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• W4213244198 abstract "This paper proposes a deep learning-based generalized empirical flow model (EFM) that can provide a fast and accurate prediction of the glottal flow during normal phonation. The approach is based on the assumption that the vibration of the vocal folds can be represented by a universal kinematics equation (UKE), which is used to generate a glottal shape library. For each shape in the library, the ground truth values of the flow rate and pressure distribution are obtained from the high-fidelity Navier-Stokes (N-S) solution. A fully connected deep neural network (DNN) is then trained to build the empirical mapping between the shapes and the flow rate and pressure distributions. The obtained DNN-based EFM is coupled with a finite element method (FEM)-based solid dynamics solver for fluid-structure-interaction (FSI) simulation of phonation. The EFM is evaluated by comparing the N-S solutions in both static glottal shapes and FSI simulations. The results demonstrate a good prediction performance in accuracy and efficiency." @default.
• W4213244198 created "2022-02-24" @default.
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• W4213244198 date "2022-02-16" @default.
• W4213244198 modified "2023-09-23" @default.
• W4213244198 title "A Deep-Learning Based Generalized Empirical Flow Model of Glottal Flow During Normal Phonation" @default.
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• W4213244198 doi "https://doi.org/10.1115/1.4053862" @default.
• W4213244198 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35171218" @default.
• W4213244198 hasPublicationYear "2022" @default.
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