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STGCNFormer: Spatio-Temporal Dual-Stream Graph Convolutional Networks and Transformers for Traffic Forecasting

IEEE Transactions on Vehicular technology 2025年

作者： Yang, Changzhi Pan, Huihui Wang, Jue Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150001 China Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Ningbo Institute of Intelligent Equipment Technology Company Ltd. Ningbo315200 China University of Science and Technology of China Department of Automation Hefei230027 China

In intelligent transportation systems, traffic prediction constitutes the fundamental component of traffic optimization, aiming to precisely forecast traffic flow. Modeling spatio-temporal interactions is a crucial step in achieving long-term and accurate predictions. Traditional methods are mostly based on single-stream networks, which alternately extract spatio-temporal features through sequential iterations. However, time and space are inherently two different modalities. Employing a single-stream network to address multi-modal tasks is suboptimal. In this paper, we propose Spatio-Temporal Dual-Stream Graph Convolutional Networks and Transformers (STGCNFormer), a dual-stream network for traffic forecasting, decoupling spatio-temporal features in parallel through a temporal stream and a spatial stream. The core of STGCNFormer is a STGCNFormer block, comprising three components. The first component is a GCN block, which restricts the kernel of graph convolution and adopts Chebyshev polynomials of the first kind to approximate the convolutional kernel, capturing spatial interactions and achieving localized convolution. Secondly, we present a Transformer block, designed as a simplified version of vanilla Transformers, which captures temporal interactions and significantly reduces computational complexity. Thirdly, we propose a cross attention mechanism, consisting of a temporal attention mechanism and a spatial attention mechanism, which serves as a bidirectional bridge for sharing information between the dual streams. Experimental results demonstrate that STGCNFormer achieves superior prediction accuracy with reduced computational complexity compared with previous state-of-the-art methods. © 1967-2012 IEEE.

关键词： Air traffic control

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A comprehensive review of electrochemical hybrid power supply systems and intelligent energy managements for unmanned aerial vehicles in public services

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Energy and AI 2022年第3期9卷 148-171页

作者： Caizhi Zhang Yuqi Qiu Jiawei Chen Yuehua Li Zhitao Liu Yang Liu Jiujun Zhang Chan Siew Hwa College of Mechanical and Vehicle Engineering The State Key Laboratory of Mechanical TransmissionsChongqing Automotive Collaborative Innovation CentreChongqing UniversityChongqing400044China College of Automation Chongqing UniversityKey Laboratory of Complex System Safety and ControlMinistry of EducationChongqing UniversityChongqing400044China School of Mechanical Engineering University of Science and Technology BeijingBeijing100083China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhou310027China Institute for Sustainable Energy/College of Sciences Shanghai UniversityShanghai 200444China School of Mechanical and Aerospace Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore Sichuan Energy Internet Research Institute Tsinghua UniversityChengdu 610213Sichuan ProvinceChina

The electric unmanned aerial vehicles (UAVs) are rapidly growing due to their abilities to perform some difficult or dangerous tasks as well as many public services including real-time monitoring, wireless coverage, search and rescue, wildlife surveys, and precision agriculture. However, the electrochemical power supply system of UAV is a critical issue in terms of its energy/power densities and lifetime for service endurance. In this paper, the current power supply systems used in UAVs are comprehensively reviewed and analyzed on the existing power configurations and the energy management systems. It is identified that a single type of electrochemical power source is not enough to support a UAV to achieve a long-haul flight;hence, a hybrid power system architecture is necessary. To make use of the advantages of each type of power source to increase the endurance and achieve good performance of the UAVs, the hybrid systems containing two or three types of power sources (fuel cell,battery, solar cell, and supercapacitor,) have to be developed. In this regard, the selection of an appropriate hybrid power structure with the optimized energy management system is critical for the efficient operation of a UAV. It is found that the data-driven models with artificial intelligence (AI) are promising in intelligent energy management. This paper can provide insights and guidelines for future research and development into the design and fabrication of the advanced UAV power systems.

关键词： Unmanned aerial vehicles(UAVs) Power supply system Fuel cell system Artificial intelligence(AI) Energy management systems

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Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks ⁎

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IFAC-PapersOnLine 2024年第10期58卷 176-181页

作者： Yi Li Shuangshuang Han Yongqiang Bai Fei-Yue Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Key Laboratory of Ocean Observation Technology MNR Tianjin China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Beijing Huairou Academy of Parallel Sensing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

关键词： VANETs Social relation strength Image data transmission MMSE detection Intelligent transportation Social networking

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Human-Robot Pose Tracking Based on CNN with Color and Geometry Aggregation 16th

Human-Robot Pose Tracking Based on CNN with Color and Geo...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Xu, Yue Zhang, Yinlong Liu, Shuai Liu, Yuanhao Liang, Wei He, Hongsheng Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9789819611508

