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A Spatiotemporal Posterior Graph Convolutional Neural Network Based on Multihead Attention With Squeeze-and-Excitation Module for Patient-Specific Epileptic Seizure prediction

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Fu, Runjie Zhang, Boyuan Xue, Binqiang Wang, Dongqing Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Qingdao Univ Coll Elect Engn Qingdao 266071 Peoples R China

The activity of epileptic lesions is closely related to the functional connectivity patterns between channel locations of the brain. Previous studies have used feature extraction methods such as Pearson correlation coefficient, mutual information, and phase locking value to calculate the functional connectivity for seizure prediction. However, these methods can only capture partial dynamical features of functional connections. Therefore, a novel spatiotemporal posterior graph convolutional neural network (WCE-MASE-PGCN) is proposed to comprehensively explore the spatiotemporal topological information of epileptic signals. The content includes: 1) The WCE is used to extract the time-frequency characteristics of epileptic signals, thereby obtaining a low-dimensional, robust embedding representation;2) The MASE are employed to construct the posterior graph of brain functional connections, which are adaptively adjusted through backpropagation algorithms;3) The posterior graph convolutional neural network (PGCN) is utilized to fully extract the features of brain functional connections;4) Experimental results conducted on publicly available CHB-MIT and Siena datasets demonstrate that posterior-based graph networks exhibit superior capabilities in feature extraction and interpretability. On the CHB-MIT dataset, the area under the curve (AUC), sensitivity ( S-n ), and false prediction rate (FPR) were measured as 0.966, 97.9%, and 0.010/h, respectively. On the Siena dataset, the AUC, specificity, and FPR were reported as 0.961, 96.5%, and 0.032/h, respectively. Compared to state-of-the-art (SOTA) methods, our approach achieved a 1.9% improvement in AUC, a 5.6% increase in S-n , and a 7.2% reduction in FPR on the CHB-MIT dataset. On the Siena dataset, it improved AUC by 3.8%, increased S-n by 6.3%, and reduced FPR by 9.7%.

关键词： Feature extraction Electroencephalography prediction algorithms Spatiotemporal phenomena Convolution Epilepsy Brain modeling Time-frequency analysis Hidden Markov models Discrete wavelet transforms Electroencephalogram multihead attention (MA) posterior graph seizure prediction squeeze-and-excitation (SE) module

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DPDRF: Dynamic Predictive Driving Risk Field Based on Multi-Agent Trajectory prediction and Digital Twins System

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第3期74卷 3651-3665页

作者： Liu, Jianhang Sheng, Xizhao Tan, Lizhuang Zhang, Wei Zhang, Peiying Liu, Kai China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China Qilu Univ Technol Shandong Comp Sci Ctr Key Lab Comp Power Network & Informat Secur Minist Educ Jinan 250014 Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Power Internet & Serv C Jinan 250014 Peoples R China China Univ Petr East China Qingdao Inst Software Coll Comp Sci & Technol Qingdao 266580 Peoples R China Tsinghua Univ State Key Lab Space Network & Commun Beijing 100084 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China

Assessing risk in traffic scenarios is crucial for advancing autonomous vehicle safety. As the number of vehicles and their interactions increase, there is a heightened need for prompt and precise risk evaluation. Traditional collision warning systems, which rely on conflict points, are limited to specific traffic scenarios and are inadequate for the dynamic demands of complex and diverse traffic environments. Additionally, current field-based risk assessment methods evaluate collision risks based only on instantaneous vehicle motion states, lacking in both timeliness and accuracy. To address these challenges, this paper proposed a novel risk assessment method. It involves constructing a vehicle-road cooperative environment using a distributed digital twins system and employs a multi-agent multi-modal trajectory prediction algorithm to forecast future vehicle motion states. By leveraging predictive multi-dimensional kinematic features, the proposed method dynamically generates future driving risk fields, effectively assesses collision risks in complex traffic scenarios by modeling multi-source collision risks within future traffic scenes. Extensive simulation results and analyses reveal that DPDRF significantly improves the average Pre-Collision Warning Time (PCWT) by 2.53 times compared to DDRPF and by 1.81 times compared to DSF. Furthermore, DPDRF reduces the average Pre-Collision Warning Error (PCWE) by 79% relative to PRF.

