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Distributed Reduced-Order Observer-Based Consensus of Multiple Euler-Lagrange Systems Over Switching Networks

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 53-61页

作者： Long, Mingkang Su, Housheng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

In this paper, we investigate leader-follower consensus (LFC) of multiple Euler-Lagrange systems (MELSs) under the case that followers can only measure partial output information of leader system, and the communication network among followers is switching and jointly connected. Firstly, in order to accurately estimate complete state information of the leader system in real time, we propose a distributed reduced-order observer, which is valid over a jointly connected switching network. Then, by applying this distributed reduced-order observer, we further design an observer-based controller for multiple Euler-Lagrange systems (MELSs) to reach leader-follower consensus (LFC). Distinct with the existing controllers for LFC of MELSs, this controller is robust for bounded external disturbances, and independent of the structure and features of Euler-Lagrange (EL) system. At last, a simulation example is given to confirm the effectiveness of the proposed distributed reduced-order observer and robust controller. Note to Practitioners-Many practical engineering applications (such as spacecrafts, mobile robot manipulators) can be modeled as multiple uncertain Euler-Lagrange systems, where each agent suffers external input disturbances, and only a portion of followers can measure partial components of leader's output. However, most relevant works assumed that the dynamic information of each agent is completely known, agents do not suffer external input disturbances, and all follower agents can measure full-dimensional output of leader. How to achieve LFC for such MELSs has become a main focus of control researches. Thus, this paper proposes an adaptive distributed reduced-order observer-based consensus algorithm for such MELSs, whose communication network is switching and jointly connected. Finally, the effectiveness of the proposed algorithm is illustrated by a simulation example.

关键词： Leader-follower consensus reduced-order observer multiple Euler-Lagrange systems model-independent switching networks

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Deep Reinforcement Learning-Based Security-Constrained Battery Scheduling in Home Energy System

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3548-3561页

作者： Wang, Bo Zha, Zhongyi Zhang, Lijun Liu, Lei Fan, Huijin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China

The home energy system today involves multiple renewable energy sources and battery energy storage systems, which can be considered as a microgrid. The battery energy storage system is a key component in the home energy system for the sake of filling the gap between the user demand and volatile energy supplies to maximize the techno and economic performances. However, the battery scheduling must suffer the stochastic nature of renewable energy resources and loads, which results in an intractable multi-period stochastic optimization problem with security constraints. An improved actor-critic-based reinforcement learning is proposed for this issue, where a distributional critic net is applied for faster and more accurate reward estimation under uncertainties, and a policy net incorporating protective secondary control is adopted to satisfy security constraints, preventing the unsafe state of batteries during the trial-and-error process. Numerical tests show that the proposed approach outperforms conventional reinforcement learning algorithms, as well as the rule-based battery scheduling approach while guaranteeing safe operation. The robustness and adaptability of the proposed method are also verified in case studies with different optimization tasks.

关键词： Batteries State of charge Security Degradation Trajectory Q-learning Monte Carlo methods Home energy system energy storage renewable energy resources security-constrained battery scheduling reinforcement learning

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Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2024年第3期15卷 1659-1668页

作者： Xu, Yifan Jiang, Xue Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Emotion recognition is a critical component of affective computing. Training accurate machine learning models for emotion recognition typically requires a large amount of labeled data. Due to the subtleness and complexity of emotions, multiple evaluators are usually needed for each affective sample to obtain its ground-truth label, which is expensive. To save the labeling cost, this paper proposes an inconsistency-based active learning approach for cross-task transfer between emotion classification and estimation. Affective norms are utilized as prior knowledge to connect the label spaces of categorical and dimensional emotions. Then, the prediction inconsistency on the two tasks for the unlabeled samples is used to guide sample selection in active learning for the target task. Experiments on within-corpus and cross-corpus transfers demonstrated that cross-task inconsistency could be a very valuable metric in active learning. To our knowledge, this is the first work that utilizes prior knowledge on affective norms and data in a different task to facilitate active learning for a new task, even the two tasks are from different datasets.

关键词： Task analysis Uncertainty Measurement uncertainty Estimation Entropy Emotion recognition Affective computing Active learning emotion classification emotion estimation transfer learning

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Late better than early: A decision-level information fusion approach for RGB-Thermal crowd counting with illumination awareness

