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IEEE Conference on Decision and control

作者： Sheng Dong Weiguo Xia Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we focus on the optimization of large-scale multi-agent systems, where agents collaboratively optimize the sum of local objective functions through their own continuous and/or discrete decision variables, subject to global coupling constraints and local constraints. The resulting Mixed-Integer Nonlinear Programmings (MINLPs) are NP-hard, non-convex, and large-scale. Therefore, this paper aims to design distributed algorithms to find feasible suboptimal solutions with a guaranteed bound. To this end, considering dual decomposition as an effective method to decompose large-scale constraint-coupled optimization problems, we first show, based on the convexification effects of large-scale MINLPs, that the primal solutions from the dual are near-optimal under certain conditions. This expands recent results in Mixed-Integer Linear Programmings (MILPs) to the nonlinear case but requires additional efforts on the proof. Utilizing this result to tighten the coupling constraints, we develop a fully distributed algorithm for the tightened problem, based on dual decomposition and consensus protocols. The algorithm is guaranteed to provide feasible solutions for the original MINLP. Moreover, asymptotic suboptimality bounds are established for the obtained solution. Finally, the efficacy of the method is verified through numerical simulations.

关键词： Couplings Programming Numerical simulation Linear programming Mixed integer linear programming Consensus protocol Distributed algorithms Optimization Standards Multi-agent systems

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Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning 13

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncer...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Zhao, Tingting Li, Zuoyong Quan, Li Zeng, Kun Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Computer Science and Big Data Fuzhou University Fuzhou350121 China

ISBN: (纸本)9798350319156

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance. © 2023 IEEE.

关键词： Textures

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Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution Supplementary Material

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IEEE Transactions on Image Processing 2025年 34卷 3013-3024页

作者： Yuan, Jiang Ma, Ji Wang, Bo Hu, Weiming North China Electric Power University School of Control and Computer Engineering Beijing102206 China CASIA State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Implicit degradation modeling-based blind super-resolution (SR) has attracted more increasing attention in the community due to its excellent generalization to complex degradation scenarios and wide application range. How to extract more discriminative degradation representations and fully adapt them to specific image features is the key to this task. In this paper, we propose a new Content-decoupled Contrastive Learning-based blind image super-resolution (CdCL) framework following the typical blind SR pipeline. This framework introduces negative-free contrastive learning technique for the first time to model the implicit degradation representation, in which a new cyclic shift sampling strategy is designed to ensure decoupling between content features and degradation features from the data perspective, thereby improving the purity and discriminability of the learned implicit degradation space. In addition, we propose a detail-aware implicit degradation adapting module that can better adapt degradation representations to specific LR features by enhancing the basic adaptation unit's perception of image details, significantly reducing the overall SR model complexity. Extensive experiments on synthetic and real data show that our method achieves highly competitive quantitative and qualitative results in various degradation settings while obviously reducing parameters and computational costs, validating the feasibility of designing practical and lightweight blind SR tools. Codes and models will be available at https://***/Fieldhunter/CdCL. © 1992-2012 IEEE.

关键词： Depression Anxiety disorders Face recognition Schizophrenia Visualization Training Data collection Uncertainty Facial features Emotion recognition

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Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm

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CSEE Journal of Power and Energy Systems 2022年第4期8卷 1145-1156页

作者： Levent Yavuz Ahmet Soran AhmetÖnen Xiangjun Li S.M.Muyeen Electrical and Computer Engineering Department in Abdullah Gül University Kayseri 38080Turkey Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscat123Oman Department of Electrical Engineering Qatar UniversityDoha 2713Qatar State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems China Electric Power Rescarch InstituteBeijing 100192China

This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML *** the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)*** this purpose,power system failures are simulated by using the PSCA D-Python *** of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored ***,the proposed technique will be able to work on different systems,topologies,or data *** proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.

