Reduce Measurement Error Research Articles

An Inductive Linear Displacement Sensor With Bilateral Sensing Units

This paper presents an inductive linear displacement sensor with bilateral sensing units, which consists of a stator with excitation coils, two movers with pickup coils and a mover base. Two movers have the same structure and work on two sides of the stator to realize bilateral sensing. Bilateral sensing units contribute to reducing the negative influence from the gap change between the movers and stator on the measurement performance of the sensor, because bilateral mover structure is able to maintain the sum of two side gaps constant and thus reach good magnetic coupling with the stator. Comparison experiments of the prototypes in different working gaps have been implemented and verified that the sensor with bilateral sensing units has better repeatability and accuracy than that with unilateral sensing unit. Based on the regular errors shown in the measurement results of the proposed bilateral sensing sensor in comparison experiments, this paper also presents the detailed analysis about the sources of some dominating error components. Accordingly, a sensor prototype with improved designs for reducing measurement errors was developed. Final experimental results of the improved sensor prototype show that the accuracy is better than 15μm and the repeatability is about 7μm with less than 1.2μm resolution in the range of 0mm~288mm.

Wearable sensor validation of sports-related movements for the lower extremity and trunk

Inertial Measurement Units (IMUs), an alternative to 3D optical motion capture, are growing in popularity to assess sports-related movements. This study validated an IMU system against a “gold-standard” optical motion capture system during common sports movements. Forty-nine healthy adults performed six movements common to a variety of sports applications (cutting, running, jumping, single leg squats, and cross-over twist) while simultaneously outfitted with standard, retroreflective markers and a wireless IMU system. Bias, RMSE, precision, and maximum absolute error (MAE) were calculated to compare the two systems at the lower extremity joints and the trunk in all planes of movement and for all activities. The MAE difference between fast and slow activities for the sagittal, transverse, and frontal planes were 11.62°, 7.41°, and 5.82°, respectively. For bias, the IMU system tended to report larger angles than the optical motion capture system in the transverse and frontal planes and smaller angles in the sagittal plane. Average intraclass correlation coefficients for the sagittal, transverse, and frontal planes were 0.81±0.17, 0.38±0.19, and 0.22±0.37, respectively. When calculating a global bias across all three planes, the IMU system reported nearly identical angles (< 3.5° difference) to the optical motion capture system. The global precision across all planes was 2–6.5°, and the global RMSE was 7–10.5°. However, the global MAE was 11–23°. Overall, and with suggestions for methodological improvement to further reduce measurement errors, these results support current applications and also indicate the need for continued validation and improvement of IMU systems.

Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Attribute Self-Selection

The authors address two significant challenges in using online text reviews to obtain finegrained attribute level sentiment ratings. First, in contrast to methods that rely on word frequency, they develop a deep learning convolutional-LSTM hybrid model to account for language structure. The convolutional layer accounts for spatial structure (adjacent word groups or phrases) and LSTM accounts for sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in constructing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior attribute sentiment scoring accuracy with their model. They find three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Reviewers write to inform and vent/praise, but not based on attribute importance. The heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings. More broadly, our results suggest that social science research should pay more attention to reduce measurement error in variables constructed from text.

Influence of the implementation order of distortion correction and subset matching on digital image correlation

The shape function and lens distortion affect the accuracy of digital image correlation (DIC). Many studies have been conducted on the shape function and lens distortion correction, and an appropriate shape function with lens distortion correction can reduce measurement errors. However, the influence of the implementation order of correcting lens distortion with known distortion parameters and matching subsets with different shape functions has not been discussed. The two implementation orders are: correcting matched grid points after matching distorted subsets and correcting distorted images before matching the subsets. The former will cause undermatching, and the latter will produce additional interpolation errors. Simulations and an experiment with known lens distortion parameters were performed to evaluate the accuracy of the two implementation orders. The forward additive Gauss–Newton algorithm with first-order and second-order shape functions was applied to the measurements. The results show that the order of correcting distorted images before matching subsets has higher accuracy as compared with another order, and a second-order shape function should always be used unless the underlying deformation is known to be lower order.

