Measurement Error Problems Research Articles

Impact of measurement error on maximum hybrid exponentially weighted moving average control chart

Statistical process control provides various types of control charts for monitoring of mean and variance shifts in the industrial production process individually as well as jointly to improve and maintain the quality products. The authors proposed these control charts based on sample values selected to calculate the desired statistics assuming that these values are measured correctly. But in a real life situation, measurements of the values may suffer from errors ultimately affecting the efficiency of the control charts. A few of the researchers in the field of control charts also discussed the problem of measurement error during process monitoring and proposed solutions to avoid losses of producers. We also present a hybrid exponentially weighted moving average control chart for joint monitoring of mean as well as variance and study the effect of measurement error on the efficiency of this control chart and name it as Max-HEWMAME control chart. The impact of measurement error has been shown in the calculations and presented in the shape of average run lengths ( ) and standard deviations of run lengths ( ) using the Monte Carlo simulation method. A real life example is also included to support the simulation results.

Estimating the Problem of Non-Response and Measurement Error in Sample Survey

Estimating the Problem of Non-Response and Measurement Error in Sample Survey

Influencing Factor and Error Analysis of Operating Capacitor Voltage Transformer Harmonic Measurement

With the increasing demands of power quality monitoring and management, the problem of harmonic measurement error caused by the widespread operation of capacitor voltage transformers (CVT) in high-voltage power grids has been unavoidable. This paper analyses the influence of environmental factors on harmonic measurement error of CVT, and proposes a new method for analysing the harmonic measurement errors of operating CVT, which explains the unstable phenomenon of CVT measurement errors based on field comparison test, and provides a progressing screening method of effective analysis data. the CVT harmonic error characteristic analysis method proposed in this paper considers the impact of field installation conditions, electromagnetic environment, and other actual operating environment, which can reflect the actual harmonic error characteristics of CVT in field operation. A CVT harmonic measurement system is established based on the proposed method. The validation and effectiveness of the proposed method is illustrated on a field measurements of 1000kV CVT in Shandong Province.

Retraction Note: Simulation of sea surface temperature based on non-sampling error and psychological intervention of music education

In statistical survey, except sampling error, the error caused by other reasons is called non-sampling error. Non-sampling errors may affect survey results. This paper mainly studies the emergence of non-sampling error in the system, the corresponding history and culture and related applications, seeks the causes of the error and explores the solutions, and designs a method that can be used to measure. By using this analysis method, the percentage of non-sampling error in the total error can be adjusted through statistics. In addition, the adaptability and path of this measurement method, as well as the adjustment method for the measurement error and estimation problems found in the multi-order model are also studied. As we all know, the subtropical northeast Pacific plays an extremely important role in the whole process of transmission to the tropics. Through the investigation of the physical and mental health, interpersonal relationship, and other problems of the students in a medical college, this paper analyzes the intervention effect of this treatment method by using a variety of treatment methods such as music and song performance organized by the masses, and probes into whether this intervention method can promote the improvement of college students’ mental health level and test the effectiveness of the treatment effect. It provides an important reference for the development of physical and mental health education of college students. This paper discusses the effect of psychological intervention in music education by simulating sea surface temperature with non-sampling error.

Measurement errors in control risk regression: Acomparison of correction techniques.

Control risk regression is a diffuse approach for meta‐analysis about the effectiveness of a treatment, relating the measure of risk with which the outcome occurs in the treated group to that in the control group. The severity of illness is a source of between‐study heterogeneity that can be difficult to measure. It can be approximated by the rate of events in the control group. Since the estimate is a surrogate for the underlying risk, it is prone to measurement error. Correction methods are necessary to provide reliable inference. This article illustrates the extent of measurement error effects under different scenarios, including departures from the classical normality assumption for the control risk distribution. The performance of different measurement error corrections is examined. Attention will be paid to likelihood‐based structural methods assuming a distribution for the control risk measure and to functional methods avoiding the assumption, namely, a simulation‐based method and two score function methods. Advantages and limits of the approaches are evaluated through simulation. In case of large heterogeneity, structural approaches are preferable to score methods, while score methods perform better for small heterogeneity and small sample size. The simulation‐based approach has a satisfactory behavior whichever the examined scenario, with no convergence issues. The methods are applied to a meta‐analysis about the association between diabetes and risk of Parkinson disease. The study intends to make researchers aware of the measurement error problem occurring in control risk regression and lead them to the use of appropriate correction techniques to prevent fallacious conclusions.

