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Article Dans Une Revue IEEE Transactions on Neural Networks and Learning Systems Année : 2023

Synchronous Spatiotemporal Graph Transformer: A New Framework for Traffic Data Prediction

Tian Wang
Jiahui Chen
Jinhu Lü
Kexin Liu
Aichun Zhu
Baochang Zhang

Résumé

This paper deals with the design of sensor arrays in the context involving the localization of a few acoustic sources. Sparse approximation is known to be effective to find the source locations, but it depends on different array characteristics, such as the number of sensors and the array geometry. The present paper tackles this array design problem under the form of a sequential sensor selection procedure. The proposed method alternates between two steps. One step involves a source localization estimator, given a current set of measurement points, to obtain the estimation variance. Then, the other step selects the new point where a future measurement will maximally decrease the variance from the previous step. As such, the procedure can be applied online. Both numerical and experimental studies are conducted in an indoor nearfield configuration. Results show that the proposed approach performs better than offline state-of-the-art methods, and the presented empirical study reveals a better robustness to the model mismatches originating from the room reflections.
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Dates et versions

hal-04453714 , version 1 (12-02-2024)

Identifiants

Citer

Tian Wang, Jiahui Chen, Jinhu Lü, Kexin Liu, Aichun Zhu, et al.. Synchronous Spatiotemporal Graph Transformer: A New Framework for Traffic Data Prediction. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34 (12), pp.10589-10599. ⟨10.1109/TNNLS.2022.3169488⟩. ⟨hal-04453714⟩
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