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Recommendations are made on how to alter the performance outcome such as using alternative ground motion selection approaches (e.g., conditional mean spectrum) and having acceptance criteria based on cumulative ductility demands.
Recommendations are made on how to alter the performance outcome such as using alternative ground motion selection approaches (e.g., conditional mean spectrum), using higher fidelity brace models, and having acceptance criteria based on cumulative ductility demand or energy dissipation.
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The approach follows the general ideas of the (natural) ground motion selection algorithms proposed by Baker [4] and Wang [43] but using simulated (artificial) "spectrum-compatible" accelerograms.
Details on ground motion selection and scaling criteria are also reported.
Probabilistic and global correspondence selection approaches are discussed and compared.
The third category is based on the path selection approaches.
One approach is to implement model selection approaches.
and different variable selection approaches.
This paper presents an effective mutual information-based feature selection approach for EMG-based motion classification task.
global feature selection approach.
The proposed selection approach has two advantages.
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