Spatial–Temporal Similarity Fusion Graph Adversarial Convolutional Networks for traffic flow forecasting

Spatial–temporal dependence and similarity aware traffic flow forecasting

With the continuous development of smart cities, intelligent transportation systems (ITSs) have ushered in many breakthroughs and upgrades. As a solid foundation for an ITS, traffic flow prediction effectively helps the city to better manage intricate traffic flow. However, existing traffic flow prediction methods such as temporal graph convolutional networks(T-GCNs) ignore the dissimilarities between lanes. Thus, they cannot provide more specific information regarding predictions such as dynamic changes in traffic flow direction and deeper lane relationships. With the upgrading of intersection sensors, more and more intersection lanes are equipped with intersection sensors to detect vehicle information all day long. These spatio-temporal data help researchers refine the focus of traffic prediction research down to the lane level. More accurate and detailed data mean that it is more difficult to mine the spatio-temporal correlations between data, and modeling heterogeneous data becomes more challenging. In order to deal with these problems, we propose a heterogeneous graph convolution model based on dynamic graph generation. The model consists of three components. The internal graph convolution network captures the real-time spatial dependency between lanes in terms of generated dynamic graphs. The external heterogeneous data fusion network comprehensively considers other parameters such as lane speed, lane occupancy, and weather conditions. The codec neural network utilizes a temporal attention mechanism to capture the deep temporal dependency. We test the performance of this model based on two real-world datasets, and extensive comparative experiments indicate that the proposed heterogeneous graph convolution model can improve the prediction accuracy.

The main core purpose of artificial emotional intelligence is to recognize human emotions. Technologies such as facial, semantic, or brainwave recognition applications have been widely proposed. However, the abovementioned recognition techniques for emotional features require a large number of training samples to obtain high accuracy. Human behaviour pattern can be trained and recognized by the continuous movement of the Spatial Temporal Graph Convolution Network (ST-GCN). However, this technology does not distinguish between the speed of delicate emotions, and the speed of human behaviour and delicate changes of emotions cannot be effectively distinguished. This research paper proposes Spatial Temporal Variation Convolutional Network training for human emotion recognition, using skeleton detection technology to calculate the degree of skeleton point change between consecutive actions and using the nearest neighbour algorithm to classify speed levels and train the ST-GCN recognition model to obtain the emotional state. Application of the speed change recognition ability of the Spatial Temporal Variation Graph Convolution Network (STV-GCN) to artificial emotional intelligence calculation makes it possible to efficiently recognize the delicate actions of happy, sad, fear, and angry in human behaviour. The STV-GCN technology proposed in this paper is compared with ST-GCN and can effectively improve the recognition accuracy by more than 50%.

Traffic speed prediction is difficult because of the complicated dynamic spatiotemporal correlations. Recent studies in spatiotemporal models have achieved impressive outcomes for traffic speed prediction. But many studies use graphs in graph convolutional networks to learn spatial features that are often static. Additionally, effectively modeling long-range temporal features is crucial for prediction accuracy. In order to overcome these challenges, a Spatial–Temporal Adaptive Graph Convolutional Network with Transformer (ST_AGCNT) is designed in this paper. Specifically, an adaptive graph convolution network (AGCN) is designed to extract spatial dependency. An adaptive graph that fuses predefined matrices and learnable matrix is proposed to learn the correlations between nodes. The predefined matrices provide the model with richer prior information, while the learnable matrix can extract the dynamic nature of the nodes. And a temporal transformer (TT) is proposed to extract the long-range temporal dependency. In addition, to learn more information to achieve better results, different historical segments are modeled. Experiments conducted on a real-world traffic dataset confirm the effectiveness of the proposed model when compared to other baseline models. This model demonstrated excellent performance in prediction tasks across different time steps, effectively accomplishing traffic speed forecasting. It provides data support for improving traffic efficiency and reducing resource waste, contributing to the sustainable development of traffic management.

