounting for measurement error, leading to bias in estimates related to those variables. While measurement error corrections exist, their broad application is limited by their simplicity (e.g., Spearman's correction for attenuation), which complicates their inclusion in specialized analyses, or complexity (e.g., latent variable modeling), which necessitates large sample sizes and can limit the analytic options available. To address these limitations, a flexible multiple imputation-based approach, called true score imputation, is described, which can accommodate a broad class of statistical models. By augmenting copies of the original dataset with sets of plausible true scores, the resulting set of datasets can be analyzed using widely available multiple imputation methodology, yielding point estimates and confidence intervals calculated with respect to the estimated true score. A simulation study demonstrates that the method yields a large reduction in bias compared to treating scores as measured without error, and a real-world data example is further used to illustrate the benefit of the method. An R package implements the proposed method via a custom imputation function for an existing, commonly used multiple imputation library (mice), allowing true score imputation to be used alongside multiple imputation for missing data, yielding a unified framework for accounting for both missing data and measurement error. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Variable Selection and Estimation for Misclassified Binary Responses and Multivariate Error-Prone Predictors

In statistical analysis or supervised learning, classification has been an attractive topic. Typically, a main goal is to adopt predictors to characterize the primarily interested binary random variables. To model a binary response and predictors, parametric structures, such as logistic regression models or probit models, are perhaps commonly used approaches. However, due to the convenience of data collection, existence of non-informative variables as well as inevitability of measurement error in both responses and predictors become ubiquitous. The simultaneous appearance of these complex features make data analysis become challenging. To address those concerns, we propose a valid inferential method to deal with measurement error and handle variable selection simultaneously. Specifically, we focus on logistic regression or probit models, and propose estimating functions by incorporating corrected responses and predictors. After that, we develop the boosting procedure with error-eliminated estimating functions accommodated to do variable selection and estimation. To justify the proposed method, we examine the convergence of the boosting algorithm and rigorously establish the theoretical results. Through numerical studies, we find that the proposed method accurately retains informative predictors and gives precise estimators, and its performance is generally better than that without measurement error correction. The supplementary materials of this article, including proofs of theoretical results and computer code, are available online.

Beyond the “inverse relationship”: Area mismeasurement may affect actual productivity, not just how we understand it

AbstractMeasurement error in agricultural field area and productivity data for developing countries is widely acknowledged, but there is a shortage of evidence on what this implies, especially for farmers. By comparing self‐reported to global positioning system measurements, we investigate area measurement errors using a nationally representative dataset of Zambian smallholder maize plots. We examine the implications for researchers’ understanding of productivity, but also how actual productivity may be affected. We find land area tends to be overstated on smaller fields and understated on larger fields. Correcting measurement error strengthens evidence of an inverse relationship between field size and productivity. Input use patterns indicate farmers believe the inaccurately reported area figures, suggesting measurement errors affect input choices and thus actual productivity. Improving farmer understanding of area measurements could improve productivity. Improving the accuracy of area data will improve researchers’ understanding of productivity.

Measurement error models for spatial network lattice data: Analysis of car crashes in Leeds

Abstract Road casualties represent an alarming concern for modern societies. During the last years, several authors proposed sophisticated approaches to help authorities implement new policies. These models were usually developed considering a set of socio-economic variables and ignoring the measurement error, which can bias the statistical inference. This paper presents a Bayesian model to analyse car crash occurrences at the network-lattice level, taking into account measurement error in the spatial covariate. The suggested methodology is exemplified by considering the collisions in the road network of Leeds (UK) during 2011–2019. Traffic volumes are approximated using an extensive set of counts obtained from mobile devices and the estimates are adjusted using a spatial measurement error correction.

Two Sample Approaches to Regression Calibration for Measurement Error Correction

The goal of this work is to create methods for enhancing measurement error using regression calibration as a strategy by combining two samples, thereby increasing the relative efficiency of linear regression models. Because two or more samples are more likely to provide an accurate representation of the population than a single sample under inquiry, utilizing two samples in regression calibration is likely to produce a realistic depiction of what the actual population is when error-free. This study has generated independent estimates from two samples and combined them with weights equal to the inverse of their estimated probabilities of sample inclusion. It has also integrated two data sets into a single data set and suitably adjusted the weights on each sampled unit. The regression calibration method is most commonly used to correct predictor-response bias caused by variable measurement imperfections. Because of its simplicity, this method is often used. The fundamental principle behind regression calibration is to estimate the conditional expectation of a genuine response, given predictors measured with error and other covariates supposed to be measured without error. The predicted values are then estimated and used to assess the relationship between the response and an outcome in place of the unknown genuine response. Further information on the unobservable true predictors is required by the regression calibration program. This data is frequently obtained from a validation study that employs unbiased measurements for genuine predictors. This study has employed and compared the results obtained from the two sample approaches. Measuring errors can be produced by a variety of sources, including instrument error, laboratory error, human error, problems in documenting or executing measurements, self-reporting errors, and natural oscillations in the underlying amount. Covariate measurement error has three effects: In addition to hiding the properties of the data, which makes graphical model analysis difficult, it produces bias in parameter estimates for statistical models, resulting in a sometimes significant loss of power for detecting fascinating correlations between variables. The two sample approaches employed by the study have yielded acceptable results.