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The algorithmic approach employs eight correlation criteria which are relaxed during an iterative process.
Classical correlation criteria like the Modal Assurance Criterion require mode shapes and can therefore not be applied.
The updating objective function is formulated in such a way that two global correlation criteria are satisfied simultaneously.
A final subset of 17 states referred to as the Macran and Kind States performed best on the correlation criteria.
The correlation criteria were used successfully to quantify the initial closeness between 4 increasingly detailed FE models of an automotive oil pan.
Model correlation criteria like modal assurance criteria (MAC) are widely used in structural dynamics in order to check the spatial coincidence between two sets of vectors.
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Grassmannian frames are frames satisfying a min-max correlation criterion.
Data is classified into directional clusters by correlation criterion.
This correlation criterion is independent of scale and offset in lighting (Sutton et al.
Here, the Zero Normalized Sum of Squared Differences (ZNSSD) correlation criterion was adopted.
The extraction is achieved by minimising a cross correlation criterion and uses a genetic algorithm [49].
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