Browsing by Author "Wang, L."
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Item Metadata only Improvement in the Definition of ODM for FSV(IEEE, 2012-12-25) Zhang, G.; Duffy, A. P.; Sasse, Hugh G.; Wang, L.; Jauregui, R.It has been found that the feature-selective validation (FSV) method may demonstrate inconsistencies in the results when applied to the comparison of zero-crossing datasets. This type of data is typical of time-domain-based electromagnetic compatibility validation. This paper investigates the source of this inconsistency and proposes a solution. The reason was investigated using a set of typical transient data, which was related to the derivation of the formulas of FSV. It is demonstrated that the problem can be alleviated by enhancing the definition of the offset difference measure. The resulting enhanced performance of FSV is assessed by comparing the results with visual assessment. It is demonstrated that the improvement increases the agreement between FSV prediction and visual assessment. Meanwhile, this modification has a very limited effect on the comparison of other data structures which do not cross the axis.Item Metadata only Investigating Confidence Histograms and Classification in FSV: Part I. Fuzzy FSV(IEEE, 2013) Di Febo, D.; de Paulis, F.; Orlandi, A.; Zhang, G.; Sasse, Hugh G.; Duffy, A. P.; Wang, L.; Archambeault, B.One important aspect of the feature selective validation (FSV) method is that it classifies comparison data into a number of natural-language categories. This allows comparison data generated by FSV to be compared with equivalent “visual” comparisons obtained using the visual rating scale. Previous research has shown a close relationship between visual assessment and FSV generated data using the resulting confidence histograms. In all cases, the category membership functions are “crisp”: that is data on the FSV value axis fall distinctly into one category. An important open question in FSV-based research, and for validation techniques generally, is whether allowed variability in these crisp category membership functions could further improve agreement with the visual assessment. A similar and related question is how robust is FSV to variation in the categorization algorithm. This paper and its associated “part II” present research aimed at developing a better understanding of the categorization of both visual and FSV data using nonsquare or variable boundary category membership functions. This first paper investigates the level of improvement to be expected by applying fuzzy logic to location of the category boundaries. The result is limited improvement to FSV, showing that FSV categorization is actually robust to variations in category boundaries.Item Metadata only Investigating Confidence Histograms and Classification in FSV: Part II-Float FSV(IEEE, 2013) Di Febo, D.; Archambeault, B.; Zhang, G.; Sasse, Hugh G.; Duffy, A. P.; Wang, L.; de Paulis, F.; Orlandi, A.One important aspect of the feature selective validation (FSV) method is that it classifies comparison data into a number of natural-language categories. This allows comparison data generated by FSV to be compared with equivalent “visual” comparisons obtained using the visual rating scale. Previous research has shown a close relationship between visual assessment and FSV generated data using the resulting confidence histograms. In all cases, the category membership functions are “crisp”: that is data on the FSV-value axis falls distinctly into one category. The companion paper to this Investigating Confidence histograms and Classification in FSV: Part I. Fuzzy FSV investigated whether allowing probabilistic membership of categories could improve the comparison between FSV and visual assessment. That paper showed that such an approach produced limited improvement and, as a consequence, showed that FSV confidence histograms are robust to flexibility in category boundaries. This paper investigates the effect of redefining some, but not all, category boundaries based around the mode category. This “float” approach does show some improvement in the comparison between FSV and visual assessment.Item Metadata only Performance Improvement of FSV in a Special Situation(2011) Zhang, G.; Wang, L.; Duffy, A. P.