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Liu and Venkarasubramanian [11] applied the frequency domain decomposition method to the mode shape identification.
A study is carried out on the real-time plasma shape identification in the KSTAR device.
An application of our method to an inverse interface problem of shape identification is also presented.
A new shape identification scheme is developed and tested in numerical simulations in the presence of measurement noise.
The proposed method consists of image processing, feature analysis, particle sieving, crystal size measurement, and crystal shape identification.
Further, the new network is implemented to model a Transformer Fault Diagnosis System and Aggregate Shape Identification System.
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These tasks include: precisiation of perceptions, shape pattern identification, and pattern retranslation.
The applicability of model order will influence the accuracy and efficiency of oscillation modal frequency and damping ratio analysis, and further affect the mode shapes identification.
A serious problem for the practical applicability of these indices is their dependence on the accuracy of the mode shapes identification.
For inclusion of pattern mode we change the header information for MB type, pattern identification code, and shape of patterns.
For example, Lewis et al. showed that resting-state blood oxygenation level-dependent (BOLD) functional connectivity between visual cortex and task-relevant cortical networks changed after learning on a shape-identification task [ 11].
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