Measurement Uncertainty Research Articles

Time-varying risk aversion and international stock returns

We estimate aggregate, time-varying risk aversion inferred from options, stock returns and macroeconomic data for a panel of 8 countries. We document that, for most countries, the estimated risk aversion measure is counter-cyclical. Moreover, we show that estimated risk aversion forecasts monthly stock index returns up to 12 months ahead. This effect is statistically significant in panel regressions, and it survives the inclusion of additional control variables, such as an estimated of the variance risk premium, an investors’ sentiment index, and a measure of economic uncertainty. Finally, we show that risk aversion provides useful information to an investor who aims at timing the market. An investment strategy that uses the estimated time-varying risk aversion measure to solve a mean–variance asset allocation problem, delivers significantly positive returns.

Relativistic generalized uncertainty principle for a test particle in four-dimensional spacetime

The generalized uncertainty principle provides a promising phenomenological approach to reconciling the fundamentals of general relativity with quantum mechanics. As a result, noncommutativity, measurement uncertainty, and the fundamental theory of quantum mechanics are subject to finite gravitational fields. The generalized uncertainty principle (RGUP) on curved spacetime allows for the imposition of quantum-induced elements on general relativity (GR) in four dimensions. In the relativistic regimes, the determination of a test particle’s spacetime coordinates, [Formula: see text], becomes uncertain. There exists a specific range of coordinates where the accessibility to [Formula: see text] is notably limited. Consequently, the spacetime coordinates lack both smoothness and continuity. The quantum-mechanical calculations of the spacetime coordinates are directly linked to their measurement through the expectation value [Formula: see text]. This expectation value is dependent on [Formula: see text], which is itself limited by a minimum measurable length [Formula: see text]. A crucial finding presented in this script is the existence of a lower bound for the Hamiltonian, which implies the stability of the quantum nature of spacetime. The direct correlation between [Formula: see text] and [Formula: see text] is a significant discovery, suggesting a proportional relationship. The conjecture is made that the primary metric g carries all essential information regarding spacetime curvature and serves a role akin to the Jacobian determinant in general relativity. Moreover, the linear relationship between [Formula: see text] and the Planck length [Formula: see text] is established with a proportionality factor of [Formula: see text], where [Formula: see text] denotes the RGUP parameter. The discretization of spacetime coordinates results in the discontinuity of the test particle’s wavefunction [Formula: see text], leading to an unrealistic [Formula: see text]. It is also noted that the lower limit of [Formula: see text] is directly proportional to the fundamental tensor.

Quantification of the Uncertainty in Ultrasonic Wave Speed in Concrete: Application to Temperature Monitoring with Embedded Transducers.

This article presents an overall examination of how small temperature fluctuations affect P-wave velocity (Vp) measurements and their uncertainties in concrete using embedded piezoelectric transducers. This study highlights the fabrication of custom transducers tailored for long-term concrete monitoring. Accurate and reliable estimation of ultrasonic wave velocities is challenging, since they can be impacted by multiple experimental and environmental factors. In this work, a reliable methodology incorporating correction models is introduced for the quantification of uncertainties in ultrasonic absolute and relative velocity measurements. The study identifies significant influence quantities and suggests uncertainty estimation laws, enhancing measurement accuracy. Determining the onset time of the signal is very time-consuming if the onset is picked manually. After testing various methods to pinpoint the onset time, we selected the Akaike Information Criterion (AIC) due to its ability to produce sufficiently reliable results. Then, signal correlation was used to determine the influence of temperature (20 °C to 40 °C) on Vp in different concrete samples. This technique proved effective in evaluating velocity changes, revealing a persistent velocity decrease with temperature increases for various concrete compositions. The study demonstrated the capability of ultrasonic measurements to detect small variations in the state of concrete under the influence of environmental variables like temperature, underlining the importance of incorporating all influencing factors.

