3,206 research outputs found
Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition
Accurate spatial-temporal traffic flow forecasting is essential for helping
traffic managers to take control measures and drivers to choose the optimal
travel routes. Recently, graph convolutional networks (GCNs) have been widely
used in traffic flow prediction owing to their powerful ability to capture
spatial-temporal dependencies. The design of the spatial-temporal graph
adjacency matrix is a key to the success of GCNs, and it is still an open
question. This paper proposes reconstructing the binary adjacency matrix via
tensor decomposition, and a traffic flow forecasting method is proposed. First,
we reformulate the spatial-temporal fusion graph adjacency matrix into a
three-way adjacency tensor. Then, we reconstructed the adjacency tensor via
Tucker decomposition, wherein more informative and global spatial-temporal
dependencies are encoded. Finally, a Spatial-temporal Synchronous Graph
Convolutional module for localized spatial-temporal correlations learning and a
Dilated Convolution module for global correlations learning are assembled to
aggregate and learn the comprehensive spatial-temporal dependencies of the road
network. Experimental results on four open-access datasets demonstrate that the
proposed model outperforms state-of-the-art approaches in terms of the
prediction performance and computational cost.Comment: 11 pages, 8 figure
A prediction approach for multichannel EEG signals modeling using local wavelet SVM
Accurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal. © 2006 IEEE.published_or_final_versio
3D particle tracking velocimetry using dynamic discrete tomography
Particle tracking velocimetry in 3D is becoming an increasingly important
imaging tool in the study of fluid dynamics, combustion as well as plasmas. We
introduce a dynamic discrete tomography algorithm for reconstructing particle
trajectories from projections. The algorithm is efficient for data from two
projection directions and exact in the sense that it finds a solution
consistent with the experimental data. Non-uniqueness of solutions can be
detected and solutions can be tracked individually
- …