Relevant bibliographies by topics / Measurement error

Academic literature on the topic 'Measurement error'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 June 2024

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Journal articles on the topic "Measurement error"

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Kroc, Edward. "Generalized measurement error: Intrinsic and incidental measurement error." PLOS ONE 18, no. 6 (June 29, 2023): e0286680. http://dx.doi.org/10.1371/journal.pone.0286680.

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In this paper, we generalize the notion of measurement error on deterministic sample datasets to accommodate sample data that are random-variable-valued. This leads to the formulation of two distinct kinds of measurement error: intrinsic measurement error, and incidental measurement error. Incidental measurement error will be recognized as the traditional kind that arises from a set of deterministic sample measurements, and upon which the traditional measurement error modelling literature is based, while intrinsic measurement error reflects some subjective quality of either the measurement tool or the measurand itself. We define calibrating conditions that generalize common and classical types of measurement error models to this broader measurement domain, and explain how the notion of generalized Berkson error in particular mathematicizes what it means to be an expert assessor or rater for a measurement process. We then explore how classical point estimation, inference, and likelihood theory can be generalized to accommodate sample data composed of generic random-variable-valued measurements.
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Qin, Lihou, Qijing Liu, Maozhen Zhang, and Sajjad Saeed. "Effect of measurement errors on the estimation of tree biomass." Canadian Journal of Forest Research 49, no. 11 (November 2019): 1371–78. http://dx.doi.org/10.1139/cjfr-2019-0034.

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Diameter at breast height (DBH) is commonly used to predict the aboveground biomass (AGB) of forests and to derive biomass models for single trees; however, there is evidence that measurement errors of DBH have not been previously considered. In this study, two types of measurement errors were evaluated: errors in national forest inventory data (NFID) and errors in a calibration data set (CDS). Using Monte Carlo simulations, the uncertainties arising from these two measurement errors were quantified. In addition, the effects of measurement errors on estimates under different error assumptions were analyzed to determine how these two uncertainties change with increasing errors. The results show that CDS measurement error contributes more to the total uncertainty, whereas NFID measurement error has a negligible effect on estimating the biomass of regional forests. The uncertainties of both types of measurement error increased with increasing error assumptions; however, the uncertainties caused by CDS measurement error were noticeably larger than those caused by NFID measurement error. Thus, the greatest potential for reducing uncertainties caused by measurement error lies in increasing the accuracy of DBH measurements in CDS.
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Iskenderzade, E. B., E. D. Suleymanova, and H. S. Veliyev. "THE METHOD OF INDIRECT TWO-PART MEASUREMENTS FOR THE CONTROL OF PRODUCTION INDICATORS." Kontrol'. Diagnostika, no. 295 (January 2023): 30–32. http://dx.doi.org/10.14489/td.2023.01.pp.030-032.

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A method of indirect two-part measurements of production indicators, including two independent measurements, has been developed. An elementary example of such measurements is the measurement of rectangular areas, determination of the specific gravity of the material, etc. According to the proposed method, minimization of the total random error of indirect two-part measurement can be carried out in two cases: 1) if it is known about the equality of the total error in both measurements, but the sum of systematic errors is limited from above, then it is possible to solve the problem of determining systematic errors separately, which minimizes the total random error of a two-part indirect measurement; 2) if it is known about the equality of systematic errors in both measurements, but the sum of systematic errors is limited from above, then it is possible to solve the problem of determining the total errors separately, which minimizes the total random error of an indirect two-part measurement.
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Masse, J., J. M. Bland, J. R. Doyle, and J. M. Doyle. "Measurement error." BMJ 314, no. 7074 (January 11, 1997): 147. http://dx.doi.org/10.1136/bmj.314.7074.147.

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Luchette, Matthew, and Alireza Akhondi-Asl. "Measurement Error." Pediatric Critical Care Medicine 25, no. 3 (March 2024): e140-e148. http://dx.doi.org/10.1097/pcc.0000000000003420.

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Wu, Lei, Xizhe Zang, Guanwen Ding, Chao Wang, Xuehe Zhang, Yubin Liu, and Jie Zhao. "Joint Calibration Method for Robot Measurement Systems." Sensors 23, no. 17 (August 26, 2023): 7447. http://dx.doi.org/10.3390/s23177447.