Accurately tracking the robotic arm and human joints is crucial to ensure safety during human-robot interaction. However, traditional pose tracking methods often exhibit insufficient performance and robustness in complex environments. The variations in the robotic arm’s environment and posture make it challenging for traditional methods to accurately capture the positions and posture of its joints. Specifically, when addressing challenges such as high similarity, occlusion, background complexity, and joint recognition failures, they often struggle to provide reliable and accurate results. To address these challenges, this paper proposes a human-robot pose tracking algorithm based on a new convolutional neural network model. To enhance detection accuracy, an improved color detection module is introduced to resolve joint misclassification. A geometric perception module is designed to accurately locate joints even under occlusion. Additionally, innovative iEMA and DBB modules are incorporated. The iEMA module employs edge detection technology to dynamically adjust thresholds for correct matching by improving the edge matching process. The DBB module refines the boundary box parameters for precise localization by introducing adaptive bounding box updates in real-time. This algorithm also integrates human pose recognition, enabling real-time pose recognition, thereby facilitating more intelligent and natural human-robot interaction. The algorithm has been rigorously evaluated on a custom-designed robotic arm platform. Experimental results validate the algorithm’s effectiveness and feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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SpaGRA:Graph augmentation facilitates domain identification for spatially resolved transcriptomics

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Journal of Genetics and Genomics 2025年第1期52卷 93-104页

作者： Xue Sun Wei Zhang Wenrui Li Na Yu Daoliang Zhang Qi Zou Qiongye Dong Xianglin Zhang Zhiping Liu Zhiyuan Yuan Rui Gao Center of Intelligent Medicine School of Control Science and EngineeringShandong UniversityJinanShandong 250061China MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST Department of AutomationTsinghua UniversityBeijing 100084China Institute of Precision Medicine Peking University Shenzhen HospitalShenzhenGuangdong 518036China Department of Clinical Laboratory The Second HospitalCheeloo College of MedicineShandong UniversityJinanShandong 250033China Institute of Science and Technology for Brain-Inspired Intelligence Center for Medical Research and InnovationShanghai Pudong HospitalFudan University Pudong Medical CenterFudan UniversityShanghai 200433China

Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various ***-based geometric deep learning has gained widespread adoption for spatial domain identification ***,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain *** tackle this challenge,we propose a novel method,SpaGRA(https://***/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph *** uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive ***,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random *** results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different *** SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD ***,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.

关键词： Spatial domain identification Spatially resolved transcriptomics Multi-head graph attention networks Graph augmentation Geometric contrastive learning

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Landslide Sensitivity Mapping Based on One-Dimensional Residual Convolutional Neural Networks

Landslide Sensitivity Mapping Based on One-Dimensional Resid...

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2022 Chinese automation Congress, CAC 2022

作者： Ke, Yougang Lu, Chengda Wu, Min Yu, Wanke Niu, Ruiqing School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Institute of Geophysics and Geomatics China University of Geosciences Wuhan China

ISBN: (纸本)9781665465335

Landslide is a common geological disaster. Landslide sensitivity mapping (LSM) is the key technology for landslide monitoring, early warning and risk assessment. Deep learning shows good performance in feature extraction. This paper proposes a one-dimensional residual convolution neural network (1DRCNN), which takes Yunyang County, Chongqing City, China, located in the Three Gorges Reservoir area as the research area. Extracting 12 evaluation factors from multi-source remote sensing data and building a training set. The spatial probability of landslide occurrence is quantitatively predicted by the proposed model, and finally, the landslide sensitivity mapping is generated. Compared with the common machine learning models SVM and logistic regression, the results show that the AUC (area under curve) and accuracy of 1D-RCNN are 0.9860 and 93.38%, respectively, which proving that this method is effective and can provide a reference for disaster prevention and reduction. © 2022 IEEE.

关键词： Landslides

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Adaptive Fault-Tolerant Tracking control of Direct-Drive Servo systems with Actuator Faults and Saturation

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IEEE Transactions on Transportation Electrification 2025年

作者： Shi, Pengwei Yu, Xinghu Lin, Xiaotian Qin, Jiahu Sun, Weichao Yongjiang Laboratory Ningbo315200 China University of Science and Technology of China Department of Automation Hefei230027 China Ningbo Institute of Intelligent Equipment Technology Co. Ltd Ningbo315200 China Harbin Institute of Technology Research Institute of Intelligent Control and Systems Harbin150001 China

Direct-drive servo systems are widely used in the field of transportation, but the existence of actuator failures and saturation will threaten system safety and cause serious traffic accidents. In this paper, an adaptive fault-tolerant tracking control (AFTTC) method for direct-drive servo systems is proposed to address the complex constraint problems in traffic tasks. The system dynamics model with actuator failures and asymmetric saturation is first constructed, and an estimation algorithm with convergence characteristics is comprehensively adopted to deal with actuator failures and parameter uncertainties. In particular, a smooth function is cleverly introduced to achieve a smooth transition of the saturation inflection point output. In order to minimize the influence of various nonlinearities and uncertainties, a finite-time neural network observer is designed. It is worth mentioning that the designed controller is suitable for the above-mentioned complex multi-constraint conditions and can achieve asymptotically stable tracking of the closed-loop system. Experimental results show that compared with traditional adaptive fault-tolerant control, the proposed AFTTC method improves the steady-state performance of the system by 26.5% and 28.2%, which further improves the safety of transportation industry tasks. © 2015 IEEE.

