Die kombinierte Standardunsicherheit als Variationsproblem am Beispiel eines TurbinenradzählersThe Combined Standard Uncertainty as Variation Problem Using the Example of a Turbine Meter

2010 ◽  
Vol 77 (1) ◽  
Author(s):  
Jörg Riedel
Keyword(s):  
Standard Uncertainty ◽  
Variation Problem

Interpretation and use of standard atomic weights (IUPAC Technical Report)

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Adriaan M. H. van der Veen ◽  
Juris Meija ◽  
Antonio Possolo ◽  
David Brynn Hibbert
Keyword(s):  
Monte Carlo ◽  
Atomic Weight ◽  
Standard Uncertainty ◽  
Uncertainty In Measurement ◽  
Propagation Of Uncertainty ◽  
A Value ◽  
The Monte Carlo Method ◽  
Technical Report ◽  
Molecular Masses ◽  
Expression Of Uncertainty

Abstract Many calculations for science or trade require the evaluation and propagation of measurement uncertainty. Although relative atomic masses (standard atomic weights) of elements in normal terrestrial materials and chemicals are widely used in science, the uncertainties associated with these values are not well understood. In this technical report, guidelines for the use of standard atomic weights are given. This use involves the derivation of a value and a standard uncertainty from a standard atomic weight, which is explained in accordance with the requirements of the Guide to the Expression of Uncertainty in Measurement. Both the use of standard atomic weights with the law of propagation of uncertainty and the Monte Carlo method are described. Furthermore, methods are provided for calculating uncertainties of relative molecular masses of substances and their mixtures. Methods are also outlined to compute material-specific atomic weights whose associated uncertainty may be smaller than the uncertainty associated with the standard atomic weights.

scholarly journals Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1797
Author(s):  
Ján Vachálek ◽  
Dana Šišmišová ◽  
Pavol Vašek ◽  
Jan Rybář ◽  
Juraj Slovák ◽  
...  
Keyword(s):  
Machine Learning ◽  
Control Charts ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Conveyor Belt ◽  
Standard Uncertainty ◽  
K Nearest Neighbor ◽  
Industrial Products ◽  
Dynamic Identification ◽  
Identification Technique

The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of type A standard uncertainty and type B standard uncertainty, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e., measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.

Embracing Uncertainty: Modeling the Standard Uncertainty in Electron Probe Microanalysis—Part I

2020 ◽  
Vol 26 (3) ◽  
pp. 469-483
Author(s):  
Nicholas W. M. Ritchie
Keyword(s):  
Surface Roughness ◽  
Expert System ◽  
Coating Thickness ◽  
Electron Probe ◽  
Uncertainty Modeling ◽  
Measurement Model ◽  
Standard Uncertainty ◽  
X Ray ◽  
Error Bars ◽  
New Framework

AbstractThis is the first in a series of articles which present a new framework for computing the standard uncertainty in electron excited X-ray microanalysis measurements. This article will discuss the framework and apply it to a handful of simple, but useful, subcomponents of the larger problem. Subsequent articles will handle more complex aspects of the measurement model. The result will be a framework in which sophisticated and practical models of the uncertainty for real-world measurements. It will include many long overlooked contributions like surface roughness and coating thickness. The result provides more than just error bars for our measurements. It also provides a framework for measurement optimization and, ultimately, the development of an expert system to guide both the novice and expert to design more effective measurement protocols.

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.