Abstract: Restoring damaged character photographs (DCPs) is essential for preserving cultural and personal memories, yet traditional methods are labor-intensive and often require significant manual intervention. The degradation of photographs due to factors such as creases, spots, water damage, and exposure to light poses a considerable challenge to conventional restoration techniques. Manual restoration, typically performed using software like Photoshop, is not only time-consuming but also demands a high level of expertise and meticulous effort, making it impractical for large-scale applications. In this study, we propose an innovative solution to automate the restoration process by employing a practical generative adversarial network (GAN) architecture. Our approach addresses the limitations of traditional methods and aims to significantly reduce the time and effort required for photo restoration while improving the quality of the restored images. The proposed method comprises a novel three-step pipeline designed to tackle the complexities of DCPs effectively. First, we initiate the process by collecting a diverse dataset, including clear character photographs (CCPs), real DCPs, and dirty masks that illustrate common damage patterns. This comprehensive dataset forms the foundation for training our models. Second, we utilize a Residual U-Net GAN (RUGAN) to learn the spoilage patterns of DCPs and generate realistic fake DCPs from the CCPs and dirty masks. RUGAN leverages the structural and textural information from the clear images and dirty masks to produce fake damaged images that closely mimic real-world damage. Finally, we train a restoration model known as the Residual U-Net conditional GAN (RUCGAN) using the paired fake DCPs and CCPs.

Searching for local communities is an important research challenge that allows for personalized community discovery and supports advanced data analysis in various complex networks, such as the World Wide Web, social networks, and brain networks. The evolution of these networks over time has motivated several recent studies to identify local communities in temporal networks. Given any query nodes, Community Search aims to find a densely connected subgraph containing query nodes. However, existing community search approaches in temporal networks have two main limitations: (1) they adopt pre-defined subgraph patterns to model communities, which cannot find communities that do not conform to these patterns in real-world networks, and (2) they only use the aggregation of disjoint structural information to measure quality, missing the dynamic of connections and temporal properties. In this paper, we propose a query-driven Temporal Graph Convolutional Network (CS-TGN) that can capture flexible community structures by learning from the ground-truth communities in a data-driven manner. CS-TGN first combines the local query-dependent structure and the global graph embedding in each snapshot of the network and then uses a GRU cell with contextual attention to learn the dynamics of interactions and update node embeddings over time. We demonstrate how this model can be used for interactive community search in an online setting, allowing users to evaluate the found communities and provide feedback. Experiments on real-world temporal graphs with ground-truth communities validate the superior quality of the solutions obtained and the efficiency of our model in both temporal and interactive static settings.

Human skeleton sequences provide important information for human action recognition. Compared with video data, it has lower sensitivity to light and lower data redundancy. Many traditional skeleton-based methods generally believe that all joints in a skeleton sequence are of the same contribution for an action. This paper proposes a novel action recognition method based on an attention and temporal graph convolutional network (ATGCN). The network could automatically learn spatial and temporal features from skeleton data. The attention mechanism is applied to weight joints to obtain their contributions in an action. Experiments conducted NTU RGB+D and WorkoutUOW-18 datasets have demonstrated the effectiveness of the proposed method.

This monograph presents studies concerning the challenging topic of Human Action Recognition, which has recently become of increasing importance to those working in the field of Computer Vision. It contains an overview of the research literature on tennis movements recognition based on video, images, motion capture data, and data registered using various sensors. In this monograph, three novel methods of tennis movements recognition are presented in detail with success, achieving high performance of the proposed classifiers. They all utilize Graph Convolutional Neural networks (GCN) solutions. Two methods are also applied using attention modules which improve feature extraction and thus make the classification better. The Spatial- -Temporal Graph Convolutional Network, the Attention-Temporal Graph Convolutional Network and the Dual Attention Temporal Graph Convolutional Network are applied for recognition of the most frequently used tennis moves, such as: forehand, backhand, volley forehand, and volley backhand. Moreover, the forehand and backhand are further divided into two phases: 1) the preparation, starting from the player’s positioning until the moment just before the ball makes contact with the racket, 2) the hit together with the racket swinging until the move is finished. These separations allow detailed analysis. For the purpose of this study, a 3DTennisDS dataset was created that contains motion capture data of the forehand, backhand, volley forehand, and volley backhand. They were registered via the Vicon motion capture system. Three proposed neural networks methods were verified based on the gathered dataset. In this monograph, the interpretability aspect of GCN was also presented. This issue is important and was examined by presenting feature maps and heatmaps obtained after selected convolutional operations. These results showed how individual filters in GCNs extract features that are relevant for recognizing tennis movements.

