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PPDO:a privacy-preservation-aware delay optimization task-offloading algorithm for collaborative edge computing

Frontiers of information technology & Electronic engineering 2025年第1期26卷 27-41页

作者： Chao JING Jianwu XU College of Computer Science and Engineering Guilin University of TechnologyGuilin 541004China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin 541004China

Although collaborative edge computing(CEC)systems are beneficial in enhancing the performance of mobile edge computing(MEC),the issue of user privacy leakage becomes prominent during task *** address this issue,we design a privacy-preservation-aware delay optimization task-offloading algorithm(PPDO)in a CEC *** considering location and usage pattern privacy protection,we establish a privacy task model to interfere with the edge server and ensure user *** address the extra delay arising from privacy protection,we subsequently leverage a Markov decision processing(MDP)policy-iteration-based algorithm to minimize delays without compromising *** simultaneously accelerate the MDP operation,we develop an extension that improves the PPDO by optimizing the action ***,a comprehensive simulation was conducted using the edge user allocation(EUA)*** results demonstrated that PPDO achieves an optimal trade-off between privacy protection and delay with a minimum delay compared with existing ***,we examined the advantages and disadvantages of improving PPDO.

关键词： Collaborative edge computing Task offloading Privacy protection Markov decision process

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Noncooperative Game-Based Composite Voltage Regulation for Microgrid With Switching Topologies and Prescribed Performance

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IEEE Transactions on Network science and engineering 2025年第3期12卷 1720-1731页

作者： Liu, Yuezhi Chen, Yong Zhang, Longjie Wang, Hongru Hafez, Esam University of Electronic Science and Technology of China School of Automation Engineering Sichuan Chengdu611731 China Sichuan Province Engineering Technology Research Center for Electric Vehicle Driving System and Safety Sichuan Chengdu611731 China College of Electrical and Information Engineering Zhengzhou niversity of Light Industry Zhengzhou450002 China Aswan University Faculty of Energy Engineering Aswan81528 Egypt

In this paper, the voltage regulation issue of the microgrid with the switching topologies, nonlinear coupling and prescribed performance is investigated. Firstly, to remove the requirement of the global topology information, a noncooperative game layer with the fully distributed Nash equilibrium seeking (FDNES) approach is devised. In this study, the noncooperative game is performed over the switching communication topologies. Furthermore, the voltage regulation layer is formulated, which integrates the adaptive coupling observer (ACO) and prescribed performance based composite voltage regulation (PPCVR) methods. Specifically, in the ACO, an adaptive observer gain is devised to attenuate the damaging peak dynamics. For the PPCVR, the prescribed performance function based dynamic surface control strategy is formulated such that the voltage tracking error can be restrained according to the specified constraints, which will enhance the voltage regulation performance. Additionally, the PPCVR utilizes the coupling estimation by the ACO to compensate the nonlinear coupling dynamics. It is shown that the signals of the closed-loop system are semi-global uniformly and ultimately bounded (SGUUB). Finally, comparative studies and hardware-in-loop experiment by RT-LAB platform are performed to illustrate the effectiveness of the proposed voltage regulation strategy. © 2013 IEEE.

关键词： Microgrids

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Research on Improving Wavelet Thresholding Method for Noise Reduction of Current Ripples in Motors

Research on Improving Wavelet Thresholding Method for Noise ...

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International Conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Qin, Xu Wang, Wei School of Electronic and Information Engineering Tiangong University Tianjin China Tianjin Key Laboratory of Optoelectronic Detection Technology and System Tianjin China Engineering Research Center of Ministry of Education for High Power Semiconductor Lighting Application System Tianjin China

ISBN: (纸本)9789819620456

The vigorous development of new energy vehicles has brought new opportunities and challenges to related industries. In order to gain greater competitive advantages, major automobile manufacturers have begun to focus on using motor current ripple signals directly to achieve various functions in a cheaper and more integrated way. However, the current ripple signal generated when the motor rotates is mixed with a lot of noise. If it is to be put into practical application, it needs to be denoised first, such as the improved wavelet threshold denoising method studied in this paper. First, the denoising principle and the shortcomings of the traditional method are introduced, and the design of the improved method is given on this basis. Subsequently, verification is conducted using simulated signals and actual current ripple signals, with SNR (Signal to Noise Ratio) and RMSE (Root Mean Square Error) used as quantitative evaluation criteria for comparing and analyzing the results. Finally, the experiments have confirmed that the improved threshold method proposed in this paper outperforms traditional methods in noise removal, proving its significance and value for further research. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Noise abatement