Capacitive Online Corn Moisture Content Sensor Considering Porosity Distributions: Modeling, Design, and Experiments

An online corn moisture content measurement device would be a key technology for providing accurate feedback information for industrial drying processes to enable the dynamic tracking and closed-loop control of the process. To overcome the problem of large measurement error caused by the characteristics of the corn flow state and the pore distribution when a parallel plate capacitor is applied to the online moisture content measurement process, in this study, we summarized the constraint conditions of the sensor’s structure parameters by mathematical modeling and calculated the optimal sensor design size. Moreover, the influence of porosity variation on moisture content measurement was studied by using the designed sensor. In addition, a mathematical model for calculating corn moisture content was obtained for the moisture content range of 14.7% to 26.4% w.b., temperature of 5 °C to 35 °C, and porosity of 38.4% to 44.6%. The results indicated that the fluctuation in the online moisture content measurement value was obviously reduced after the porosity compensation. The absolute error of the measured moisture content value was −0.62 to 0.67% w.b., and the average of absolute values of error was 0.32% w.b. The main results provide a theoretical basis and technical support for the development of intelligent industrial grain–drying equipment.

Estimating spatiotemporal patterns of deaths by COVID-19 outbreak on a global scale

ObjectiveOur main objective is to estimate the trend of deaths by COVID-19 on a global scale, considering the six continents.Study designThe study design was a retrospective observational study conducted using...

RETRACTED ARTICLE: Simulation of sea surface temperature based on non-sampling error and psychological intervention of music education

In statistical survey, except sampling error, the error caused by other reasons is called non-sampling error. Non-sampling errors may affect survey results. This paper mainly studies the emergence of non-sampling error in the system, the corresponding history and culture and related applications, seeks the causes of the error and explores the solutions, and designs a method that can be used to measure. By using this analysis method, the percentage of non-sampling error in the total error can be adjusted through statistics. In addition, the adaptability and path of this measurement method, as well as the adjustment method for the measurement error and estimation problems found in the multi-order model are also studied. As we all know, the subtropical northeast Pacific plays an extremely important role in the whole process of transmission to the tropics. Through the investigation of the physical and mental health, interpersonal relationship, and other problems of the students in a medical college, this paper analyzes the intervention effect of this treatment method by using a variety of treatment methods such as music and song performance organized by the masses, and probes into whether this intervention method can promote the improvement of college students’ mental health level and test the effectiveness of the treatment effect. It provides an important reference for the development of physical and mental health education of college students. This paper discusses the effect of psychological intervention in music education by simulating sea surface temperature with non-sampling error.

The device and experimental study of the monitoring of the layered soil settlement

In order to solve the problems of large measurement error, low efficiency, complex equipment operation and difficult maintenance of layered soil settlement, this paper designed a new monitoring device of layered soil settlement based on Hall effect. A number of Hall elements are arranged on the monitoring circuit board at equal spacing, and the circuit board is fixed in the aluminum tube and sealed. The aluminum tube with multiple settlement magnetic rings is drilled into the earth vertically, and the settlement magnetic rings are distributed in different soil layers. The settlement of the soft soil will move the magnetic ring, causing its position to change, which will cause the change of magnetic flux density around Hall element, and the output voltage of Hall element will also change, and then the voltage signal will be transmitted to Hall signal concentrator through RS-485 bus communication mode. The Hall signal concentrator stores the signal in FLASH and communicates with the upper computer through using the LORA wireless communication module. Through data processing and model analysis in the upper computer, the function between Hall voltage and variation of displacement of the magnetic ring is fitted. Many experimental results indicated that the error of the monitoring device of layered settlement is within 1.0567mm, and the accuracy is high. It can provide real-time monitoring to different soil layers, and the data can be exported at any time, so it has a wider application prospect.

Identifying resting locations of a small elusive forest carnivore using a two-stage model accounting for GPS measurement error and hidden behavioral states

BackgroundStudies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data.MethodsWe developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event.ResultsParameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively.ConclusionsWe demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.

Dealing with measurement error in list experiments: Choosing the right control list design

List experiments are widely used in the social sciences to elicit truthful responses to sensitive questions. Yet, the research design commonly suffers from the problem of measurement error in the form of non-strategic respondent error, where some inattentive participants might provide random responses. This type of error can result in severely biased estimates. A recently proposed solution is the use of a necessarily false placebo item to equalize the length of the treatment and control lists in order to alleviate concerns about respondent error. In this paper we show theoretically that placebo items do not in general eliminate bias caused by non-strategic respondent error. We introduce a new option, the mixed control list, and show how researchers can choose between different control list designs to minimize the problems caused by inattentive respondents. We provide researchers with practical guidance to think carefully about the bias that inattentive respondents might cause in a given application of the list experiment. We also report results from a large novel list experiment fielded to over 4900 respondents, specifically designed to illustrate our theoretical argument and recommendations.