Dynamic multi-scale spatial–temporal graph convolutional network for traffic flow prediction

When considering agricultural commodity transaction data, long sampling intervals or data sparsity may lead to small samples. Furthermore, training on small samples can lead to overfitting and makes it hard to capture the fine-grained fluctuations in the data. In this study, a multi-scale forecasting approach combined with a Generative Adversarial Network (GAN) and Temporal Convolutional Network (TCN) is proposed to address the problems related to small sample prediction. First, a Time-series Generative Adversarial Network (TimeGAN) is used to expand the multi-dimensional data and t-SNE is utilized to evaluate the similarity between the original and synthetic data. Second, a greedy algorithm is exploited to calculate the information gain, in order to obtain important features, based on XGBoost. Meanwhile, TCN residual blocks and dilated convolutions are used to tackle the issue of gradient disappearance. Finally, an attention mechanism is added to the TCN, which is beneficial in terms of improving the forecasting accuracy. Experiments are conducted on three products, garlic, ginger and chili. Taking garlic as an example, the RMSE of the proposed method was reduced by 1.7% and 1% when compared to the SVR and RF models, respectively. Its R2 accuracy was also improved (by 4.3% and 3.4%, respectively). Furthermore, TCN-attention and TCN were found to require less time compared to GRU and LSTM. The accuracy of the proposed method increased by about 5% when compared to that without TimeGAN in the ablation study. Moreover, compared with TCN, the Gated Recurrent Unit (GRU), and the Long Short-term Memory (LSTM) model in the multi-scale price forecasting task, the proposed method can better utilize small samples and high-dimensional data, leading to improved performance. Additionally, the proposed model is compared to the Transformer and TimesNet models in terms of its accuracy, deployment cost, and other metrics.

Forced oscillation location based on temporal graph convolutional network

Current forecasting methods for complex geographic scenes often fail to adequately account for both intrinsic factors and external environmental variables, which limits their predictive accuracy. This study proposes Scene-GCN, a Graph Convolutional Network (GCN)-based model that utilizes a spatio-temporal knowledge graph (ST-KG) to integrate static and dynamic environmental factors with the scene’s spatio-temporal characteristics to predict trends. First, variations at each monitoring target point are abstracted as geo-processes, and instantaneous monitoring values are represented as geographic states, forming a coupled “process-state” ST-KG model tailored for monitoring networks in complex geographic scenes. Environmental knowledge, both dynamic and static, is then embedded within the nodes of the ST-KG, capturing historical and real-time contextual influences through feature computation. Next, a temporal graph convolutional network (T-GCN) cell is developed to quantify the relationships between spatio-temporal features, environmental knowledge, and target predictions. Experimental evaluation on a real-world time-series dataset for monitoring surface deformation in a tailings dam shows that our method outperforms baseline models. Ablation studies further show that the integration of the ST-KG enhances forecasting performance, reducing root mean square error (RMSE) by 49.2% compared to a T-GCN model without ST-KG. Additionally, leveraging a graph neural network framework improves the model’s interpretability over traditional time-series forecasting methods.

Rice is a staple food for more than half of the world’s population. Furthermore, rice is the main export crop of Thailand which produces 21% world’s market share. However, weedy rice is a major counterproductive plant that reduces rice productivity by more than 80% in Thailand. Previous research attempted to develop image classification models to recognize types of rice using images captured in closed environments, which is not practical for farmers with typical mobile phone cameras. This research develops a specific Generative Adversarial Network (GAN) architecture to translate an input image from a typical mobile phone cameras into the closed environment setting. Our GAN architecture can translate mobile phone images and achieves 90.06% weedy rice recognition accuracy, as compared to 58.10% without the translation.

Temporal graph convolutional network soft sensor for molecular weight distribution prediction

Temporal graph convolutional network for multi-agent reinforcement learning of action detection