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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3690-3702页

作者： Xiao, Jiang-Wen Fang, Hongliang Wang, Yan-Wu Huazhong University of Science and Technology Ministry of Education School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China

Short-term residential load forecasting is essential to demand side response. However, the frequent spikes in the load and the volatile daily load patterns make it difficult to accurately forecast the load. To deal with these problems, this article proposes a smoothing clustering method for daily load clustering and a pooling-ensemble model for one day ahead load forecasting. The whole short-term load forecasting framework in this article contains three steps. Specifically and first, the states of the residents are obtained by clustering the daily load curves with the proposed smoothing clustering method. Second, a weighted mixed Markov model is built to predict the probability distribution of the load state in the next day. Third, multiple predictors in the pooling-ensemble model are selected for different states and the load is forecasted by weighing the results of the multiple predictors based on the predicted states. Results of the case studies and comparison studies on two public datasets verify the advantages of the smoothing clustering method and the pooling-ensemble model. © 2020 IEEE.

关键词： Probability distributions

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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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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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Echo state network based symbol detection in chaotic baseband wireless communication

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Digital Communications and Networks 2023年第6期9卷 1319-1330页

作者： Huiping Yin Chao Bai Haipeng Ren Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of TechnologyXi'an710048China Xi'an Key Laboratory of Intelligent Equipment Xi'an Technological UniversityXi'an710021China

The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting ***,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold *** unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)*** contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using *** proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative *** this approach,the calculation complexity is reduced compared to the previous ESN-based *** proposed method achieves better BER performance compared to the previous *** reason for this superior result is ***,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was ***,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information *** results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.

关键词： Chaotic baseband wireless communication system(CBWCS) Inter-symbol interference(ISI) Echo state network(ESN) Threshold prediction

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Deployment of Heterogeneous Multi-Agent Systems via a PDE Approach

Deployment of Heterogeneous Multi-Agent Systems via a PDE Ap...

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2023 China Automation Congress, CAC 2023

作者： Man, Jingtao Zeng, Zhigang Xiao, Qiang Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan China

ISBN: (纸本)9798350303759

Spatial deployment of large-scale heterogeneous multi-agent systems (HMASs) over desired 2D or 3D curves is investigated in this paper. With assumption that HMASs consist of numerous first-order agents (FOAs) and second-order agents (SOAs) that could obtain local information of desired curves and their positions relative to their closest neighbors, the collective dynamics of large-scale HMASs are modeled as heterogeneous partial differential equations (PDEs). In particular, this paper introduces series-dependent topological weights between neighboring agents, which are more versatile and practical than constant topological weights commonly used in previous studies. A novel single-point control scheme is proposed, where an informed agent is situated between the last FOA and first SOA. This operation could not only ensure successful implementation of spatial deployment, but also guarantee well-posedness of the constructed heterogeneous error PDEs. By utilizing inequality techniques, sufficient conditions for exponential convergence of error system are derived. A numerical example is presented to demonstrate effectiveness of the proposed approaches. © 2023 IEEE.

关键词： Multi agent systems

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Protecting Multiple Types of Privacy Simultaneously in EEG-Based Brain-Computer Interfaces

Protecting Multiple Types of Privacy Simultaneously in EEG-B...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Meng, Lubin Jiang, Xue Jia, Tianwang Wu, Dongrui School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan China

ISBN: (纸本)9781665410205

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation, text input, games, and so on. However, EEG signals inherently carry rich personal information, necessitating privacy protection. This paper demonstrates that multiple types of private information (user identity, gender, and BCI-experience) can be easily inferred from EEG data, imposing a serious privacy threat to BCIs, To address this issue, we design perturbations to convert the original EEG data into privacy-protected EEG data, which conceal the private information while maintaining the primary BCI task performance. Experimental results demonstrated that the privacy-protected EEG data can significantly reduce the classification accuracy of user identity, gender and BCI-experience, but almost do not affect at all the classification accuracy of the primary BCI task, enabling user privacy protection in EEG-based BCIs. © 2024 IEEE.

关键词： Differential privacy

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controllability of neighborhood Corona product networks

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Science China(Information Sciences) 2024年第12期67卷 327-328页

作者： Bo LIU Xuan LI Qiang ZHANG Junjie HUANG Housheng SU Ministry of Education Key Laboratory for Intelligent Analysis and Security Governance of Ethnic Languages School of Information Engineering Minzu University of China School of Mathematical Sciences Inner Mongolia University Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology

controlling networks aims to study the models, structures,and related dynamics of complex networks. The primary problem of controlling networks is to determine whether they are controllable. Nowadays, controllability has been widely studied and applied to system engineering and control theory, power systems, aerospace, and quantum systems. Various classical criteria include the Gram matrix criterion, Kalman rank criterion, and PBH test.

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Recursive Least Squares Identification With Variable-Direction Forgetting via Oblique Projection Decomposition

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 547-555页

作者： Kun Zhu Chengpu Yu Yiming Wan Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology Beijing Institute of Technology Chongqing Innovation Center

In this paper,a new recursive least squares(RLS)identification algorithm with variable-direction forgetting(VDF)is proposed for multi-output *** objective is to enhance parameter estimation performance under non-persistent *** proposed algorithm performs oblique projection decomposition of the information matrix,such that forgetting is applied only to directions where new information is *** proofs show that even without persistent excitation,the information matrix remains lower and upper bounded,and the estimation error variance converges to be within a finite ***,detailed analysis is made to compare with a recently reported VDF algorithm that exploits eigenvalue decomposition(VDF-ED).It is revealed that under non-persistent excitation,part of the forgotten subspace in the VDF-ED algorithm could discount old information without receiving new data,which could produce a more ill-conditioned information matrix than our proposed *** simulation results demonstrate the efficacy and advantage of our proposed algorithm over this recent VDF-ED algorithm.

关键词： Non-persistent excitation oblique projection recursive least squares(RLS) variable-direction forgetting(VDF)

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th International Conference on intelligent control and Information processing, ICICIP 2025

作者： Huang, Zhixuan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 IEEE.

关键词： Contrastive Learning

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