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Harnessing Trend Theory to Enhance Distributed Proximal Point Algorithm Approaches for Multi-Area Economic Dispatch Optimization

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computers, Materials & Continua 2025年第3期82卷 4503-4533页

作者： Yaming Ren Xing Deng College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China

The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power *** complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly *** the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization *** proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs *** study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the *** PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence ***,the convergence efficiency of the PPA is significantly influenced by the parameter *** address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational *** computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch *** simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.

关键词： Multi-area economic dispatch problem proximal point algorithm trend theory

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Cooperative UAV Clustering for Fair Coverage of Communication Regions

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Intelligent and Converged Networks 2025年第1期6卷 1-19页

作者： Wu, Jiehong Gu, Linpeng Jia, Zhongli Wu, Jinsong School of Computer Science Shenyang Aerospace University Shenyang110136 China School of Artificial Intelligence Guilin University of Electronic Technology Guilin541004 China

Cooperative unmanned aerial vehicles (UAVs) cluster technology is considered a prospective solution for area coverage problems, enabling network access and emergency communications in remote areas. In this paper, we investigate how to control UAV cluster to achieve long-term and stable regional coverage while maintaining link connectivity and minimizing energy consumption, given the limited communication range and energy consumption of the UAVs themselves. To this end, we propose a cooperative UAV cluster strategy based on multi-agent deep reinforcement learning (MADRL) to achieve fair coverage of communication regions, which we call MADRL-based cooperative UAV cluster strategy (MADRL-CUCS). Our solution is a centralized training distributed execution architecture and defines a cluster structure for leader UAVs and follower UAVs. Under the premise of comprehensively considering the maximum coverage, we use a new energy efficiency function to minimize energy consumption, so as to extend the network lifetime of the UAVs cluster networks. The new fairness index and collision avoidance factor are used to ensure that the UAV cluster achieve effective and secure regional coverage. We adopt depth first search algorithm to check the link connectivity of the UAVs during the coverage process. Experiments show that the MADRL-CUCS algorithm outperforms the benchmark algorithm. © 2020 Tsinghua university Press.

关键词： Deep reinforcement learning

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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction

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Digital Communications and Networks 2025年第2期11卷 537-546页

作者： Feng Zeng Zheng Zhang Jinsong Wu School of Computer Science and Engineering Central South University School of Artificial Intelligence Guilin University of Electronic Technology The Department of Computer Science Universidad de Chile

In task offloading, the movement of vehicles causes the switching of connected RSUs and servers, which may lead to task offloading failure or high service delay. In this paper, we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking scheduling. Then, a Bi-LSTM-based model is proposed to predict the trajectories of vehicles. The service area is divided into several equal-sized grids. If the actual position of the vehicle and the predicted position by the model belong to the same grid, the prediction is considered correct, thereby reducing the difficulty of vehicle trajectory prediction. Moreover, we propose a scheduling strategy for delay optimization based on the vehicle trajectory prediction. Considering the inevitable prediction error, we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers, thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task offloading. Simulation results show that, compared with other classical schemes, the proposed strategy has lower average task offloading delays.

关键词： Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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MTOClus: Multi-Type Objects Clustering in Heterogeneous Information Networks 3

MTOClus: Multi-Type Objects Clustering in Heterogeneous Info...

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3rd International Conference on Communications, Information System, and Data science, CISDS 2024

作者： Liang, Yongjie Hu, Wujie Wu, Junjie Wu, Jinzhao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Electric Engineering Guangxi University Nanning China Guilin University of Aerospace Technology Guilin China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

Clustering is crucial for analyzing heterogeneous information networks (HINs). Mainly state-of-the-art algorithms often focus on single-type node clustering, overlooking the clustering of multiple node types and requiring manual setting of cutoff distances and clustering centers. This paper introduces a clustering algorithm integrating multi-type objects, named MTOClus, which generates multiple similarity matrices for different node types and assigns weights to each matrix to indicate their significance. By aggregating weighted similarity matrices, it creates a distance matrix. MTOClus automates cluster center selection through node sorting and mitigates the impact of outliers by utilizing the K-Nearest Neighbors approach to compute the cutoff distance. Experimental results across four datasets consistently show that MTOClus outperforms six other algorithms, underscoring its superior clustering efficacy. © 2025 SPIE.