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Robot measurement systems with a binocular planar structured light camera (3D camera) installed on a robot end-effector are often used to measure workpieces’ shapes and positions. However, the measurement accuracy is jointly influenced by the robot kinematics, camera-to-robot installation, and 3D camera measurement errors. Incomplete calibration of these errors can result in inaccurate measurements. This paper proposes a joint calibration method considering these three error types to achieve overall calibration. In this method, error models of the robot kinematics and camera-to-robot installation are formulated using Lie algebra. Then, a pillow error model is proposed for the 3D camera based on its error distribution and measurement principle. These error models are combined to construct a joint model based on homogeneous transformation. Finally, the calibration problem is transformed into a stepwise optimization problem that minimizes the sum of the relative position error between the calibrator and robot, and analytical solutions for the calibration parameters are derived. Simulation and experiment results demonstrate that the joint calibration method effectively improves the measurement accuracy, reducing the mean positioning error from over 2.5228 mm to 0.2629 mm and the mean distance error from over 0.1488 mm to 0.1232 mm.
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Chesher, Andrew, and Christian Schluter. "Welfare Measurement and Measurement Error." Review of Economic Studies 69, no. 2 (April 2002): 357–78. http://dx.doi.org/10.1111/1467-937x.00209.

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Adamczak, Stanisław, Jacek Świderski, and Urszula Kmiecik-Sołtysiak. "Estimation of the uncertainty of the roundness measurement with a device with rotary spindle." Mechanik 90, no. 10 (October 9, 2017): 912–14. http://dx.doi.org/10.17814/mechanik.2017.10.145.

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The paper presents the estimation of uncertainty of roundness measurement using the Talyrond 73 by analyzing the sources of measurement errors such as measuring noise, signal drift, radial spindle error, repeatability, sensor gain error and uncertainty of measurement standards. The study included the following measurements: roller bearing, glass hemisphere and flick standard.
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Fang, Yiming, Zhaoyao Shi, Yanqiang Sun, and Pan Zhang. "Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method." Applied Sciences 14, no. 3 (January 24, 2024): 1004. http://dx.doi.org/10.3390/app14031004.

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A gear integrated error, a combination of individual and composite errors, carries richer information and has long been a key target of classic gear error measurement techniques. However, in the age of intelligent manufacturing, the classic methods for gear integrated error measurement are no longer able to meet the emerging requirements of large-scale gears and real-time online measurement. To address this gap, a novel approach to obtaining the gear integrated error based on GTC−Facet (Gaussian template convolution-Facet) is proposed. This method accurately pinpoints the sub-pixel contour of gears in images, enabling a quick derivation of the gear integrated error curve. From this curve, other individual and composite errors can be analyzed. The gear error information obtained through our method has higher measurement accuracy, achieving a positioning accuracy of 3.6 μm for the gear profile. Moreover, during the measurement process, the measured gear remains unclamped, and the entire measurement process can be completed within 0.35 s, which is much faster than classic methods. Our method meets the demands of online measurements and provides a new avenue for gear error measurement.
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Jacob, Vinodkumar, M. Bhasi, and R. Gopikakumari. "Impact of Human Factors on Measurement Errors." International Journal of Measurement Technologies and Instrumentation Engineering 1, no. 4 (October 2011): 28–44. http://dx.doi.org/10.4018/ijmtie.2011100103.

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Measurement is the act or the result, of a quantitative comparison between a given quantity and a quantity of the same kind chosen as a unit. It is for observing and testing scientific and technological investigations and generally agreed that all measurements contain errors. In a measuring system where both a measuring instrument and a human being taking the measurement using a preset process, the measurement error could be due to the instrument, the process or human error. This study is devoted to understanding the human errors in measurement. Work and human involvement related factors that could affect measurement errors have been identified. An experimental study has been conducted using different subjects where the factors were changed one at a time and the measurements made by them recorded. Errors in measurement were then calculated and the data so obtained was subject to statistical analysis to draw conclusions regarding the influence of different factors on human errors in measurement. The findings are presented in the paper.
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Dissertations / Theses on the topic "Measurement error"

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Bashir, Saghir Ahmed. "Measurement error in epidemiology." Thesis, University of Cambridge, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264544.

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Wang, Qiong. "Robust Estimation via Measurement Error Modeling." NCSU, 2005. http://www.lib.ncsu.edu/theses/available/etd-08112005-222926/.

