The development of location-based services facilitates the use of location data for detecting urban events. Currently, most studies based on location data model the pattern of an urban dynamic and then extract the anomalies, which deviate significant...
Fault diagnosis of bearings in rotating machinery is a critical task. Vibration signals are a valuable source of information, but they can be complex and noisy. A transformer model can capture distant relationships, which makes it a promising solutio...
Long-term prediction of physical systems on irregular unstructured meshes is extremely challenging due to the spatial complexityof meshes and the dynamic changes over time; namely, spatial dependence and temporal dependence. Recently, graph-based nex...
Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and d...
Accurate traffic flow prediction plays a critical role in intelligent transportation systems, supporting traffic management, congestion mitigation, and efficient utilization of road resources. Advances in neural network-based methods, particularly gr...
Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence...
To address the core challenges of multivariate nonlinear coupling and long-term temporal dependency in 4D UAV trajectory prediction, this study proposes an innovative model named KAN-Former. On a 21-dimensional multimodal UAV dataset, KAN-Former achi...
Digital Twins, which are virtual representations of physical systems mirroring their behavior, enable real-time monitoring, analysis, and optimization. Understanding and identifying the temporal dependencies included in the multivariate time series d...
Multivariate time series prediction models perform the required operation on a specific window length of a given input. However, capturing complex and nonlinear interdependencies in each temporal window remains challenging. The typical attention mech...
Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming a...
Stock return prediction is a pivotal yet intricate task in financial markets, challenged by volatility and multifaceted dependencies. This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networ...
In recent years, video research has dealt with high-frame-rate (HFR) content. Even though low or standard frame rates (SFR) that correspond to values less than 60 frames per second (fps) are still covered. Temporal conversions are applied accompanied...
This paper addresses the Multi-Athlete Tracking (MAT) problem, which plays a crucial role in sports video analysis. There exist specific challenges in MAT, e.g., athletes share a high similarity in appearance and frequently occlude with each other, m...
Skeleton-based action recognition, which aims to classify human actions through the coordinates of body joints and their connectivity, is a significant research area in computer vision with broad application potential. Although Graph Convolutional Ne...
Traffic flow prediction can provide effective support for traffic management and control and plays an important role in the traffic system. Traffic flow has strong spatio-temporal characteristics, and existing traffic flow prediction models tend to e...
This paper presents a novel multi-view 3D object detection framework, Long-Term Spatial–Temporal Bird’s-Eye View (LST-BEV), designed to improve performance in autonomous driving. Traditional 3D detection relies on sensors like LiDAR, but...
Accurate PM2.5 prediction is essential for effective urban air quality management. However, existing methods often struggle to capture the complex, nonlinear, and coupled spatiotemporal dynamics in long-term air pollution evolution. Most existing mod...
In recent years, traffic flow forecasting has attracted the great attention of many researchers with increasing traffic congestion in metropolises. As a hot topic in the field of intelligent city computing, traffic flow forecasting plays a vital role...
Traffic forecasting plays an important role in intelligent transportation systems. However, the prediction task is highly challenging due to the mixture of global and local spatiotemporal dependencies involved in traffic data. Existing graph neural n...
Taxi demand forecasting plays an important role in ride-hailing services. Accurate taxi demand forecasting can assist taxi companies in pre-allocating taxis, improving vehicle utilization, reducing waiting time, and alleviating traffic congestion. It...
Modeling spatiotemporal representations is one of the most essential yet challenging issues in video action recognition. Existing methods lack the capacity to accurately model either the correlations between spatial and temporal features or the globa...
Recently, STDenseNet (SpatioTemporal Densely connected convolutional Network) showed remarkable performance in predicting network traffic by leveraging the inductive bias of convolution layers. However, it is known that such convolution layers can on...
Cloud image prediction is a spatio-temporal sequence prediction task, similar to video prediction. Spatio-temporal sequence prediction involves learning from historical data and using the learned features to generate future images. In this process, t...
The involvement of the dopamine system in modulating time perception has been widely reported. Clinical conditions (e.g., Parkinson’s disease, addictions) that alter dopaminergic signaling have been shown to affect motor timing and perceived du...
Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional sp...
To tackle the limitations in simultaneously modeling long-term dependencies in the time dimension and nonlinear interactions in the feature dimension, as well as their inability to fully reflect the impact of real-time load changes on spatial depende...
Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading...
The accuracy of short-term wind power forecasting (STWPF) is crucial for the stable operation of power systems. To address the issue of insufficient capture of spatio-temporal dependencies in existing models, which leads to low prediction accuracy, t...
Traffic flow prediction is essential for smart city management and planning, aiding in optimizing traffic scheduling and improving overall traffic conditions. However, due to the correlation and heterogeneity of traffic data, effectively integrating...
Reliable forecasting of satellite telemetry is critical for spacecraft health management and mission planning. However, conventional data-driven methods often struggle to effectively capture both the long-term dependencies and local dynamics inherent...
Previous studies on Sound Event Localization and Detection (SELD) have primarily focused on CNN- and Transformer-based designs. While CNNs possess local receptive fields, making it difficult to capture global dependencies over long sequences, Transfo...
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a signif...
In steel production, the blast furnace is a critical element. In this process, precisely controlling the temperature of the molten iron is indispensable for attaining efficient operations and high-grade products. This temperature is often indirectly...
Short-term origin–destination (OD) prediction in urban rail transit (URT) is vital for improving URT operation. However, due to the problems such as the unavailability of the OD matrix of the current day, high dimension and long-range spatio-te...
Predicting the cooling capacity of converter valves is crucial for maintaining the stability and efficiency of high-voltage direct current (HVDC) systems. This task involves handling complex, multi-dimensional time-series data with strong inter-varia...
Bitcoin transaction anomaly detection is essential for maintaining financial market stability. A significant challenge is capturing the dynamically evolving transaction patterns within transaction networks. Dynamic graph models are effective for char...
The ability to accurately repeat meaningless nonwords or lists of spoken digits in correct order have been associated with vocabulary acquisition in both first and second language. Individual differences in these tasks are thought to depend on the ph...
The stochastic analysis in the scale domain (instead of the traditional lag or frequency domains) is introduced as a robust means to identify, model and simulate the Hurst–Kolmogorov (HK) dynamics, ranging from small (fractal) to large scales exhibit...
Traffic flow forecasting is the foundation of intelligent transportation systems. Accurate traffic forecasting is crucial for intelligent traffic management and urban development. However, achieving highly accurate traffic flow prediction is challeng...
Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods...
Efficient operation of urban metro systems depends on accurate passenger flow predictions, a task complicated by intricate spatiotemporal correlations. This paper introduces a novel spatiotemporal graph neural network (STGNN) designed explicitly for...
In recent years, traffic forecasting has gradually become a core component of smart cities. Due to the complex spatial-temporal correlation of traffic data, traffic flow prediction is highly challenging. Existing studies are mainly focused on graphic...
Accurate prediction of drilling speed is essential in mechanical drilling operations, as it improves operational efficiency, enhances safety, and reduces overall costs. Traditional prediction methods, however, are often constrained by delayed respons...
A stable and reliable power system is crucial for human daily lives and economic stability. Power load forecasting is the foundation of dynamically balancing between the power supply and demand sides. However, with the popularity of renewable energy...
Traffic flow prediction plays an important role in intelligent transportation systems. However, accurately modeling traffic dynamics remains challenging due to complex temporal correlations and spatial interactions across road networks. In this work,...
As the core component of aircraft systems, aeroengines require accurate Remaining Useful Life (RUL) prediction to ensure flight safety, which serves as a key part of Prognostics and Health Management (PHM). Traditional RUL prediction methods primaril...
Traffic systems are quintessential complex systems, characterized by nonlinear interactions, multiscale dynamics, and emergent spatiotemporal patterns over complex networks. These properties make traffic prediction highly challenging, as it requires...
Traffic forecasting’s key challenge is to extract dynamic spatial-temporal features within intricate traffic systems. This paper introduces a novel framework for traffic prediction, named Local-Global Spatial-Temporal Graph Convolutional Networ...
Emotion recognition plays an important role in human–computer interactions. Recent studies have focused on video emotion recognition in the wild and have run into difficulties related to occlusion, illumination, complex behavior over time, and audito...
Traffic prediction is a popular research topic in the field of Intelligent Transportation System (ITS), as it can allocate resources more reasonably, relieve traffic congestion, and improve road traffic efficiency. Graph neural networks are widely us...
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