Was the mid-2000s drop in the British job change rate genuine or a survey design effect?

The year-on-year job change rate fell sharply, from 18% in 2005 to around 13% in 2006, according to British Household Panel Survey (BHPS) estimates. This fall coincides with the introduction of dependent interviewing to the BHPS, intended to reduce measurement error and improve consistency. Estimates from models of job change misclassification (Hausman et al., 1998) show that reduced measurement error cannot account for the fall in the job change rate. This suggests that the fall was genuine.

The Case for Cognitive Interviewing in Survey Item Validation: A Useful Approach for Improving the Measurement and Assessment of Substance Use Disorders

To accurately identify substance use disorders, we must be confident of our ability to define and measure the construct itself. To date, research has demonstrated that the ways in which substance use disorder criteria are operationalized or assessed can significantly affect the information we obtain from these diagnoses. For example, differing operationalizations of the same construct, such as impaired control over substance use, can result in markedly different estimates of prevalence. This points to the need for approaches that aim to improve the validity of diagnostic assessments during the measure development phase. We performed a scoping review of the cognitive interviewing literature, a technique that aims to provide a systematic way of identifying and reducing measurement error associated with the structure and content of assessment items. Along with this, we apply cognitive interviewing to items assessing alcohol tolerance. We argue that cognitive interviewing is well suited for reducing measurement error in substance use disorder assessment items. Incorporating cognitive interviewing into the item generation stage of measure development for substance use disorder assessments is a worthwhile endeavor for improving validity.

Data validation and reconciliation for error correction and gross error detection in multiphase allocation systems

In upstream oil and gas production, flow measurement errors are common at multiple locations in the production system. These measurement errors lead to imbalances, which are reconciled by using allocation methods. The allocation method should redistribute the imbalance in a fair manner to all involved stakeholders. In this paper, data validation and reconciliation (DVR) is proposed as an alternative allocation method in multiphase production systems. DVR is a model-based optimization method that exploits redundancies in process data to minimize random measurement errors, and simultaneously provides a basis for the detection of gross errors. To study the applicability of DVR compared to conventional allocation methods, a model for generic multiphase production systems is developed, and the corresponding DVR problem is formulated. In order to deal with nonlinear flow effects (e.g. interphase mass transfer) the model is linearized around the operating conditions and the DVR problem is solved iteratively. From the simulation studies, it is concluded that DVR reduces measurement errors by up to 56% for gas allocation systems and 33% for multiphase systems. Gross errors in interior locations of the network can be located precisely, while allocating gross errors to exterior locations is generally not possible, due to limited network redundancy. Compared to conventional methods, DVR provides a better means for the detection of gross errors, and results in more accurate attribution of oil/gas ownership and more fair division of revenues between stakeholders.

Improving the Efficiency of Measurements of Combustible Gases Concentration by a Thermocatalytic Sensor

Thermocatalytic sensors are widely used in gas analysis systems and have high reliability and low cost. However, errors in measuring the concentration of combustible gases related to the non-linearity of the conversion characteristic and the influence of ambient temperature fluctuations significantly limit the scope of their application.Purpose of reseach. Development of a method for measuring gas concentration by thermocatalytic sensors, which allows reducing measurement errors by tuning out due to ambient temperature influence and linearization of the conversion characteristic.Problems. They are as follows: to develop a method for temperature stabilization of a thermocatalytic sensor; to make a structural and functional scheme for the sensor activation; to obtain a mathematical description of the method and substantitation for tuning out as a result of temperature influence; to experimentally confirm the possibility of linearization of the sensor conversion function in the thermal stabilization mode.Methods. The mathematical description of the method applies the theory of heat transfer and the theory of electrical circuits with discrete signals. When analyzing existing solutions and synthesizing the device, methods for calculating circuits with nonlinear elements and the theory of measurement systems have been used. The real conversion function has been obtained through an experimental method.Results. A method for measuring gas concentration by a thermocatalytic sensor with the use of a microcontroller and PWM has been developed. It allows reducing errors due to tuning out as a result of ambient temperature influence. A mathematical description of the method has been given. An experiment has been performed. It demonstrates the effectiveness of using temperature stabilization to linearize the conversion characteristic.Conclusion. The paper proposes a method for temperature stabilization of thermocatalytic gas sensors. The method makes it possible to increase the accuracy of measurements by tuning out due to the influence of temperature fluctuations and linearization of the conversion function. The possibility of linearization of the sensor function has been experimentally confirmed. It characterizes the dependence of the output signal on the concentration of combustible gas. Using this method allows you to reduce the cost of the sensor, improve the quality factors of the sensor, such as the reliability and stability of parameters.

