Components of measurement uncertainty from a measurement model with two stages involving two output quantities

2015 ◽  
Vol 146 ◽  
pp. 305-312 ◽  
Author(s):  
Dimitrios Theodorou ◽  
Ypatia Zannikou ◽  
Fanourios Zannikos
Keyword(s):  
Measurement Uncertainty ◽  
Measurement Model ◽  
Two Stages

Estimation of uncertainty in olfactometry

2009 ◽  
Vol 59 (7) ◽  
pp. 1409-1413 ◽  
Author(s):  
T. Higuchi
Keyword(s):  
Measurement Uncertainty ◽  
Interlaboratory Comparison ◽  
Collaborative Study ◽  
Estimation Procedure ◽  
Measurement Model ◽  
Standard Uncertainty ◽  
Expanded Uncertainty ◽  
Collaborative Test ◽  
Measurement Results ◽  
Estimation Of Uncertainty

Estimation of uncertainty in odour measurement is essential to the interpretation of the measurement results. The fundamental procedure for the estimation of measurement uncertainty comprises the specification of the measurement process, expression of the measurement model and all influences, evaluation of the standard uncertainty of each component, calculation of the combined standard uncertainty, determination of a coverage factor, calculation of the expanded uncertainty and reporting. Collaborative study such as interlaboratory comparison of olfactometry yields performance indicators of the measurement method including repeatability and reproducibility. Therefore, the use of collaborative test results for measurement uncertainty estimation according to ISO/TS 21748 and ISO 20988 is effective and reasonable. Measurement uncertainty of the triangular odour bag method was estimated using interlaboratory comparison data from 2003 to 2007 on the basis of the simplest model of statistical analysis, and the expanded uncertainty of odour index ranged between 3.1 and 6.7. On the basis of the establishment of the estimation procedure for uncertainty, a coherent interpretation method for the measurement results will be proposed and more effective and practical quality control of olfactometry will be available.

scholarly journals Method of kurtosis in estimating the measurement uncertainty during calibration of the electrical resistance measures using a potentiometer

2021 ◽  
pp. 30-34
Author(s):  
Igor Zakharov ◽  
Olesia Botsiura ◽  
Valerii Semenikhin
Keyword(s):  
Measurement Uncertainty ◽  
Electrical Resistance ◽  
Voltage Drop ◽  
Measurement Model ◽  
Standard Uncertainty ◽  
The Monte Carlo Method ◽  
Resistance Measure ◽  
Distribution Laws ◽  
Single Size ◽  
Good Agreement

Calibration of electrical resistance measures is considered by the indirect method, which is realized through measuring the voltage drop across the series-connected reference and calibrated resistors. The biases of the estimates of the measurand and the combined standard uncertainty due to the nonlinearity of the measurement model were calculated. The distribution laws of the input quantities in the calculation of the expanded uncertainty were taken into account by the kurtosis method. An example of measurement uncertainty evaluating during calibration of single-size electrical resistance measure R331 with a nominal resistance of 1000 Ω by comparing its value using a potentiometer R345 with the value of the calibrated reference standard is considered. Estimates of the measurand and its standard and expanded uncertainties obtained using the proposed method showed good agreement with the estimates obtained using the Monte Carlo method.

CALIBRATION OF SPECTRAL IRRADIANCE SOURCES USING A FIBER-COUPLED SYSTEM

2021 ◽  
Author(s):  
H.T.B Lee ◽  
Y.C. Chau ◽  
H.S.B. Lam
Keyword(s):  
Measurement Uncertainty ◽  
Light Source ◽  
Spectral Range ◽  
Coupled System ◽  
Measurement Model ◽  
Spectral Irradiance ◽  
Calibration Laboratory ◽  
Calibration Setup

The standards and Calibration Laboratory has recently developed the calibration service for the enclosed-type irradiance light source in the spectral range from 300 nm to 850 nm. The calibration is based on the source-based method measured by a fiber-coupled system. In this paper, the calibration setup, measurement model and the associated uncertainty are presented. The expanded measurement uncertainty is estimated to be less than 3,3 % over the measured spectral range which can support the needs from the testing and certification industry.