The Uncertainty Analysis of Microwave Resonant Cavity System for Wetness Measurement

2014 ◽  
Vol 643 ◽  
pp. 176-182
Author(s):  
Jun Ke Zhang ◽  
Tin Ge ◽  
Jing Yu Liu ◽  
Ming Li ◽  
Xiao Zhe Yan
Keyword(s):  
Sampling Error ◽  
Resonant Cavity ◽  
Water Film ◽  
Steam Pressure ◽  
Frequency Measurement ◽  
Standard Uncertainty ◽  
Microwave Cavity ◽  
System Uncertainty ◽  
Cavity System ◽  
Parameters Measurement

Steam pressure and resonant frequency of microwave cavity are important measure parameters of microwave wetness measurement system. The measuring precision of pressure and frequency directly affect the accuracy of wetness measurement. This paper takes different pressure and wetness of steam as example to analyze the parameter uncertainty of measurement relationship and deduces standard uncertainty of wetness measurement. The system comprehensive uncertainty is identified, when the parameters measurement, cavity heat expansion, sampling error, sedimentary water film and so on are considered. The result shows that the system uncertainty is less than 0.004%. The system uncertainty introduced by pressure measurement is small and can be neglected, but the system uncertainty from frequency measurement has a great effect. The precisely measurement of resonance frequency is key to ensure the accuracy of the system.

scholarly journals The NIST Johnson Noise Thermometry System for the Determination of the Boltzmann Constant

2017 ◽  
Vol 122 ◽  
Author(s):  
Nathan E. Flowers-Jacobs ◽  
Alessio Pollarolo ◽  
Kevin J. Coakley ◽  
Adam C. Weis ◽  
Anna E. Fox ◽  
...  
Keyword(s):  
International System ◽  
Standard Uncertainty ◽  
Boltzmann Constant ◽  
Correlated Noise ◽  
Johnson Noise ◽  
Noise Thermometry ◽  
Relative Standard ◽  
System Of Units ◽  
Johnson Noise Thermometry

In preparation for the redefinition of the International System of Units (SI), five different electronic measurements of the Boltzmann constant have been performed using different Johnson noise thermometry (JNT) systems over the past seven years. In this paper, we describe in detail the JNT system and uncertainty components associated with the most recent National Institute of Standards and Technology (NIST) determination of the Boltzmann constant: k = 1.380642 9(69) × 10−23 J/K, with a relative standard uncertainty of 5.0 × 10−6 and relative offset of −4.05 × 10−6 from the Committee on Data for Science and Technology (CODATA) 2014 recommended value. We discuss the input circuits and the approach we used to match the frequency response of two noise sources. We present new measurements of the correlated noise of the 4 K on-chip resistors in the quantum-accurate, pseudorandom, voltage-noise source, which we used to estimate the correlated, frequency-dependent, nonthermal noise in our system. Finally, we contrast our system with those used in other measurements and speculate on future improvements.

scholarly journals Thermal Conductivities of Choline Chloride-Based Deep Eutectic Solvents and Their Mixtures with Water: Measurement and Estimation

Molecules ◽  
2020 ◽  
Vol 25 (17) ◽  
pp. 3816
Author(s):  
Taleb H. Ibrahim ◽  
Muhammad A. Sabri ◽  
Nabil Abdel Jabbar ◽  
Paul Nancarrow ◽  
Farouq S. Mjalli ◽  
...  
Keyword(s):  
Atmospheric Pressure ◽  
Choline Chloride ◽  
Deep Eutectic Solvents ◽  
Standard Uncertainty ◽  
Heat Transfer Fluids ◽  
Wide Range ◽  
Different Temperatures ◽  
Experimental Values ◽  
Water Measurement ◽  
Thermal Conductivities

The thermal conductivities of selected deep eutectic solvents (DESs) were determined using the modified transient plane source (MTPS) method over the temperature range from 295 K to 363 K at atmospheric pressure. The results were found to range from 0.198 W·m−1·K−1 to 0.250 W·m−1·K−1. Various empirical and thermodynamic correlations present in literature, including the group contribution method and mixing correlations, were used to model the thermal conductivities of these DES at different temperatures. The predictions of these correlations were compared and consolidated with the reported experimental values. In addition, the thermal conductivities of DES mixtures with water over a wide range of compositions at 298 K and atmospheric pressure were measured. The standard uncertainty in thermal conductivity was estimated to be less than ± 0.001 W·m−1·K−1 and ± 0.05 K in temperature. The results indicated that DES have significant potential for use as heat transfer fluids.

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