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35th Chinese control and Decision Conference (CCDC)

作者： Yan, Jiayu Wan, Yiming Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

ISBN: (纸本)9798350334722

As lithium-ion batteries are widely used in different fields, the thermal effect is of serious concern. How to achieve accurate temperature estimation in real time is the main challenge of current research. To address this problem, we propose a real-time distributed moving horizon estimation (RT-DMHE) based on partial differential equations describing thermal dynamics of a lithium-ion battery pack. It decomposes the real-time centralized moving horizon estimation (RT-CMHE) into multiple local estimators that run in parallel with information exchange from adjacent subsystems. Simulation validation shows that the root mean square error of temperature estimates from the proposed RT-DMHE is smaller than that of an existing distributed Kalman filter in literature. Moreover, compared to the RT-CMHE, the proposed RT-DMHE achieves comparable estimation accuracy while vastly reducing the average computation time per sample.

关键词： lithium-ion battery pack distributed moving horizon estimation real-time computation state-of-temperature estimation

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Presketched DNA Origami Canvas for Polymerase-Driven DNA Kirigami

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ACS NANO 2023年第17期17卷 17265-17272页

作者： Chen, Kuiting Xie, Chun Chen, Zhekun Wang, Shichen Hu, Yingxin Xu, Fei Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc Intelligent Control Educ Minist China Wuhan 430074 Hubei Peoples R China Shijiazhuang Tiedao Univ Coll Informat Sci & Technol Shijiazhuang 050043 Hebei Peoples R China

Reconfigurable DNA origami provides a versatile tool to manipulate the conformation of matter on the nanometer scale. Typically, the DNA kirigami method enables the transformation of an origami structure from an initial shape to another predesigned shape by reconfiguring the staple strands. In a regular origami structure, since the perfectly matched and densely packed DNA duplexes block the removal of staple strands, the construction of finely trimmed "sub-origami" structures by the DNA kirigami method has remained challenging. Herein, we proposed a strategy to construct the presketched DNA origami canvas, where the offcut area in the canvas was sketched by loosely fixed staple strands with single-base insertion, to enhance the fineness of polymerase-driven DNA kirigami. We successfully trimmed presketched two-dimensional rectangular canvas, three-dimensional Mo''bius strip, and genie bottle canvases into complex letter patterns, supercoiled rings, and nanorods, respectively. Finally, we demonstrated a size-controlled DNA kirigami system: a presketched 6HB origami was trimmed into a set of shorter nanowires with predefined lengths, which quantitatively characterized the fineness of the improved DNA kirigami. The presketched origami design was a general method that applied to both 2D and 3D DNA origami structures in square and honeycomb lattices. Loosening DNA origami structures by introducing single-base insertions provides a practical approach to constructing dynamic components when designing DNA nanomachines. Furthermore, the delicate trimming of the DNA origami canvas driven by polymerase may inspire strategies for graphical information encryption and storage.

关键词： DNA nanotechnology DNA origami reconfigurableDNA nanostructure polymerase-triggered strand displacement DNA kirigami

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Designing a completely distributed observer with robustness against disturbances

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2024年第12期34卷 7912-7935页

作者： Wang, Xiaoling Zhang, Ankang Su, Housheng Chen, Guanrong Nanjing Univ Posts & Telecommun Coll Automat & AI Nanjing 210003 Peoples R China Jiangsu Engn Lab IOT Intelligent Robots IOTRobot Nanjing Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Wuhan 430074 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

Distributed observer is an effective scheme for state estimation of a targeted system, utilizing a network of sensors to perform local output measurements. In this article, the problem of designing a distributed observer for a linear time-invariant system with external disturbances is addressed. The objective is to perform state estimation of such a system while simultaneously mitigating the impact of external disturbances and maintaining the distributed communication characteristics among the sensors. To achieve this, a completely distributed observer with robustness against disturbances is developed by combining the high-gain observer technique and the adaptive coupling strength strategy. Initially, a traditional high-gain observer is used for the state estimation on the observable subsystem, and subsequently a high-gain observer without peaking is introduced to avoid the peaking phenomenon. Finally, numerical simulations are presented to verify the theoretical results.

