Measurement Uncertainty Research Articles

Ground, Ceiling and Wall Effect Evaluation of Small Quadcopters in Pressure-controlled Environments

Multicopters are used for a wide range of applications that often involve approaching buildings or navigating enclosed spaces. Opposed to the open spaces in obstacle-free environments commonly flown by fixed-wing unmanned aerial vehicles, multicopters frequently fly close to surfaces and must take into account the airflow variations caused by airflow rebound. Such disturbances must be identified in order to design algorithms capable of compensating them. The evaluation of ground, ceiling and wall effects using two different test stands is proposed in this work. Different propellers and sensors have been considered for testing. The first test setup used was placed inside terraXcube’s large climatic chamber allowing a precise control of temperature and pressure of around 20°C and 1000 hPa, respectively. The second test setup is located at the University of Denver (DU) Unmanned Systems Research Institute (DU2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}SRI) laboratory with a stable pressure of around 800 hPa. Two different fixed 6 degrees of freedom force-torque sensors have been used for the experiments, allowing to sample forces and moments in three orthogonal axes. The tests simulate a hovering situation of a quadcopter at different distances to either the ground, the ceiling or a wall. The influence of the propeller size, rotation speed, pressure and temperature have also been considered and used for later dimensionless coefficient comparison. A thorough analysis of the measurement uncertainty is also included based on experimental evaluations and manufacturer information. Experimental data collected in these tests can be used for the definition of a mathematical model in which the effect of the proximity to the different surfaces is evaluated.

Impact of effective refractory period personalization on arrhythmia vulnerability in patient-specific atrial computer models.

The effective refractory period (ERP) is one of the main electrophysiological properties governing arrhythmia, yet ERP personalization is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. This study evaluates the impact of integrating clinical ERP measurements into personalized in silico models on arrhythmia vulnerability. Clinical ERP measurements were obtained in seven patients from multiple locations in the atria. Atrial geometries from the electroanatomical mapping system were used to generate personalized anatomical atrial models. The Courtemanche M. et al. cellular model was adjusted to reproduce patient-specific ERP. Four modeling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) ERP distributions. Non-personalized approaches (A and B) were based on literature data, while personalized approaches (C and D) were based on patient measurements. Modeling effects were assessed on arrhythmia vulnerability and tachycardia cycle length, with sensitivity analysis on ERP measurement uncertainty. Mean vulnerability was 3.4 ± 4.0%, 7.7 ± 3.4%, 9.0 ± 5.1%, and 7.0 ± 3.6% for scenarios A-D, respectively. Mean tachycardia cycle length was 167.1 ± 12.6 ms, 158.4 ± 27.5 ms, 265.2 ± 39.9 ms, and 285.9 ± 77.3 ms for scenarios A-D, respectively. Incorporating perturbations to the measured ERP in the range of 2, 5, 10, 20, and 50 ms changed the vulnerability of the model to 5.8 ± 2.7%, 6.1 ± 3.5%, 6.9 ± 3.7%, 5.2 ± 3.5%, and 9.7 ± 10.0%, respectively. Increased ERP dispersion had a greater effect on re-entry dynamics than on vulnerability. Inducibility was higher in personalized scenarios compared with scenarios with uniformly reduced ERP; however, this effect was reversed when incorporating fibrosis informed by low-voltage areas. Effective refractory period measurement uncertainty up to 20 ms slightly influenced vulnerability. Electrophysiological personalization of atrial in silico models appears essential and requires confirmation in larger cohorts.

Definition, estimation and decoupling of the overall uncertainty of the outdoor air temperature measurement surrounding a building envelope

Definition, estimation and decoupling of the overall uncertainty of the outdoor air temperature measurement surrounding a building envelope

The streamgauging ruler: A low-cost, low-tech, alternative discharge measurement technique

