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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 246-253页

作者： Wang, Xu Wang, Deyi He, Zhaoshui Lin, Zhijie Xie, Shengli Guangdong Mechanical and Electrical Polytechnic School of Electronics and Communication Guangzhou510550 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China

Agriculture crop classification is helpful for agricultural production. However, it is challenging to classify crops from the agriculture image suffering from these problems: 1) Crops are often masked in complex backgrounds;2) There is high similarity between crop categories. To address these problems, an adaptive masking attention network (AMA-Net) is proposed for agriculture crop identification from natural images, where the adaptive masking (AM) module is developed to distinguish the crop from the complex background by selectively eliminating redundant information of feature maps, and the fair attention module is devised to identify similar crops between categories by modeling the fine-grained features. Experiments conducted on the benchmark show the effectiveness and superiority of the proposed AMA-Net, achieving the performance of 96.65%, 96.65%, 97.13%, and 96.72% on the accuracy, precision, recall, and F1-score, respectively, which is better than other state-of-the-art methods. © 2023 IEEE.

关键词： Crops Agriculture Accuracy Visualization Training Production Autonomous aerial vehicles Attention mechanisms Adaptive systems Support vector machines

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Toward High-Accuracy and Low-Latency Group Vehicle Trajectory Prediction With Linear UNet-Enhanced Fully Connected Spatial–Temporal Graph Neural Network

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IEEE Internet of Things Journal 2025年第9期12卷 11797-11811页

作者： Zhang, Zirui Huang, Xumin Yu, Rong Wu, Maoqiang Kang, Jiawen Xie, Shengli Guangdong University of Technology School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China South China Normal University School of Electronic Science and Engineering Foshan528225 China Guangdong University of Technology School of Automation 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China

Group vehicle trajectory prediction (GVTP) is important for analyzing the traffic states and optimizing the traffic management. However, existing studies have performance bottlenecks in the prediction accuracy and inference latency. To tackle the problems, we propose a linear UNet-enhanced fully connected spatial–temporal GNN (LUFC-STGNN) for GVTP. First, a spatial graph is constructed by integrating prior-based and data-driven methods to capture both explicit and implicit spatial interactions between the vehicles. After that, a comprehensive temporal graph is created to capture the varying strengths of temporal interactions between all vehicles throughout historical timestamps. Furthermore, a fully connected spatial–temporal graph combining the spatial and temporal graphs is introduced to extract the effective spatial–temporal interaction features of the vehicles through the graph convolution operation. Finally, a linear UNet-based temporal dependency encoder (LU-TDE) is designed to further enhance the model’s ability of capturing the potential temporal patterns in the vehicle interactions. The encoder with linear complexity explores the multiscale temporal dependencies from the spatial–temporal interaction features but also reducing the inference latency. Experiments results based on real-world datasets show that compared to state-of-the-art models, our model reduces the average root mean square error over the 5-s prediction horizon by 31% and 10% on the NGSIM and HighD datasets, while reducing the inference latency by at least 1.26 times. © 2014 IEEE.

关键词： Prediction models

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FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning

arXiv

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arXiv 2025年

作者： Li, Daoyuan Yang, Zuyuan Xie, Shengli School of Automation Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangdong University of Technology China Key Laboratory of iDetection and Manufacturing-IoT Ministry of Education China

Federated learning is essential for enabling collaborative model training across decentralized data sources while preserving data privacy and security. This approach mitigates the risks associated with centralized data collection and addresses concerns related to data ownership and compliance. Despite significant advancements in federated learning algorithms that address communication bottlenecks and enhance privacy protection, existing works overlook the impact of differences in data feature dimensions, resulting in global models that disproportionately depend on participants with large feature dimensions. Additionally, current single-view federated learning methods fail to account for the unique characteristics of multi-view data, leading to suboptimal performance in processing such data. To address these issues, we propose a Self-expressive Hypergraph Based Federated Multi-view Learning method (FedMSGL). The proposed method leverages self-expressive character in the local training to learn uniform dimension subspace with latent sample relation. At the central side, an adaptive fusion technique is employed to generate the global model, while constructing a hypergraph from the learned global and view-specific subspace to capture intricate interconnections across views. Experiments on multi-view datasets with different feature dimensions validated the effectiveness of the proposed method. © 2025, CC BY-NC-SA.