关键词： Digital twins Trajectory Vehicle dynamics Safety Risk management prediction algorithms Predictive models Heuristic algorithms Computational modeling Kinematics Risk assessment driving risk field trajectory prediction digital twins Internet of Vehicles

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Risk-Aware Vehicle Trajectory prediction Under Safety-Critical Scenarios

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第5期26卷 7156-7171页

作者： Wang, Qingfan Xu, Dongyang Kuang, Gaoyuan Lv, Chen Li, Shengbo Eben Nie, Bingbing Tsinghua Univ Sch Vehicle & Mobil State Key Lab Intelligent Green Vehicle & Mobil Beijing 100084 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore

Trajectory prediction is significant for intelligent vehicles to achieve high-level autonomous driving, and a lot of relevant research achievements have been made recently. Despite the rapid development, most existing studies solely focus on normal and safe scenarios while largely neglecting safety-critical scenarios, particularly those involving imminent collisions. This oversight may result in autonomous vehicles lacking the essential predictive ability in such situations, posing a significant threat to safety. To tackle these, this paper proposes a risk-aware trajectory prediction framework tailored to safety-critical scenarios. Leveraging distinctive hazardous features, we develop three core risk-aware components. First, we introduce a risk-incorporated scene encoder, which augments conventional encoders with quantitative risk information to achieve risk-aware encoding of hazardous scene contexts. Next, we incorporate endpoint-risk-combined intention queries as prediction priors in the decoder to ensure that the predicted multimodal trajectories cover both various spatial intentions and risk levels. Lastly, an auxiliary risk prediction task is implemented for the ultimate risk-aware prediction. Furthermore, to support model training and performance evaluation, we introduce a safety-critical trajectory prediction dataset and tailored evaluation metrics. We conduct comprehensive evaluations and compare our model with several SOTA models. Results demonstrate the superior performance of our model, with a significant improvement in most metrics. This prediction advancement enables autonomous vehicles to execute correct collision avoidance maneuvers under safety-critical scenarios, eventually enhancing road traffic safety.

关键词： Trajectory Measurement Predictive models Autonomous vehicles Training Vehicles Accuracy prediction algorithms Hazards Decision making Autonomous driving trajectory prediction traffic safety deep learning safety-critical scenarios collision avoidance

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Remaining Useful Life prediction Under Multiple Operating Conditions Based on a Novel Dual-Layer Temporal Convolutional Network

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IEEE SENSORS JOURNAL 2025年第1期25卷 1900-1911页

作者： Yang, Xu Chen, Dandan Huang, Jian Wu, Xia Chen, Zhiwen Li, Qing Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Innovat Sch Foshan 528300 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China

Modern mechanical equipment often works under multiple operating conditions. Ignoring the impact of conditions on the remaining useful life (RUL) of equipment will reduce the prediction accuracy. Based on this, a novel prediction method that properly integrates multidimensional state monitoring data with operating condition data is proposed to evaluate the impact of operating conditions on equipment RUL. First, to extract feature representations incorporating operational information, a dual-layer temporal convolutional network (TCN) structure is designed to simultaneously model the state data and operating condition data at the current time. A convolutional self-attention mechanism (CSA) is also constructed in this structure to fully capture spatiotemporal information in state data. Then, the time-varying future condition probabilities are computed based on the future condition data and integrated with the extracted features to mitigate the impact of future condition changes upon equipment RUL. Finally, extensive experiments conducted on two datasets demonstrate the excellent prediction performance of the proposed method under multiple operating conditions.

关键词： Data models Sensors Accuracy Hidden Markov models Degradation Convolutional neural networks Predictive models Feature extraction Adaptation models prediction algorithms Multiple operating conditions remaining useful life (RUL) prediction self-attention mechanism temporal convolutional network (TCN)

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FSGA: Motion prediction Method Based on Future Scene Graph Attention

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第6期26卷 8268-8282页

作者： Xu, Nan Chen, Chaoyi Chu, Liang Guo, Chong Pang, Tianshu Jilin Univ Coll Automot Engn Changchun 130025 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

Accurately predicting the trajectories of surrounding agents is critically important for the safe operation of autonomous vehicles. However, complex environments not only increase the computational cost but also introduce multimodality in the agent's future motions, presenting significant challenges for motion prediction. Therefore, we propose FSGA, a trajectory prediction framework that constructs a relationship graph between agents and lanes in future spacetime and utilizes a graph attention mechanism to analyze the interactions between these future spacetime agents and lanes. We employ a vectorized representation method to model the traffic scene as a graph. Through this vectorized scene representation, FSGA extracts spatio-temporal features from agents' historical trajectories, including interactions between agents, and between agents and lanes. To reduce computational costs, a lane scoring mechanism is employed, allowing the model to focus only on the lanes that have a significant impact on the current agent's motion. To address complexity and multimodality of future trajectories, we introduce a branching tree goal prediction structure that models multiple possible motion trends by representing waypoints and endpoints at different temporal stages. Based on these predicted goals, we construct a relationship graph that captures the associations between agents and lanes in future spacetime and obtains the interactions between agents and lanes, improving trajectory prediction accuracy. Experiments show that FSGA achieves state-of-the-art performance on Argoverse1.