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Targeted-tuning competitive acidic CO2RR via metalloid antagonism sites

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Materials Today 2025年 83卷 54-63页

作者： Sheng, Beibei Cao, Dengfeng Qi, Zhenghang Shou, Hongwei Xia, Yujian Su, Xiaozhi Chen, Shuangming Wu, Chuanqiang Liu, Hengjie Chimtali, Peter Joseph Chu, Yongheng Liu, Chongjing Wu, Xiaojun Song, Li National Synchrotron Radiation Laboratory CAS Center for Excellence in Nanoscience Key Laboratory of Precision and Intelligent Chemistry School of Nuclear Science and Technology University of Science and Technology of China Hefei230029 China SINOPEC Shanghai Research Institute of Petrochemical Technology Co. Ltd. Shanghai201208 China Engineering Laboratory of Chemical Resources Utilization in South Xinjiang of Xinjiang Production and Construction Corps College of Chemistry and Chemical Engineering Tarim University Alar843300 China School of Chemistry and Materials Science CAS Center for Excellence in Nanoscience and Synergetic Innovation of Quantum Information & Quantum Technology Hefei National Laboratory for Physical Sciences at the Microscale University of Science and Technology of China Hefei230026 China Advanced Light Source Lawrence Berkeley National Laboratory Berkeley CA94720 United States Shanghai Synchrotron Radiation Facility Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201204 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information Technology Anhui University Hefei230601 China

To mitigate the high separation costs associated with conventional neutral/alkaline electrocatalysis for CO2 reduction reactions (CO2RR), acidic CO2RR offers economic advantages and improved efficiency in CO2 utilization. However, it typically involves the cleavage of M−H bonds at a relatively negative potential, leading to the predominant formation of H2 and poor HCOOH selectivity. Herein, we develop a facile solid-phase thermal diffusion approach to controllably synthesize a novel metalloid-metal single atom alloys (m-SAAs) electrocatalyst Te1Bi with unique metalloid antagonistic sites, thus enabling high-efficient acidic CO2-to-HCOOH conversion. Electrochemical test and operando synchrotron radiation multi-techniques (SRMS) characterization reveal that metalloid Te sites bring steric hindrance effect and blocks *H coupling. Furthermore, it actively adsorbs OH− species as a proton source, allowing for effective separation of protons and electrons in space. Thus, leading to enhanced hydrogenation in acidic CO2RR to produce HCOOH. The flow cell test results demonstrate that the carefully designed Te1Bi catalyst exhibits a milder reaction potential, along with higher HCOOH Faraday efficiency (∼94.5 %) and single-pass carbon efficiency (SPCE, ∼40 %) in acidic media. This work significantly expands the family of SAAs and offers a novel perspective to analyze the regulation of competitive reactions through site-specific modifications for industrial acidic CO2RR. © 2024 Elsevier Ltd

关键词： Synchrotron radiation

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Improved ADRC PMSM Control system Based on Compensation Function Observer

Improved ADRC PMSM Control System Based on Compensation Func...

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International Conference of Electrical, Electronic and Networked Energy systems, EENES 2024

作者： Wu, Chuangyi Wang, Wei School of Electronic and Information Engineering Tiangong University Tianjin China Tianjin Key Laboratory of Optoelectronic Detection Technology and System Tianjin China Engineering Research Center of Ministry of Education for High Power Semiconductor Lighting Application System Tianjin China

