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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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Shared Latent Embedding Learning for Multi-View Subspace Clustering

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IEICE Transactions on Information and Systems 2024年第1期E107.D卷 148-152页

作者： Liu, Zhaohu Song, Peng Mu, Jinshuai Zheng, Wenming School of Computer and Control Engineering Yantai University Yantai264005 China Southeast University Nanjing210096 China

Most existing multi-view subspace clustering approaches only capture the inter-view similarities between different views and ignore the optimal local geometric structure of the original data. To this end, in this letter, we put forward a novel method named shared latent embedding learning for multi-view subspace clustering (SLE-MSC), which can efficiently capture a better latent space. To be specific, we introduce a pseudo-label constraint to capture the intra-view similarities within each view. Meanwhile, we utilize a novel optimal graph Laplacian to learn the consistent latent representation, in which the common manifold is considered as the optimal manifold to obtain a more reasonable local geometric structure. Comprehensive experimental results indicate the superiority and effectiveness of the proposed method. © 2024 Institute of Electronics, Information and Communication, Engineers, IEICE. All rights reserved.

关键词： Embeddings

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TD-SAS: A Trust-Aware and Decentralized Speed Advisory System for Energy-Efficient Autonomous Vehicle Platoons

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-16页

作者： Li, Shike Li, Jianbin Liang, Yin Zhang, Hengyang Wu, Sixing Wang, Shen Cheng, Long School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Science University College Dublin Ireland

Autonomous vehicle platooning benefits significantly from the Consensus Speed Advisory System (CSAS), an emerging technology that recommends a consensus speed to reduce energy consumption. However, managing trust to ensure system security and identify malicious nodes poses a considerable challenge in these autonomous environments. Furthermore, while CSAS optimizes the total energy consumption of the platoon, it may inadvertently result in increased energy use for specific vehicles, discouraging their continued participation and hindering an efficient formation of a platoon. This paper proposes a trust-aware and decentralized speed advisory system (TD-SAS) to address these challenges. TD-SAS employs a consortium blockchain for managing trust nodes, providing non-repudiation and tamper resistance for reputation data. Capitalizing on this platform, we use a multi-weight subjective logic model for precise reputation value calculation. Additionally, we present a trust-aware consensus speed recommendation scheme capable of adapting its recommendations to vehicular reputation variations. To mitigate the potential disincentives for vehicles experiencing increased energy consumption, we incorporate an incentive mechanism to encourage their re-engagement with TD-SAS. Comprehensive security analysis and extensive simulation experiments confirm the robustness and effectiveness of TD-SAS, emphasizing its potential for enhancing the energy efficiency and security of autonomous vehicle platoons. IEEE

关键词： Blockchain

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Switching event-triggered self-adjusting prescribed performance force control of electrohydraulic load simulator

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ISA Transactions 2025年 162卷 217-226页

作者： Li, Jiafeng Ji, Ruihang Yan, Hao Shan, Jiefeng Chen, Zanwei School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore

This paper addresses the problem of switching event-triggered self-adjusting prescribed performance force control of electro-hydraulic load simulator (EHLS). A reduced-order fuzzy speed state observer for speed estimation is designed and a self-adjusting performance boundary is devised to mitigate the commutation jitter problem. The switching event-triggered mechanism dynamically adjusts the triggering conditions to optimize resource utilization. To reduce the complexity of the traditional inverse stepping control, the dynamic surface technique is adopted. The results demonstrate that the proposed control method guarantees the boundedness of the closed-loop system states and confines the tracking error to a small range near the equilibrium point within a finite time horizon. Finally, the effectiveness of the proposed method is demonstrated through experimentation. © 2025 International Society of Automation

关键词： Closed loop control systems

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Spatiotemporal sequence data mining: Traffic flow prediction algorithm based on Recurrent Neural Networks

Advances in Transportation Studies

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Advances in Transportation Studies 2024年 64卷 25-40页

作者： Liu, Y.P. Liu, B.N. Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan030008 China Department of Traffic Equipment and Control Engineering School of Traffic Electrical and Control Engineering North China University of Technology Beijing100043 China

Traffic congestion is one of the most common problems in the transportation system. In urban planning and construction, traffic congestion increases the difficulty of control and scheduling, hindering the pace of urban modernization development. The development of intelligent transportation systems has to some extent solved these problems, among which traffic flow prediction is particularly crucial. A period processing layer containing concatenation, dimension amplification, and diffusion convolution was studied and designed. On the grounds of this, a graph neural network prediction model combining spatiotemporal period characteristics was designed. And it further introduced a traffic flow prediction method on the grounds of dynamic topology maps. This model combined adaptive topology generation and parameter learning mechanisms, synchronously processing the complex correlation of traffic data and the heterogeneity between nodes through gated recurrent neural networks. The outcomes indicated that the research model is more excellent than other graph learning methods in three evaluation indicators: Mean square error, mean absolute percentage error, and root mean square error. Their values were 15.63%, 9.87%, and 25.08%, respectively. The evaluation on all four datasets showed excellent performance of the research model, confirming the effectiveness of its adaptive topology and parameter learning strategy. This study had important practical significance for improving the traffic management system, alleviating traffic pressure, and enhancing the travel experience of urban residents. © 2024, Aracne Editrice. All rights reserved.