Indirect optical geometry measurements with a stream of particles as micro probes

The manufacturing of micro-structured components with varying geometries and materials requires precise and versatile applicable geometry measurement techniques. Optical measurement approaches enable high-precision measurements, but they rely on cooperative surfaces. To overcome the dependency on the optical surface response, the surface of a measured object is determined indirectly using an aerosol flow with fluorescent particles and a confocal fluorescence microscope. To precisely extract the surface position from the scanned fluorescence signal of the particles in the surrounding air, a model-based signal processing is proposed. To assess the measurement capabilities and the influence of the aerosol supply flow, geometry and flow measurements are performed on a flat plate geometry and a step geometry. As a result, a measurement uncertainty of ▪ with no systematic error was achieved for the flat plate experiment, despite the different boundary layer flows at the different measurement positions. In contrast to this, a significant systematic measurement error occurred in the wake flow region of the step geometry because of flow separation and, thus, no detectable particles directly behind the step. In summary, the findings clarify an in principle achievable sub-micrometre resolution with improved optics and a large number of detected particles, but also emphasise the necessity of a sufficient particle supply for enabling indirect optical geometry measurements.

Optimizing the uncertainty of measurements on a coordinate measuring machine when controlling complex geometric surfaces

The object of this study is the process of optimizing measurement uncertainty on a coordinate measuring machine (CMM) when inspecting complex geometric surfaces. The problem addressed was insufficient accuracy and efficiency of measurements of complex parts on CMMs under production conditions. A method for optimizing measurement uncertainty has been devised, which includes a mathematical model of the measurement process and an adaptive algorithm for optimizing the control strategy, based on the Monte Carlo method. The model takes into account the geometry of surfaces and CMM characteristics, while the algorithm dynamically adjusts measurement parameters. The results demonstrate a reduction in measurement uncertainty by 15–20 % and a reduction in inspection time by 10–12 % compared to conventional methods. This is achieved by taking into account the specificity of complex surface geometry and an adaptive approach. The uniqueness of the developed method is the ability to automatically adapt to different types of CMMs and measured objects, optimizing the number and location of measurement points, the speed of probe movement, and its contact force with the surface. The method takes into account not only the geometric parameters of objects but also the characteristics of the CMM itself, which allows for high accuracy. The method is particularly effective for parts with complex geometry, in which conventional methods often lead to significant errors. Practical application is possible at machine-building enterprises for quality control of complex parts, especially in serial production. The implementation of the developed method allows for improving product quality and reducing production costs by 8–10 % due to optimization of the control process and reduction of defects