ISBN: (纸本)9789819620791

The Adaptive Disturbance Rejection Control (ADRC) is widely used in nonlinear systems such as Permanent Magnet Synchronous Motors (PMSM) due to its adaptability, strong robustness, and ease of implementation. However, traditional ADRC performs poorly in high-order nonlinear systems, as its performance mainly depends on the nonlinear function representing the total disturbance. To address this issue, this paper proposes an improved ADRC based on the Compensation Function Observer (CFO). The CFO belongs to the type III system, exhibiting significantly improved signal processing performance compared to the type I Extended State Observer (ESO), and it can attenuate the influence of the nonlinear function in the compensation stage. Additionally, in order to alleviate the difficulty of parameter tuning in ADRC, fuzzy control is introduced in the error feedback loop. The feasibility of the proposed method is verified by establishing a Matlab/Simulink simulation model. The simulation results demonstrate that, compared to traditional ADRC, the new ADRC exhibits better response speed and disturbance rejection for common signals. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： MATLAB

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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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MCAGN: Multilayer Coupled Attention Graph Networks for Composites’ Damage Detection

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IEEE Sensors Journal 2025年第10期25卷 18553-18564页

作者： Ma, Jitong Zhao, Sen Yang, Zhengyan Bao, Wenqiang Wang, Jie Wu, Zhanjun Dalian Maritime University School of Information Science and Technology Dalian116026 China Jiangnan University School of Fiber Engineering and Equipment Technology Wuxi214122 China Dalian University of Technology State Key Laboratory of Structural Analysis for Industrial Equipment Dalian116024 China

Damage detection in carbon-fiber-reinforced polymer (CFRP) composites is essential for maintaining structural integrity and preventing catastrophic accidents. Recently, machine-learning-based damage detection methods have garnered increasing attention. However, existing approaches often face challenges in extracting relational information about damage, both within guided wave paths and between damage samples. These limitations result in insufficient detection stability under varying training sample conditions and increase the risk of model overfitting when relying solely on high-dimensional information extracted from guided wave signals. To address these challenges, this article proposes a multilayer graph network with a coupled attention mechanism based on feature fusion. In the proposed method, by encoding sensor coordinates and fusing guided wave signal features, positional information can be integrated into the guided wave signals. A multilayer graph network is further used to extract signal features, while the interlayer coupled attention mechanism and multilayer graph construction fully explore the relational information associated with the damage. Experimental datasets collected from real CFRP composites demonstrate the effectiveness of the proposed method. Comparative results show that the multilayer graph neural network (GNN) achieves superior stability and accuracy, even with limited training samples and noisy training samples. These findings highlight the robustness and practicality of the proposed approach for damage detection in CFRP composites. © 2001-2012 IEEE.

关键词： Graph neural networks

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DD-Net: Dynamic Network Architecture for Optimized Curve Segmentation and Reduce Computational Redundancy 27th

DD-Net: Dynamic Network Architecture for Optimized Curve Seg...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Yunxiang Chen, Li Wang, Yubo Feng, Zhida Tian, Jing School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan China Institute of Systems Science National University of Singapore Singapore Singapore

ISBN: (纸本)9783031783821

Curvilinear Structure segmentation has numerous applications in various fields, including providing a better understanding of defects such as cracks on roads or walls, thereby assessing the structural safety of buildings. Numerous curve segmentation methods based on Convolutional Neural Networks(CNN) have been developed in recent years. However, preserving feature integrity remains a challenge. To address this issue, this paper introduces a double dynamic network, called DD-Net, which consists of a dynamic structure for training and dynamic masking for inference. Firstly, to enhance the nonlinearity of the model, a paradigm to auto-adjust the CNN structure via a trainable module is formed for better fine-grained feature extraction. This dynamic structure (DS) paradigm enables a trade-off to extract or discard the feature data in the model training. Due to the data heterogeneity between the training and inference data, models may contain some useless nodes that are less effective during inference. The dynamic masking (DM) is proposed to omit the useless nodes based on the score difference between the train and inference feature statistics, thereby reducing redundant computations. To further improve the model’s performance on thin-curve segmentation and to preserve feature integrity, we introduce a non-curve suppression (NCS) module. This module focuses on background information while considering foreground prediction to address noisy conditions. The experimental results show that our DD-Net achieves promising results on three benchmark datasets and outperforms state-of-the-art curve segmentation models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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MM-UKAN++: A Novel Kolmogorov–Arnold Network-Based U-Shaped Network for Ultrasound Image Segmentation