Densities of liquid Re, Os, and Ir, and their temperature dependence measured by an electrostatic levitator

Densities in the liquid phase for molten refractory metals (Re, Os, and Ir) whose values are over 18,000 kg/m3 have been re-measured using an electrostatic levitator. The sample position control of electrostatically levitated sample was revised to suppress sample oscillations and to reduce measurement errors on the volume calculation by image analysis. Therefore, the measured density data showed a clearer temperature dependence compared to our previous measurements. The measured density of Re, Os, and Ir can be respectively expressed as ρRe(T) = 18.9 × 103–0.62 × (T-Tm) kg/m3, ρOs(T) = 19.4 × 103–0.92 × (T-Tm)kg/m3, and ρIr(T) = 19.6–0.83 × (T-Tm) kg/m3, where Tm is the melting temperature of each metal.

A hierarchical data reconciliation based on multiple time-delay interval estimation for industrial processes

With the increasing demand for energy conversation and high efficiency, data quality is of great important to the operation management and monitoring in industrial applications. Data reconciliation, as a data processing technology, provides great potential to improve quality of process data, and is widely used to reduce measurement error and estimate unmeasured parameters. However, there are reactors connected in series in the long-running industrial processes so that liquid material information is difficult to mark and trace, and the liquid material has different residence times in each reactor due to the differences in the internal structure and operation mode. The time-delay in different reactors may be various and time-varying. In this paper, to solve these problems, a multiple time-delay interval estimation based hierarchical data reconciliation method is put forward. First, the multiple time-delay interval estimation is developed according to the process mechanism analysis and modeling. Then, an improved discrete state transition solution approach is presented to solve the data time-matching with multiple time-delay interval estimation for different reactors. Finally, a hierarchical data reconciliation frame is built by data characteristics. The feasible of the proposed data reconciliation method is verified utilizing the industrial application results.

Investigating stable and dynamic aspects of student motivation using generalizability theory

BackgroundThe Academic Motivation Scale (AMS) is a commonly used 28‐item measure of motivation orientation. However, the scale has not been examined using methods to distinguish between state and trait aspects of motivation. We applied Generalizability Theory to appraise the psychometric properties of the AMS and to differentiate items measuring state or trait motivation within the AMS.MethodsOne hundred and thirty medical students completed the 28‐item AMS at three time points. Generalizability Theory was applied to examine the generalizability of the AMS (total and sub‐scales) and to estimate state and trait aspects of student motivation.ResultsThe overall AMS showed high generalizability (G = .93) in assessing student motivation across student populations and occasions, whereas the AMS subscales demonstrated low levels of generalizability. The majority of the AMS items scored high for the state component index suggesting movement between motivational priorities while maintaining the overall stability of the individual motivation levels.ConclusionsThe total AMS is suitable as a reliable measurement of motivation among student populations with high generalizability of scores across students and assessment occasions. Measuring trait motivation using the total AMS score instead of subscale scores reduces measurement error and optimises instrument reliability.

