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1. Modeling Floating Potential Conductors Using Discontinuous Galerkin Method

Author

Liang Chen, Ming Dong, and Hakan Bagci

Subjects
Discontinuous Galerkin method, electrostatics, finite element method, floating potential conductors, magnetostatics, plasmonic-enhanced photoconductive antenna, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Isolated conductors appear in various electrostatic problems. In simulations, an equipotential condition with an undefined/floating potential value is enforced on the surface of isolated conductors. In this work, a numerical scheme making use of the discontinuous Galerkin (DG) method is proposed to model such conductors in electrostatic problems. A floating-potential boundary condition, which involves the equipotential condition together with a total charge condition, is “weakly” enforced on the conductor surfaces through the numerical flux of the DG method. Compared to adaptations of the finite element method used for modeling conductors, this proposed method is more accurate, capable of imposing charge conditions, and simpler to implement. Numerical results, which demonstrate the accuracy and applicability of the proposed method, are presented.

Published
2020
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2. Emotion Recognition From Full-Body Motion Using Multiscale Spatio-Temporal Network.

Author

Wang, Tao, Liu, Shuang, He, Feng, Dai, Weina, Du, Minghao, Ke, Yufeng, and Ming, Dong

Abstract

Body motion is an important channel for human communication and plays a crucial role in automatic emotion recognition. This work proposes a multiscale spatio-temporal network, which captures the coarse-grained and fine-grained affective information conveyed by full-body motion and decodes the complex mapping between emotion and body movement. The proposed method consists of three main components. First, a scale selection algorithm based on the pseudo-energy model is presented, which guides our network to focus not only on long-term macroscopic body expressions, but also on short-term subtle posture changes. Second, we propose a hierarchical spatio-temporal network that can jointly process posture covariance matrices and 3D posture images with different time scales, and then hierarchically fuse them in a coarse-to-fine manner. Finally, a spatio-temporal iterative (ST-ITE) fusion algorithm is developed to jointly optimize the proposed network. The proposed approach is evaluated on five public datasets. The experimental results show that the introduction of the energy-based scale selection algorithm significantly enhances the learning capability of the network. The proposed ST-ITE fusion algorithm improves the generalization and convergence of our model. The average classification results of the proposed method exceed 86% on all datasets and outperform the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Published
2024
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