In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion clas...
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networksdoi:10.1109/taffc.2018.2817622Tengfei SongWenming ZhengPeng SongZhen CuiIEEE
Compared with the Koopman models previously used in biophysics and fluid dynamics, the introduction of graph convolutional neural networks enables parameter sharing between the atoms and an encoding of local environments that is invariant to permutation, rotation, and reflection. This symmetry facilitates ...
Graph neural network-based anomaly detection in multivariate time series. In: Proc. Thirty-Fifth AAAI Conference on Artificial Intelligence, 4027–4035 (AAAI Press, 2021). https://ojs.aaai.org/index.php/AAAI/article/view/16523. Hundman, K., Constantinou, V., Laporte, C., Colwell, I. &...
Among different structures of AEs, the convolutional autoencoder (CAE) (including convolutional VAE), is by far the most widespread architecture. However, it can be cumbersome to apply CAE for unstructured data, for instance, in CFD with irregular meshes. To address this bottleneck, graph neural...
Nanowire Networks (NWNs) belong to an emerging class of neuromorphic systems that exploit the unique physical properties of nanostructured materials. In addition to their neural network-like physical structure, NWNs also exhibit resistive memory switchin
Graph of singular value decay. (a): Singular value decay of solution snapshot, (b): Singular value decay of temporal snapshot for the first spatial basis vector. The Galerkin and LSPG space–time ROMs solve the Equation (62) with the target parameter ( μ 1 , μ 2 ) = ( 0.2 , ...
Classification of time-series images using deep convolutional neural networks. In Proceedings of the Tenth International Conference on Machine Vision, 2017, Vienna, Austria, 13 April 2018; pp. 242–249. [CrossRef] 23. Matthews, H.; de Jong, G.; Maal, T.; Claes, P. Static and Motion ...
Kaliningraph treats string adjacency and graph adjacency as the same. To construct a graph, simply enumeratewalks. This can be done using a raw string, in which case unique characters will form the vertex set. Whitespace delimits walks: ...
Subsequently, a graph-convolutional neural network-based surrogate learns parameter-dependent low-dimensional latent dynamics on the coarsest representation. Following surrogates are trained on residuals using finer resolutions, allowing for multiple surrogates with varying hardware requirements and increasing ...