关键词： Trajectory Feature extraction Market research Computational modeling Predictive models Semantics Accuracy prediction algorithms Encoding Autonomous vehicles Motion prediction deep learning graph neural network graph attention mechanism

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Co-Evolving Traffic State Parameters prediction Based on Mechanism-Data Blending Driven Deep Learning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 3084-3100页

作者： Dong, Hanxuan Zhang, Hailong Ding, Fan Tan, Huachun North Univ China Sch Elect & Control Engn Taiyuan 030051 Peoples R China North Univ China Sch Mech Engn Taiyuan 030051 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China Beijing Inst Technol Adv Res Inst Multidisciplinary Sci Beijing 100081 Peoples R China

Traffic state prediction, a classical task for traffic management, is a central component of intelligent transport systems to maintain safe and efficient operation. While extensive and intensive research has been conducted on traffic state prediction, most studies have concentrated on enhancing the accuracy of specific traffic state parameters. However, traffic state is a co-evolutionary multivariate time series with various parameters such as flow, velocity, occupancy, etc. At the same time, traffic state data will inevitably be lost during collection. So accurate traffic prediction still faces the following challenges: First, how to deal with the complex missing situations in observational data? Second, how to learn the co-evolutionary relationships between different traffic state parameters while mining the high-dimensional spatio-temporal traffic state patterns? In this paper, we propose a mechanism-data blending-driven co-evolving traffic state parameter prediction method: multi-parameter hybrid tensor deep learning networks (MHT-Net), which consists of a multi-parameter tensor graph convolutional network (MTGCN) and a tensor recurrent neural network (T-RNN). MTGCN implements knowledge embedding of synergistic mechanisms between traffic parameters, ensuring that the road network spatial dependency and the synergistic influence relationship of the parameters can be obtained simultaneously;T-RNN is used to learn high-dimensional temporal features of traffic states. Experiment results on a real-world dataset from Jiangsu province outperform the state-of-the-art baselines, demonstrating the efficacy of the proposed method and providing an effective tool for traffic state prediction with missing values. A mechanism-data blending driven co-evolving traffic state parameter prediction method, multi-parameters hybrid tensor deep learning networks (MHT-Net) is proposed, which implements knowledge embedding of synergistic mechanisms between traffic parameters and learn th

关键词： Tensors Time series analysis Data models Roads Deep learning Predictive models Accuracy prediction algorithms Electronic mail Detectors Traffic state prediction co-evolving traffic state parameters mechanism-data blending driven model tensor deep learning data missing

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A Long-Term-Planning Learning Strategy to Coordinate Viewport prediction and Video Transmission in 360 Video Streaming

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第3期24卷 1792-1804页

作者： Zhang, Guanghui Guo, Jing Xiao, Mengbai Yu, Dongxiao Aggarwal, Vaneet Cheng, Xiuzhen Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Peoples R China Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong 999077 Peoples R China Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Fueled by Metaverse, 360 degrees video streaming has seen tremendous growth in the past years. However, our measurement reveals that current 360 degrees streaming systems suffer from a dilemma that severely limits QoE. On the one hand, viewport prediction requires the shortest possible prediction distance for high predicting accuracy;On the other hand, video transmission requires more buffered data to compensate for bandwidth fluctuations otherwise substantial playback rebuffering would be incurred. There is so far no existing method that can break this dilemma so the QoE optimization for 360 degrees video streaming was naturally bottlenecked. This work is the first attempt to tackle this challenge by developing QUTA - a novel learning-based streaming system. Specifically, according to our measurement, three kinds of internal streaming parameters have significant impacts on the prediction distance, namely, download pause, data rate threshold, and playback rate. On top of this, we design a new long-term-planning (LTP) continuous control deep reinforcement learning method that tunes the parameters dynamically based on the network condition and the streaming context. Extensive evaluations based on real system prototypes show that QUTA not only improves the prediction accuracy and QoE performance by up to 68.4% but also exhibits strong temporal and spatial robustness.