CONCERTO at APEX On-sky performance in continuum

Context. CarbON CII line in post-rEionisation and ReionisaTiOn epoch (CONCERTO) instrument is a low-resolution mapping spectrometer based on lumped element kinetic inductance detector (LEKIDs) technology, operating at 130-310 GHz. It was installed on the 12-metre APEX telescope in Chile in April 2021 and was in operation until May 2023. CONCERTO’s main goals were the observation of [CII]-emission line fluctuations at high redshift and of the Sunyaev–Zel’dovich (SZ) signal from galaxy clusters. Aims. We present the data processing algorithms and the performance of CONCERTO in continuum by analysing the data from the commissioning and scientific observations. Methods. We developed a standard data processing pipeline to proceed from the raw data to continuum maps. Using a large dataset of calibrators (Uranus, Mars, and quasars) acquired in 2021 and 2022 at the APEX telescope across a wide range of atmospheric conditions, we measured the CONCERTO continuum performance and tested its stability against observing conditions. Further, using observations on the COSMOS field and observations targeting a distant sub-millimetre galaxy in the UDS field, we assessed the robustness of the CONCERTO performance on faint sources and compared our measurements with expectations. Results. The beam pattern is characterised by an effective full width at half maximum (FWHM) of 31.9 ± 0.6″ and 34.4 ± 1.0″ for high-frequency (HF) and low-frequency (LF) bands, respectively. The main beam is slightly elongated with a mean eccentricity of 0.46. Two error beams of ~65″ and ~130″ are characterised, allowing us to estimate a main beam efficiency of ~0.52. The field of view is accurately reconstructed and presents coherent distortions between the HF and LF arrays. LEKID parameters were robustly determined for 80% of the read tones. Cross-talks between LEKIDs are the first cause of flagging, followed by an excess of eccentricity for ~10% of the LEKIDs, all located in a given region of the field of view. Of the 44 scans of Uranus selected for the absolute photometric calibration, 72.5% and 78.2% of the LEKIDs were selected as valid detectors with a probability >70%. By comparing the Uranus measurements with a model, we obtain calibration factors of 19.5±0.6 Hz Jy−1 and 25.6±0.9 Hz Jy−1 for HF and LF, respectively. The point-source continuum measurement uncertainties are 3.0% and 3.4% for the HF and LF bands, ignoring the uncertainty in the model (which is <2%). This demonstrates the accuracy of the methods we deployed to process the data. Finally, the RMS of CONCERTO maps is verified to evolve as proportional to the inverse square root of the integration time. The measured noise-equivalent flux densities (NEFDs) for HF and LF are 115±2 mJy beam−1 s1/2 and 95±1 mJy beam−1 s1/2, respectively, obtained using CONCERTO data on the COSMOS field for a mean precipitable water vapour (pwv) and elevation of 0.81 mm and 55.7 deg. Conclusions. CONCERTO has unique capabilities in fast dual-band spectral mapping at ~30 arcsec resolution and with a ~18.5 arcmin instantaneous field of view. CONCERTO’s performance in continuum is perfectly in line with expectations.

Development and validation of a LC-MS method for the quantification of microcystins in fish for their monitoring in environmental and food context

Cyanobacterial blooms in aquatic ecosystems can result in the accumulation of cyanotoxins in other organisms. Fish can potentially accumulate large amounts of cyanotoxins through their direct interaction with contaminated water and the ingestion of cyanobacterial cells. As a human food source, this can result in a potential health risk. Investigating and monitoring this risk requires analytical methods sufficiently robust to be suitable for different fish species. This study, therefore, aimed to optimize and validate an UHPLC-MS/MS method for the quantification of eight microcystin congeners and nodularin in five different fish species. The limits of detection and quantification were 1 and 3 μg kg−1, respectively. The linearity of the method was very good, with calculated R2 values above 0.99. The mean recovery of the method was within the acceptable range of 70.0%–120.0 %, the repeatability was not higher than 12.6 %, and the highest intra-laboratory reproducibility was 18.7 %. The estimated range for the expanded measurement uncertainty was between 29 % and 58 %. Moreover, multiple fish from Flemish waters were analyzed to show the method's applicability. Multiple microcystin congeners were detected (up to 88.3 μg kg−1 sum of analyzed microcystins) in the liver and viscera of perch and sander. Small amounts of MC-LR and MC-YR (up to 6.1 μg kg−1) were also detected in perch muscle tissue.

Uncertainties in measurements of bubbly flows using phase-detection probes

The analysis of bubbly two-phase flows is challenging due to their turbulent nature and the need for intrusive phase-detection probes. However, accurately characterizing these flows is crucial for safely designing critical infrastructure such as dams and their appurtenant structures. The combination of dual-tip intrusive phase-detection probes with advanced signal processing algorithms enables the assessment of pseudo-instantaneous 1-D velocity time series; for which the limitations are not fully fathomed. In this investigation, we theoretically define four major sources of error, which we quantify using synthetically generated turbulent time series, coupled with the simulated response of a phase-detection probe. Based on the analysis of 1010 simulated bubble trajectories, our findings show that typical high-velocity flows in hydraulic structures hold up to 15% error in the mean velocity estimations and up to 35% error in the turbulence intensity estimations for the most critical conditions, typically occurring in the proximity of the wall. Based on thousands of simulations, our study provides a novel data-driven tool for the estimation of these baseline errors (bias and uncertainties) in real-word phase-detection probe measurements of bubbly flows (air concentrations c<40%).