关键词： Clustering algorithms

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Crystal structure, phase compositions and microwave dielectric properties of Gd1-xCaxV1-xMoxO4 ceramics

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Ceramics International 2025年第15期51卷 20124-20130页

作者： Zhang, Yuheng Chen, Junqi Chen, Jinwu Zhou, Yang Wu, Xiaoyu Tang, Ying Fang, Liang Xiang, Huaicheng College of Physics and Electronic Information Engineering Guilin University of Technology Guilin541004 China Guangxi Universities Key Laboratory of Non-Ferrous Metal Oxide Electronic Functional Materials and Devices Guangxi Key Laboratory of Optical and Electronic Materials and Devices College of Materials Science and Engineering Guilin University of Technology Guilin541004 China School of Mechanical Engineering Guilin University of Aerospace Technology Guilin541004 China

Gd1-xCaxV1-xMoxO4 (0 ≤ x ≤ 1) solid-solution/composite ceramics with low permittivity were synthesized via the solid-state reaction method, and their crystal structures, phase compositions, and microwave dielectric properties were investigated. As the substitution of Ca2+ and Mo6+ increased, phase evolutions were observed: tetragonal zircon (I41/amd) for x 1/a) for x > 0.8. GdVO4, a tetragonal zircon, exhibited a low Εr (11.48) and a near-zero τf (−13.2 ppm/°C), yet its Q × f value is suboptimal (9400 GHz). This limitation stems from its low structural stability, attributed to the prevalent edge-sharing polyhedra and high magnetic susceptibility of Gd. With the increased substitution of Ca2+ and Mo6+, the Q × f value of Gd1-xCaxV1-xMoxO4 ceramics rose to 79,300 GHz (x = 1), while the τf value concurrently dropped to −61.7 ppm/°C. The inherent factors affecting the microwave dielectric properties were determined by Raman scattering and infrared reflection (IR) spectra, dielectric temperature spectrum, magnetic susceptibility, and thermal expansion analysis. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Crystal structure

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A two-stage feature redundancy minimization methodology framework for motor imagery EEG classification

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Multimedia Tools and Applications 2025年 1-21页

作者： Li, Heng Lu, Zhongwei Mo, Yun Feng, Bao Yu, Tianyou Guangxi Beitou GULF Investment Group.CO LTD Nanning530000 China Guangxi Beitou IT Innovation Technology Investment Group.CO LTD Nanning530000 China School of Electronic Information and Automation Guilin University of Aerospace Technology Guilin541004 China School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China School of Automation Science and Engineering South China University of Technology Guangzhou510000 China

The temporal-frequency-spatial features of motor imagery electroencephalogram (EEG) signals provide comprehensive information for classification. However, these features also introduce significant redundancy and increase the feature dimension, complicating the classification task. Feature selection methods can identify subject-specific features and eliminate redundant information. Nevertheless, current feature selection approaches typically only remove features unrelated to the classification task labels, without addressing redundancy between features. To minimize feature redundancy, this paper proposes a two-stage framework combining feature selection and feature decorrelation. In the first stage, feature selection methods are employed to eliminate redundant and noisy information, thereby obtaining feature subsets closely related to the motor imagery task. In the second stage, the orthogonal transformation of principal component analysis (PCA) is utilized to remove correlations between features. Furthermore, kernel PCA (KPCA) and sparse PCA (SPCA) are designed to capture nonlinearity and sparsity among features, respectively. The optimal number of principal components for these PCA methods is determined using tenfold cross-validation, with classification accuracy as the evaluation criterion. The proposed method's effectiveness is validated on four motor imagery EEG datasets, achieving the highest average accuracies of 89.63%, 83.02%, 80.83%, and 79.40%, respectively. Experimental results demonstrate that the proposed methods significantly improve classification performance, with methods combining KPCA showing greater effectiveness. Additionally, the proposed methods enhance the separability of features, yielding good classification results even with simple classifiers like Fisher linear discriminant analysis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Linear transformations