Measurement Uncertainty in Spatial Models: A Bayesian Dynamic Measurement Model

2018 ◽  
Vol 27 (3) ◽  
pp. 302-319 ◽  
Author(s):  
Sebastian Juhl
Keyword(s):  
Measurement Uncertainty ◽  
Statistical Models ◽  
Latent Variables ◽  
Political Competition ◽  
Spatial Models ◽  
Measurement Model ◽  
Item Response Model ◽  
Spatial Econometric ◽  
Spatial Dependencies ◽  
Party Family

According to spatial models of political competition, parties strategically adjust their ideological positions to movements made by rival parties. Spatial econometric techniques have been proposed to empirically model such interdependencies and to closely convert theoretical expectations into statistical models. Yet, these models often ignore that the parties’ ideological positions are latent variables and, as such, accompanied by a quantifiable amount of uncertainty. As a result, the implausible assumption of perfectly measured covariates impedes a proper evaluation of theoretical propositions. In order to bridge this gap between theory and empirics, the present work combines a spatial econometric model and a Bayesian dynamic item response model. The proposed model accurately accounts for measurement uncertainty and simultaneously estimates the parties’ ideological positions and their spatial interdependencies. To verify the model’s utility, I apply it to recorded votes from the sixteen German state legislatures in the period from 1988 to 2016. While exhibiting a notable degree of ideological mobility, the results indicate only moderate spatial dependencies among parties of the same party family. More importantly, the analysis illustrates how measurement uncertainty can lead to substantively different results which stresses the importance of appropriately incorporating theoretical expectations into statistical models.

Calibration of Ultrasonic Flaw Detectors in accordance with ISO 22232-1:2020

2021 ◽  
Author(s):  
C.F. Au Yeung ◽  
◽  
H.F. Tsang ◽  
S.L. Yang ◽  
C.M. Tsui
Keyword(s):  
Measurement Uncertainty ◽  
Measurement Model ◽  
Ultrasonic Flaw ◽  
Background Measurement ◽  
Loop Antennas

This paper presents the procedure developed at SCL for the calibration of loop antennas for test frequencies from 9 kHz to 30 MHz in accordance with CISPR 16-1-6:2014+AMD1:2017. The background, measurement model and uncertainty components are introduced and discussed. The expanded measurement uncertainty is estimated as 1.2 dB.

Study on Uncertainty of Gas Monitoring Dynamic Calibration System

2011 ◽  
Vol 105-107 ◽  
pp. 1970-1974 ◽  
Author(s):  
Li De Fang ◽  
Xiu Ming Xiang ◽  
Xiao Ting Li ◽  
Li Li Pang ◽  
Xiao Jie Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Measurement Uncertainty ◽  
Monitoring System ◽  
Rapid Development ◽  
Measurement Model ◽  
Quality Monitoring ◽  
Dynamic Calibration ◽  
Air Quality Monitoring ◽  
Calibration System

With the rapid development of industry and transportation, air pollution is worsening, therefore, monitoring of air pollution components is more and more heeded. In this study, based on the measurement model and the composition of the air quality monitoring system on line, the mathematical model of the measurement system value transmission was analyzed, and the uncertainty components were calculated respectively, a conclusion that the main factor of the overall system uncertainty is the uncertainty of system itself in the existing air quality monitoring system was drawn, so measurement uncertainty of the calibration system was focused on the research. The effects of the Zero-gas, calibration gases and gas mass flow controller on the uncertainty of the calibration system was experimented and analyzed, and measurement uncertainty of the dynamic calibration system was evaluated.

scholarly journals Determination of Competence, Training, and Character of Loyalty Through Personnel Professionalism as Intervening Variables in Guskamla Batam-Indonesia

2019 ◽  
Vol IV (II) ◽  
pp. 448-457
Author(s):  
Eko Agus Susanto ◽  
Chablullah Wibisono ◽  
Indrayani
Keyword(s):  
Structural Model ◽  
Measurement Model ◽  
Model Analysis ◽  
Study Data ◽  
Inner Model ◽  
Competence Training ◽  
Saturated Sample ◽  
Two Stages ◽  
Intervening Variables

Guskamla Batam personnel always improve Personnel Professionalism and its quality, both directly and indirectly, but some problems exist, and solutions must be sought. This study used a saturated sample taken from the Census Technique using proportional random sampling, namely personnel in the Guskamla Batam organizational units. The five staff consisting of 120 personnel, were made into the population, the sample in this study. Data analysis techniques in this study using AMOS. There are two stages to the group in analyzing SEM: (1) Measurement Model Analysis (Outer Model), (2) Structural Model Analysis (Inner Model). For Loyalty (LOY) R2 = 0, 789 then the amount of Determination 78.9%. Thus, it can be stated that Loyalty changes are influenced by Competence, Training, Character, and Personnel Professionalism of 78.9%. thus, the problem of the Character of some personnel to support operations is still not optimal, has been answered.

