Forecasting Sparse Traffic Congestion Patterns Using Message-Passing RNNS

Iyer, Shiva R.; An, Ulzee; Subramanian, Lakshminarayanan

doi:10.1109/icassp40776.2020.9052963

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…Essentially, the ability of RNNs to utilize past experiences enables them to model partially observed and variable length Markov decision processes 1174,1175 (MDPs). Applications of RNNs include directing vision 1138,1139 , image captioning 1176,1177 , language translation 1178 , medicine 77 , natural language processing 1179,1180 , playing computer games 24 , text classification 1049 , and traffic forecasting 1181 . Many RNNs are combined with CNNs to embed visual media 1139 or words 1182,1183 , or to process RNN outputs 1184,1185 .…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

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Section: Recurrent Neural Networkmentioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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“…Bao et al (2017) showed the use of sparse strain sensors to estimate the full stress state of a structure using a Fourier basis. Iyer et al (2020) used recurrent neural networks (NNs) and sparse observations for reconstructing and forecasting road traffic.…”

Section: Introductionmentioning

confidence: 99%

Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data

Carter

Voogt

Soares

et al. 2021

DCE

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Recent work has demonstrated the use of sparse sensors in combination with the proper orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity fields in a variety of flows. The present work investigates the fidelity of such techniques applied to a stalled NACA 0012 aerofoil at $ {Re}_c=75,000 $ at an angle of attack $ \alpha ={12}^{\circ } $ as measured experimentally using planar time-resolved particle image velocimetry. In contrast to many previous studies, the flow is absent of any dominant shedding frequency and exhibits a broad range of singular values due to the turbulence in the separated region. Several reconstruction methodologies for linear state estimation based on classical compressed sensing and extended POD methodologies are presented as well as nonlinear refinement through the use of a shallow neural network (SNN). It is found that the linear reconstructions inspired by the extended POD are inferior to the compressed sensing approach provided that the sparse sensors avoid regions of the flow with small variance across the global POD basis. Regardless of the linear method used, the nonlinear SNN gives strikingly similar performance in its refinement of the reconstructions. The capability of sparse sensors to reconstruct separated turbulent flow measurements is further discussed and directions for future work suggested.

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