Proposal for a Method to Measure the Range of Dart-Throwing Motion

PurposeTo examine the reliability of a novel technique to measure the range of the dart-throwing motion.MethodsTwo raters measured the range of the dart-throwing motion in 40 healthy subjects. For the measurement, subjects were asked to perform a simulated hammering motion using various experimental tools (a real hammer, a thick wooden rod, and a thin wooden rod). The inclination angle of the tool in the vertical plane was measured with a manual goniometer at the maximal position of radial extension and ulnar flexion. The sum of these angles was defined as the range of the dart-throwing motion. To evaluate relative interrater reliability, intraclass correlation coefficients were calculated. To account for absolute reliability, Bland-Altman analysis was performed.ResultsIntraclass correlation coefficients ranged 0.72 to 0.86. Bland-Altman analysis revealed that some systematic errors existed when the measurement was carried out with the real hammer or the thin rod, but not the thick rod. The 95% confidence intervals of minimal detectable change for the thick rod were 36.0° and 35.8° for the dominant and nondominant sides, respectively. Measured values between the experimental tools were similar.ConclusionsRelative reliability was shown to be good or moderate for each set of measurements. Some refinements are required to reduce measurement error. Accuracy of measurement should also be confirmed.Clinical relevanceNo standardized methods for measuring the range of the dart-throwing motion have yet been established. Our technique can be performed rapidly and with easily available materials, producing reliable measurements for the range of the dart-throwing motion.

Using open-source software and digital imagery to efficiently and objectively quantify cover density of an invasive alien plant species

The most commonly used method for measuring vegetation cover is visual estimation, which is highly subjective, potentially leading to measurement errors. This poses serious implications to the assessment and continued management of plant species cover, for example in the control of invasive plant species. Morphological analysis of digital imagery has, to date, been primarily applied in the classification of landscape features. Our novel application of morphological image analysis provides an objective method for detection and accurate cover assessment of an invasive alien plant species (IAS), giving reduced measurement errors when compared to visual estimation. Importantly, this method is entirely based on free software. Guidos Toolbox is a collection of generic raster image processing routines, including Morphological Spatial Pattern Analysis (MSPA), which classifies and quantifies features according to shape. MSPA was employed in this study to detect and quantify cover of invasive Petasites pyrenaicus (Winter heliotrope) in digital images of 1 m × 1 m plots. Its efficacy was compared to that of two other methods- GIS Digitisation (used as an accurate baseline) and Visual Estimation (standard method). We tested the limit of MSPA usability on images of varying complexity, i.e. “simple”, intermediate” or “complex”, depending on presence/absence of other vascular plant species and the species richness of plot. Our results show good agreement between all three methods. MSPA measurement of P. pyrenaicus cover was most closely aligned with the GIS Digitisation (concordance correlation coefficients of 0.966). Visual Estimation was less closely aligned with GIS Digitisation (concordance correlation coefficients of 0.888). However, image complexity resulted in differing levels of agreement; with the closest agreement being achieved between MSPA and GIS Digitisation when used on images of lower and higher complexity. MSPA consistently provides higher accuracy and precision for P. pyrenaicus cover measurement than the standard Visual Estimation method. Our methodology is applicable to a range of focal vegetation species, both herbaceous and graminoid. Future application of MSPA for larger-scale surveying and monitoring via remote sensing is discussed, potentially reducing resource demands and increasing cover measurement consistency and accuracy. We recommend this method forms part of vegetation management toolkits for not only environmental managers, but for anyone concerned with plant cover assessment, from agricultural systems to sustainable resource use.

Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review

Development and dissemination of sustainable practices are key to enhance agricultural productivity in developing countries and to curtail potential negative externalities. Rigorous adoption/impact evaluations provide valuable lessons to enhance the capacity of agricultural research-for-development (R4D) systems in this context. Conventional evaluation studies rely solely on farm-household surveys for data. Generation of survey data however requires considerable financial and human capital, and the process often misses several important explanatory variables, ignores the longer-term impacts, and suffers from measurement errors. Complementary data sources are explored to make the evaluations more robust and rigorous. Here we review 54 studies that used satellite data to estimate adoption and impact of agricultural practices in developing countries. Some evidence on successful application of satellite data in high-income countries is also provided. The main findings of the paper are threefold: (1) satellite data have been successfully used to detect agricultural practices, such as cropping intensity, tillage, crop residue cover, irrigation, and soil and water conservation; (2) only a few studies have estimated the yield impacts of agricultural practices, although the estimation of crop yields with satellite data is fairly developed; and (3) only a small number of studies have explored impact estimation beyond the biophysical sphere. Estimation of certain environmental impacts of agricultural practices is possible through satellite data, although only a few studies have carried it out. Not many have assessed the economic impacts of interventions. We conclude that satellite data analysis allows information access with little delay and over longer periods, provide a unique set of variables over wide geographies, and reduce measurement error in certain variables. However, more interdisciplinary research is necessary to speed up the uptake of this alternative data source in R4D evaluations.

GOES-R series image navigation and registration performance assessment tool set

An image navigation (NAV) and registration (INR) performance assessment tool set (IPATS) was developed to assess the US Geostationary Operational Environmental Satellite R-series (GOES-R) Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) INR performance. IPATS produces five INR metrics for level 1B ABI images: navigation, channel-to-channel registration, frame-to-frame registration, swath-to-swath registration, and within-frame registration. IPATS also produces one INR metric for GLM: navigation of background images. The high-precision INR metrics produced by IPATS are critical to INR performance evaluation and long-term monitoring. IPATS INR metrics also provide feedback to INR engineers for tuning the navigation algorithms and parameters to further refine INR performance. IPATS utilizes a modular algorithm design to allow the user-selectable data processing sequence and configuration parameters. We first describe the algorithmic design and the implementation of IPATS. Next, it describes the investigation of the optimization of the configuration parameters to reduce measurement errors. Finally, sample INR performance is presented, including GOES-16 and GOES-17 ABI NAV performance from postlaunch test to November 2019 and the comparison of example 24-h INR performance against the mission performance requirements. The INR assessment results show that both GOES-R ABIs are in compliance with the mission INR requirements.

Selecting response anchors with equal intervals for summated rating scales.

Summated rating scales are ubiquitous in organizational research, and there are well-delineated guidelines for scale development (e.g., Hinkin, 1998). Nevertheless, there has been less research on the explicit selection of the response anchors. Constructing survey questions with equal-interval properties (i.e., interval or ratio data) is important if researchers plan to analyze their data using parametric statistics. As such, the primary objectives of the current study were to (a) determine the most common contexts in which summated rating scales are used (e.g., agreement, similarity, frequency, amount, and judgment), (b) determine the most commonly used anchors (e.g., strongly disagree, often, very good), and (c) provide empirical data on the conceptual distance between these anchors. We present the mean and standard deviation of scores for estimates of each anchor and the percentage of distribution overlap between the anchors. Our results provide researchers with data that can be used to guide the selection of verbal anchors with equal-interval properties so as to reduce measurement error and improve confidence in the results of subsequent analyses. We also conducted multiple empirical studies to examine the consequences of measuring constructs with unequal-interval anchors. A clear pattern of results is that correlations involving unequal-interval anchors are consistently weaker than correlations involving equal-interval anchors. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Mathematical Models of Conservative Objects of Control

Trend analysis and record charts different technological parameter in the heat power, chemical and oil refining industry showed, that most of them are oscillatory in nature. In order to reduce the amplitude of the oscillations, filters are used, and for the actual value of the measured quantity — their average value. Research has found, that the oscillatory-pulse nature of trends and diagrams of measured values is observed in multi-parameter technological objects of control, which have valve valve strapping on both input and output material, heat or energy flows. It is shown that valve-type regulating organs, which are used in automatic control systems, together with the technological apparatus create partial objects, which work on a conservative type. This creates new dyna­mic objects with three or more private conservative systems, which together create a complex multi-para­meter technological control object with the oscillatory-pulse nature of the measured parameters. It was found that in many cases for such complex objects of control the average value of the measured parameter, defined with the trend, can differ significantly from its actual value. This leads to the appearance of significant measurement errors, and consequently, to an incorrect assessment of the quality of manufactured pro­ducts. The task is to study the causes of the appearance of such vibrational-pulse systems, their properties, devia­tions of average readings along the trend from the actual value of the measured parameter, and also develop methods for reducing measurement errors. The paper presents mathematical and physical models of such conservative control systems, obtained on the basis of the theory of rheological transformations, as well as the results of their research. It is shown that the vibrational-pulse trends of the measurement parameters are a set of angular frequencies of private objects, the sum of which creates an oscillatory-pulse form of the signals of the measuring parameters.

