Lingqiang Chen
Jiangnan University
9 Papers
Lingqiang Chen is an academic researcher from Jiangnan University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 1, co-authored 2 publications.
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Papers
FedAGCN: A traffic flow prediction framework based on federated learning and Asynchronous Graph Convolutional Network
TL;DR: In this paper , the authors proposed a deep learning framework (FedAGCN) based on federated learning and asynchronous graph convolutional networks to predict traffic flow accurately in real time.
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A Missing Type-Aware Adaptive Interpolation Framework for Sensor Data
TL;DR: This paper proposed a missing type-aware interpolation framework (IMA) for data loss problems in city-wide environmental monitoring systems that contain many scattered stations, which considers three aspects of information, i.e., spatiotemporal, all attributes of one measurement, and all values and accordingly develops three methods to estimate the missing data.
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A hypergrid based adaptive learning method for detecting data faults in wireless sensor networks
TL;DR: A Hypergrid based Adaptive Detection of Faults (HADF) method, which adopts hypergrid and statistical analysis to recognize three types of faults in the sensor data, including outliers, stuck-at faults, and noisy faults is provided.
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Traffic flow prediction using multi-view graph convolution and masked attention mechanism
TL;DR: Tang et al. as mentioned in this paper proposed a deep learning model including a dilated temporal causal convolution module, multi-view diffusion graph convolution, and masked multi-head attention module (TGANet).
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Colorization of infrared images based on feature fusion and contrastive learning
TL;DR: Wang et al. as mentioned in this paper designed an improved generator structure on the basis of Unet, adding dense convolutional blocks and skip connections to integrate low-level detail information with high-level semantic information.
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