Application Research of Power System Automation Based on Electrical Automation Technology

Power is an indispensable energy in our daily life, and the power system is to manage and maintain the normal supply of electricity, which directly affects the stability of the entire power network. At present, China’s power network is in an important upgrading stage, from the traditional power system to intelligent power system, in this process, automatic electrical technology plays an important role. Therefore, based on electrical automation technology, this paper puts forward the application research of power system automation. This paper deeply studies the power system and the application of automation technology in power system in China. It is considered that PLC and automatic power detection technology, which are extended from electrical automation technology, play an important role in the whole power system operation process. But at present, there are still many deficiencies in the power system based on electrical automation technology, including the problem of large measurement error. Therefore, this paper proposes an improved optimization method. The experiment shows that the maximum error of the improved method is 0.125%, especially in the case of synchronous current power grid frequency of 50 Hz, the error rate is 0. The results show that the optimized method has good measurement accuracy. According to the actual application of the existing electrical automation technology in the power system, this paper puts forward the optimization and improvement measures, and forecasts and analyzes the future application development trend.

Research on the Temperature Control System of 6KA Neodymium Oxide Electrolysis Furnace with BAS_PID Algorithm

In order to solve the obvious temperature fluctuations in the cell during the neodymium oxide electrolysis process, the traditional PID control algorithm has a large lag, the timeliness of the multi-variable electrolysis temperature control system is poor, and the manual parameter adjustment is difficult, the beetle PID automatic temperature control system is proposed. Through the control of the key variable cathode current density, speed up the system to adjust the output speed; use the orthogonal temperature measurement method to replace the traditional single point temperature measurement, and use the domain temperature to represent the point temperature to solve the problem of large temperature measurement errors. This algorithm model is based on MATLAB2017 platform for modeling simulation and experimental research. Compared with traditional PID, this method has better adaptive ability and robust performance. The maximum deviation between the actual electrolysis temperature and the set target temperature is 45°C, and the overshoot is 3.3 %, to meet the process index requirements in the actual production process, and provide a reference case for the improvement of neodymium oxide electrolysis process.

Research on Human Motion Monitoring Method Based on Multi-Joint Constraint Filter Model

In recent years, with the increasing application of human motion monitoring technology in virtual reality, medical health, human-computer interaction, and other fields, higher requirements have been put forward for measurement accuracy. Aiming at the problem of large accumulative measurement error and multi-joint constraint in the traditional inertial measurement system, a multi-joint constrained filter model human motion monitoring method based on data fusion is proposed. Firstly, an accelerometer and magnetometer are used to construct the initial attitude angle calculation model of the human body at rest. Secondly, combined with gyroscope, accelerometer and magnetometer, the attitude optimal estimation model based on Extended Kalman Filter (EKF) algorithm for human body motion was established. Then, a global coordinate system of human multi-joints is established, and the joint constraint is introduced into the attitude optimal estimation model by using the estimation projection method, and the multi-joint constraint filtering model of human motion monitoring is constructed. Finally, a multi-inertial measurement unit (MIMU) human motion monitoring system was built, and the simulation and experimental analysis were carried out. The experimental results show that the maximum angular measurement error is about 1° after filtering with the motion monitoring method proposed in this paper. Compared with the existing algorithm, the accuracy of the three angle measurements is improved by 38.2%, 39.3% and 67.4%, respectively. Based on this, the human motion monitoring system designed can effectively monitor human motion.

Design of monitoring system for redundant communication power supply based on ZigBee

Aiming at the problems of poor maintenance effect and large measurement error of traditional communication power supply monitoring system, a redundant communication power supply monitoring system based on ZigBee is designed. The sensor and power supply cable are installed, the battery working state parameters are collected and cleaned, compressed and fused. The MSP430F149 single chip microcomputer is used to realize the high-speed processing of field information and optimize the configuration of hardware structure. The software maintenance process of the complete redundant communication power supply monitoring system is designed. The logic editing technology is used to correct, adjust and improve the system software. The mathematical model of hardware maintenance is established. The most reasonable monitoring and measurement of redundant communication power supply is calculated by ZigBee method to realize the effective monitoring of communication power supply. The experimental results show that the hardware operation effect of the design system is good, and the measurement error can be effectively reduced.

