Feature selective validation (FSV) for validation of computational electromagnetics (CEM). Part I - The FSV method

A goal for the validation of computational electromagnetics (CEM) is to provide the community with a simple computational method that can be used to predict the assessment of electromagnetic compatibility (EMC) data as it would be undertaken by individuals or teams of engineers. The benefits of being able to do this include quantifying the comparison of data that has hitherto only been assessed qualitatively, to provide the ability to track differences between model iterations, and to provide a means of capturing the variability and range of opinions of groups and teams of workers. The feature selective validation (FSV) technique shows great promise for achieving this goal. This paper presents a detailed analysis of the FSV method, setting it firmly in the context of previous comparison techniques; it suggests the relationship between validation of graphically presented data and the psychology of visual perception. A set of applicability tests to judge the effectiveness of computer-based CEM validation techniques is also proposed. This paper is followed by a detailed comparison with visual assessment, which is presented in Part II.

A goal for the validation of computational electromagnetics (CEM) is to provide the community with a simple computational method that can be used to predict the assessment of electromagnetic compatibility (EMC) data as it would be undertaken by individuals or teams of engineers. The benefits of being able to do this include quantifying the comparison of data that has hitherto only been assessed qualitatively, to provide the ability to track differences between model iterations, and to provide a means of capturing the variability and range of opinions of groups and teams of workers. The feature selective validation (FSV) technique shows great promise for achieving this goal. This paper presents a detailed analysis of the FSV method, setting it firmly in the context of previous comparison techniques; it suggests the relationship between validation of graphically presented data and the psychology of visual perception. A set of applicability tests to judge the effectiveness of computer-based CEM validation techniques is also proposed. This paper is followed by a detailed comparison with visual assessment, which is presented in Part II. © 2006 IEEE.