Aeroelastic Influence of Blade-Shaft Coupling in a 1 1/2-Stage Axial Compressor

Abstract The design process of turbomachinery, is often constraint by aeroelastic phenomena. The design choices are limited by possible structural failure, which can be caused by high vibration amplitudes. In particular, damping has an important impact on these phenomena. In the absence of friction, damping is mainly created by aerodynamics. In this paper, additional damping created by the shaft will be investigated. This becomes relevant when blade vibrations with nodal diameters 1 and -1 couple structurally with shaft vibrations. To investigate the blade-shaft coupling, a simulation process is set up based on a full structural dynamic model of the blisk-shaft assembly and a harmonic balance CFD model to account for the aeroelastic effects. In addition, mistuning identification is performed based on an experimental modal analysis at standstill. All results are incorporated into a structural reduced order model that calculates the vibrational behavior of the blading. These results are compared to damping determined during operation using an acoustic excitation system and measured forced frequency responses. The numerical results agree well with the experimental results, i.e. within the measurement uncertainty. Furthermore, the blade-shaft coupling results in significant changes of the eigenfrequencies and damping. As a consequence, damping increases by up to twelve times due to the coupling. This reduces amplitudes by a factor of nine for the mistuned blade responses. Consequently, higher structural safety factors can be achieved by taking the blade-shaft coupling into account so that the remaining potentials in the aerodynamic design could be better exploited.

Adsorption of Selected Herbicides on Activated Carbon from Single- and Multi-Component Systems-Error Analysis in Isotherm Measurements.

The aim of this study is to examine the influence of various factors on the precision and repeatability of the experimental determination of herbicide adsorption isotherms. Studies were conducted for the activated carbon RIB as an adsorbent and three herbicides as adsorbates: 2,4-dichlorophenoxyacetic acid (2,4-D), 4-chlorophenoxyacetic acid (4-CPA), and 3-chlorophenoxypropionic acid (3-CPP). The herbicide adsorption process was carried out in single-component and multi-component modes (the herbicide was adsorbed in the presence of an accompanying substance, i.e., 4-nitroaniline (4-NA)). Due to the significant contribution of the competition phenomenon in the adsorption process, which is important, e.g., in multi-component environmental systems, a qualitative and quantitative analysis of herbicide adsorption in the presence of a competing substance was presented. This work presents, among other things, the influence of adsorbent heterogeneity (grain size) on measurement uncertainties. The spread of standard deviations for solutions requiring dilution during spectrophotometric measurements was discussed, indicating that dilutions contribute to increasing measurement uncertainties. The heterogeneity parameters of the Freundlich equation for the studied adsorption systems were analyzed; the 2,4-D/RIB system was indicated as the most energetically heterogeneous. Differentiation of the experimental conditions (pH, temperature) allowed us to assess their impact on the efficiency and mechanism of adsorption. A high repeatability of experimental isotherms was obtained for the multi-component system. The accuracy of quantitative determination of equilibrium concentrations for the tested two-component systems was assessed based on the measured UV-Vis spectra, and the adsorption of herbicides from single- and multi-component systems was compared.

