比较FSV和人类对数据比较的反应

B. Archambeault, S. Connor, A. Duffy
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引用次数: 17

摘要

特征选择验证(FSV)方法的开发是为了反映工程师在计算电磁学验证过程中评估可视化数据时所采取的方法。FSV(特征选择验证)技术已被证明与许多不同数据集的人体调查结果有很好的一致性。一致性较好的数据集的调查结果显示标准差很小,而一致性较好的数据集的调查结果显示标准差明显较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing FSV and human responses to data comparisons
The feature selective validation (FSV) method was developed with the specific goal to mirror the approach taken by engineers when assessing data presented visually during the validation of computational electromagnetics. The FSV (feature selective validation) technique has been demonstrated to have good agreement with the human survey results for a number of different data sets. Survey results for data sets in good agreement show a very small standard deviation, however, data sets with fair agreement have a significantly higher standard deviation.
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