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2nd International Conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Pei, Wenliang Hua, Changchun Cui, Hailong Zhao, Guanglei Ma, Zhuang The Institute of Electrical Engineering The Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Hebei China Tangshan University Hebei China

ISBN: (纸本)9798400710681

This paper solves the event-triggered robust consensus problem of disturbed multi-agent systems (MASs) in fully distributed fashion. First, a distributed extended state observer (DESO) is developed for appropriately estimating system states and external disturbances. Then, by utilising these estimates, a fully distributed event-triggering control protocol, that composes of an adaptive distributed controller to avoid using global information while maintaining desired consensus property, and a novel hybrid dynamic event-triggering mechanism (ETM) to save communication resources, is put forward for the MASs. It is notable that an auxiliary differential equation is incorporated in the triggering condition, with which, positive minimum interevent times (MIETs) are available to ease the hardware implementation, while being with adaptive coefficients, the proposed hybrid dynamic ETM can be independent of any global information. Finally, the obtained main results are testified via a numerical example. © 2024 Copyright held by the owner/author(s).

关键词： State estimation

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Hierarchical Uncertainty Characterization of Monthly Net Load in Renewable Power Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 3515-3527页

作者： Yang, Xiao Li, Yuanzheng Zhao, Yong Hao, Guokai Ishizaki, Takayuki Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China China Belt & Rd Joint Lab Measurement & Control Te Wuhan 430074 Peoples R China Tokyo Inst Technol Grad Sch Engn Tokyo 1528552 Japan

The accurate characterization of net load uncertainty can ensure the economy and stability of renewable power systems operation. In existing studies, Gaussian mixture model (GMM) and Dirichlet process mixture model (DPMM) are powerful tools for characterizing the uncertainty of monthly net load. However, the correlation among the Gaussian components and the common distribution characteristics among the monthly net load are not considered in these studies, which may lead to lower characterization accuracy. To solve these issues, we propose a hierarchical uncertainty characterization method for monthly net load considering Gaussian component reduction and correlation. Firstly, the method obtains the Gaussian mixture components and the label information of the annual net load by the Expectation-Maximum (EM) algorithm. Then, the Gaussian components of the monthly net load are corrected by the label information, which helps extract the common distribution characteristics among the monthly net load. On this basis, the corrected Gaussian components are considered as the base distribution. A component-preserving Expectation-Maximum (CPEM) algorithm is developed for component reduction. This realizes the uncertainty characterization of the monthly net load with high accuracy and lower time consumption. Importantly, the temporal correlation of net load is converted into the correlation among the Gaussian components, which are explicitly characterized by the Spearman coefficient. Finally, the superiority of the proposed method is verified with actual data collected in Australia. Note to Practitioners-In this article, we address the issue of monthly net load uncertainty characterization in renewable power systems. A hierarchical uncertainty characterization method is proposed to enable the distribution characteristic extraction of monthly net load. Most existing works on uncertainty characterization use GMM and DPMM, where the internal information in the net load data (e.g., di

关键词： Net load uncertainty characterization distribution characteristics component reduction component correlation

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Bilevel Optimization Framework for Multiregional Integrated Energy Systems Considering 6G Network Slicing and Battery Energy Storage Capacity Sharing

IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY

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IEEE OPEN JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY 2025年 6卷 396-414页

作者： Cui, Kun Chi, Ming Zhao, Yong Liu, Zhi-Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Amidst the escalating challenges of global warming and energy crises, the rapid development of distributed renewable energy resources has emerged as a critical strategy. Regional integrated energy systems (RIESs) have garnered significant attention for their potential to integrate and optimize both distributed renewable energy resources and conventional energy facilities. This article presents a bilevel optimization framework for the electricity-storage coupling market in multi-RIES, considering the integration of 6G network slicing technology and battery energy storage (BES) capacity sharing. The upper-level model maximizes the profit of generation units by optimizing their bidding strategies, while the lower-level model aims to maximize social welfare through market clearing. The proposed line search-based global Levenberg-Marquardt algorithm addresses the limitations of existing algorithms with necessary and innovative improvements to tackle the challenge of global convergence in nonsmooth optimization problems. Numerical case studies validate the effectiveness of the proposed framework, demonstrating enhanced BES utilization, increased renewable energy generation, and improved social welfare. The results also highlight the sensitivity of social welfare to communication costs, underscoring the importance of careful cost calibration.

关键词： 6G mobile communication Renewable energy sources Optimization Costs Resource management Couplings Industrial electronics Information processing Indexes Electricity Climate change Battery energy storage (BES) capacity sharing bilevel optimization device-to-device (D2D) communication 6G network slicing electricity-storage coupling market multiregional integrated energy system (RIES)

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Interval Observer-Based Coordination control for Discrete-Time Multi-Agent Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 4102-4114页

作者： Luo, Miaohong Su, Housheng Zheng, Wei Xing Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW 2751 Australia

In this article, the coordination control problem of discrete-time multiagent systems (MASs) affected by uncertainties, namely unknown initial states and external disturbances, is considered. Inspired by the interval observer constructed by the single system, the definition of distributed interval observer for discrete-time MASs is given, in which the control protocol of each agent obtained by solving a modified algebraic Riccati equation depends on the bounded information of the interval observer connected to itself and its neighbors. By the cooperativity theory and Lyapunov stability theory, it is established that the distributed interval observer can not only access some information about MASs at any instant, that is, the upper and lower bounds of each component of the agent state, but also realize the cooperative behavior of MASs under some essential conditions involving network synchronization and the unstable eigenvalue of the system matrix. In addition, with the help of a new time-varying transformation matrix, the new interval observer is constructed to eliminate the non-negative constraint. Finally, two numerical simulations are provided to confirm the validity of the derived results.