scholarly journals Backpacker Identity: Scale Development and Validation

2019 ◽  
Vol 59 (2) ◽  
pp. 281-294 ◽  
Author(s):  
Ganghua Chen ◽  
Lijun Zhao ◽  
Songshan (Sam) Huang
Keyword(s):  
Scale Development ◽  
Social Identity Theory ◽  
Reliability And Validity ◽  
Measurement Model ◽  
Three Dimension ◽  
Self Evaluation ◽  
Two Stages ◽  
Development And Validation ◽  
Group Self ◽  
Practical Implications

Backpacker identity has attracted growing attention in backpacker tourism research. However, there still lacks a valid scale to measure backpacker identity. Guided by Social Identity Theory (SIT), this study aims to develop and validate a scale to measure backpacker identity in the Chinese context. The study used two Chinese backpacker samples in two stages (Study 1, n=190; Study 2, n=323) to establish the psychometric properties of a backpacker identity scale (BIS). Following the process of scale development, a three-dimension (i.e., self-categorization, group self-value, and group self-evaluation) backpacker identity measurement model was identified. The refined scale with 16 measurement items was finally identified with sufficient reliability and validity. Theoretical and practical implications were discussed.

Modelling complex measurement processes for measurement uncertainty determination

2020 ◽  
Vol 37 (3) ◽  
pp. 494-516
Author(s):  
Tobias Mueller ◽  
Meike Huber ◽  
Robert Schmitt
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Measurement Uncertainty ◽  
Measurement Model ◽  
Continuous Functions ◽  
Content Type ◽  
The Neural Network ◽  
Continuous Output ◽  
Complex Measurement ◽  
Uncertainty Determination

Purpose Measurement uncertainty is present in all measurement processes in the field of production engineering. However, this uncertainty should be minimized to avoid erroneous decisions. Present methods to determine the measurement uncertainty are either only applicable to certain processes and do not lead to valid results in general or require a high effort in their application. To optimize the costs and benefits of the measurement uncertainty determination, a method has to be developed which is valid in general and easy to apply. The paper aims to discuss these issues. Design/methodology/approach This paper presents a new technique for determining the measurement uncertainty of complex measurement processes. The approximation capability of artificial neural networks with one hidden layer is proven for continuous functions and represents the basis for a method for determining a measurement model for continuous measurement values. Findings As this method does not require any previous knowledge or expertise, it is easy to apply to any measurement process with a continuous output. Using the model equation for the measurement values obtained by the neural network, the measurement uncertainty can be derived using common methods, like the Guide to the expression of uncertainty in measurement. Moreover, a method for evaluating the model performance is presented. By comparing measured values with the output of the neural network, a range in which the model is valid can be established. Combining the evaluation process with the modelling itself, the model can be improved with no further effort. Originality/value The developed method simplifies the design of neural networks in general and the modelling for the determination of measurement uncertainty in particular.

scholarly journals Example of Monte Carlo Method Uncertainty Evaluation for Above-Water Ocean Colour Radiometry

2020 ◽  
Vol 12 (5) ◽  
pp. 780 ◽  
Author(s):  
Agnieszka Białek ◽  
Sarah Douglas ◽  
Joel Kuusk ◽  
Ilmar Ansko ◽  
Viktor Vabson ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Measurement Uncertainty ◽  
Measurement Model ◽  
Uncertainty Evaluation ◽  
Ocean Colour ◽  
Monte Carlo Approach ◽  
Radiometric Measurements ◽  
Downwelling Irradiance

We describe a method to evaluate an uncertainly budget for the in situ Ocean Colour Radiometric measurements. A Monte Carlo approach is chosen to propagate the measurement uncertainty inputs through the measurements model. The measurement model is designed to address instrument characteristics and uncertainty associated with them. We present the results for a particular example when the radiometers were fully characterised and then use the same data to show a case when such characterisation is missing. This, depending on the measurement and the wavelength, can increase the uncertainty value significantly; for example, the downwelling irradiance at 442.5 nm with fully characterised instruments can reach uncertainty values of 1%, but for the instruments without such characterisation, that value could increase to almost 7%. The uncertainty values presented in this paper are not final, as some of the environmental contributors were not fully evaluated. The main conclusion of this work are the significance of thoughtful instrument characterisation and correction for the most significant uncertainty contributions in order to achieve a lower measurements uncertainty value.

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