A probabilistic machine learning framework for stiffness tensor estimation of carbon composite laminate

The potential uses of carbon composite material are vast, particularly in the civil, mechanical and aero-structures. Nonetheless, the practical utilization of carbon composite faces challenges due to complex experimental methods and imprecise algorithms for inverting material properties. Characterization of full set of stiffness tensor for an anisotropic material in particular orthotropic material can be considered as a complex non-linear inversion problem. The inversion process requires a robust optimization algorithm and forward models. The complexity of this inversion process impedes the practical utility in large-scale automation and in real-time structural health monitoring (SHM) application. To cater this, in this work an advanced probabilistic machine learning framework based on multi-output Gaussian process regression (moGPR) is proposed as an inversion algorithm. The inversion algorithm proposed is based on probabilistic framework that utilises the dispersion of Lamb waves as an input for optimal estimation of stiffness tensor. Experimental measurements of full wavefield data of propagating waves are conducted by scanning laser Doppler vibrometer. Further, an optimal training dataset is generated by employing a forward model based on stiffness matrix method (SMM). In the presence of measurement uncertainties, the effectiveness of the optimal prediction algorithm is showcased through a comparison between the estimated parameter and its ground truth. This comparison reveals that the proposed inversion algorithm is both efficient and robust, delivering satisfactory performance even in the presence of substantial measurement uncertainties.

Uncovering the correlation between neurotransmitter-specific functional connectivity and multidimensional anxiety in a non-clinical cohort.

Research on anxiety faces challenges due to the wide range of symptoms, making it difficult to determine if different aspects of anxiety are linked to distinct neurobiological processes. Both alterations in functional brain connectivity (FC) and monoaminergic neurotransmitter systems are implicated as potential neural bases of anxiety. We aimed to investigate whole-brain FC involving monoaminergic nuclei and its association with anxiety dimensions in 178 non-clinical participants. Nine anxiety-related scales were used, encompassing trait and state anxiety scores, along with measures of cost-probability, hypervigilance, reward-punishment sensitivity, uncertainty, and trait worry. Resting-state functional magnetic resonance imaging data were acquired, focusing on seven brainstem regions representing serotonergic, dopaminergic, and noradrenergic nuclei, with their FC patterns voxel-wise correlated with the scales. All models underwent family-wise-error correction for multiple comparisons. We observed intriguing relationships: trait and state anxiety scores exhibited opposing correlations in FC between the dorsal raphe nucleus and the paracingulate gyrus. Additionally, we identified shared neural correlates, such as a negative correlation between the locus coeruleus and the frontal pole. This connection was significantly associated with scores on measures of probability, hypervigilance, reward sensitivity, and trait worry. These findings underscore the intricate interplay between anxiety dimensions and subcortico-cortical FC patterns, shedding light on the underlying neural mechanisms governing anxiety.

Design, construction, and calibration of a weighable lysimeter for measuring water requirements of field crops for data-scarce areas of Ethiopia

A simple weighable lysimeter was designed, constructed, and calibrated for measuring the water requirements and crop coefficients of shallow-rooted crops. It has a portable weighing mechanism to quantify changes in soil moisture content. The weighing mechanism consists of a horizontal steel bar, a hydraulic car jack, both ends hooked vertical steel bars, a stand, and a digital weighing balance. As the lysimeter's inner tank, a plastic drum with a 55 cm diameter that yields 0.24 m2 of internal area was used. A performance test was conducted, and a rating curve was developed at the beginning of the growing seasons in 2023 and 2024 to evaluate the sensitivity of the weighing mechanism in determining crop water use. Linearity, repeatability, and measurement uncertainty tests were conducted. The linearity error was ±0.04 kg and ±0.03 kg for the years 2023 and 2024, respectively, which is within the allowable limit. The weighing system repeats the measurement perfectly with no error (repeatability error was zero). The combined uncertainty of the measurements was 0.023 kg, representing the cumulative effect of individual errors. The lysimeter weighing system is capable of detecting variations in mass as small as 0.2 mm of moisture content. Therefore, the developed lysimeter can be used to determine water requirements and crop coefficients of shallow-rooted crops for the design and management of irrigation in data-scarce areas of Ethiopia.

Statistical Modeling of Within-Laboratory Precision Using a Hierarchical Bayesian Approach.