关键词： Federated learning

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Joint Driving Mode Selection and Resource Management in Vehicular Edge Computing Networks

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IEEE Internet of Things Journal 2025年

作者： Yang, Chao Chen, Jihuang Huang, Xumin Lian, Jianyu Tang, Yanqun Chen, Xin Xie, Shengli Guangdong University of Technology Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration School of Automation Guangzhou510006 China Sun Yat-Sen University School of Electronics and Communications Shenzhen518107 China

Connected and automated vehicles (CAVs) have emerged as an efficient solution to improve the driving experience in the intelligent transportation systems (itss), in which the targeted vehicle (TV) can switch between the human driven (HD) and autonomous driven (AD) modes to act as server or terminal in vehicular edge computing networks (VECNs). However, due to the dynamic nature of traffic networks and the moving of vehicles, distribution of computational resources is imbalanced and variable, it is a challenge to design the cooperative resource management scheme for the whole journey of vehicle users. In this paper, we propose a joint driving model selection and resource management scheme for TV in each road segment, to maximize the vehicle users' satisfaction of the whole journey. For the complex formulated joint optimization problem, we design a three-stage hierarchical optimization (3SHO) framework, using deep Q-network (DQN) for driving mode optimization in the first stage and deep deterministic policy gradient (DDPG) for optimizing resource management under different selected driving modes. And a terminal-server matching mechanism is introduced to enable dynamic service quality improvement for TV. Specially, we design a new user satisfaction function with the quality of service, traffic revenue, and the gap between expected and actual revenues of users are considered. Experimental results showcase the robust convergence of the 3SHO algorithm, the adaptness to dynamic traffic networks, and the capacity to enhance user satisfaction significantly. © 2014 IEEE.

关键词： Quality of service

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Solo Adaptation for Distributed Synchronization With Application to Event-Triggered Linear Multi-Agent Systems

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IEEE Transactions on Automatic Control 2025年

作者： Chen, Ci Zheng, Zhiyang Lewis, Frank L. Xie, Kan Xie, Shengli Guangdong University of Technology Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education School of Automation Guangzhou510006 China Guangdong University of Technology School of Automation Guangdong Guangzhou510006 China The University of Texas at Arlington UTA Research Institute Fort Worth76118 United States Guangdong University of Technology 111 Center for Intelligent Batch Manufacturing Based on IoT Technology School of Automation Guangzhou China Guangdong University of Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of Education School of Automation Guangzhou510006 China

We study a solo adaptation-based protocol design problem for distributed synchronization of event-triggered linear multi-agent systems (MAS) over general directed graphs. We show that a solo adaptive gain, a locally static control gain matrix, and a distributed state measurement are sufficient for obtaining adaptive distributed synchronization of linear MAS over directed graphs without any prior knowledge of the global communication network. We then extend the solo adaptation structure to formulate an event-triggered synchronization control protocol. The main feature of our result is that the time for triggering a subsequent event is now computed using the distributed event-triggered state measurement, rather than the continuous-time state measurement. © 1963-2012 IEEE.

关键词： Continuous time systems

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MFDFormer: A Unified Multiscale Frequency Domain MetaFormer Framework for EEG-based Chronic Pain Recognition

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IEEE Internet of Things Journal 2025年

作者： Qiu, Shihan Xie, Kan Yang, Junjie Yang, Qiyu Zhang, Bo Gu, Yuhong Xie, Feng Qiu, Rongjian Xie, Shengli Bai, Wenfang Guangdong University of Technology School of Automation China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China Guangdong University of Technology Guangzhou510006 China School of Automation Guangdong University of Technology China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Guangzhou510006 China Guangzhou510080 China

Wearable electroencephalogram (EEG) devices have shown great potential in enabling real-Time monitoring of subtle changes in brain activity, providing new possibilities for the assessment and management of chronic pain. However, recognizing pain-related biomarkers from EEG data remains a complex, multi-Task challenge. Most existing research focuses on single-Task approaches and rarely addresses this issue within a unified framework. In this paper, we propose a novel deep neural network (DNN) model called multiscale frequency domain MetaFormer (MFDFormer), which is designed to simultaneously predict the presence, type, and intensity of chronic pain. The proposed MFDFormer comprises two primary sub-networks: multiscale feature extractor (MFE) and frequency domain MetaFormer (FDFormer) encoder. The MFE extracts diverse EEG features through convolutions with different kernel sizes, while a self-Attention mechanism is integrated into MFE to emphasize the importance of inter-dependency among these features. The FDFormer encoder refines the output MFE features using causal convolutions to capture local patterns and projects them into a higher-dimensional representation domain. Additionally, it incorporates spatial and temporal frequency domain learners in parallel to effectively capture the spatial-Temporal information of EEG data. Based on the publicly available BFCP dataset, the proposed MFDFormer demonstrates superior performance over state-of-The-Art algorithms, achieving accuracies of 97.59%, 96.02%, and 83.48% in pain or non-pain state classification (P/NSC), pain type classification (PTC), and pain intensity classification (PIC) tasks, respectively. This paper proposes a unified, end-To-end DNN-based framework for multi-Task chronic pain recognition, providing a reliable solution with the potential to advance pain diagnosis and management in IoT and smart wearable applications. © 2014 IEEE.