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NEUROCOMPUTING 2024年 594卷

作者： Cheng, Jian Feng, Chen Xiao, Yang Cao, Zhiguo Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In this paper, we make the first research effort to address the RGB-Thermal (RGB-T) crowd counting problem with the decision -level late fusion manner. Being different from the existing pixel -level or feature -level fusion methods, our proposition chooses to fuse the density maps yielded by RGB and thermal counterparts via spatially adaptive weighting with RGB illumination -aware attention. Our key intuition to conduct RGB-T density map fusion lies in 2 main folders. First, compared with the raw RGB-T images or convolutional feature maps, RGB-T density maps contain stronger counting -wise semantic meanings. Secondly, they are also of high spatial resolution for revealing fine local details. To fuse them adaptively, a spatial weighting map for each modality, together with an illumination -related RGB weight is generated. In this way, the issues of RGB illumination awareness and local counting pattern characterization ability are concerned jointly. To the best of our knowledge, we are the first to leverage RGB-T crowd counting concerning these 2 issues in a unified way. Meanwhile, cross -modality feature interaction is conducted between RGB and thermal modalities to facilitate spatial weighting map generation. The experiments on 2 well -established RGB-T crowd counting datasets ( i.e. , RGBT-CC and DroneRGBT) verify the superiority of our proposition.

关键词： Crowd counting RGB-T image Decision-level fusion

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Hierarchical Feature Warping and Blending for Talking Head Animation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第8期34卷 7301-7314页

作者： Zhang, Jiale Liu, Chengxin Xian, Ke Cao, Zhiguo Huazhong Univ Sci & Technol Minist Educ Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Talking head animation transforms a source anime image to a target pose, where the transformation includes the change of facial expression and head movement. In contrast to existing approaches that operate on the low-resolution image (256 x 256), we study this task at a higher resolution, e.g., 512 x 512 . High-resolution talking head animation, however, raises two major challenges: i) how to achieve smooth global transformation while maintaining rich details of anime characters under large-displacement pose variations;ii) how to address the shortage of data, because no related dataset is publicly available. In this paper, we present a Hierarchical Feature Warping and Blending (HFWB) model, which tackles talking head animation hierarchically. Specifically, we use low-level features to control global transformation and high-level features to determine the details of anime characters, under the guidance of feature flow fields. These features are then blended by selective fusion units, outputting transformed anime images. In addition, we construct an anime pose dataset-AniTalk-2K, aiming to alleviate the shortage of data. It contains around 2000 anime characters with thousands of different face/head poses at a resolution of 512 x 512 . Extensive experiments on AniTalk-2K demonstrate the superiority of our approach in generating high-quality anime talking heads over state-of-the-art methods.

关键词： Head image resolution Task analysis Generators Vectors Animation Iterative methods Talking head animation generative adversarial networks pose transformation anime image generation anime dataset

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Optimal Spatial-Temporal Scheduling for EVs With Stochastic Behaviors and Possible Inauthentic Information

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IEEE TRANSACTIONS ON SMART GRID 2024年第1期15卷 729-742页

作者： Wang, Yan-Wu Cao, Zhong Xiao, Jiang-Wen Liu, Xiao-Kang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

This paper studies the spatial-temporal scheduling for electric vehicles with stochastic charging behaviors. A new spatial-temporal scheduling model is proposed to achieve both the spatial coordination and temporal coordination for EV users and is modeled as a non-convex optimization problem which can be solved by the coordinate descent method. Further, a new billing method of charging cost is proposed based on the Vickrey-Clarke-Groves to avoid possible inauthentic information from users. Specially, the individual rationality and incentive compatibility of the method are theoretically proved. Moreover, a stochastic optimization problem is proposed to describe the stochastic charging behavior of users, which can fit the real application better than the deterministic one. Consequently, an efficient charging strategy is obtained via chance constraint method. Simulation results and comparison studies show the effectiveness of both the model and the billing method.

关键词： Electric vehicle charging strategy chance constraint inauthentic information coordinate descent method

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Pseudo label Fusion With Uncertainty Estimation for Semi-Supervised Cropping Box Regression

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 8157-8171页

作者： Pan, Zhiyu Cui, Jiahao Wang, Kewei Wu, Yizheng Cao, Zhiguo Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Minist Educ Wuhan 430074 Peoples R China

Cropping box regression algorithms re-frame the images with predicted cropping boxes for better composition quality, which can save considerable manpower and time for massive image retouching work. Yet, recent learning-based cropping box regression algorithms require expert annotations, which makes the scale of training limited. This consequently incurs a performance bottleneck. To address this issue, previous works seek the help from auxiliary datasets of related tasks, e.g., the composition classification. However, the domain gap between related tasks and the likewise restricted scale of auxiliary datasets are still limiting factors. Hence, our work provides a novel semi-supervised framework that can learn better re-framing knowledge with unlimited unlabeled data. We make use of the unlabeled data via pseudo-labeling, where the model learns from the pseudo labels generated from a temporal ensemble version of itself. To prevent the model learns from its own mistakes, a.k.a. the problem of confirmation bias, we propose to rectify the mistakes by fusing multiple candidate pseudo labels into the better ones. The fusion procedure is based on the uncertainty estimation for each boundary of the candidate cropping boxes. The multiple candidates are from the proposed aesthetic region proposal network. Extensive experimental results explain how the uncertainty-based pseudo label fusion procedure overcomes the confirmation bias and demonstrate the superiority of our semi-supervised cropping box regression framework.