Reproducibility has been well-studied in the field of food analysis; the relative standard deviation is said to follow the Horwitz curve with certain exceptions. However, little systematic research has been done on predicting repeatability or intermediate precision. We developed a regression method to estimate within-laboratory standard deviations using hierarchical Bayesian modeling and analyzing duplicate measurement data obtained from actual laboratory tests. The Hamiltonian Monte Carlo method was employed and implemented using R with Stan. The basic structure of the statistical model was assumed to be a chi-squared distribution, the fixed effect of the predictor was assumed to be a nonlinear function with a constant term and a concentration-dependent term, and the random effects were assumed to follow a lognormal distribution as a hierarchical prior. By analyzing over 300 instances, we obtained regression results that fit well with the assumed model, except for moisture, which was a method-defined analyte. The developed method applies to a wide variety of analytes measured using general principles, including spectroscopy, GC, and HPLC. Although the estimated precisions were within the HorRat(r) criteria, some cases using high-sensitivity detectors, such as mass spectrometers, showed standard deviations below that range. We propose utilizing the within-laboratory precision predicted by the model established in this study for internal quality control and measurement uncertainty estimation without considering the sample matrices. Performing statistical modeling on data from double analysis, which is conducted as a part of internal quality controls, will simplify the estimation of the precision that fits each analytical system in a laboratory.

An estimation method for bias error of measurements by utilizing process data, an incidence matrix and a reference instrument for data validation and reconciliation: Part 2 Extension to the energy balance relation

Abstract Ensuring data reliability is becoming increasingly important for further applications of AI, IoT, and digital twins. One promising technology for ensuring data reliability is data validation and reconciliation (DVR), which minimizes the uncertainty of measurements based on statistics. DVR has been widely used for the operation and maintenance of nuclear power plants in Europe and the United States in recent years. The most important input for DVR analysis is measurement uncertainty. The catalog value provided by sensor manufacturers includes the measurement uncertainty, but in reality, the actual measurement uncertainty is often smaller. Previous studies have proposed several methods for evaluating the actual measurement uncertainty based on process data, which have been confirmed to be effective for evaluating random errors. It is important to note that bias errors also contribute significantly to the measurement uncertainty. In our previous paper, we proposed a method for estimating bias error using process data, an incidence matrix, and a reference instrument. The proposed method was limited to a mass balance relation, i.e., flow rate measurements. In this paper, we extend the method to include an energy balance relation by considering energy conservation in addition to mass conservation. This extension enables the evaluation of measurement uncertainty for flow rate and temperature. The proposed method was validated with two benchmark problems. It was found to be applicable to various flow conditions, including physically fluctuating flow, such as that observed in the feedwater flow in nuclear power plants.

The importance of adding unbiased Argo observations to the ocean carbon observing system

The current coverage of direct, high-quality ship-based observations of surface ocean pCO2 includes large gaps in time and space, and has been declining since 2017. These ocean observations provide the basis for the data products that reconstruct surface ocean pCO2 and estimate ocean carbon uptake. Improved data coverage is needed to advance our understanding of the ocean carbon sink and air–sea CO2 exchange. Targeted sampling from autonomous platforms, such as biogeochemical floats, combined with traditional shipboard measurements represents a promising path forward to improve surface ocean pCO2 reconstructions. However, floats provide indirect pCO2 estimates derived from pH, and thus have higher uncertainty and are biased compared to direct shipboard measurements. Here, we use a Large Ensemble Testbed (LET) of Earth System Models and the pCO2-Residual method to reconstruct surface ocean pCO2 globally to test the impact of additional float observations, both with and without measurement uncertainties. Through comparison to the ‘model truth’, the LET allows for robust evaluation of the reconstructions. With only shipboard sampling, surface ocean pCO2 is overestimated, and the 2000–2016 global ocean carbon sink is underestimated by 0.1 Pg C year−1. Additional float observations significantly reduce this underestimation, and deviate from the ‘model truth’ by as little as 0.01 Pg C year−1, even when floats have random uncertainties of ± 11 μatm. However, systematic bias in the float observations significantly degrades the accuracy of pCO2 reconstructions, leading to an even stronger underestimation of the global ocean carbon sink of up to 0.32 Pg C year−1. We conclude that adding float-based observations to the global observing system can significantly improve reconstructions of global surface ocean pCO2, but only if these data are unbiased.