The streamgauging ruler, a.k.a. transparent velocity-head rod, is an inexpensive, easy, and quick tool for conducting wading discharge measurements in open-channel flows. It provides reliable velocity and discharge measurements when the right measuring conditions, especially minimum flow velocity, are met. The principle is simple: depth-averaged velocity can be computed from the water level difference between the upstream and downstream sides of a plastic board placed into the flow perpendicular to the flow direction. The model developed by INRAE (commercially available for €210) is a little more expensive than previously published models but it significantly improves the ease-of-use and measurement quality. Comparison experiments with reference measurements performed in a laboratory flume and at various field sites confirm the accuracy of the semi-empirical velocity rating established by Pike et al. (2016). Over most of the investigated cross-sections, the discharge measurements are generally within 10% of the reference discharge, when the velocity is greater than 0.2 m/s. However, operator-related effects (site selection, number and distribution of verticals, adjustment and reading of the sliding rulers) can lead to larger errors, hence operator training and care are essential.A first evaluation of the velocity uncertainty related to the velocity-head reading is proposed in the form of an equation that can be used in existing methods for calculating discharge measurement uncertainty. As the method is extremely simple and quick, it is well suited for rapid discharge estimates, training or demonstration, citizen science programmes, or cooperation with services with limited resources and/or lacking specialized expertise in hydrometry. As of July 2024, 304 instruments had been built and released to diverse users around the world, along with a simple discharge computing spreadsheet, a video tutorial, and a field memo.

Simultaneous quantification of 2-pyrrolidone-5-carboxylic acid (PCA), urocanic acid (UCA), and histidine (His) in the stratum corneum by HPLC-PDA

Located in the epidermis, the stratum corneum is the most superficial layer of the skin, acting as a barrier against aggression from the external environment, preventing dehydration, and maintaining the water balance of the skin. The stratum corneum contains the Natural Hydration Factor (NMF), a mixture of hygroscopic molecules derived from filaggrin. The NMF includes 2-pyrrolidone-5-carboxylic acid (PCA), urocanic acid (UCA), and histidine (His), target biomarkers that were extracted by tape-stripping from the stratum corneum of participants for quantification in HPLC-PDA. By extracting the stratum corneum, we developed a protocol to optimize, through audience definition, the quantification of NMF biomarkers. Chromatographic analysis was performed using a YMC-Triart C18 chromatographic column, with a gradient elution of mobile phase composed of triethylammonium phosphate and acetonitrile mixture, and a photodiode array detector. HPLC-PDA procedure was selective, linear (in the range from 0.2 to 5.0 µg/mL), accurate (recovery from 92.7 to 115.1 %), precise (RSD from 0.3 to 12.1 %), and with proper detection and quantification limits. The measurement uncertainty was evaluated from validation data, with combined standard uncertainty values of 0.025–0.12 µg/mL (2.1–5.6 %), 0.004–0.28 µg/mL (2.4–12.6 %), and 0.016–0.16 µg/mL (3.2–7.9 %) for His, PCA, and UCA, respectively. Statistical analyses were performed using Monte Carlo simulation and the Mann-Whitney test, as our results were not homoscedastic and deviated from normality. The results indicate that the best audience for quantifying biomarkers were participants up to 35 years old, with all phototypes, and, preferably, female.

A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology

Among a variety of virtual metrology models, dynamic generative latent variable models (DGLVMs) have proven to be an effective tool, owing to outstanding advantages in dealing with complicated data characteristics including temporal correlations, variable colinearities, high dimensionality, and process and measurement uncertainties. Presently, the vast majority of DGLVMs are developed upon the assumption that data are Gaussian-distributed, which is however, non-robust against outlying data and leads the DGLVMs to compromised performance, as the Gaussian distribution is susceptible to outliers that would skew the estimated parameters of DGLVMs. In view of this, this paper proposes a semi-supervised robust learning (SsRL) framework for the DGLVMs by designing an ad-hoc fully robust model structure that handles the outlying data by means of the heavy-tail properties of Student’s t distribution. In addition, the SsRL-DGLVM framework is initiated as a concrete model using probabilistic slow feature analysis (denoted RoSsPSFA) for which a rigorous training algorithm without resorting to any approximations is developed. The performance of the RoSsPSFA is thoroughly evaluated by both numerical and real-world industrial cases, showing the RoSsPSFA’s better immunity against outlying data and higher generalization accuracy.

Towards millimetre-wave spectroscopy of human blood using an open-ended waveguide

Broadband dielectric spectroscopy of blood above 50 GHz is scarce due to hardware limitations, affordability, and complexity. This study explores the use of a rectangular waveguide as a probe for analysing human blood in the 55 to 95 GHz range, at temperatures from 23 °C to 36 °C, and Haemoglobin levels between 0 and 16 g/dL, focusing on practical aspects such as sample preparation and the setup’s mechanical, thermal, and calibration requirements. The research highlights the effectiveness of the Short-Open-Load calibration method, evidenced by precise measurements of saline solutions. Furthermore, analysis of 84 human blood samples indicates that while Haemoglobin levels introduce some uncertainty in dielectric constant measurements, dielectric loss readings remain consistent across temperature and Haemoglobin variations, with a low uncertainty of under 2.65%, indicating the potential use of this parameter in future millimetre-wave techniques for non-invasive testing of blood analytes.