关键词： Electroencephalography

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Temporal-Spatial Scheduling of Energy and Computation Resources for Charging and Computing Service Vehicles

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IEEE Internet of Things Journal 2025年

作者： Rong, Shichu Deng, Xiongtian Zhong, Weifeng Huang, Xumin Kang, Jiawen Xie, Shengli Yuen, Chau Guangdong University of Technology School of Automation Key Laboratory of Intelligent Detection and Manufacturing Internet of Things Ministry of Education Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China Guangdong University of Technology School of Automation 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore

The growing adoption of electric vehicles (EVs) and expansion of Internet-of-Things (IoT) in-vehicle applications enhance vehicle intelligence and connectivity but also drive higher demand for both charging and computing services. Charging and Computing Stations (CCSs), integrating bidirectional chargers and edge computing servers and allowing optimal joint energy-computation management, has been taken as an effective solution to address this demand. This paper introduces a new concept called charging and computing service vehicle (CCSV) fleets, which are equipped with high-capacity batteries and edge servers, serving as mobile resources to support the stationary CCSs at different locations in a wide area. We propose a two-timescale model integrating temporal-spatial scheduling, charging/discharging management, and computation task offloading of the CCSV fleets. Our goal is to minimize the total system cost by optimizing the energy-computation coordination between the mobile CCSV fleets and the stationary CCSs. We construct an extended time-space network (TSN) with congestion nodes, providing a clearer depiction of the time-varying congestion conditions in the traffic network. For practical implementation, we develop a heuristic based on the convex-concave procedure (CCP) and penalty alternating direction method (PADM) to solve the problem quickly. Simulation results in a traffic network based on Guangzhou city demonstrate that the proposed model effectively leverages the mobility and multi-dimensional resources of the CCSV fleets to reduce the system cost significantly. © 2014 IEEE.

关键词： Traffic congestion

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A Fast Generation Algorithm of Communication Topology for Three-dimensional Optimally Persistent Formation 33

A Fast Generation Algorithm of Communication Topology for Th...

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第33届中国控制与决策会议

作者： Guoqiang Wang Qianwei Lv Xiaoduo Li Xin Cao He Luo School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Engineering Research Center for Intelligent Decision-making & Information Systems Technologies Ministry of Education Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security of China

ISBN: (纸本)9781665431293

To solve the communication topology generation problem of three-dimensional optimally persistent formation with leader constraint,a fast generation algorithm based on minimum cost arborescence,arc addition and path reverse operation is *** with the existing algorithms,this algorithm has a shorter calculation ***,the simulation results further verify the correctness and effectiveness of the algorithm.

关键词： Communication topology Leader constraint Three-dimensional optimal persistent formation Minimum cost arborescence Arc addition Path reverse operation

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Research on Optimizing Intersection Signals Based on TRANSYT Single Timing Software 16

Research on Optimizing Intersection Signals Based on TRANSYT...

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16th COTA International Conference of Transportation Professionals: Green and Multimodal Transportation and Logistics, CICTP 2016

作者： Song, Bao-Bao Zhang, Xu Tian, Rong-Rong Xu, Zhi-Jun School of Civil Engineering and Architecture Henan Univ. of Technology Zhengzhou450001 China Key Laboratory ITS Technology Optimization and Integration Ministry of Public Security Hefei230088 China

ISBN: (数字)9780784479896

ISBN: (纸本)9780784479896

This paper aims to combine the use of both TRANSYT and VISSIM software in an attempt to reduce roadway traffic saturation while also combatting stop delay and queue lengths to improve the efficiency of traffic through an intersection. Investigation into and analysis of current conditions on Huang-Shan Road and Xiang-Zhang Avenue in Hefei city allows the TRANSYT and VISSIM models to be created and the two models to be compared. After using the TRANSYT model for signal optimization under two conditions, pedestrians are introduced. The model is combined with the VISSIM simulation software to verify the optimized results. The research shows that combining the software can reduce the road saturation effectively and reduce the both stop delay and queue length to improve the efficiency of the intersection. © 2016 ASCE.

关键词： Efficiency

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The design and optimization of overlapping phase combination for isolated intersection 4

The design and optimization of overlapping phase combination...

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4th International Conference on Transportation Information and Safety, ICTIS 2017

作者： Liang, Zijun Chen, Hong Qin, Chenchen Zhou, Yulin Fang, Wei Chang'An University Xi'an China Key Lab of Urban ITS Technology Optimization and Integration Ministry Hefei China Institute of Intelligent Machines Chinese Academy of Sciences Heifei China

ISBN: (纸本)9781538604373

To reduce the waste of green time as a result of unbalance of non-conflict streams, a method of overlapping phase was introduced. The design and optimization for overlapping phase combination is proposed in the paper. There are two steps of optimization in the process. Firstly to select a best group among the possible phase combination including overlapping phase by calculating the minimal cycle flow ratio of the group, secondly to use the object of minimum total delay based Particle Swarm optimization approach to obtain optimal green time for each phase. The method is applied at the intersection of Xiuning Road and Hezuohua Road in Hefei, China. It shows that the waste of green time is diminished by overlapping phase. The total delay is reduced in large scale. The result was proved by simulation of Vissim and showed the method is effective in upgrading the traffic signal control program. © 2017 IEEE.

关键词： Traffic signals

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