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We introduce a new method to robustifying inference that can be applied in any situation where a parametric likelihood is available. The key feature is that data from the postulated parametric models are assumed to be measured with error where the measurement error distribution is chosen to produce the occasional gross errors found in data. We show that the tails of the error-contamination model control the properties (boundedness, redescendingness) of the resulting influence functions, with heavier tails in the error contamination model producing more robust estimators. In the application to location-scale models with independent and identically distributed data, the resulting analytically-intractable likelihoods are approximated via Monte Carlo integration. In the application to time series models, we propose a Bayesian approach to the robust estimation of time series parameters. We use Markov Chain Monte Carlo (MCMC) to estimate the parameters of interest and also the gross errors. The latter are used as outlier diagnostics.
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Johansson, Fredrik. "Essays on measurement error and nonresponse /." Uppsala : Department of Economics, Uppsala University, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7920.

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Boaventura, Guimareas Dumangane Montezuma. "Essays on duration response measurement error." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368683.

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AHMAD, SHOAIB. "Finite Precision Error in FPGA Measurement." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-49646.

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Finite precision error in digital signal processing creates a threshold of quality of the processed signal. It is very important to agree on the outcome while paying in terms of power and performance. This project deals with the design and implementation of digital filters FIR and IIR, which is further utilized by a measurement system in order to correctly measure different parameters. Compared to analog filters, these digital filters have more precise and accurate results along with the flexibility of expected hardware and environmental changes. The error is exposed and the filters are implemented to meet the requirements of a measurement system using finite precision arithmetic and the results are also verified through MATLAB. Moreover with the help of simulations, a comparison between FIR and IIR digital filters have been presented.

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Digital filters and FPGA
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Cao, Chendi. "Linear regression with Laplace measurement error." Kansas State University, 2016. http://hdl.handle.net/2097/32719.

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Master of Science
Statistics
Weixing Song
In this report, an improved estimation procedure for the regression parameter in simple linear regression models with the Laplace measurement error is proposed. The estimation procedure is made feasible by a Tweedie type equality established for E(X|Z), where Z = X + U, X and U are independent, and U follows a Laplace distribution. When the density function of X is unknown, a kernel estimator for E(X|Z) is constructed in the estimation procedure. A leave-one-out cross validation bandwidth selection method is designed. The finite sample performance of the proposed estimation procedure is evaluated by simulation studies. Comparison study is also conducted to show the superiority of the proposed estimation procedure over some existing estimation methods.
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Hirst, William Mark. "Outcome measurement error in survival analysis." Thesis, University of Liverpool, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366352.

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Lo, Sau Yee. "Measurement error in logistic regression model /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?MATH%202004%20LO.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.
Includes bibliographical references (leaves 82-83). Also available in electronic version. Access restricted to campus users.
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Fang, Xiaoqiong. "Mixtures-of-Regressions with Measurement Error." UKnowledge, 2018. https://uknowledge.uky.edu/statistics_etds/36.

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Finite Mixture model has been studied for a long time, however, traditional methods assume that the variables are measured without error. Mixtures-of-regression model with measurement error imposes challenges to the statisticians, since both the mixture structure and the existence of measurement error can lead to inconsistent estimate for the regression coefficients. In order to solve the inconsistency, We propose series of methods to estimate the mixture likelihood of the mixtures-of-regressions model when there is measurement error, both in the responses and predictors. Different estimators of the parameters are derived and compared with respect to their relative efficiencies. The simulation results show that the proposed estimation methods work well and improve the estimating process.
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De, Nadai Michele. "Measurement Error Issues in Consumption Data." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3421980.