Waveform Difference Feature-Based Protection Scheme for Islanded Microgrids

Unlike conventional synchronous generators, the fault characteristics of inverter-based distributed generators (IBDGs) are mainly determined by the inverter control strategies. Their limited fault current contributions make the protection design of islanded microgrids (MGs) become a major challenge. This article focuses on the fault characteristics and protection scheme design of islanded MGs. First, the phase differences between the positive-sequence current fault components (PSC-FCs) at both terminals of fault line are analyzed under different network topologies and fault severities. It points out the phase characteristic differences between internal and external faults. Then, on basis of this to overcome the problems of the phase measurement error and protection setting, a waveform difference feature-based protection scheme is proposed. The protection criteria are formulated using the first peak sign and time information of PSC-FCs between both terminals of line, which are extracted by the mathematical morphology (MM) technology. Only relatively low bandwidth communication is required to achieve accurate fault identification. Finally, the simulation results on a 10-kV islanded MG model demonstrate that the proposed scheme can identify internal faults reliably in both looped and radial islanded MGs under different fault resistances and measurement noises.

Retracted] Pose Estimation under Visual Sensing Technology and Its Application in Art Design

The study is aimed at solving the problem of large measurement errors caused by the binocular camera in traditional 3D art design, which leads to inaccurate 3D information of the target. The contour information extraction in the process of human motion pose reconstruction is easily affected by the noise in the image. Therefore, a binocular stereo vision system is built first and it integrates image acquisition, camera calibration, and image processing. The dedistortion method is used to process the image because it can reduce errors. Second, a three‐dimensional human motion pose reconstruction model is implemented, the Gaussian template is used to remove the noise in the image frame, and the change detection template (CDM) is used to solve the problem of background “exposure” and “occlusion.” Finally, simulation experiments are designed to verify the system and model designed. Since the research on the application of pose estimation based on visual sensing technology in art design is still blank, such research has great significance and provides a reference for the research in the field. The literature analysis is used to expound and analyze the application of pose estimation based on visual sensing technology in visual communication design and environmental art design: (1) although the binocular stereo vision system causes some errors in the measurement, the overall error is controlled within 2% and the accuracy is high, which proves that it can be applied to the acquisition of three‐dimensional information of the target in art design; (2) there is a high degree of fitting between the video sequence data created by the three‐dimensional human motion pose reconstruction model designed and the real motion data, which indicates that this method has high accuracy in processing image sequences and the feasibility of applying it to human pose reconstruction in three‐dimensional art design is high; (3) through the analysis of the existing literature, it is found that most of the current visual‐based attitude assessment studies are carried out by using network cameras combined with computers, and the quality of the obtained images is low. The combination of binocular stereo sensor and attitude estimation technology can be applied to the design of advertising, animation, games, and packaging, making the behavior of virtual characters in animation and games more vivid. The combination provides convenience for the collection of environmental spatial information and object attitude information, the formulation of a design scheme, and real‐time monitoring of construction in environmental art design. The purpose of this study is to provide an important theoretical basis for the technical upgrading of art design.

On-Line Measurement of Chip Temperature Based on Blocking Leakage Current of the Insulated-Gate Bipolar Transistor Module in the High-Temperature Reverse-Bias Test

In high-temperature reverse-bias test of an insulated-gate bipolar transistor module, the problem of self-heating in chip resulting from power loss arises with high frequency. To address this problem, the law of the variation in leakage current with temperature for the reverse-bias state of a device is derived and utilized to establish a temperature calibration curve, with which the online measurement of the insulate-gate bipolar transistor module chip temperature can be implemented directly by real-time monitoring of device leakage current in high-temperature reverse-bias test. The method we proposed solves the problem of large measurement error in chip temperature obtained by traditional thermal resistance calculation method. In addition, real-time chip temperature can be monitored without introducing additional test circuit or HTRB interruption experiment. To demonstrate the effectiveness of the proposed method, the switching small-current temperature measurement method is used to make comparison, and the experiment result indicates that the temperature of the chip in the blocking test can be obtained with high precision by using the leakage current measurement method presented in this work.

Dealing with covariate measurement error in a clustered cross-sectional survey

Many surveys are often complex cross-sectional studies that involve clustered data. Such surveys can have the additional complexity of the measurement error problem. Ignoring the measurement error ...

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to mine variables of interest from available data, followed by the inclusion of those variables into an econometric framework, with the objective of estimating causal effects. Recent work highlights that, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables are likely to suffer from bias due to measurement error. We propose a novel approach to mitigate these biases, leveraging the ensemble learning technique known as the random forest. We propose employing random forest not just for prediction, but also for generating instrumental variables to address the measurement error embedded in the prediction. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make different mistakes, i.e., have weakly correlated prediction errors. A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the other trees serve as its instruments. Simulation experiments demonstrate the efficacy of the proposed approach in mitigating estimation biases, and its superior performance over an alternative method (simulation-extrapolation), which has been suggested by prior work as a reasonable method of addressing the measurement error problem.