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IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2025年第4期72卷 498-514页

作者： Zhang, Boheng Huang, Haorui Shen, Yi Sun, Mingjian Harbin Institute of Technology Department of Control Science and Engineering Heilongjiang Harbin150001 China Pohang University of Science and Technology Department of Convergence IT Engineering Pohang37673 Korea Republic of Harbin Institute of Technology Suzhou Research Institute Jiangsu Suzhou215000 China Harbin Institute of Technology Department of Information Science and Engineering Shandong Weihai264200 China Harbin Institute of Technology Shandong Weihai264200 China Shandong Laboratory of Advanced Biomaterials and Medical Devices Shandong Weihai264209 China

Ultrasound (US) imaging is an important and commonly used medical imaging modality. Accurate and fast automatic segmentation of regions of interest (ROIs) in US images is essential for enhancing the efficiency of clinical and robot-assisted diagnosis. However, US images suffer from low contrast, fuzzy boundaries, and significant scale variations in ROIs. Existing convolutional neural network (CNN)-based and transformer-based methods struggle with model efficiency and explainability. To address these challenges, we introduce MM-UKAN++, a novel U-shaped network based on Kolmogorov-Arnold networks (KANs). MM-UKAN++ leverages multilevel KAN layers as the encoder and decoder within the U-network architecture and incorporates an innovative multidimensional attention mechanism to refine skip connections by weighting features from frequency-channel and spatial perspectives. In addition, the network effectively integrates multiscale information, fusing outputs from different scale decoders to generate precise segmentation predictions. MM-UKAN++ achieves higher segmentation accuracy with lower computational cost and outperforms other mainstream methods on several open-source datasets for US image segmentation tasks, including achieving 69.42% IoU, 81.30% Dice, and 3.31 mm HD in the BUSI dataset with 3.17 G floating point of operations (FLOPs) and 9.90 M parameters. The excellent performance on our automatic carotid artery US scanning and diagnostic system further proves the speed and accuracy of MM-UKAN++. Besides, the good performance in other medical image segmentation tasks reveals the promising applications of MM-UKAN++. The code is available on GitHub. © 1986-2012 IEEE.

关键词： Image segmentation

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Location Privacy Preservation Crowdsensing With Federated Reinforcement Learning

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 1877-1894页

作者： You, Zhichao Dong, Xuewen Liu, Ximeng Gao, Sheng Wang, Yongzhi Shen, Yulong Xidian University School of Computer Science and Technology The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Xi'an710071 China Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'an710071 China Fuzhou University College of Computer and Data Science Fuzhou350025 China Central University of Finance and Economics School of Information Beijing102206 China California State Polytechnic University Pomona Department of Computer Information Systems PomonaCA91768 United States Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Key Laboratory of Network and System Security Xi'an710071 China

Crowdsensing has become a popular method of sensing data collection while facing the problem of protecting participants' location privacy. Existing location-privacy crowdsensing mechanisms focus on static tasks and participants without considering sensing tasks' time requirements and participants' mobility, which cannot achieve satisfactory collected data quality and task completion in crowdsensing with dynamic tasks and participants. Inspired by this, we proposed a location-preservation crowdsensing mechanism, FedSense, considering dynamic tasks and participants based on federated learning (FL) and reinforcement learning (RL). In FedSense, through RL's outstanding decision-making ability, participants select sensing tasks to perform by well-trained RL models without uploading location information to servers for task allocation. We propose an independent tasks selection environment that defines actions, states, and rewards of RL to enable FedSense to achieve satisfactory task completion and data quality while preserving location privacy. Besides, FedSense applies an asynchronous FL aggregation algorithm that reduces participants' network stabilization and device computing ability requirements. Analysis proves that participants' location information does not leave the local device during the model training and task selection process, effectively avoiding privacy leakage. Simulation shows that compared with existing location-preservation crowdsensing mechanisms, FedSense achieves the highest task completion and sensing accuracy for dynamic tasks and participants. © 2024 IEEE.

关键词： Data privacy

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