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Data available in commonly employed consumer surveys, like the \emph{Consumer Expenditure Survey} in the US, are widely known to be affected by measurement errors. Ignoring the effect of such errors in the estimation of consumption models may result in severely biased estimates of the quantities of interest. In this thesis I consider identification of three different models of consumption behavior allowing for the presence of measurement errors. Identification is particularly difficult to achieve due to the high non-linearity of the specifications involved and to peculiarities of consumptions models. In fact in many instances, allowing for mismeasured covariates also implies correlated measurement errors also in the dependent variable. This further complicates the identification of the model, invalidating most of the non-linear errors in variables results in the literature. The core of the thesis is made of three Chapters. In the first Chapter I consider identification of a particular specification of Engel curves when unobserved expenditure is endogenous and measured with error. In the second Chapter I study identification of a general non-linear errors in variables model allowing for correlated measurement errors on both sides of the equation. In the third Chapter I derive identification and estimation of the distribution of consumption when only expenditure and the number purchases are observed.
I dati disponibili nelle più comuni indagini sui consumatori, come la Consumer Expenditure Survey negli Stati Uniti, sono noti per essere affetti da errori di misura. Ignorare l'effetto di questi errori nella stima di modelli di consumo può portare a stime distorte delle quantità di interesse. Questa tesi discute l'identificazione di tre differenti modelli di comportamento dei consumatori in presenza di errori di misura. L'identificazione risulta particolarmente difficile a causa della elevata non-linearità delle specificazioni utilizzate e di alcune peculiarità proprie dei modelli con dati di consumo. In molti casi infatti, la presenza di covariate misurate con errore implica errori di misura correlati nella variabile dipendente. Questo complica ulteriormente l'identificazione del modello, invalidando la maggior parte dei risultati presenti nella letteratura su errori di misura in modelli non-lineari. Il contenuto della tesi è discusso in tre Capitoli. Il primo Capitolo discute l'identificazione di una particolare specificazione di curve di Engel quando la spesa totale non osservata è endogena e misurata con errore. Il secondo Capitolo studia l'identificazione di un modello non-lineare molto generale con errori di misura correlati su entrambi i lati dell'equazione. Il terzo Capitolo ottiene identificazione e stima della distribuzione di consumo quando solo la spesa e il numero di acquisti sono osservati.
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Books on the topic "Measurement error"

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Chesher, Andrew. Wefare measurement and measurement error. Bristol: University of Bristol, Department of Economics, 1999.

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Fuller, Wayne A., ed. Measurement Error Models. Hoboken, NJ, USA: John Wiley & Sons, Inc., 1987. http://dx.doi.org/10.1002/9780470316665.

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Measurement error models. New York: Wiley, 1987.

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Hearn, Chase P. Q-circle measurement error. Hampton, Va: Langley Research Center, 1991.

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Chesher, Andrew. Measurement error bias reduction. Bristol: University of Bristol, Department of Economics, 1998.

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S, Bradshaw Edward, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Q-circle measurement error. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1991.

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S, Bradshaw Edward, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Q-circle measurement error. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1991.

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S, Bradshaw Edward, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Q-circle measurement error. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1991.

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W, Van Ness John, ed. Statistical regression with measurement error. London: Arnold, 1999.

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Yi, Grace Y., Aurore Delaigle, and Paul Gustafson. Handbook of Measurement Error Models. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315101279.

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Book chapters on the topic "Measurement error"

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Buonaccorsi, John P., and Aurore Delaigle. "Measurement Error." In The Work of Raymond J. Carroll, 1–154. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05801-6_1.

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Suen, Yi Nam, and Ester Cerin. "Measurement Error." In Encyclopedia of Quality of Life and Well-Being Research, 3907–9. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_1758.

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Scott, Haleigh M., and Susan M. Havercamp. "Measurement Error." In Encyclopedia of Autism Spectrum Disorders, 1–2. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-6435-8_1644-3.

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Caulcutt, Roland. "Measurement Error." In Research Methods for Postgraduates: Third Edition, 275–86. Oxford, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118763025.ch27.

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Hutchins, Tiffany, Giacomo Vivanti, Natasa Mateljevic, Roger J. Jou, Frederick Shic, Lauren Cornew, Timothy P. L. Roberts, et al. "Measurement Error." In Encyclopedia of Autism Spectrum Disorders, 1817–18. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_1644.

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Buzas, Jeffrey S., Leonard A. Stefanski, and Tor D. Tosteson. "Measurement Error." In Handbook of Epidemiology, 1241–82. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-09834-0_19.

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Scott, Haleigh M., and Susan M. Havercamp. "Measurement Error." In Encyclopedia of Autism Spectrum Disorders, 2831–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_1644.

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Suen, Yi Nam, and Ester Cerin. "Measurement Error." In Encyclopedia of Quality of Life and Well-Being Research, 4217–19. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_1758.

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Charman, Tony, Susan Hepburn, Moira Lewis, Moira Lewis, Amanda Steiner, Sally J. Rogers, Annemarie Elburg, et al. "Error of Measurement." In Encyclopedia of Autism Spectrum Disorders, 1158. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_100547.

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Hong, Han. "Measurement Error Models." In The New Palgrave Dictionary of Economics, 8607–15. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2619.

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Conference papers on the topic "Measurement error"

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Henderson, Robert K. "Measurement error revisited." In Photomask Technology and Management, edited by Frank E. Abboud and Brian J. Grenon. SPIE, 1999. http://dx.doi.org/10.1117/12.373367.