Dual beam optical coherence tomography angiography for decoupling axial velocity gradient

Axial velocity gradient (AVG) in the optical coherence tomography angiography (OCTA) signal affects measurement accuracy when the flow is not perpendicular to the scanning beam. We developed a dual beam OCTA method to decouple the contribution of AVG from the decorrelation signal. Decoupling is first verified by phantom experiments which reduces measurement uncertainty from 1.5 to 0.7% (standard deviation). We also tested the method in human skin in vivo and the results indicate that the contribution of AVG to decorrelation signal is reduced.

Electric Drive Units: A Set-Up for Investigating Function, Efficiency, and Dynamics

High-speed electric drive units promise improved power density and, theoretically, driving range of battery electric vehicles. An essential step of the development process is extensive testing of the drive unit on a test rig. In particular, at a high rotational speed level, experimental testing can be challenging. This paper describes a test rig for investigating the overall function of a high-speed drive unit and the transmission’s efficiency and dynamics. The high-speed drive unit developed in the Speed4E research project was the reference drive unit. The test rig is based on the concept of electrical power circulation. Thus, the test rig can be used universally for different drive unit designs and operating modes. A reaction torque measurement unit was developed to enable measurements at high rotational speeds. Simultaneously, this unit allows robust measurements at low costs. The expected measurement uncertainties of torque, rotational speed, transmission efficiency, and power losses were calculated using the Monte Carlo method. The results demonstrate that the developed torque measurement unit combines precise torque measurement with a robust design and low costs, making it competitive with state-of-the-art solutions for torque measurement at high speeds.

The promise and peril of comparing fluorescence lifetime in biology revealed by simulations.

Signaling dynamics are crucial in biological systems, and biosensor-based real-time imaging has revolutionized their analysis. Fluorescence lifetime imaging microscopy (FLIM) excels over the widely used fluorescence intensity imaging by allowing the measurement of absolute signal levels, independent of sensor concentration. This capability enables the comparison of signaling dynamics across different animals, body regions, and timeframes. However, FLIM's advantage can be compromised by factors like autofluorescence in biological experiments. To address this, we introduce FLiSimBA, a flexible computational framework for realistic F luorescence Li fetime Sim ulation for B iological A pplications. Through simulations, we analyze the signal-to-noise ratios of fluorescence lifetime data, determining measurement uncertainty and providing necessary error bars for lifetime measurements. Furthermore, we challenge the belief that fluorescence lifetime is unaffected by sensor expression and establish quantitative limits to this insensitivity in biological applications. Additionally, we propose innovations, notably multiplexed dynamic imaging that combines fluorescence intensity and lifetime measurements. This innovation can transform the number of signals that can be simultaneously monitored, thereby enabling a systems approach in studying signaling dynamics. Thus, by incorporating diverse factors into our simulation framework, we uncover surprises, identify limitations, and propose advancements for fluorescence lifetime imaging in biology. This quantitative framework supports rigorous experimental design, facilitates accurate data interpretation, and paves the way for technological advancements in fluorescence lifetime imaging.

Beyond Risk: A Measure of Distribution Uncertainty

This paper addresses the critical yet often overlooked concept of distribution uncertainty (ambiguity) in decision making, emphasizing its importance alongside traditional outcome uncertainty (risk). It introduces a novel quantitative measure of ambiguity that accurately captures distribution uncertainty. This measure enhances empirical models, yielding more reliable parameter estimates and improving decision-making processes. The study demonstrates the practical value of this ambiguity measure using financial market decision making as an example. The measure helps identify and adjust for uncertainties in underlying distributions, supporting more robust financial models and better risk management. The findings advocate for integrating ambiguity considerations into data analytics models and developing more reliable methodologies for empirical research and practical applications. This study promotes a nuanced understanding of uncertainty, offering significant implications for research methodologies and practical risk management across various fields.

