Because of the measurement errors, the result ỹ = f(x̃1, . . . , x̃n) of processing the measurement results x̃1, . . . , x̃n is, in general, different from the value y = f(x1, . . . , xn) that we would obtain if we knew the exact values x1, . . . , xn of all the inputs. In the linearized case, we can use numerical differentiation to estimate the resulting difference ∆y = ỹ− y; however, this require...

How to estimate parameters from observations subject to errors and uncertainty? Very often, the measurement errors are random quantities that can be adequately described by the probability theory. When we know that the measurement errors are normally distributed with zero mean, then the (asymptotically optimal) Maximum Likelihood Method leads to the popular least squares estimates. In many situ...

The determination of salinity values from SI traceable densimetry and refractometry measurement results enable traceability the former. In this work, a short study metrological compatibility determinations via these two methods for sodium chloride in ultrapure water solutions different standard seawaters (OSIL ERM) solutions, [35.0; 215.0] g kg −1 interval, is presented. It evidenced referred i...

The suggested methods for solving fault diagnosis and estimation problems are based on the use of Jordan canonical form. diagnostic observer, virtual sensor, interval, sliding mode observer design considered. Algorithms have been developed to solve these both linear nonlinear systems, considering presence external disturbances measurement noise. It has shown that form allows reducing dimensions...

The traditional engineering approach to error estimation assumes that we know the probabilities of different values of measurement error ∆xi def = e xi − xi. Yet in many practical situations, we only know the upper bound ∆i for this error. Hence after the measurement, the only information that we have about xi is that it belongs to the interval xi def = [e xi−∆i, e xi+∆i]. In this case, we have...

It is known that in general, statistical analysis of interval data is an NP-hard problem: even computing the variance of interval data is, in general, NP-hard. Until now, only one case was known for which a feasible algorithm can compute the variance of interval data: the case when all the measurements are accurate enough { so that even after the measurement, we can distinguish between diierent...

This article generalizes the tolerance interval approach for assessing agreement between two methods of continuous measurement for repeated measurement data — a common scenario in applications. The repeated measurements may be longitudinal or they may be replicates of the same underlying measurement. Our approach is to first model the data using a mixed model and then construct a relevant asymp...

In order to handle noisy measurement data, an interval type-2 fuzzy logic system (T2FLS) is proposed. In this paper, the evolution of soft fault diagnosis for analog circuits is explored based on a type-2 fuzzy logic system (T2FLS) which can handle measurement uncertainties. In order to evaluate the values of the faulty components accurately, a type-2 fuzzy rule base is adopted to describe the ...

The intraclass correlation coefficient (ICC) and the standard error of measurement (SEM) are two reliability coefficients that are reported frequently. Both measures are related; however, they define distinctly different properties. The magnitude of the ICC defines a measure's ability to discriminate among subjects, and the SEM quantifies error in the same units as the original measurement. Mos...

AIM Myocardial performance index (MPI) is usually measured with pulsed wave Doppler (PWD). Our aim was to assess the degree of agreement between PWD and a method based on tissue Doppler imaging (TDI). METHODS AND RESULTS Seventy-five patients with prior myocardial infarction and 20 healthy subjects underwent measurement of time intervals and MPI with PWD and pulsed TDI at septal and lateral s...