Spatial-temporal information fusion graph convolutional network for bearing remaining useful life prediction

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%.

LightGCAN: A lightweight graph convolutional attention network for user preference modeling and personalized recommendation

Traffic prediction has attracted a lot of attention in recent years. However, it is challenging due to the dynamic and heterogeneous spatial–temporal correlations. Most of the existing methods adopt the attention mechanism to capture the dynamic spatial features, but it is difficult for the attention mechanism to learn the real-time change of spatial dependencies, which restrict accurate spatial dependencies learning. Furthermore, most methods are out at elbows when solving heterogeneous spatial–temporal data. To overcome these problems, we propose a novel traffic prediction model called Dynamic spatial–temporal Heterogeneous Graph Convolution Network. Different from the existing methods, we have designed a dynamic localized graph and a corresponding adaptive localized graph convolution network, which are capable of simultaneously capturing dynamic and heterogeneous spatial correlations. We propose a gated adaptive temporal convolution network to capture the temporal heterogeneity of traffic data and enjoy global receptive fields. Finally, a global correlations fusion network is provided to incorporate the global spatial–temporal correlations. Compared with 11 baselines, our proposed model achieves state-of-the-art performance in the accuracy of prediction.

The popularity of open source has witnessed significant growth in the past two decades. The sustainability and success of open source projects rely heavily on the active community of contributors, who have the freedom to join or depart voluntarily. The churn of core developers can have detrimental effects on the continuous development and progress of these communities. Therefore, previous studies have aimed to identify the factors that influence developer churn and reveal its impact on open source communities. However, there is currently a lack of research on predicting whether a developer will leave or retain within a given timeframe. This article aims to bridge this gap by introducing a novel model called context-aware dynamic graph neural network (ConDyGNN), which is designed specifically for predicting developer churn in open source communities. By leveraging historical data, this model takes into consideration various factors such as developers’ activities, collaborative networks, and community context information. To capture both temporal dependencies and spatial correlations among different developers, temporal convolutional network (TCN) and graph convolutional network (GCN) modules are employed in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ConDyGNN</i>. Furthermore, a context-aware module based on a multihead attention mechanism is proposed to effectively integrate macrolevel community information and enhance the predictive performance. The superiority of the proposed model is empirically demonstrated through extensive comparative experiments and an ablation study.

Graph convolutional networks (GCNs) have revealed great promise in skeleton-based gait recognition by modeling human joints as a spatiotemporal graph. However, most current spatiotemporal GCN (ST-GCN) methods rely on a fixed graph topology and a static partitioning scheme. This fixed approach limits the model’s ability to capture complex relationships between distant body joints. Besides, Temporal Convolutional Networks (TCNs) are commonly used to model temporal dynamics. However, they have shortcomings in capturing temporal patterns at multiple scales. To overcome these constraints, this paper introduces a new architecture, i.e., SATGCN, that integrates a graph attention combination module (GACM) with a multi-scale temporal convolutional network (MSTCN). In GACM, a fusion approach is used, in which the attention module dynamically learns joint relationships with GCN at each frame. It enables the network to capture local spatial topology and long-range dependencies between physically distant body parts. Following the spatial module, the multi-scale TCN analyzes joint features over time at multiple scales, capturing both short- and long-range motion patterns across the gait sequence. The model is evaluated on the CASIA-B gait recognition dataset under normal, bag-carrying, and clothing conditions, achieving accuracies of 96.97%, 93.10%, and 90.65%, respectively. The performance is about 0.97%, 1.80%, and 0.65% better than that of previous works in the three conditions. The results reveal the effectiveness of combining an attention-guided GCN with multi-scale temporal convolution for skeleton-based gait recognition.

Local and global self-attention enhanced graph convolutional network for skeleton-based action recognition

Spatial-temporal dual-channel adaptive graph convolutional network for remaining useful life prediction with multi-sensor information fusion

With the increasing complexity of global crude oil trade, traditional prediction methods have difficulty capturing the spatiotemporal characteristics of trade flows. This paper proposes a global crude oil trade flow prediction method based on a graph neural network (GNN). The crude oil trade network is modeled using a dynamic heterogeneous graph, and a spatiotemporal joint modeling is performed by combining a graph convolution network (GCN), a graph attention network (GAT), and a temporal convolution network (TCN). This method can simultaneously capture the spatial dependence and temporal dynamics of trade flows, thereby improving the accuracy of predictions. A series of experiments have verified the effectiveness and robustness of the model when processing large-scale crude oil trade data, especially in the face of data imbalance and abnormal value interference, the model still maintains good prediction ability. The research in this paper provides a new way of thinking for the prediction of crude oil trade flows and provides a useful reference for modeling complex spatio-temporal networks.

