Experiment and Analysis of Measurement Error of a Wide Range Current Transformer

Abstract The repeatability of findings is the key factor behind scientific reliability, and the failure to reproduce scientific findings has been termed the ‘replication crisis’. Geometric morphometrics is an established tool in evolutionary biology. However, different operators (and/or different methods) could act as large sources of variation in the data obtained. Here, we investigated inter-operator error in geometric morphometric protocols on complex shapes of Liolaemus lizards, as well as measurement error in three taxa varying in their difficulty of digitalization. We also examined the potential for these protocols to discriminate among complex shapes in closely related species. We found a wide range of inter-operator error, contributing between 19.5% and 60% to the total variation. Moreover, measurement error increased with the complexity of the quantified shape. All protocols were able to discriminate between species, but the use of more landmarks did not imply better performance. We present evidence that complex shapes reduce repeatability, highlighting the need to explore different sources of variation that could lead to such low repeatability. Lastly, we suggest some recommendations to improve the repeatability and reliability of geometric morphometrics results.

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Electromagnetic and thermal parameter identification method for best prediction of temperature distribution on transformer tank covers

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-08-2014-0217 ◽

2015 ◽

Vol 34 (2) ◽

pp. 485-495 ◽

Cited By ~ 2

Author(s):

Patricia Penabad Durán ◽

Paolo Di Barba ◽

Xose Lopez-Fernandez ◽

Janusz Turowski

Keyword(s):

Temperature Distribution ◽

Measurement Error ◽

Parameter Identification ◽

Objective Function ◽

Measurement Errors ◽

Optimization Algorithms ◽

Identification Problem ◽

Content Type ◽

Identification Method ◽

Wide Range

Purpose – The purpose of this paper is to describe a parameter identification method based on multiobjective (MO) deterministic and non-deterministic optimization algorithms to compute the temperature distribution on transformer tank covers. Design/methodology/approach – The strategy for implementing the parameter identification process consists of three main steps. The first step is to define the most appropriate objective function and the identification problem is solved for the chosen parameters using single-objective (SO) optimization algorithms. Then sensitivity to measurement error of the computational model is assessed and finally it is included as an additional objective function, making the identification problem a MO one. Findings – Computations with identified/optimal parameters yield accurate results for a wide range of current values and different conductor arrangements. From the numerical solution of the temperature field, decisions on dimensions and materials can be taken to avoid overheating on transformer covers. Research limitations/implications – The accuracy of the model depends on its parameters, such as heat exchange coefficients and material properties, which are difficult to determine from formulae or from the literature. Thus the goal of the presented technique is to achieve the best possible agreement between measured and numerically calculated temperature values. Originality/value – Differing from previous works found in the literature, sensitivity to measurement error is considered in the parameter identification technique as an additional objective function. Thus, solutions less sensitive to measurement errors at the expenses of a degradation in accuracy are identified by means of MO optimization algorithms.

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Quantitation of measurement error with Optimal Segments: basis for adaptive time course smoothing

AJP Endocrinology and Metabolism ◽

10.1152/ajpendo.1993.264.6.e902 ◽

1993 ◽

Vol 264 (6) ◽

pp. E902-E911 ◽

Cited By ~ 10

Author(s):

D. C. Bradley ◽

G. M. Steil ◽

R. N. Bergman

Keyword(s):

Measurement Error ◽

Measurement Errors ◽

Time Course ◽

Smooth Curve ◽

Random Group ◽

Smooth Curves ◽

Original Curve ◽

Wide Range ◽

Data Points ◽

Time Courses

We introduce a novel technique for estimating measurement error in time courses and other continuous curves. This error estimate is used to reconstruct the original (error-free) curve. The measurement error of the data is initially assumed, and the data are smoothed with "Optimal Segments" such that the smooth curve misses the data points by an average amount consistent with the assumed measurement error. Thus the differences between the smooth curve and the data points (the residuals) are tentatively assumed to represent the measurement error. This assumption is checked by testing the residuals for randomness. If the residuals are nonrandom, it is concluded that they do not resemble measurement error, and a new measurement error is assumed. This process continues reiteratively until a satisfactory (i.e., random) group of residuals is obtained. In this case the corresponding smooth curve is taken to represent the original curve. Monte Carlo simulations of selected typical situations demonstrated that this new method ("OOPSEG") estimates measurement error accurately and consistently in 30- and 15-point time courses (r = 0.91 and 0.78, respectively). Moreover, smooth curves calculated by OOPSEG were shown to accurately recreate (predict) original, error-free curves for a wide range of measurement errors (2-20%). We suggest that the ability to calculate measurement error and reconstruct the error-free shape of data curves has wide applicability in data analysis and experimental design.