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Usami, Shogo. "Construction of Quantum Error Correcting Code for Specific Position Errors." In QUANTUM COMMUNICATION, MEASUREMENT AND COMPUTING. AIP, 2004. http://dx.doi.org/10.1063/1.1834413.

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Wang, Shih-Ming, Han-Jen Yu, and Hung-Wei Liao. "An Efficient Volumetric-Error Measurement Method for Five-Axis Machine Tools." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-83000.

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Accurate measurement of volumetric errors plays an important role for error compensation for multi-axis machines. The error measurements for volumetric errors of five-axis machines are usually very complex and costly than that for three-axis machines. In this study, a direct and simple measurement method using telescoping ball-bar system for volumetric errors for different types of five-axis machines was developed. The method using two-step measurement methodology and incorporating with derived error models, can quickly determine the five degrees-of-freedom (DOF) volumetric errors of five-axis machine tools. Comparing to most of the current used measurement methods, the proposed method provides the advantages of low cost, high efficiency, easy setup, and high accuracy.
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Michaeli, L., J. Saliga, P. Dolinsky, and I. Andras. "Compensation of dual slope ADC error caused by dielectric absorption." In 2017 11th International Conference on Measurement. IEEE, 2017. http://dx.doi.org/10.23919/measurement.2017.7983536.

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Chaojun, Gu, and Panida Jirutitijaroen. "Topology error processing based on forecast measurement errors." In 2014 Power Systems Computation Conference (PSCC). IEEE, 2014. http://dx.doi.org/10.1109/pscc.2014.7038475.

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Gould, Christopher J., Francis G. Goodwin, and William R. Roberts. "Overlay measurement: hidden error." In Microlithography 2000, edited by Neal T. Sullivan. SPIE, 2000. http://dx.doi.org/10.1117/12.386496.

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Wang, Shih-Ming, Han-Jen Yu, Yi-Hung Liu, and Da-Fun Chen. "An On-Machine Error Measurement System for Micro-Machining." In ASME 2007 International Manufacturing Science and Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/msec2007-31053.

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Technology development trends towards the ability to manufacture ever smaller parts and feature sizes with increased precision and decreased cost. Micro machining is one of the important manufacturing methods to fulfill the requirements from the industry. The objective of this paper is to develop an on-machine error measurement system that can identify the micro machining errors for error compensation so that the machining accuracy of a meso-scale machine tool (mMT) can be enhanced. Because of the difficulty in handling and repositioning the miniature workpiece, the error measurement system should be non-contact and on-machine executable. To meet this requirement, a vision-based error measurement system integrating image re-constructive technology, camera pixel correction, and model comparison algorithm error was developed in this study. The proposed measurement system consists of a CCD with CCTV lens, a precision 3-DOF platform, image re-construction sub-system, and contour error calculation sub-system. By adopting Canny Edge Detection algorithm and camera pixel calibration method, the contour of a machined workpiece can be identified and compared to the pixel-based theoretical contour model of the workpiece to determine the micro machining errors. Because the system does not have to remove the machined workpiece from the CNC machine tool, errors due to re-installing and re-positioning can be avoided. To prove the feasibility of the developed algorithm and system, measurement results obtained from the vision-based measurement system were compared with the measurements of CMM, and error compensation experiment conducted on a 3-DOF mMT was also conducted. The results have shown the good feasibility and effectiveness of the developed system.
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MAPP, P. "ERROR MECHANISMS IN SPEECH INTELLIGIBILITY MEASUREMENTS." In Intelligible Measurement 2006. Institute of Acoustics, 2023. http://dx.doi.org/10.25144/17843.

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Sarovar, Mohan. "Continuous Quantum Error Correction." In QUANTUM COMMUNICATION, MEASUREMENT AND COMPUTING. AIP, 2004. http://dx.doi.org/10.1063/1.1834397.

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Siegel, Mel, and M. L. Leary. "Range from focus error." In Measurement Technology and Intelligent Instruments, edited by Li Zhu. SPIE, 1993. http://dx.doi.org/10.1117/12.156361.

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Reports on the topic "Measurement error"

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Haines, Tomar. PR-218-103608-R01 ILI Tool Calibration based on In-ditch Measurement with Related Uncertainty. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 2013. http://dx.doi.org/10.55274/r0010825.