关键词： Decision tree(DT) ensemble machine learning algorithm fault detection islanding operation k-Nearest Neighbor(kNN) linear discriminant analysis(LDA) logistic regression(LR) Naive Bayes(NB) self-healing algorithm

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GraphFed: A Personalized Subgraph Federated Learning Framework for Non-IID Graphs 20

GraphFed: A Personalized Subgraph Federated Learning Framewo...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Deng, Pan Liu, Xuefeng Niu, Jianwei Hu, Chunming Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9798350324334

Recently, federated graph learning has attracted significant attention, as subgraphs of a global graph may often distribute across different institutions and are subject to privacy restrictions. However, inevitable data heterogeneity among sub-graphs can lead to poor performance of Graph Neural Networks (GNNs) trained by vanilla federated learning algorithm, which has been neglected before. To address this problem we look at subgraphs and find that there are usually overlapping nodes among them. Inspired by the common overlapping nodes among subgraphs, we propose GraphFed, a subgraph federated learning framework which solve the challenge in terms of both data augmentation and personalized federated learning. GraphFed uses common overlapping nodes among subgraphs to locate lost node information and applies data augmentation by retrieving lost nodes and forming common node sets that mitigates data distribution difference. Furthermore, GraphFed treats the overlapping nodes as bridges to calculate the distance among different subgraphs, which implies the similarity of the graph data distribution. Then, we use the distance to perform personalized federated learning. Empirical results and analysis on three real-world graph datasets with graph federated learning settings demonstrate the effectiveness of our proposed framework. © 2023 IEEE.

关键词： Graph neural networks

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Hybrid-Triggered H∞ sliding mode control for Networked control System With Deception Attacks

Hybrid-Triggered H∞ sliding mode control for Networked Cont...

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第35届中国过程控制会议

作者： Xiangyang Qu Zhiwen Wang Pan Zhang Feng Wang College of Electrical and Information Engineering Lanzhou University of Technology Gansu Key Laboratory Advanced Control for Industrial Processes

This paper studies the H∞ sliding mode control(SMC) problem for a class of networked control system(NCS) with hybrid event-triggered scheme and deception attacks. To alleviate the communication burden, a hybrid event-triggered mechanism that combines the time-triggered mechanism(TTM) and event-triggered mechanism(ETM) via a Bernoulli random variable is presented. Then, taking into account the influence of deception attacks on system performance, an observer-based output feedback H∞ SMC method is proposed. By using the linear matrix inequality technique and Lyapunov stability theory, the system is asymptotically stable and with given H∞ performance. In addition, an observer-based SMC law is designed to ensure that the system state trajectory reaches a predefined sliding region. Finally, a numerical simulation is used to verify the effectiveness of the proposed method.

关键词： Networked control system sliding mode control hybrid-triggered mechanism deception attacks

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A hierarchical battery pack balancing method based on capacitance and inductance series connection

A hierarchical battery pack balancing method based on capaci...

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第35届中国过程控制会议

作者： Zheming Yan Aimin An Zhe Guan Zhipeng Luo College of Electrical and Information Engineering Lanzhou University of Technology Key Laboratory of Gansu Advanced Control for Industrial Processes

Aiming at the problem of battery inconsistency in the series use of lithium battery packs, and considering that the non-adjacent battery packs in the battery equalization circuit are restricted by the equalization circuit, a new equalization topology based on the layered equalization circuit in series of capacitors and inductors is proposed. The balance circuit as a whole uses a double-layer balance topology, the bottom circuit uses CUK chopper circuit, the top circuit combined with the high precision of inductive energy storage balance and the fast balance of capacitive energy storage balance, the use of inductance capacitor series balance circuit. The experimental results show that the equalization speed is improved, the equalization time is shortened, and the expected equalization effect is achieved.