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Design and Simulation of Electromagnetic Wide-range Current Transformer

10.1109/aeeca52519.2021.9574311 ◽

2021 ◽

Author(s):

Bing Lu ◽

Xiwen Chen ◽

Jun Zhang ◽

Xu Wang ◽

Can Ding

Keyword(s):

Current Transformer ◽

Wide Range

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Dynamic measurement error detection method of high voltage current transformer based on reinforcement learning

10.1109/icwcsg53609.2021.00090 ◽

2021 ◽

Author(s):

Hui Song ◽

Linjie Hu

Keyword(s):

Reinforcement Learning ◽

Measurement Error ◽

High Voltage ◽

Error Detection ◽

Detection Method ◽

Dynamic Measurement ◽

Current Transformer

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Harmonic Measurement Error Mechanism and Performance Improvement of Direct-Current Fiber-Optic Current Transformer

Chinese Journal of Lasers ◽

10.3788/cjl201744.0910002 ◽

2017 ◽

Vol 44 (9) ◽

pp. 0910002 ◽

Cited By ~ 1

Author(s):

李传生 Li Chuansheng ◽

赵伟 Zhao Wei ◽

王家福 Wang Jiafu ◽

邵海明 Shao Haiming

Keyword(s):

Measurement Error ◽

Performance Improvement ◽

Direct Current ◽

Fiber Optic ◽

Current Transformer ◽

And Performance ◽

Harmonic Measurement

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11. Surveys

Political Research ◽

10.1093/hepl/9780198820628.003.0011 ◽

2020 ◽

pp. 284-308

Author(s):

Sandra Halperin ◽

Oliver Heath

Keyword(s):

Measurement Error ◽

Survey Research ◽

Random Sampling ◽

Sampling Error ◽

Questionnaire Design ◽

Wide Range ◽

Asking Questions ◽

The Common ◽

Interviewing Techniques ◽

Insight Into

This chapter discusses the principles of survey research as well as the issues and problems associated with different stages of the research design process. In particular, it examines questionnaire design, sample design, and interviewing techniques, along with the common sources of error that affect survey research and what can be done to try and avoid or minimize them. Although surveys have several weaknesses, they are widely used in political research to investigate a wide range of political phenomena. They combine two things: obtaining information from people by asking questions and random sampling. When done well, surveys provide an accurate and reliable insight into what ordinary people think about politics and how they participate in politics. The chapter considers the elements of a survey that need to be addressed, namely: questionnaire design, measurement error, sampling design, sampling error, and interview mode.

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Measuring Political Democracy

Comparative Political Studies ◽

10.1177/0010414005277083 ◽

2005 ◽

Vol 38 (8) ◽

pp. 939-970 ◽

Cited By ~ 81

Author(s):

Kirk Bowman ◽

Fabrice Lehoucq ◽

James Mahoney

Keyword(s):

Measurement Error ◽

20Th Century ◽

Central American ◽

Data Sources ◽

Primary Sources ◽

Political Democracy ◽

Index Construction ◽

Wide Range ◽

Cross National

Recent writings concerning measurement of political democracy offer sophisticated discussions of problems of conceptualization, operationalization, and aggregation. Yet they have less to say about the error that derives from the use of inaccurate, partial, or misleading data sources. Drawing on evidence from five Central American countries, the authors show this data-induced measurement error compromises the validity of the principal, long-term cross-national scales of democracy. They call for an approach to index construction that relies on case expertise and use of a wide range of data sources, and they employ this approach in developing an index of political democracy for the Central American countries during the 20th century. The authors’ index draws on a comprehensive set of secondary and primary sources as it rigorously pursues standards of conceptualization, operationalization, and aggregation. The index’s value is illustrated by showing how it suggests new lines of research in the field of Central American politics.

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