关键词： Adaptive control systems

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A Stacking Ensemble Learning Method for Non-Intrusive Appliance Load Monitoring

A Stacking Ensemble Learning Method for Non-Intrusive Applia...

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Chinese control and Decision Conference, CCDC

作者： Xudong Cheng Ding Li Wenkai Hu Jinghao Xu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Non-Intrusive Load Monitoring (NILM) collects the overall power consumption information from the power panel with advanced metering infrastructure, and obtains the operating states and power consumption information of each electrical appliance through load decomposition algorithms, so as to come up with appropriate strategies to reduce energy consumption and improve energy efficiency, which is of great significance for energy conservation and reduction. In recent years, NILM methods based on deep learning algorithms have received much attention and achieved good results, but the type of features extracted by a single deep learning model is limited, resulting in the model’s inability to overcome the bottleneck of accurate decomposition. To address the above problems, this paper proposes a deep ensemble learning framework for NILM, which takes the total active power sequence as input and synthesizes the advantages of multiple deep learning neural network models to further improve the performance of appliance identification and power disaggregation. Experiments are designed and conducted on the UK-DALE dataset to verify the effectiveness of the proposed deep ensemble learning method for NILM. The experimental results show that the method proposed in this paper has more advantages and is more promising in terms of various metrics compared to single deep learning approaches.

关键词： Deep learning Training Load monitoring Power demand Accuracy Stacking Neural networks

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Short-Term Load Forecasting for Buildings Based on Dynamic Weighted edRVFL Model

Short-Term Load Forecasting for Buildings Based on Dynamic W...

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Chinese automation Congress (CAC)

作者： Junwei Xie Meng Yuan Zhezhuang Xu Chi Liu Yuxiong Xia Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Fuzhou China Fujian Huading Intelligent Manufacturing Technology Co. Ltd Fuzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Short-term load forecasting of buildings plays a key role in demand management, however, when dealing with massive data, the prevalent practice of training and testing the system as a whole overlooks pattern variations inside data and potentially limits predictive performance. To address this issue, this paper proposes an ensemble deep random vector functional link (edRVFL) model that combines adaptive ensemble clustering and dynamic weighting strategy for load forecasting of an industrial building. Firstly, the adaptive ensemble clustering that combines ordering points to identify the clustering structure and K nearest neighbor is employed to determine the electricity load usage patterns. Subsequently, the improved particle swarm optimization algorithm is applied to adjust the weights of load curves in different typical day scenarios. Finally, a dynamic weighting strategy is designed to dynamically assign model weights to two clusters, where one matches the type of predicted load pattern for the day being forecasted and another for the cluster of the previous day. The experiment in a real building verifies the effectiveness of the proposed algorithm, which has higher prediction accuracy than other comparison algorithms including one-week interval and different ensemble clustering.

关键词： Adaptation models Load forecasting Heuristic algorithms Buildings Clustering algorithms Predictive models Prediction algorithms Vectors Forecasting Load modeling

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Continuous Flow Synthesis of Mesalazine via Data-Driven Nonlinear Model Predictive control

Continuous Flow Synthesis of Mesalazine via Data-Driven Nonl...

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European control Conference (ECC)

作者： Ismael Castillo Jakob Rehrl Martin Steinberger Martin Horn Institute of Automation and Control Graz University of Technology Graz Austria Research Center Pharmaceutical Engineering GmbH Inffeldgasse 13/2 Graz Austria Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Institute of Automation and Control Graz University of Technology Graz Austria

The synthesis of Mesalazine via data-driven modelling and a control scheme for the underlying complex continuous flow chemistry process is presented. The challenges of modelling continuous flow synthesis of Mesalazine are overcome by the usage of Neuro-Fuzzy Models together with the so-called Local Linear Model Tree (NFM-LoLiMoT) training algorithm based on data from a highly detailed simulator for the reactor. A state-space representation of the NFM-LoLiMoT allows the implementation of a Non-linear Model Predictive control (NMPC) strategy in order to perform output tracking and fulfil all input and output constraints. The NMPC scheme guarantees stability through the approximation of an Infinite Horizon cost function using a terminal cost and terminal state constraints. The proposed method provides a systematic approach that can be applied for different setup configurations and reduces the time-consuming process of first-principles modelling of the chemical processes. Simulations of a hydrogenation reactor for the synthesis of Mesalazine are presented to show the performance of the introduced method.

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