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Keifner and Associates was awarded task 2 and 3 of the project EC-4-2, "ILI Tool Calibration based on In-ditch Measurement with Related Uncertainty". The project comprised of three tasks as follows: Develop a process for either confirming adequacy of vendor claimed random error component, or recalibrating the random error component of ILI measurement uncertainty. Task 1 was completed by ApplusRTD and a report was generated under a separate contract no: PR-366-103606. Develop a process for examining errors, and determine methods to correct for these errors, recognizing that lack of knowledge about the accuracy of in-ditch measurement will limit error correction. Develop comments and recommendations on the number of confirmation measurements required to provide a statistically defensible basis for adjusting vendor claimed tool error. Kiefner reported on these latter two tasks as its part of the work for this project.
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Aruoba, S. Boraǧan, Francis Diebold, Jeremy Nalewaik, Frank Schorfheide, and Dongho Song. Improving GDP Measurement: A Measurement-Error Perspective. Cambridge, MA: National Bureau of Economic Research, April 2013. http://dx.doi.org/10.3386/w18954.

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Baker, Scott, Lorenz Kueng, Steffen Meyer, and Michaela Pagel. Measurement Error in Imputed Consumption. Cambridge, MA: National Bureau of Economic Research, September 2018. http://dx.doi.org/10.3386/w25078.

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Nybom, Martin, Toru Kitagawa, and Jan Stuhler. Measurement error and rank correlations. The IFS, April 2018. http://dx.doi.org/10.1920/wp.cem.2081.2818.

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George and Hawley. PR-015-09605-R01 Extended Low Flow Range Metering. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2010. http://dx.doi.org/10.55274/r0010728.

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Abstract:
Natural gas meters are often used to measure flows below their minimum design flow rate. This can occur because of inaccurate flow projections, widely varying flow rates in the line, a lack of personnel available to change orifice plates, and other causes. The use of meters outside their design ranges can result in significant measurement errors. The objectives of this project were to examine parameters that contribute to measurement error at flow rates below 10% of a meters capacity, determine the expected range of error at these flow rates, and establish methods to reduce measurement error in this range. The project began with a literature search of prior studies of orifice, turbine, and ultrasonic meters for background information on their performance in low flows. Two conditions affecting multiple meter types were identified for study. First, temperature measurement errors in low flows can influence the accuracy of all three meter types, though the effect of a given temperature error can differ among the meter types. Second, thermally stratified flows at low flow rates are known to cause measurement errors in ultrasonic meters that cannot compensate for the resulting flow profiles, and the literature suggested that these flows could also affect orifice plates and turbine meters. Several possible ways to improve temperature measurements in low flows were also identified for further study. Next, an analytical study focused on potential errors due to inaccurate temperature measurements. Numerical tools were used to model a pipeline with different thermowell and RTD geometries. The goals were to estimate temperature measurement errors under different low-flow conditions, and to identify approaches to minimize temperature and flow rate errors. Thermal conduction from the pipe wall to the thermowell caused the largest predicted bias in measured temperature, while stratified temperatures in the flow caused relatively little temperature bias. Thermally isolating the thermowell from the pipe wall, or using a bare RTD, can minimize temperature bias, but are not usually practical approaches. Insulation of the meter run and the use of a finned thermowell design were practical methods predicted to potentially improve measurement accuracy, and were chosen for testing.
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Ghazarians, Alan, Subrata Sanyal, and Dennis H. Jackson. Application of Uniform Measurement Error Distribution. Fort Belvoir, VA: Defense Technical Information Center, March 2016. http://dx.doi.org/10.21236/ad1007537.

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Stefanski, L. A., and R. J. Carroll. Covariate Measurement Error in Logistic Regression. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada160277.

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Collard-Wexler, Allan, and Jan De Loecker. Production Function Estimation and Capital Measurement Error. Cambridge, MA: National Bureau of Economic Research, July 2016. http://dx.doi.org/10.3386/w22437.

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Liu, Yilu, Jose R. Gracia, Paul D. Ewing, Jiecheng Zhao, Jin Tan, Ling Wu, and Lingwei Zhan. Impact of Measurement Error on Synchrophasor Applications. Office of Scientific and Technical Information (OSTI), July 2015. http://dx.doi.org/10.2172/1212367.

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Vickers, Jr, and Ross R. Measurement Error in Maximal Oxygen Uptake Tests. Fort Belvoir, VA: Defense Technical Information Center, November 2003. http://dx.doi.org/10.21236/ada454282.

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