关键词： Observers Uncertainty Protocols Linear matrix inequalities Multi-agent systems Synchronization Output feedback Laplace equations Dynamical systems Topology Coordination control discrete-time multiagent systems (MASs) interval observer uncertainty

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Real-Time EEG-Based Driver Drowsiness Detection Based on Convolutional Neural Network With Gumbel-Softmax Trick

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IEEE SENSORS JOURNAL 2025年第1期25卷 1860-1871页

作者： Feng, Weibin Wang, Xiaoping Xie, Jialan Liu, Wanqing Qiao, Yinghao Liu, Guangyuan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain inspired Intelligent Syst Wuhan 430074 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Southwest Univ Inst Affect Comp & Intelligent Informat Proc Chongqing 400715 Peoples R China

Nowadays, severe traffic accidents attributed to driver drowsiness have become increasingly frequent, prompting a widespread concern among researchers in electroencephalography (EEG)-based driver drowsiness detection. However, due to the significant differences in EEG signals between participants, the prevalence of redundant information in multichannel EEG data, and the computational burden in combining channel selection with neural networks, achieving an accurate and efficient real-time driver drowsiness recognition remains challenging. To overcome these limitations, this article proposes a novel deep learning framework that utilizes a separable convolutional neural network (CNN) to mine the intricate spatiotemporal information in EEG signals, combined with the channel selection layer to jointly optimize EEG channels and network parameters. This layer employs an efficient embedded Gumbel-Softmax technique for discrete sampling and differentiable approximation. To prevent the introduction of duplicate channels, we impose penalties on the row sums of the selection matrix to encourage the selection neurons to learn distinct channels, enabling the neural network to train in an end-to-end manner. The proposed model achieves an average accuracy of 80.84% and an F1 score of 79.65% in cross-subject drowsiness identification for 11 subjects on the publicly available sustained-attention driving task dataset. Compared to the results of recent relevant works, our model exhibits superior performance, surpassing state-of-the-art (SOTA) deep learning methods by 1.47%. Furthermore, building upon the model's advantages, we have further actualized a real-time driver drowsiness detection graphical user interface (GUI), providing a practical reference for real-world applications.

关键词： Electroencephalography Brain modeling Neurons Feature extraction Training Accuracy Vectors Target tracking Reviews Real-time systems Driver drowsiness detection electroencephalography (EEG) graphical user interface (GUI) Gumbel-Softmax trick real-time

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Curve-Suppression-Based Event-Triggered Mechanisms for Quasi-Synchronization of Fuzzy Delayed Neural Networks on Time Scales

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3174-3187页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Lai, Jingang Wuhan University of Science and Technology School of Artificial Intelligence and Automation Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent control of Education Ministry of China Wuhan430074 China

The vast majority of published event-triggered mechanisms (ETMs) are constructed based on measurement errors, which introduces a problem naturally that they are updated when the measurement errors exceed the thresholds although the current obtained sampling states can make systems converge well. With this problem in mind, we redesign ETMs for quasi-synchronization of T-S fuzzy neural networks (FNNs) with time delays on time scales. First, a novel ETM is designed for continuous-time FNNs with time-varying delays to achieve quasi-synchronization, with which synchronization errors is suppressed to globally exponentially converge to a ball. Second, we introduce the ETM for continuous-time FNNs to discrete-time FNNs, owing to the existence of discrete-time states, the Lypunov function of synchronization errors run over the exponentially decay curve, but it can be suppressed to evolve under another exponentially decay curve. Third, for FNNs on time scales with constant and time-varying delays, we estimate the forward jump operator of the Lyapunov functions and design ETMs to guarantee that the Lypunov functions evolve under the exponentially decay curves, so quasi-synchronization can be achieved. Last but not least, we prove that Zeno behavior will not happen and four numerical examples are introduced to verify the validity and the superiority of the proposed ETMs in reducing information transmission. © 2013 IEEE.

关键词： Continuous time systems

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A Robotic Tactile Excavation System for Excavating Objects Buried in Granular Materials

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2025年

作者： Deng, Linan Wang, Yasen Yue, Zuogong Li, Zhi Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Robotic excavation of objects buried in granular materials (GM) is challenging due to the lack of visual information. In this work, we present a robotic tactile excavation system (RTES) for exploring and excavating unknown objects buried in GMs. We first design an underactuated tactile finger with multimodal tactile sensing, including sensing contact pressure and fingertip motion. A contact detection model is established to identify GMs and buried objects in real-time based on the multimodal sensor data. Then, a 3-D occupancy map of GMs is constructed to guide the object grasping. Finally, a tactile excavation (TE) policy is proposed to explore and excavate buried objects, which mainly includes two rounds of rough excavation and one round of fine excavation. Experiments on contact detection, shape detection, and TE are conducted to evaluate the effectiveness of RTES, where RTES can successfully excavate various unknown objects from GMs.

