Measurement Uncertainty Interval Research Articles

Measurement uncertainty interval in case of a known relationship between precision and mean.

Background: Measurement uncertainty is typically expressed in terms of a symmetric interval y±U, where y denotes the measurement result and U the expanded uncertainty. However, in the case of heteroscedasticity, symmetric uncertainty intervals can be misleading. In this paper, a different approach for the calculation of uncertainty intervals is introduced. Methods: This approach is applicable when a validation study has been conducted with samples with known concentrations. In a first step, test results are obtained at the different known concentration levels. Then, on the basis of precision estimates, a prediction range is calculated. The measurement uncertainty for a given test result can then be obtained by projecting the intersection of the test result with the limits of the prediction range back onto the axis of the known values, now interpreted as representing the measurand. Results: It will be shown how, under certain circumstances, asymmetric uncertainty intervals arise quite naturally and lead to more reliable uncertainty intervals. Conclusions: This article establishes a conceptual framework in which measurement uncertainty can be derived from precision whenever the relationship between the latter and concentration has been characterized. This approach is applicable for different types of distributions. Closed expressions for the limits of the uncertainty interval are provided for the simple case of normally distributed test results and constant relative standard deviation.

Application of Machine Learning to Express Measurement Uncertainty

The continuing increase in data processing power in modern devices and the availability of a vast amount of data via the internet and the internet of things (sensors, monitoring systems, financial records, health records, social media, etc.) enabled the accelerated development of machine learning techniques. However, the collected data can be inconsistent, incomplete, and noisy, leading to a decreased confidence in data analysis. The paper proposes a novel “judgmental” approach to evaluating the measurement uncertainty of the machine learning model that implements the dropout additive regression trees algorithm. The considered method uses the procedure for expressing the type B measurement uncertainty and the maximal value of the empirical absolute loss function of the model. It is related to the testing and monitoring of power equipment and determining partial discharge location by the non-iterative, all-acoustic method. The example uses the dataset representing the correlation of the mean distance of partial discharge and acoustic sensors and the temperature coefficient of the sensitivity of the non-iterative algorithm. The dropout additive regression trees algorithm achieved the best performance based on the highest coefficient of determination value. Most of the model’s predictions (>97%) fell into the proposed standard measurement uncertainty interval for both “seen” and “unseen” data.

Interferences, a neglected error source for clinical assays

In clinical assays, interfering substances can cause large errors, which in turn can result in severe patient harm. Yet, perhaps because such errors are rare, not much attention is devoted to interferences. This is evident in specifications, which often focus on most, but not all of the results. Methods such as total error analysis, six sigma, and measurement uncertainty intervals are all useful but fail to properly account for interferences, and the Clinical Laboratory Standards Institute standard on interferences has misleading reporting advice. Suggestions are given to improve specifications, protocols, analyses, and reporting methods.

A sensitivity analysis of a personalized pulse wave propagation model for arteriovenous fistula surgery. Part A: Identification of most influential model parameters

Previously, a pulse wave propagation model was developed that has potential in supporting decision-making in arteriovenous fistula (AVF) surgery for hemodialysis. To adapt the wave propagation model to personalized conditions, patient-specific input parameters should be available. In clinics, the number of measurable input parameters is limited which results in sparse datasets. In addition, patient data are compromised with uncertainty. These uncertain and incomplete input datasets will result in model output uncertainties. By means of a sensitivity analysis the propagation of input uncertainties into output uncertainty can be studied which can give directions for input measurement improvement. In this study, a computational framework has been developed to perform such a sensitivity analysis with a variance-based method and Monte Carlo simulations. The framework was used to determine the influential parameters of our pulse wave propagation model applied to AVF surgery, with respect to parameter prioritization and parameter fixing. With this we were able to determine the model parameters that have the largest influence on the predicted mean brachial flow and systolic radial artery pressure after AVF surgery. Of all 73 parameters 51 could be fixed within their measurement uncertainty interval without significantly influencing the output, while 16 parameters importantly influence the output uncertainty. Measurement accuracy improvement should thus focus on these 16 influential parameters. The most rewarding are measurement improvements of the following parameters: the mean aortic flow, the aortic windkessel resistance, the parameters associated with the smallest arterial or venous diameters of the AVF in- and outflow tract and the radial artery windkessel compliance.

