Validating the FSV Method using reverberation chamber measurements

Hankins, Genevieve J.; Lewis, Dennis

doi:10.1109/isemc.2010.5711370

Cited by 3 publications

(4 citation statements)

References 3 publications

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“…GDM is calculated using the original definition [10], which is the root summed square of the ADM i and FDM i terms, as shown in (11) and (12):…”

Section: Calculation Of Fsv Indexesmentioning

confidence: 99%

“…Going back to the objective of this section, the calculated sensitivity coefficients (see (15) through (24)) may be used to derive the uncertainty of the output quantity GDM described by (11) and (12). We may assume that the elements of the input data vectors are independent.…”

Section: Law Of Propagation Of Uncertainty and First-order Derivativesmentioning

confidence: 99%

“…Several validation methods have been proposed in various fields of science; for example, those applied to economics, crystallography, and electromagnetics, and they have been reviewed, discussed, and quantitatively assessed and confronted for some test cases [9]. The relatively recent Feature Selective Validation (FSV) method [10] is a complex and complete validation tool with wide applicability to several fields of engineering, with particular emphasis on electronic and electrical engineering: pure EMC applications were presented in 2010 from studying reverberating chambers [11], and near-and far field radiation from heatsinks [12]; a thorough validation of a railway line model against experimental data is discussed in [13], with considerations on the influence of second-order system elements; counterfeiting detection via electromagnetic fingerprint is instead evaluated in [14]; finally, the modeling of a high voltage direct current system is discussed in [15]. Older examples can be found, especially in the field of microelectronics, when models of semiconductor devices began to appear at different levels of complexity [1], and are now widely available resources in many desktop circuit simulators.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty and Sensitivity of the Feature Selective Validation (FSV) Method

Bongiorno¹,

Mariscotti

2022

Electronics

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The FSV method is a recognized validation tool that initially assesses the similarity between data sets for electromagnetic measurements and models. Its use may be extended to many problems and applications, and in particular, with relation to electrical systems, but it should be characterized in terms of its uncertainty, as for measurement tools. To this aim, the Guide to the Expression of Uncertainty in Measurement (GUM) is applied for the propagation of uncertainty from the experimental data to the Feature Selective Validation (FSV) quantities, using Monte Carlo analysis as confirmation, which ultimately remains the most reliable approach to determine the propagation of uncertainty, given the significant FSV non-linearity. Such non-linearity in fact compromises the accuracy of the Taylor approximation supporting the use of first-order derivatives (and derivative terms in general). MCM results are instead more stable and show sensitivity vs. input data uncertainty in the order of 10 to 100, highly depending on the local data samples value. To this aim, normalized sensitivity coefficients are also reported, in an attempt to attenuate the scale effects, redistributing the observed sensitivity values that, however, remain in the said range, up to about 100.

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“…GDM is calculated using the original definition [10], which is the root summed square of the ADM i and FDM i terms, as shown in (11) and (12):…”

Section: Calculation Of Fsv Indexesmentioning

confidence: 99%

Section: Law Of Propagation Of Uncertainty and First-order Derivativesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Uncertainty and Sensitivity of the Feature Selective Validation (FSV) Method

Bongiorno¹,

Mariscotti

2022

Electronics

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“…Some studies have demonstrated the influence of the noisy signals on the validation methods [10–12]. However, the studies have not exposed the need to assess exactly what the expert is evaluating.…”

Section: Introductionmentioning

confidence: 99%

Analysis of pre‐processing techniques when using validation methods in computational electromagnetic simulations

Jauregui

Ventosa

Silva

et al. 2013

IET Science, Measurement & Technology

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In computational electromagnetic simulations, most validation methods have been developed to quantify the similarity of two datasets, making them an objective tool for test engineers to discuss data on a similar basis. However, these methods do not always agree with the expert's opinion. The main problem when a validation method is used, is not taking into account the 'preprocessing' required by the expert to analyse a particular characteristic in the comparison. In this study, two different preprocessing techniques are used to show the influence they have on the results of the validation methods in relation to the opinion of the expert. The Institution of Engineering and Technology 2013.Peer ReviewedPostprint (published version

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