scholarly journals KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

2022 ◽  
pp. 1-11
Author(s):  
Jiawei Zhu ◽  
Xing Han ◽  
Hanhan Deng ◽  
Chao Tao ◽  
Ling Zhao ◽  
...  
Keyword(s):  
Convolutional Network ◽  
Traffic Forecasting ◽  
Temporal Graph

scholarly journals A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

2021 ◽  
Vol 10 (7) ◽  
pp. 485
Author(s):  
Jiandong Bai ◽  
Jiawei Zhu ◽  
Yujiao Song ◽  
Ling Zhao ◽  
Zhixiang Hou ◽  
...  
Keyword(s):  
Road Network ◽  
Temporal Dynamics ◽  
Technological Problem ◽  
Traffic Flows ◽  
Convolutional Network ◽  
Temporal Dimension ◽  
Time Points ◽  
The Road ◽  
Traffic Forecasting ◽  
Temporal Graph

Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In the spatial dimension, due to the connectivity of the road network, the traffic flows between linked roads are closely related. In the temporal dimension, although there exists a tendency among adjacent time points, the importance of distant time points is not necessarily less than that of recent ones, since traffic flows are also affected by external factors. In this study, an attention temporal graph convolutional network (A3T-GCN) was proposed to simultaneously capture global temporal dynamics and spatial correlations in traffic flows. The A3T-GCN model learns the short-term trend by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional network. Moreover, the attention mechanism was introduced to adjust the importance of different time points and assemble global temporal information to improve prediction accuracy. Experimental results in real-world datasets demonstrate the effectiveness and robustness of the proposed A3T-GCN. We observe the improvements in RMSE of 2.51–46.15% and 2.45–49.32% over baselines for the SZ-taxi and Los-loop, respectively. Meanwhile, the Accuracies are 0.95–89.91% and 0.26–10.37% higher than the baselines for the SZ-taxi and Los-loop, respectively.

scholarly journals Cyber—Physical Attack Detection in Water Distribution Systems with Temporal Graph Convolutional Neural Networks

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1247
Author(s):  
Lydia Tsiami ◽  
Christos Makropoulos
Keyword(s):  
Neural Networks ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Attack Detection ◽  
Convolutional Network ◽  
Irreversible Damage ◽  
Physical Attack ◽  
Temporal Graph ◽  
Graph Neural Networks

Prompt detection of cyber–physical attacks (CPAs) on a water distribution system (WDS) is critical to avoid irreversible damage to the network infrastructure and disruption of water services. However, the complex interdependencies of the water network’s components make CPA detection challenging. To better capture the spatiotemporal dimensions of these interdependencies, we represented the WDS as a mathematical graph and approached the problem by utilizing graph neural networks. We presented an online, one-stage, prediction-based algorithm that implements the temporal graph convolutional network and makes use of the Mahalanobis distance. The algorithm exhibited strong detection performance and was capable of localizing the targeted network components for several benchmark attacks. We suggested that an important property of the proposed algorithm was its explainability, which allowed the extraction of useful information about how the model works and as such it is a step towards the creation of trustworthy AI algorithms for water applications. Additional insights into metrics commonly used to rank algorithm performance were also presented and discussed.

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

scholarly journals STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

2020 ◽  
Vol 34 (02) ◽  
pp. 1342-1350 ◽  
Author(s):  
Uttaran Bhattacharya ◽  
Trisha Mittal ◽  
Rohan Chandra ◽  
Tanmay Randhavane ◽  
Aniket Bera ◽  
...  
Keyword(s):  
Real World ◽  
Classification Accuracy ◽  
Emotion Perception ◽  
Convolutional Network ◽  
Human Emotion ◽  
Convolutional Networks ◽  
Variational Autoencoder ◽  
Gait Features ◽  
Temporal Graph ◽  
Perceived Emotion

We present a novel classifier network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Given an RGB video of an individual walking, our formulation implicitly exploits the gait features to classify the perceived emotion of the human into one of four emotions: happy, sad, angry, or neutral. We train STEP on annotated real-world gait videos, augmented with annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE). We incorporate a novel push-pull regularization loss in the CVAE formulation of STEP-Gen to generate realistic gaits and improve the classification accuracy of STEP. We also release a novel dataset (E-Gait), which consists of 4,227 human gaits annotated with perceived emotions along with thousands of synthetic gaits. In practice, STEP can learn the affective features and exhibits classification accuracy of 88% on E-Gait, which is 14–30% more accurate over prior methods.

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