关键词： Excavation Buried object detection Sensors Robots Robot sensing systems Grippers Grasping Three-dimensional displays Sensor arrays Force Granular materials (GMs) robotic excavation tactile sensing underactuated gripper

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Matrix-Weight-Based controllability of Second-Order Signed Multi-agent Networks

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CIRCUITS SYSTEMS AND SIGNAL processing 2025年第4期44卷 2406-2423页

作者： Liu, Bo Li, Songlu Huang, Junjie Su, Housheng Minzu Univ China Sch Informat Engn Key Lab Ethn Language Intelligent Anal & Secur Gov MOE Beijing 100081 Peoples R China Inner Mongolia Univ Sch Math Sci Hohhot 010021 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China

This study addresses the controllability of second-order signed multi-agent networks based on matrix weights. Some controllability conditions of signed second-order multi-agent networks under matrix-weighted topology for the heterogeneous networks and homogenous networks are built, respectively. In addition, the second-order controllability of signed networks with matrix-weights under structurally balanced conditions is considered with selecting appropriate leaders. Finally, simulations and examples are provided to demonstrate the theoretical results.

关键词： controllability Multi-agent networks Signed networks Structural balance

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Adaptive Homophily Clustering: Structure Homophily Graph Learning With Adaptive Filter for Hyperspectral image

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Ding, Yao Zhang, Zhili Kang, Weijie Yang, Aitao Zhao, Junyang Feng, Jie Hong, Danfeng Zheng, Qinghe Xian Res Inst High Technol Key Lab Intelligent Control Xian 710025 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Shandong Management Univ Sch Intelligent Engn Jinan 250357 Peoples R China

Hyperspectral image (HSI) clustering is a fundamental yet challenging task that typically operates without training labels. Recent advancements in deep graph clustering methods have shown promise for HSI due to their ability to effectively encode spatial structural information. However, limitations such as inadequate utilization of structural information, poor feature representation, and weak graph update capabilities hinder their performance. In this article, we propose an adaptive homophily structure graph clustering (AHSGC) method for HSI. Our approach begins with the generation of homogeneous regions to process HSI and construct the initial graph. Next, we design an adaptive filter graph encoder that captures both high and low-frequency features for subsequent processing. We then develop a graph embedding clustering self-training decoder using KL Divergence to generate pseudo-labels for network training. To enhance graph learning, we introduce homophily-enhanced structure learning, which updates the graph based on the clustering task. This involves estimating node connections through orient correlation estimation and dynamically adjusting graph edges via graph edge sparsification. Finally, we implement joint network optimization to facilitate self-training and graph updates, with K-means used to express latent features. The clustering accuracy on three datasets is 83.60%, 63.65%, and 86.03%, the FLOPs are 3.57G, 30.62G, and 2.95G. The source code will be available at https://***/DY-HYX.

关键词： Training Correlation Clustering methods Source coding Adaptive filters Estimation Reinforcement learning Decoding Optimization Hyperspectral imaging Adaptive filter graph encoder homophily-enhanced structure learning hyperspectral image (HSI) clustering joint network optimization

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Dual-Camera All-in-Focus Neural Radiance Fields

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2025年第5期47卷 4075-4089页

作者： Luo, Xianrui Wu, Zijin Peng, Juewen Sun, Huiqiang Cao, Zhiguo Lin, Guosheng Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Nanyang Technol Univ NTU Singapore 639798 Singapore

We present the first framework capable of synthesizing the all-in-focus neural radiance field (NeRF) from inputs without manual refocusing. Without refocusing, the camera will automatically focus on the fixed object for all views, and current NeRF methods typically using one camera fail due to the consistent defocus blur and a lack of sharp reference. To restore the all-in-focus NeRF, we introduce the dual-camera from smartphones, where the ultra-wide camera has a wider depth-of-field (DoF) and the main camera possesses a higher resolution. The dual camera pair saves the high-fidelity details from the main camera and uses the ultra-wide camera's deep DoF as reference for all-in-focus restoration. To this end, we first implement spatial warping and color matching to align the dual camera, followed by a defocus-aware fusion module with learnable defocus parameters to predict a defocus map and fuse the aligned camera pair. We also build a multi-view dataset that includes image pairs of the main and ultra-wide cameras in a smartphone. Extensive experiments on this dataset verify that our solution, termed DC-NeRF, can produce high-quality all-in-focus novel views and compares favorably against strong baselines quantitatively and qualitatively. We further show DoF applications of DC-NeRF with adjustable blur intensity and focal plane, including refocusing and split diopter.

关键词： Cameras Neural radiance field image color analysis Smart phones Three-dimensional displays Rendering (computer graphics) Training Apertures Geometry Neural networks all-in-focus dual-camera novel view synthesis

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