Lixin Wang

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.

Objective Selection of Minimum Acceptable Mesh Refinement for EMC Simulations

Optimization of computational electromagnetics (CEM) simulation models can be costly in both time and computing resources. Mesh refinement is a key parameter in determining the number of unknowns to be processed. In turn, this controls the time and memory required for a simulation. Hence, it is important to use only a mesh that is good enough for the objectives of the simulation, whether for direct handling of high-fidelity EM models or, even more importantly, for setting up low-fidelity models in a variable-fidelity optimization. On the other hand, in the early stages of an optimization process, a relatively coarse mesh can show whether the governing parameters of the simulation are being appropriately modeled. As the simulation geometry approaches its target, the mesh definition becomes more refined. This letter presents initial results for an approach to identifying the minimum acceptable mesh coarseness based on the projected evolution of FSVs global difference measure when a model is refined from a very crude representation rather than the more usual high-fidelity model. Future work to verify the generality of this letter could provide substantial savings in time and effort for CEM analysis in EMC.

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.

Dimension-Reduced Sparse Grid Strategy for a Stochastic Collocation Method in EMC Software

Stochastic collocation method (SCM), a prevailing uncertainty analysis method, has been successfully implemented in electromagnetic compatibility (EMC) simulation, especially in EMC commercial software. However, the “curse of dimensionality” problem (dimensionality means the number of uncertain variables) limits the application of the SCM. This paper proposes a novel sparse grid strategy in order to improve the computational efficiency of the SCM, especially in high-dimensionality case. In the proposed strategy, it is revealed that the number of the collocation points is in proportion to the dimensionality. By simulating two shielding effectiveness analysis examples in CST software, the feasibility of the proposed method can be presented clearly, with the help of the feature selective validation method.

Validity Evaluation of the Uncertain EMC Simulation Results

Uncertainty analysis is widely used in todays electromagnetic compatibility simulations in order to include the variation and tolerance caused by realistic nonidealities. However, previous research has neglected to investigate the validity evaluation of uncertainty analysis results. In this paper, a novel validity evaluation method, named mean equivalent area method (MEAM), is proposed to quantify the difference between the reference data and the simulation results, particularly for validity evaluation of the uncertainty analysis results in computational electromagnetics. Comparing with other existing works or confirmed conclusion in published references, the performance of MEAM is presented. Furthermore, in order to improve both efficiency and accuracy of the uncertainty analysis method (Monte–Carlo method or generalized polynomial chaos method), a scheme based on MEAM is presented to examine whether the uncertainty analysis method has reached convergence. Finally, the selection of the reference data and the limitation of MEAM are illustrated in detail in Section VI.

Comparison of Three-Dimensional Datasets by Using the Generalized n-Dimensional ( n-D) Feature Selective Validation (FSV) Technique

Automatic methods to evaluate the validity of computational electromagnetics computer modeling and simulations have widespread applications. The feature selective validation (FSV) method is a heuristic technique which has been shown to give a broad agreement with a visual assessment for one-dimensional data. As a heuristic technique, extending the dimensionality is an important target for the improvement and development of FSV. One of the major challenges in the development of n-dimensional (n-D) FSV is the difficulty of obtaining visual assessment results, since, the visual comparison of three- and higher dimensional data is difficult or even impossible. This paper formulates the comparison of 3-D data based on an established generalized n -D-FSV approach. The performance of the approach is investigated by means of the Laboratory for Image and Video Engineering Video Quality Database which provides subjective scores of 150 distorted videos. A statistical evaluation of the relative performance of FSV and other publicly available full-reference video quality assessment algorithms is presented. Further, parameter tuning is performed to improve the agreement of 3-D FSV results and subjective scores. The proposed approach is finally applied to the self-referenced validation of an electromagnetic simulation model to identify and locate the continuous variation of electric field within a region of space.

Downsampled and Undersampled Datasets in Feature Selective Validation (FSV)

Feature selective validation (FSV) is a heuristic method for quantifying the (dis)similarity of two datasets. The computational burden of obtaining the FSV values might be unnecessarily high if datasets with large numbers of points are used. While this may not be an important issue per se it is an important issue for future developments in FSV such as real-time processing or where multidimensional FSV is needed. Coupled with the issue of dataset size, is the issue of datasets having “missing” values. This may come about because of a practical difficulty or because of noise or other confounding factors making some data points unreliable. These issues relate to the question “what is the effect on FSV quantification of reducing or removing data points from a comparison-i.e., down- or undersampling data?” This paper uses three strategies to achieve this from known datasets. This paper demonstrates, through a representative sample of 16 pairs of datasets, that FSV is robust to changes providing a minimum dataset size of approximately 200 points is maintained. It is robust also for up to approximately 10% “missing” data, providing this does not result in a continuous region of missed data.

Uncertainty analysis of random field coupling to Stochastic cables

The Stochastic Collocation Method is introduced to field-to-wire coupling simulation to analyze the uncertainty caused by the parameters of the cables geometrical structure and incident field. The accuracy of Stochastic Collocation Method can be validated by the results calculated by Monte Carlo. Furthermore, the proposed method has high efficiency, and requires no modification to solver.