Efficient identification of key nodes is crucial to optimizing detector deployment and enhancing traffic monitoring in intelligent transportation systems. However, existing approaches often struggle to adapt to dynamic traffic variations, leading to suboptimal coverage and increased deployment costs. We propose bidirectional temporal convolutional graph attention networks (BTC-GATs) to address these limitations. This novel framework integrates bidirectional attention mechanisms to capture upstream and downstream dependencies, temporal convolutional networks for multiscale feature extraction, and graph attention networks for spatial information aggregation. BTC-GATs incorporates adaptive temporal modeling to capture nonlinear traffic dynamics, gradient-based variation analysis to quantify node influence, and a ranking mechanism that fuses attention coefficients with topological attributes to further enhance robustness and interoperability. In addition, a key node coverage study is conducted to examine the trade-off between accuracy and deployment efficiency. Extensive experiments on the California Highway PeMS04 dataset demonstrate that BTC-GATs outperforms benchmark methods in key node identification, offering superior accuracy and stability. Further analysis confirms its robustness under varying traffic conditions and initialization settings, highlighting its potential as a scalable, adaptive, and cost-effective solution for intelligent traffic monitoring. By facilitating efficient sensor placement, BTC-GATs contributes to improved data collection and congestion management in large-scale transportation networks.

Detecting anomalous traffic behaviors with seasonal deep Kalman filter graph convolutional neural networks

A novel anomaly detection method for multivariate time series based on spatial-temporal graph learning

Remaining useful life (RUL) prediction is crucial in the field of engineering, which can reduce the frequency of accidents and the maintenance cost of machinery. With the increasing complexity of rotating machinery, the data analysis methods based on deep learning have become the mainstream methods of prediction work. However, most of the current RUL prediction methods only use single-sensor data as input, which cannot effectively use multi-sensor data. In addition, as an advanced deep learning prediction method, temporal convolutional network (TCN) only uses the past time information of vibration data to determine the current health status of bearings, while ignoring the importance of future time information of vibration data. To solve the above problems, a bearing RUL prediction method based on multi-sensor data fusion and bidirectional-temporal attention convolutional network (Bi-TACN) is proposed in this paper. In multi-sensor data fusion, multi-sensor data are combined into multi-channel data, and a channel-weighted attention is designed to emphasize the importance of each sensor data. Compared with traditional multi-sensor data fusion, the proposed fusion method allows deep prediction networks to learn more useful feature information from multi-sensor data. Then, Bi-TACN is developed to predict the RUL of bearings. Bi-TACN is mainly composed of the forward TCN block and the backward TCN block, both of which can learn the past and future time information of multi-sensor data simultaneously. Moreover, a temporal attention mechanism is embedded in Bi-TACN to adaptively calibrate the weights of the two TCN blocks, so as to achieve dynamic feature fusion of past and future time information. RUL prediction experiments are carried out through Xi’an Jiao tong University bearing dataset and PHM 2012 bearing dataset respectively. Compared with the advanced prediction methods, the proposed method can accurately predict the RUL of more types of bearings and has low prediction errors.

Skeleton-based human action recognition has great potential for human behavior analysis owing to its simplicity and robustness in varying environments. This paper presents a spatial and temporal attention-enhanced graph convolution network (STAEGCN) for human action recognition. The spatial-temporal attention module in the network uses convolution embedding for positional information and adopts multi-head self-attention mechanism to extract spatial and temporal attention separately from the input series of the skeleton. The spatial and temporal attention are then concatenated into an entire attention map according to a specific ratio. The proposed spatial and temporal attention module was integrated with an adaptive graph convolution network to form the backbone of STAEGCN. Based on STAEGCN, a two-stream skeleton-based human action recognition model was trained and evaluated. The model performed better on both NTU RGB+D and Kinetics 400 than 2s-AGCN and its variants. It was proven that the strategy of decoupling spatial and temporal attention and combining them in a flexible way helps improve the performance of graph convolution networks in skeleton-based human action recognition.

Cloud‐assisted physical education teaching is an important direction for the development of smart education. However, the data processing and transmission of videos severely restrict the real‐time performance of action analysis. To this end, this paper proposes an efficient edge cloud‐assisted student movement recognition framework based on the graph convolutional network and human skeleton data. On the edge server, we use the YOLO‐pose algorithm to generate robust human skeleton sequences and design an improved spatial–temporal dual stream graph convolutional neural network with an early fusion structure, which introduces the node weight module and the dynamic graph module to exploit long‐distance dependency relationships of nodes. In the cloud server, we use a federated learning framework based on the density clustering mechanism to collaboratively train and aggregate parameters of models scattered across edge nodes. The experimental results show that our proposed model achieves excellent recognition accuracy on the self‐built sports action dataset, providing an effective solution for intelligent and real‐time feedback in outdoor sports teaching.

Effective remaining useful life (RUL) prediction of bearings is essential for the predictive maintenance of rotating machinery. However, the effectiveness of many existing RUL prediction methods depends on expert experience and signal processing algorithms, which limiting the application of these methods in real-life scenarios. This study proposes a novel end-to-end deep learning framework consisting of a multi-scale attention-based dilated causal convolutional (MADCC) module and a multi-layer temporal convolutional network (MTCN) to predict the RUL of bearings using raw vibration data. First, the MADCC module extracts multi-scale temporal features of the bearing degradation process (BDP) and provides fused feature vectors (FFVs) containing comprehensive BDP information for the MTCN module. Subsequently, the MTCN module mines deep temporal dependencies hidden in the FFV to predict the RUL of bearings. Ablation experiments are conducted to analyze the contribution of the framework’s components. Three evaluation metrics (root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2)) are used to verify the effectiveness of the proposed framework and other state-of-the-art methods on two public bearing datasets. The experimental results show that the proposed framework achieves the lowest RMSE and MAE and the highest R 2, demonstrating excellent performance and potential for RUL prediction of bearings.