Snow cover accumulation and melting measurements taken using new automated loggers at three study locations

Snow water equivalent (SWE) exactly reflects the hydrological importance of accumulated snow cover. Taking manual measurement is both time consuming and laborious, and one major disadvantage of them is the sporadic information they give about SWE trends during the winter season. This disadvantage is eliminated through the use of an automated device capable of monitoring temporal changes of SWE practically continuously. This study investigated the testing of a new SWE monitoring device installed at three study locations over three winter seasons (2014–2017). The device measures SWE by weighing snow cover and is equipped with a fencing component expected to reduce measurement errors due to snow bridging. It is also equipped with an ultrasonic snow depth and a snowmelt seepage sensors. The evaluation indicated that an hourly interval for collecting SWE data may not be sufficient for providing an accurate description of snow cover trends and for tracking winter hydrology during rain-on-snow events. Changeable winters increase the chance of measurement errors and complicate making evaluations of snow cover. The installed component did not fully prevent snow bridging, and the occurrence of predisposed climatic conditions was site specific. Apart from snow bridging events, the measurement errors did not exceed 7 mm, i.e. maximally from 7 to 26% of SWE. Snowmelt at each device was accelerated by up to one day. The risk of the influence of ground freeze on the snowmelt seepage sensor and measurement apparatus was seen during low snow cover and occurred mainly at the lowest altitude. The calculated evaporation from snow in each particular season ranged between 3.9% and 8.3%; the average values of evaporation increased with decreasing altitude.

Measuring Simultaneous Emotions: Existing Problems and a New Way Forward

AbstractStudies on discrete emotions typically work to evoke one emotion at a time. Yet many political phenomena cause multiple emotions. Threats, for example, cause, anger, and fear, have diametrically opposing behavioral consequences. As a result, the effect of experimental treatments can be masked by the countervailing influence of emotions with similar affect. This issue is exacerbated by existing measures of negative emotions, such as the Positive and Negative Affect Schedule (PANAS). We show that the PANAS is contaminated by systematic measurement error, as negative affect produced by one emotion influences responses on the other. To overcome this, we develop an alternative version of the PANAS that allows respondents to select which emotions they are feeling, then rate the severity. This technique accurately captures respondent’s emotional reactions, reducing measurement error and thus decreasing the correlation between fear and anger. The tactics we developed have broad relevance for experimental researchers analyzing emotional responses to politics.

Lens aberration compensation in interference microscopy

Emergence of products that feature functional surfaces with complex geometries, such as freeform optics in consumer electronics and augmented reality and virtual reality, requires high-accuracy non-contact surface measurement. However, large discrepancies are often observed between the measurement results of optical methods and contact stylus methods, especially for complex surfaces. For interference microscopy, such as coherence scanning interferometry, the three-dimensional surface transfer function provides information about the instrument spatial frequency passband and about lens aberrations that can result in measurement errors. Characterisation and phase inversion of the instrument's three-dimensional surface transfer function yields an inverse filter that can be applied directly to the three-dimensional fringe data. The inverse filtering is shown to reduce measurement errors without using any data processing or requiring any a priori knowledge of the surface. We present an experimental verification of the characterisation and correction process for measurements of several freeform surfaces and an additive manufactured surface. Corrected coherence scanning interferometry measurements agree with traceable contact stylus measurements to the order of 10 nm.