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An Analysis of Material Movement Trajectory in Eccentric Vibration Transport Equipment 4th

An Analysis of Material Movement Trajectory in Eccentric Vib...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Wei, Li Liu, Xiaogang School of Mechanical and Automotive Engineering Guangxi University of Science and Technology Liuzhou545000 China School of Automotive Engineering Guilin Institute of Aerospace Industry Guilin541000 China

ISBN: (纸本)9789819778867

A vibration transport device with an eccentricity of 3 mm was designed for an automated detection machine of condoms. The composition and principle of the device were introduced, and the kinematics of the vibration groove and condoms were analyzed, and corresponding mathematical expressions were derived. Finally, the accuracy of the theoretical results of the kinematics of the vibration groove and condoms was verified through simulation using Adams and MATLAB software. © The Author(s) 2025.

关键词： MATLAB

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ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smoke Detection

ACRL-10K: A Dataset for Air Conditioner Refrigerant Leak Smo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jiang, Yuhan Wang, Zhongyuan Hua, Jie Liang, Jinbi Xu, Zengmin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350368741

In this paper, we introduce a new dataset for air conditioner refrigerant leak smoke detection, called ACRL-10K. The dataset is designed to develop algorithms for detecting refrigerant leak smoke faults during air conditioner recycling. It contains a total of 10, 724 images covering three common scenarios of air conditioner refrigerant leak: loading port, refrigerant extraction, and disassembly. All images are sourced from 656 video segments of real air conditioner recycling scenarios, captured by surveillance cameras deployed on an environmental company's recycling production line. The annotations for the refrigerant leak smoke in the images are performed by industry experts to ensure high accuracy and consistency. To the best of our knowledge, ACRL-10K is the first dataset specifically designed for refrigerant leak smoke detection. Based on the ACRL-10K dataset, we present the performance of mainstream object detectors, including the YOLO series, as a baseline to conduct the following work: 1) preliminarily summarize the challenges of using the dataset for refrigerant leak smoke detection;2) show the detection results of benchmark methods;and 3) make a comparison to identify the strengths and weaknesses of the baseline algorithms. In practice, the ACRL-10K dataset would hopefully advance research and applications in refrigerant leak early warning during air conditioner dismantling and recycling processes. © 2025 IEEE.

关键词： Air Conditioner Dataset Object Detection Refrigerant Leak Smoke Detection

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FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks 18th

FedDCT: A Dynamic Cross-Tier Federated Learning Framework in...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Xian, Youquan Gan, Xiaoyun Yao, Chuanjian Li, Dongcheng Wang, Peng Liu, Peng Zhao, Ying Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin54104 China School of Computer Science and Engineering Guangxi Normal University Guilin54104 China School of Business Guilin University of Electronic Technology Guilin54104 China

ISBN: (纸本)9783031714634

Federated Learning (FL), as a privacy-preserving machine learning paradigm, trains a global model across devices without exposing local data. However, resource heterogeneity and inevitable stragglers in wireless networks severely impact the efficiency and accuracy of FL training. In this paper, we propose a novel Dynamic Cross-Tier Federated Learning framework (FedDCT). Firstly, we design a dynamic tiering strategy that dynamically partitions devices into different tiers based on their response times and assigns specific timeout thresholds to each tier to reduce single-round training time. Then, we propose a cross-tier device selection algorithm that selects devices that respond quickly and are conducive to model convergence to improve convergence efficiency and accuracy. Experimental results demonstrate that the proposed approach under wireless networks outperforms the baseline approach, with an average reduction of 54.7% in convergence time and an average improvement of 1.83% in convergence accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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