关键词： Mean square error

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Conditional Feature Generative Adversarial Network for Fault Diagnosis of Axial Piston Pump

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IEEE Sensors Journal 2025年第11期25卷 20907-20919页

作者： Chen, Dong-Ning Hu, Dong-Bo Wang, Hao-Wen Xian, Qing-Gui Yao, Cheng-Yu Deng, Ran-Yang Yanshan University School of Mechanical Engineering Qinhuangdao066004 China Yanshan University Hebei Key Laboratory of Industrial Computer Control Engineering Qinhuangdao066004 China

A conditional feature generative adversarial network (CFGAN) for small sample data augmentation was proposed in this paper to address the issue of the scarcity of fault samples in axial piston pump fault diagnosis. With one-dimensional data as input, a feature extractor was constructed in this method to extract features, bringing about an original deep feature dataset. Subsequently, this dataset was employed to train the generation model for enhancing the training efficiency. Additionally, the Wasserstein distance was introduced to improve the stability of the model training. Conditional labels were added to the inputs of the generator and discriminator to guide the model's training and generate high-quality feature datasets. Afterward, the effectiveness of the proposed data generation method was verified through the data obtained from the Paderborn Bearing Dataset and an axial piston pump fault simulation testbed. The fault diagnosis accuracy of the augmented dataset was significantly improved compared to the non-augmented dataset. Furthermore, a multi-scale convolutional neural network was established and utilized different-sized convolutional kernels to extract and fuse multi-scale information from the samples. This approach enabled the capture of more comprehensive fault state features, contributing to enhanced diagnostic accuracy. The experimental data of the axial piston pump validate that the diagnostic accuracy with fused multi-sensor signals was higher compared to a single sensor. © 2001-2012 IEEE.

关键词： Generative adversarial networks

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Cost-aware cloud workflow scheduling using DRL and simulated annealing

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Digital Communications and Networks 2024年第6期10卷 1590-1599页

作者： Yan Gu Feng Cheng Lijie Yang Junhui Xu Xiaomin Chen Long Cheng School of Control and Computer Engineering North China Electric Power UniversityBeijing 100026China School of Mathematics Southwest Jiaotong UniversityChengdu 610032China Department of Computer Science University of ReadingReading RG66AHUK

Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging *** several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch tasks and not well-suited for real-time *** address this issue,researchers have started exploring the use of Deep Reinforcement Learning(DRL).However,the existing models are limited in handling independent tasks and cannot process workflows,which are prevalent in cloud computing and consist of related *** this paper,we propose SA-DQN,a scheduling approach specifically designed for real-time cloud *** approach seamlessly integrates the Simulated Annealing(SA)algorithm and Deep Q-Network(DQN)*** SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server,serving as a crucial feature of the task for the neural network to *** provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.

关键词： Cloud computing Deep reinforcement learning Simulated annealing algorithm Job scheduling Workflow

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Generating diverse clothed 3D human animations via a generative model

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Computational Visual Media 2024年第2期10卷 261-277页

作者： Min Shi Wenke Feng Lin Gao Dengming Zhu School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Data-driven garment animation is a current topic of interest in the computer graphics *** approaches generally establish the mapping between a single human pose or a temporal pose sequence,and garment deformation,but it is difficult to quickly generate diverse clothed human *** address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion *** the heart of our method is a twostage ***,we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder(Transformer-CVAE).Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder-decoder *** the learned latent space comes from varied human motions,our method can generate a variety of styles of motions given a specific motion *** means of a novel beginning of sequence(BOS)learning strategy and a self-supervised refinement procedure,our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial *** verify our ideas *** is the first generative model that directly dresses human animation.

关键词： Transformer garment animation conditional variational autoencoder(CVAE) computer graphics

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Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

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computers, Materials & Continua 2024年第3期78卷 4343-4361页

作者： Yunchang Liu Fei Wan Chengwu Liang School of Computer and Data Science Henan University of Urban ConstructionPingdingshan467036China School of Electrical and Control Engineering Henan University of Urban ConstructionPingdingshan467036China

Traffic flow prediction plays a key role in the construction of intelligent transportation ***,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very *** of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between ***,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial *** paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic *** combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic *** on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.

关键词： Intelligent transportation graph convolutional network traffic flow DTW algorithm attention mechanism

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Predicting circRNA-Disease Associations by Using Multi-Biomolecular Networks Based on Variational Graph Auto-Encoder with Attention Mechanism

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Chinese Journal of Electronics 2024年第6期33卷 1526-1537页

作者： Jing YANG Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology

CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.

关键词： Knowledge engineering Attention mechanisms Computational modeling Biological system modeling Predictive models Feature extraction Heterogeneous networks Topology Data mining Diseases

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