关键词： Streaming media Quality of experience Bit rate prediction algorithms Accuracy Bandwidth Trajectory Training Resource management Optimization 360 degrees video streaming mobile network virtual reality

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Enhancing Reliability in AI-Based CSI prediction: A Proxy-Based Performance Monitoring Approach

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第4期73卷 2602-2615页

作者： Jiang, Chengyong Guo, Jiajia Wen, Chao-Kai Jin, Shi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Natl Sun Yat Sen Univ Inst Commun Engn Kaohsiung 80424 Taiwan

Artificial intelligence (AI)-based channel state information (CSI) prediction, aimed at enhancing CSI accuracy and reducing overhead, has shown significant advancements over traditional model-based prediction methods. Despite these advantages, its practical deployment has been hindered by unreliable prediction performance due to AI instability. This study introduces a reliable AI-based CSI prediction framework by implementing a proxy-based performance monitoring mechanism. Specifically, we deploy a lightweight proxy at the user equipment (UE), trained via knowledge distillation to accommodate the UE's limited capacities. This proxy mimics the output of the CSI prediction network at the base station (BS) side, enabling the UE to monitor the accuracy of the predicted CSI and prevent undesirable outcomes. To overcome the deployment challenges in operational systems, we detail the practical implementation procedures of our proposed method, covering both offline training and online operation phases. Simulation results show that our proxy-based monitor can achieve over 90% consistency with the CSI prediction network at the BS side and avoid over 85% of unsatisfactory prediction outcomes under various practical considerations, demonstrating remarkable generalization capabilities across different configurations.

关键词： Monitoring Reliability Accuracy Predictive models Precoding Training Artificial intelligence Downlink prediction algorithms Massive MIMO CSI prediction artificial intelligence performance monitoring

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prediction of Dissolved Alcohol Concentrations in Transformer Oil and Aging Evaluation of Cellulose Insulation

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2025年第2期32卷 1238-1245页

作者： Zhang, Heng Zhang, Yuan Jiang, Zaijun Fan, Xianhao Li, Wen Liu, Chuying Zhang, Enze Wu, Thomas Liu, Jiefeng Guangxi Univ Guangxi Key Lab Power Syst Optimizat & Energy Tech Nanning 530004 Peoples R China State Grid Jining Power Supply Co Jining 272000 Shandong Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Alcohol (methanol and ethanol) markers in transformer oil have progressively played a crucial role in assessing the aging state of cellulose paper. There is a lack of reported studies on the prediction of alcohol concentration in transformer oil. Given this, this work presents an approach that employs an atomic orbit search optimized support vector machine (AOS-SVM) to predict the alcohol concentration. Initially, oil-paper insulation samples are prepared, and the alcohol concentrations are determined. Furthermore, methanol and ethanol concentrations in oil were predicted using AOS-SVM, and the MSE of the prediction results reached 0.0428 and 0.0064, respectively. Eventually, a quantitative model combining methanol, ethanol, and degree of polymerization (DP) was constructed to evaluate the aging of insulating paper, which verified the reliability of the proposed model. In this regard, the proposed method exhibits reliable predictive performance in terms of alcohol concentration in oil, thereby contributing to the effective evaluation of the aging state of insulating paper.

关键词： Aging Oils Electrons Methanol Ethanol Power transformer insulation Oil insulation prediction algorithms Support vector machines Predictive models Aging evaluation concentration prediction intelligent algorithm oil-paper insulation power transformer

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An Adaptive Wind Power prediction Model Based on Cross-Attention Mechanism With Incremental Alignment

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 12857-12870页

作者： Jiang, Li Wang, Yifan Lu, Zhaowei Gao, Yuebing Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

Wind energy is a clean energy source that can effectively reduce pollution. Efficient and accurate wind power forecasting helps optimize the operation and scheduling of wind farms, thereby enhancing energy utilization. The intensity of wind energy is closely related to weather information, and these data are in the form of time series. Currently, most time series forecasting models use multi-head self-attention mechanisms and gated recurrent units. However, the self-attention mechanism has disorderly characteristics, which may disrupt the data sequence and lead to the loss of temporal information;meanwhile, gated recurrent units have limited effectiveness in capturing information among multiple variables, making it challenging to significantly improve prediction accuracy. To address these shortcomings, this study innovatively introduces a cross-attention mechanism into the core prediction algorithm and proposes a new forecasting module, the CA module, which can capture information among variables while maintaining the orderliness of the time series. Furthermore, to address the time lag between weather information and power data, this study designs a novel data alignment algorithm based on increments and combines it with variational mode decomposition to achieve multi-source feature alignment, further improving prediction accuracy. Additionally, a reinforcement learning algorithm is used as a hyperparameter optimization framework during the forecasting process to determine the optimal hyperparameters for different tasks, thus enhancing the model's accuracy and practicality. The experiments are based on weather and corresponding power data from four wind power stations in eastern China during spring, including ablation, comparison, and generalization experiments. The results show that the proposed model improves wind power output forecasting accuracy by more than 15% across different time scales compared to current mainstream algorithms, with short- to medium-term pre

关键词： Meteorology Time series analysis Wind power generation Accuracy Predictive models Forecasting Computational modeling prediction algorithms Data models Long short term memory Wind power prediction incremental dynamic time alignment cosine similarity cross-attention mechanism reinforcement learning

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