关键词： equalization capacitance equilibrium battery packs control strategy

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InterRep: A Visual Interaction Representation for Robotic Grasping

InterRep: A Visual Interaction Representation for Robotic Gr...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yu Cui Qi Ye Qingtao Liu Anjun Chen Gaofeng Li Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University and the Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recently, pre-trained vision models have gained significant attention in motor control, showcasing impressive performance across diverse robotic learning tasks. While previous works predominantly concentrate on the significance of the pre-training phase, the equally important task of extracting more effective representations based on existing pre-trained visual models remains unexplored. To better leverage the representation capabilities of pre-trained models for robotic grasping, we propose InterRep, a novel interaction representation method that possesses not only the strengths of pre-trained models, known for their robustness in noisy environments and their proficiency in recognizing essential features, but also the capacity of capturing dynamic interaction details and local geometric features during the grasping process. Based on the novel representation, we introduce a deep reinforcement learning method to learn generalizable grasping policies. The experimental results demonstrate that our proposed representation outperforms the baselines in terms of both training speed and generalization. For the generalized grasping tasks with dexterous robotic hands, our method boasts a success rate nearly 20% higher than methods using the global features of the entire image from pre-trained models. In addition, our proposed representation method demonstrates promising performance when applied to a different robotic hand and task. It also exhibits excellent performance on real robots with a success rate of 70%.

关键词： Training Visualization Motor drives Shape Grasping Feature extraction Robustness

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Construction and application of a novel WGAN-CNN-based predicting approach for dust concentration at underground coal mine working faces

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Environmental Science and Pollution Research 2024年第27期31卷 39271-39284页

作者： Zhou, Banghao Qin, Botao Zhou, Qun Sun, Daowei Chen, Pengpeng Yang, Kai Pan, Qingyan Li, Huizhen Key Laboratory of Gas and Fire Control for Coal Mines China University of Mining and Technology Ministry of Education Jiangsu Xuzhou221116 China School of Safety Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China School of Computer Science & amp Technology China University of Mining and Technology Jiangsu Xuzhou221116 China

To enhance the real-time monitoring and early-warning capabilities for dust disasters in underground coal mine, this paper presents a novel WGAN-CNN-based prediction approach to predict the dust concentration at underground coal mine working faces. Dust concentration, wind speed, temperature, and methane concentration were collected as the original data due to their nonlinear relationship. The consistency between the generated and original data distributions was verified through PCA dimensionality reduction analysis. The predictive performance of this approach was assessed using five metrics (R2, EVS, MSE, RMSE, and MAE) and compared with three other algorithms (Random Forest Regressor, MLP Regressor, and LinearSVR). The findings indicate that a majority of the generated data falls within the distribution range of the real dataset, exhibiting reduced levels of volatility and dispersion. The R2 values of prediction results are all above 98%, and the MSE values are between 0.0007 and 0.0106. The proposed approach exhibits superior predictive accuracy and robust model generalization capabilities compared to alternative algorithms, thereby enhancing the real-time monitoring and early-warning level of dust disasters in underground coal mine. This will facilitate the realization of advanced prevention and control measures for dust disasters, showcasing a wide range of potential applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Disasters

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Optimization Scheduling of Multi-Energy Microgrid Based on the QUBO Quantum Computing Model 19th

Optimization Scheduling of Multi-Energy Microgrid Based on t...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Wang, Baonan Wang, Hui Zhang, Dan College of Computer Science and Technology Shanghai University of Electric Power Shanghai China State Key Laboratory of Power System Operation and Control Dept. of Electrical Engineering Tsinghua University Beijing China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9789819608966

Current optimization methods for microgrid scheduling face issues such as insufficient precision in energy distribution, high operational costs, and inefficiency. In response to these challenges, an optimization scheduling method for multi-energy microgrids based on the QUBO (Quadratic Unconstrained Binary Optimization) quantum computing model is proposed in this paper, alongside a discretization approach based on a grid partitioning strategy. To validate the feasibility of addressing microgrid optimization scheduling problems in a quantum computing environment, a QUBO model is constructed and simulation studies are conducted in the qbsolv environment provided by D-Wave. The results indicate that operational costs are significantly reduced with an increase in the number of grids, particularly a reduction of 4.52% when the number of grids increases from five to ten. This method not only effectively enhances the precision of electricity supply and energy utilization efficiency but also reduces unnecessary electrical waste and economic costs. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Microgrids

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