关键词： Task analysis Uncertainty Annotations Semisupervised learning Object detection Data models Multitasking image cropping cropping box regression semi-supervised learning uncertainty estimation

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DFIMat: Decoupled Flexible Interactive Matting in Multi-person Scenarios 17th

DFIMat: Decoupled Flexible Interactive Matting in Multi-per...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Jiao, Siyi Zeng, Wenzheng Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819609710

Interactive portrait matting refers to extracting the soft portrait from a given image that best meets the user’s intent through their inputs. Existing methods often underperform in complex scenarios, mainly due to three factors. (1) Most works apply a tightly coupled network that directly predicts matting results, lacking interpretability and resulting in inadequate modeling. (2) Existing works are limited to a single type of user input, which is ineffective for intention understanding and also inefficient for user operation. (3) The multi-round characteristics have been under-explored, which is crucial for user interaction. To alleviate these limitations, we propose DFIMat, a decoupled framework that enables flexible interactive matting. Specifically, we first decouple the task into 2 sub-ones: localizing target instances by understanding scene semantics and the flexible user inputs, and conducting refinement for instance-level matting. We observe a clear performance gain from decoupling, as it makes sub-tasks easier to learn, and the flexible multi-type input further enhances both effectiveness and efficiency. DFIMat also considers the multi-round interaction property, where a contrastive reasoning module is designed to enhance cross-round refinement. Another limitation for multi-person matting task is the lack of training data. We address this by introducing a new synthetic data generation pipeline that can generate much more realistic samples than previous arts. A new large-scale dataset SMPMat is subsequently established. Experiments verify the significant superiority of DFIMat. With it, we also investigate the roles of different input types, providing valuable principles for users. Our code and dataset will be public. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Large datasets

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Distributed robust iterative learning control for AC microgrids with external disturbances

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Science China(Information Sciences) 2025年

作者： Hongyu SU Jingang LAI School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent ControlMinistry of Education Huazhong University of Science and Technology

This study proposes a distributed secondary control scheme based on distributed robust iterative learning control(DRILC) for islanded alternating current(AC) microgrids subjected to external disturbances. By employing the decoupled sliding mode consensus approach, voltage regulation, frequency restoration, and accurate active power sharing can be achieved within a finite time on the proposed novel integral terminal sliding mode(ITSM) manifold. Furthermore, the appropriate iterative update law in the ITSM-based controller is utilized to learn and eliminate external disturbances as effectively as possible. In the proposed control scheme, the iterative learning control and sliding mode control are designed to function in a complementary manner, which enhances the performance of the secondary control scheme for multi-objective regulations. The stability criteria and robustness to external disturbances of the closed-loop microgrid system in the iteration and time domains are also rigorously derived with the help of the Lyapunov direct method. Finally, the effectiveness of the proposed secondary control scheme is validated by case studies of an islanded AC microgrid test system in the MATlab/SimPowerSystems software environment.

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Surface-PMSM Sensorless control Strategy Based on Reduced-Order Linear Kalman Filter Cooperating With Prediction Error Rolling Compensation

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IEEE Transactions on Power Electronics 2025年第8期40卷 10804-10815页

作者： Xu, Sang Shen, Anwen Tang, Qipeng Wang, Miaomiao Luo, Pan Luo, Xin Xu, Jinbang National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

Kalman filter (KF) is increasingly attracted for sensorless control of surface permanent magnet synchronous motors due to its strong robustness against measurement and system noise. However, the conventional method suffers from poor position and speed estimation accuracy under dynamic conditions and high computational cost. In order to solve these problems, an enhanced sensorless control strategy based on reduced-order linear Kalman filter (RLKF) cooperating with prediction error rolling compensation is introduced in this article. Differing from conventional methods, a reduced-order linear state equation encompassing rotor position, speed, and torque disturbance is constructed. Then, by obtaining the prediction error of system output using the motor current equation in the predictive reference frame, the optimal estimation of rotor position and speed in the KF algorithm is achieved based on the reduced-order model. This not only simplifies the calculation process but also enhances the position and speed estimation accuracy under dynamic conditions. Meanwhile, a prediction error rolling compensation algorithm is developed to minimize the estimation deviations caused by motor parameter variations. In addition, the system's dynamic performance is further improved with an adopted speed controller based on torque disturbance feedforward and acceleration feedback. Finally, experimental results verify the effectiveness and feasibility of the proposed strategy. © 1986-2012 IEEE.

关键词： Permanent magnets

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