Evolutionary variational inference for Bayesian generalized nonlinear models

In the exploration of recently developed Bayesian Generalized Nonlinear Models (BGNLM), this paper proposes a pragmatic scalable approximation for computing posterior distributions. Traditional Markov chain Monte Carlo within the populations of the Genetically Modified Mode Jumping Markov Chain Monte Carlo (GMJMCMC) algorithm is an NP-hard search problem. To linearize them, we suggest using instead variational Bayes, employing either mean-field approximation or normalizing flows for simplicity and scalability. This results in an evolutionary variational Bayes algorithm as a more scalable alternative to GMJMCMC. Through practical applications including inference on Bayesian linear models, Bayesian fractional polynomials, and full BGNLM, we demonstrate the effectiveness of our method, delivering accurate predictions, transparency and interpretations, and accessible measures of uncertainty, while improving the scalability of BGNLM inference through on the one hand using a novel variational Bayes method, but, on the other hand, enabling the use of GPUs for computations.

Evaluation of Uncertainty in Linear Cylindrical Diameter Measurement based on Coordinate Measuring Machines

According to the measurement method of linear cylindrical diameter based on coordinate measuring machine (CMM), establish a black box evaluation model for the uncertainty of CMM linear cylindrical diameter measurement, and analyze the sources of each uncertainty. Combining measurement examples, this paper elaborates on the uncertainty evaluation process of linear cylindrical diameter measurement based on CMM, calculates the extended uncertainty of linear cylindrical diameter measurement based on CMM, and provides a reference for the uncertainty evaluation of linear cylindrical diameter measurement based on CMM.

Uncertainty analysis for free-space three-dimensional measurement of electromagnetic pulse

As the polarization direction is unknown in free-space three-dimensional (3D) electromagnetic pulse (EMP) measurement, the components in three directions are usually measured first, and then the total vector is calculated. The waveform (magnitude) and polarization direction define the 3D process. Because of the uncertainty produced in the component measurement, there is also uncertainty in the calculated 3D process. This paper investigates the propagation of uncertainty during the total vector calculation process. The magnitude and polarization angle uncertainty propagation formulas are derived through analysis. The results show that the uncertainty of the calculated magnitude is less than the maximal measurement uncertainty of the three components, and the uncertainty of the polarization angle is less than sqrt(2) times the maximal uncertainty of the three components divided by the magnitude of the measured field. Finally, a Monte-Carlo (MC) simulation is run to validate the results of the analysis. The simulation results agree well with the analysis results.

Fast uncertainty assessment of in-service thrust control for turbofan engines: An equivalent model using Taylor expansion

An equivalent model using Taylor expansion (TEEM) is proposed for a fleet of engines regulated by the industrial baseline controller, aiming at the fast assessment of in-service thrust control under gas path and measurement uncertainties in advance. TEEM is fulfilled initially by the equivalent transformation of measurement uncertainties into engine set-point distribution, followed by a first-order Taylor expansion on the steady-state aero-thermal engine model along the average degradation curve of engine fleets. Simulations are conducted at take-off states on a validated aero-thermal turbofan engine model with publicly available uncertainty statistics on a desktop computer. Results show that the equivalent transformation is successful for both new and degraded engine fleets. Moreover, TEEM can estimate the mean and standard deviation values of key engine control parameters including thrust with the maximum errors of 0.04 % and 3.85 % for new engine fleets, 0.04 % and 6.25 % for degraded engine fleets, respectively, compared to the classic sample-based Monte-Carlo simulations (MCS). Computational time for TEEM and MCS for the tested life cycle are 3,018,564s and 137.52s accordingly, which means 99.995 % simulation time reduction is achieved by TEEM. Hence, the accuracy and efficiency of the proposed model for uncertainty assessment of thrust control of turbofan engines are confirmed.

