Results 21 to 30 of about 4,390,707 (282)
Sequence Image Datasets Construction via Deep Convolution Networks
Remote Sensing, 2021 Remote-sensing time-series datasets are significant for global change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental factors ...
Xing Jin, Ping Tang, Zheng Zhang
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Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network
Applied Sciences, 2022 Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on.
Zichao He, Chunna Zhao, Yaqun Huang
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A Seismic Phase Recognition Algorithm Based on Time Convolution Networks
Applied Sciences, 2022 Over recent years, frequent earthquakes have caused huge losses in human life and property. Rapid and automatic earthquake detection plays an important role in earthquake warning systems and earthquake operation mechanism research.
Zhenhua Han +5 more
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Fourier Graph Convolution Network for Time Series Prediction
Mathematics, 2023 The spatio-temporal pattern recognition of time series data is critical to developing intelligent transportation systems. Traffic flow data are time series that exhibit patterns of periodicity and volatility.
Lyuchao Liao +3 more
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Deep Temporal Convolution Network for Time Series Classification
Sensors, 2021 A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data.
Bee Hock David Koh +4 more
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Quantum stochastic convolution cocycles II [PDF]
, 2008 Schurmann's theory of quantum Levy processes, and more generally the theory of quantum stochastic convolution cocycles, is extended to the topological context of compact quantum groups and operator space coalgebras.
Skalski, Adam G., Lindsay, J. Martin
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Sequence Image Interpolation via Separable Convolution Network
Remote Sensing, 2021 Remote-sensing time-series data are significant for global environmental change research and a better understanding of the Earth. However, remote-sensing acquisitions often provide sparse time series due to sensor resolution limitations and environmental
Xing Jin +5 more
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Journal of Advanced Transportation, 2022 Accurate traffic prediction is a powerful factor of intelligent transportation systems to make assisted decisions. However, existing methods are deficient in modeling long series spatio-temporal characteristics. Due to the complex and nonlinear nature of
Shanchun Zhao, Xu Li
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Restoration of a blurred photographic image of a moving object obtained at the resolution limit
Российский технологический журнал, 2023 Objectives. When processing images of the Earth’s surface obtained from satellites, the problem of restoring a blurry image of a moving object is of great practical importance.
V. B. Fedorov +2 more
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Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement
Applied Sciences, 2022 In this paper, we propose a fully convolutional neural network based on recursive recurrent convolution for monaural speech enhancement in the time domain. The proposed network is an encoder-decoder structure using a series of hybrid dilated modules (HDM)
Zhendong Song +3 more
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