Hugh Sasse

Factors influencing the successful validation of transient phenomenon modelling

An increased requirement for validation of computational electromagnetic simulation and modelling through the publication of IEEE Standard 1597.1 brings to light some interesting issues surrounding the validation of transients. The structure of a transient event has three particular regions of interest that can have an influence on the results, of which only two are generally well defined. These are the initial quiescent phase from t = 0 to the transient event; the transient event itself up to the point where the energy has fallen to a predefined limit, and the post-transient phase where residual energy is still present in the system. This latter region is generally ill-defined and changes the way that a validation comparison should be made, from, for example a frequency domain coupling study where the region of interest is usually well defined. This study looks at the influence of the three regions on the validation results and suggests how the Feature Selective Validation (FSV) method can be applied in transient studies.

Offset Difference Measure Enhancement for the Feature-Selective Validation Method

The feature-selective validation (FSV) method is proving itself to be a robust and helpful technique to quantify visually complex measurement sets, such as those resulting from computational electromagnetic validation exercises or experimental repeatability studies. This paper reports on an enhancement to this technique that includes data related to the level of dc difference (i.e., offset) between two sets of results, hitherto disregarded within the method. This offset difference measure (ODM) contributes to the amplitude difference measure (ADM) and ensures that the ADM and global difference measure values reflect the level of disagreement between the two traces even if this is the only difference between the two. The paper describes the background to this development and provides details of the selection and implementation of the ODM measure.

Analyzing Transient Phenomena in the Time Domain Using the Feature Selective Validation (FSV) Method

The increasing application of simulation tools to increasingly complex problems makes the use of validation tools essential to improve confidence in the veracity of those simulation results. IEEE Standard 1597.1 is the first true standard for the validation of computational electromagnetics method. This standard uses the feature selective validation (FSV) method as the key quantification tool. However, despite its many advantages, there have been some interesting issues surrounding the validation of transients. This paper presents a new approach to the validation of a set of generally representative transient types using the FSV method and shows how the previously experienced limitations can be overcome. In order to analyze the main parameters associated with transient comparison, a survey which included 20 experts was conducted. This information was used to identify the significant regions that need to be taken into account in the transient comparison. Finally, using the statistics obtained by the experts, a new solution was defined and its improvement over the existing approach was demonstrated.

Challenges in developing a multidimensional Feature Selective Validation implementation

Feature Selective Validation (FSV) was incorporated as core component of IEEE Std 1597.1. Recently, discussions have moved from ‘is quantitative comparison of data needed?’ to ‘how can this be applied to multidimensional data?’ The purpose of this paper is to present some of the latest thinking about the key challenges in developing a 1D model to unlimited dimensionality. In particular, the paper will present: (1) a revised mathematical framework for FSV, e.g. using tensor notation to simplify the mathematical representation. (2) A discussion on how the performance of n Dimensional FSV can be verified against human perception. (3) A review on the effects of data that oscillates between positive and negative data points, e.g. transients.

Progress in the development of a 2D Feature Selective Validation (FSV) method

The feature selective validation (FSV) technique is becoming a favored approach to quantifying the comparison of numerical and / or experimental data for validation purposes. It is a heuristic approach and, therefore, has scope for developments, enhancements and refinements from researchers particularly interested in formal validation, particularly of computational electromagnetics. One area that is clearly ripe for development is in extending the current dasiaFSVpsila FSV approach to two or more independent axes, for example to compare surface currents over a whole body. As the central tenet of FSV is to mirror the perceptions of a group of experts, higher levels of dimensionality provide substantial challenges for calibration. However, a first step in this development is gaining experience and understanding of the quantification of multidimensional data. Building on previous work, this paper concludes with a set of recommendations for the full development of two dimensional FSV.

Over Coupled Ring Resonator-Based Add/Drop Filters

The performance of add/drop filters based on over coupled ring resonators is analyzed and simulated. Over coupling improves the performance of the filters by providing a wider bandwidth over which high crosstalk suppression is achieved. Interband and intraband crosstalk associated with wavelength division multiplexing in optical filters are examined as a function of coupling coefficients. The simulation results show that, in over coupled double ring resonators, the crosstalk suppression bandwidth is increased in comparison with that of critically coupled ring resonators. Increased bandwidth will allow such filters to be used for higher data rate signals, and moreover, for different modulation techniques (such as return to zero RZ and nonreturn to zero NRZ).

Improvement in the Definition of ODM for FSV

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.

Comparison of Data With Multiple Degrees of Freedom Utilizing the Feature Selective Validation Method

The feature selective validation method has been shown to provide results that are in broad agreement with the visual assessment of a group of engineers for line, 1-D, data. An implementation using 2-D Fourier transforms and derivatives have been available for some years, but verification of the performance has been difficult to obtain. Further, that approach does not naturally scale well for 3-D and higher degrees of freedom, particularly if there are sizable differences in the number of points in the different directions. This paper describes an approach based on repeated 1-D FSV analyses that overcomes those challenges. The ability of the 2-D case to mirror user perceptions is demonstrated using the LIVE database. Its extension to n-dimensions is also described and includes a suggestion for weighting the algorithm based on the number of data points in a given “direction.”

Applying the Analytic Hierarchy Process (AHP) to an FSV-Based Comparison of Multiple Datasets

This paper presents research extending the feature selective validation (FSV) method to quantitatively compare multiple datasets. The analytic hierarchy process is introduced to weight different FSV results when comparison is conducted by comparing multiple numerical results with their referencing counterparts. Only pairwise comparisons are required which simplify the process of comprehensive validation. The efficiency of this approach is demonstrated by the validation of a simplified crosstalk model.

Investigating Confidence Histograms and Classification in FSV: Part II-Float FSV

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.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.