Robust acceleration schedule design for gas turbine engine using multilayer perceptron network with adaptive sample class weighting

The acceleration schedule is crucial for generating acceleration control references for the gas turbine engine (GTE) and ensuring optimal acceleration performance. However, under harsh operating conditions, GTEs may encounter difficult-to-diagnose pressure sensor faults, which lead to inaccurate control references and decreased acceleration performance. This paper proposes a data-driven robust acceleration schedule (RAS) design method to tackle this issue, including fault data augmentation and adaptive sample class weighting (ASCW). Fault data augmentation generates multiple pressure fault sample classes from a normal acceleration schedule dataset. Due to the redundant pressure sensor configuration, the RAS can reconstruct these classes and generate accurate control references when a single pressure sensor fails. The ASCW employs a multilayer perceptron network to reconstruct the normal and fault sample classes accurately. Proportional integral regulation adjusts their weights during training to ensure balanced reconstruction precision. Simulation cases were conducted to verify the effectiveness of the RAS under actual GTE conditions, including engine-model mismatches, performance deterioration, flight envelope, and measurement uncertainty. The results demonstrate that the RAS ensures superior acceleration performance of GTEs across the full flight envelope in both normal and single pressure sensor fault scenarios. Additionally, the ASCW achieves reconstruction precision of 0.068 %, 0.080 %, 0.080 %, 0.082 %, and 0.077 % for normal and fault sample classes, respectively, prioritizing the precision of the normal sample class and balancing the precision of fault sample classes.

Uncertainty evaluation in density and viscosity of nanofluids at different temperatures using Gaussian process regression-based Monte-Carlo simulations

Uncertainty in experimental measurements is likely to be incorporated due to various limitations in measurement instruments, experimental procedures, quantity of chemicals, etc. Obtaining the true properties of nanofluids is a difficult task. Hence, the evaluation of the dynamic viscosity and density is strenuous. The present work aims to study the influence of uncertainties in temperature and concentration on density and dynamic viscosity of Graphene nanoparticle/distilled water, multiwall carbon nanotube/distilled water, and alumina/distilled water nanofluids using Gaussian Process Regression (GPR) learning algorithm guided Monte-Carlo simulations. The GPR algorithm is employed to predict the properties of the nanofluids which are then fed to Monte-Carlo simulations to predict the uncertainty. The noise and uncertainties are introduced in the mole fraction and temperature artificially and are validated using the Kolmogorov-Smirnov test and Quantile-Quantile plot. The influence of different noise levels in the data is also carried out.

An efficient protocol for commissioning radiophotoluminescence dosimeters for radiotherapy dosimetry audits

PurposeThe aim of this study was to develop an efficient protocol for the commissioning of 1000 radiophotoluminescence dosimeters (RPLDs) for use in postal dosimetry audits in radiotherapy. This involved the determination of correction factors necessary to reduce measurement uncertainty and ensure accurate dose measurements. MethodsThe commissioning process started with the RPLDs subjected to a series of controlled irradiations to determine their individual nominal response. Experiments were also conducted to assess the influence of irradiation position, reading position, and ambient temperature on the dosimeter readings all which were accounted for calculating individual sensitivity correction factors (SCFs) for each dosimeter. Statistical analysis was performed to evaluate the variability of SCFs depending on normalization group. An additional investigation simulating different dosimetry audit batches was conducted to study the effect of SCF variability on dosimetry audit measurements. ResultsThe adopted commissioning protocol required irradiation position correction factors (0.990–0.996), readout tray position correction factors (0.992–1.01) and room temperature corrections (∼0.4 % per ° C). This enabled the calculation of SCFs for a batch of 1000 RPLDs and the analysis found the majority of SCFs falling within the range of 0.985–1.015. The standard deviations of the SCF distributions were approximately 1% for all normalization groups. It was observed that SCFs normalized to the entire batch of 1000 dosimeters could be effectively used for smaller audit batches, with an additional uncertainty contribution of up to 0.2%. This minimal increase in uncertainty is acceptable within the context of dosimetry audits. ConclusionsThe developed protocol for commissioning RPLDs provides a reliable method for ensuring accurate dose measurements in postal radiotherapy dosimetry audits. The correction factors applied during the commissioning process were thoroughly described to effectively minimize measurement uncertainty. The findings support the use of SCFs normalized to large dosimeter batches for smaller audit groups, thereby streamlining the dosimetry audit process. Future research should focus on the long-term stability of SCFs to further enhance the reliability of RPLD-based dosimetry audits.

Evaluation of Uncertainty in Cylindricity Measurement based on Coordinate Measuring Machine

Evaluation of Uncertainty in Cylindricity Measurement based on Coordinate Measuring Machine

Evaluation of Uncertainty in Linear Plane Angle Measurement based on Coordinate Measuring Machines

Evaluation of Uncertainty in Linear Plane Angle Measurement based on Coordinate Measuring Machines