Carbohydrate Deficient Transferrin in a Driver's License Regranting Program

Carbohydrate deficient transferrin (CDT) is a common diagnostic marker for detecting chronic alcohol abuse. For over 2.5 years, it has been used in traffic medicine among subjects applying for driver's license renewal or regranting in Belgium. We report on data collected during the program and provide an estimation of an applicable cut-off point in forensic situations. Using this cut-off, the success of the driver's license regranting program is evaluated. CDT was assayed at Ghent University Hospital by capillary zone electrophoresis, measured on the Capillarys 2™ system, in 3977 subjects applying for driver's license regranting. Determination of a cut-off was done by using Bhattacharya statistics and by adding a measurement uncertainty interval. The outcome of the program was evaluated by monitoring CDT values for 163 subjects during one entire year. In 3977 subjects (3481 males and 496 females), CDT values were significantly higher in men compared with women, but there is no need for a gender-specific cut-off value. Drunk drivers under the age of 30 have significantly lower CDT values than older subjects, and a separate cut-off could be calculated. A general cut-off of 2.3% CDT was calculated for the entire study population. Using this cut-off value for evaluating the outcome of the program for 163 subjects, the percentage offenders at the beginning (29%) decreased to 8% after 1 year. Applying a marker for chronic alcohol abuse such as CDT for driver's license renewal or regranting is a powerful tool. Analysis of data collected over 2.5 years reveals a favorable outcome of the program and a useful cut-off point could be determined.

Discussion on the Gaussian assumption in flow rate measurements using a primary weighing method

Flow rate measurement is a common task in many hydraulic infrastructures included in systems with a large impact on the economy. The quality requirements that such measurement must fulfil imply the best knowledge of the measurement results (estimates and measurement uncertainties). Methods such as those given by the Guide for the Expression of Uncertainty in Measurement (GUM) have been widely used as tools to evaluate measurement uncertainties. However, such methods have implicit assumptions on the nature of the mathematical models and the applicability conditions, which are not often taken into account by their users, who apply them regardless of the specific nature of the actual metrological problems. One such assumption is that the output probability function is Gaussian, which is true only if some input conditions are met. In practice, many metrological problems are described by mathematical models with non-ideal conditions, the measurement uncertainty solutions thus being quite different from those predicted by the GUM method. The development of metrological studies has shown that the Monte Carlo method is suitable to deal with non-ideal problems and has several advantages. One such advantage is particularly useful for the specific problem of flow rate measurement using a primary weighing method: the ability to give information on the output quantity probability function. In this way, it is possible not only to obtain the output quantity estimate but also to test the normality of the output measurement uncertainty interval, which in fact has a non-Gaussian shape.

Probing turbulence with three-dimensional particle-tracking velocimetry

Recent developments in three-dimensional particle-tracking velocimetry (3-D PTV) are reviewed. The instrumentation, data processing algorithm, and applicability range of the 3-D PTV developed at the University of Tokyo are introduced, and then its application to turbulence measurements is presented with some of the author's perspectives on future 3-D PTV research directions. The 3-D PTV has become a powerful tool for measuring an instantaneous distribution of all three velocity components in turbulent flows. With a highly automated digital image processing technique, motions of neutrally buoyant tracer particles suspended in a flow are tracked, and the three-dimensional displacement of each particle is efficiently calculated from a set of consecutive TV frames; velocity vectors are readily determined by dividing the displacement by the time interval. The results obtained in several turbulent shear flows demonstrate that the measurement uncertainty intervals can be made sufficiently small by the employment of three TV cameras and a precise camera calibration procedure. This technique is most unique in providing not only conventional one-point turbulence statistics but also information on the spatial structure of a flow field, for example, multiple-point correlation tensors. These characteristics are particularly advantageous for studying the quasi-coherent turbulence structures in various complex flows.

Design and Testing of a New Size Classifying Isokinetic Sequential Aerosol Sampler

Two versions of a size-classifying isokinetic sequential aerosol sampler (SCISAS) have been designed, built, tested, and deployed in a field program in the southwestern US. The SCISAS units can operate at unattended sites, exposing four or more filter types simultaneously, in two size ranges, for six sampling time intervals. Design considerations included theoretical estimates of aerosol particle losses in the 0-15 {mu}m size range. SCISAS prototypes have been tested to evaluate their sampling efficiency as a function of flow rate, the sensitivity of the sampling efficiency to isokinetic matching within the SCISAS sampling stack, the equivalency of their sampling ports, and their passive deposition characteristics. The prototypes were also compared to several other types of aerosol filtration samplers already in common use. These tests show that particle loss mechanisms within the SCISAS usually cause no more than 5% losses, and that the SCISAS units agree, within one to two measurement uncertainty intervals, with other types of aerosol samplers.