Measurement uncertainty evaluation of equivalent roughness in hydraulic pipes

This paper addresses the quantification of the dispersion of equivalent roughness values obtained from the experimental study of hydraulic pipes used, for example, in water supply networks. This quantity is mainly used in the determination of the friction factor related to pipe fluid flow. In this context, non-linear and complex mathematical models, such as the Colebrook-White equation, are applied to characterize the equivalent roughness of hydraulic pipes composed of different types of materials. However, knowledge about the measurement uncertainty of the obtained estimates is still reduced, having a direct impact in the conformity assessment of this type of hydraulic component and in the technical comparison between different types of pipes (materials and manufacturers). The paper describes the application of a Monte Carlo method (MCM) in the measurement uncertainty evaluation of equivalent roughness. In addition to presenting the theoretical and experimental background, the paper describes the measurement uncertainty propagation, from the probabilistic formulation of the input quantities up to the output quantity. A numerical example, based on experimental data retrieved from field testing of hydraulic pipes integrated in a large-scale agricultural irrigation network, is shown in the paper, illustrating the suitability, advantages, and limitations of the proposed approach.

Traceability and measurement uncertainty of non-removable field flowmeters using clamp-on ultrasonic flowmeters as reference

This study examines the traceability and measurement uncertainty of in situ hydraulic calibration of flowmeters using clamp-on ultrasonic flowmeters as reference. The procedure compares the equipment readings with the reference ones. Measurement uncertainty evaluation uses Guide to the expression of Uncertainty in Measurement (GUM) formulation, considering the linearity conditions of the mathematical models applied. Experimental values are used to test the procedure and its suitability for actual cases where the expected accuracy needs to be achieved.

Improvement of metrology infrastructure in the area of extreme impedance calibrations

The paper describes how the Laboratory for Electrical Measurements at the Ss. Cyril and Methodius University in Skopje, an accredited calibration laboratory, enhanced its calibration and measurement capabilities for extreme values of electrical impedance. This was conducted through development of new calibration methods for instruments that measure extreme electrical resistance and inductance. The paper also explains how these methods were validated to ensure traceability and how the measurement uncertainty in impedance instruments calibration was innovatively estimated, by deploying the data fusion concept to increase the metrology infrastructure capacity. As the testing facilities for electrical quantities in Southeast Europe are not sufficiently developed, and some calibration areas of electrical instruments are not well provided by the existing laboratories, despite the economic and scientific demands, the relevance of this metrology infrastructure upgrade is evident.

Calibration methods for high frequencies: Development and validation

The paper presents an introduction of an advanced method for calibration of high-frequency instruments, such as oscilloscopes, frequency counters and function generators that operate at the frequencies between 1 MHz and GHz range. Based on conducted thorough survey of the needs for calibration of high-frequency measurement devices in the region of Southeast Europe, and the identified calibration and measurement capability gap in comparison to the international metrology offer, the Laboratory for Electrical Measurements at Ss. Cyril and Methodius University in Skopje developed new methods, following the general recommendations of the EURAMET cg-7 Calibration Guide. An original approach in the design of the experimental procedure, and a novel data fusion concept for the evaluation of the measurement uncertainty is deployed. The paper also investigates and resolves some challenges of setting up an unbroken measurement traceability chain, and uncertainty estimation for calibration in the domain of high frequencies. The established and accredited calibration capabilities are essential for the region of Southeast Europe, where the metrology facilities for the calibration/testing of high frequency electronic devices are inadequate to meet the conformity assessment needs of the quickly growing automotive supply chain sector, and the needs of other electrical and electronic industries.