关键词： adaptive coupling gain distributed observer external disturbance high-gain observer robustness

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A transactive energy cooperation scheduling for hydrogen-based community microgrid with refueling preferences of hydrogen vehicles

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APPLIED ENERGY 2025年 377卷

作者： Zhang, Xiao-Yan Wang, Cenfeng Xiao, Jiang-Wen Wang, Yan-Wu 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 State Grid Zhejiang Elect Power Co Ltd Econ & Technol Res Inst Hangzhou 310008 Peoples R China

Hydrogen, as a high specific energy green carrier, is promising for power generation and transportation. This paper proposes a transactive energy cooperation framework for an end-user-oriented hydrogen-based community microgrid considering refueling preferences of hydrogen vehicles (HVs) and industrial hydrogen demand to minimize social cost. In this framework, an aggregator obtains stacking profits in both the electricity and hydrogen markets through flexible interconversion, storage, and interaction of electricity and hydrogen. For resident users, the refueling preferences of HVs are analyzed to reduce cost through demand response (DR). A novel energy density-weighted asymmetric Nash bargaining (DANB) method is designed to fairly assess the combined contribution, considering both electricity and hydrogen bargaining metrics for end- users. An alternating optimization procedure with the adaptive alternating direction method of multipliers (ADMM) algorithm is designed to solve the mixed-integer linear programming (MILP) problem in the social operation cost minimization process. Comparison results show the algorithm convergence advantage, the cost-effectiveness and scalability of the cooperation framework, as well as the rationality of the DANB and the flexibility of HVs in DR. Besides, the optimal configuration of the battery, the hydrogen tank, and the electrolyzer in the framework is explored, alongside the economic feasibility of electricity-hydrogen-electricity technology.

关键词： Hydrogen-based microgrid Hydrogen demand response Combined contribution assessment Alternating optimization procedure with alternating direction method of multipliers

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Variable Reconstruction for Evolutionary Expensive Large-Scale Multiobjective Optimization and Its Application on Aerodynamic Design

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第4期12卷 719-733页

作者： Lin, Jianqing He, Cheng Tian, Ye Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China

Expensive multiobjective optimization problems (EMOPs) are complex optimization problems exacted from real-world applications, where each objective function evaluation (FE) involves expensive computations or physical experiments. Many surrogate-assisted evolutionary algorithms (SAEAs) have been designed to solve EMOPs. Nevertheless, EMOPs with large-scale decision variables remain challenging for existing SAEAs, leading to difficulties in maintaining convergence and diversity. To address this deficiency, we proposed a variable reconstruction-based SAEA (VREA) to balance convergence enhancement and diversity maintenance. Generally, a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables. Thus, the population can be rapidly pushed towards the Pareto set (PS) by optimizing low-dimensional weight variables with the assistance of surrogate models. Population diversity is improved due to the cluster-based variable reconstruction strategy. An adaptive search step size strategy is proposed to balance exploration and exploitation further. Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task. Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs.

关键词： Optimization Computational modeling Iron Convergence Linear programming Predictive models Particle swarm optimization Kernel Costs Clustering algorithms Aerodynamic design large-scale optimization multiobjective evolutionary algorithm surrogate model variable reconstruction

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Distributed Finite-Time Observer for Multiple Line Outages Detection in Power Systems

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第3期54卷 1768-1778页

作者： Chen, Yu Liu, Zhi-Wei Wen, Guanghui Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Southeast Univ Sch Math Dept Syst Sci Nanjing 210096 Peoples R China

Fast detection of power line outages is critical for maintaining the stable operation of the power system. The aim of this article is to address the real time detection problem of multiple line outages in power systems. To effectively tackle the high-computational complexity issues associated with traditional approaches, we propose a multiple line outages detection algorithm that utilizes a distributed finite-time observer. The proposed method utilizes only local measurements and information from neighboring buses to update local observer for each bus. The proposed observer is mathematically proven to converge in a finite time, ensuring rapid detection of multiple line outages. Finally, simulation results demonstrate the effectiveness and rapidity of the proposed detection algorithm.

关键词： Distributed computing finite-time observer multiple line outages detection topology identification

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A radial visualization method based on knee point information for many-objective optimization

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APPLIED SOFT COMPUTING 2025年 175卷

作者： Li, Gui Xiao, Renbin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

Numerous high-dimensional solutions for many-objective optimization problems (MaOPs) usually impose a high cognitive burden on decision makers (DMs). Pareto front (PF) of MaOPs can express the problem characteristics, and then provide prior knowledge for solving the MaOPs. However, the existing high-dimensional visualization methods usually do not establish the relationship between PF information and decision making. Therefore, a novel radial visualization (RadViz) method called KRadViz that incorporates knee point information is proposed to visualize the information of PF shape and aid decision making. The relationship between the optimized performance information and PF shape is established, and the PF shape identification method is constructed. KRadViz is constructed by combining the optimization performance and PF shape. Three preferred solution selection methods are proposed to quickly screen out a few preferred solutions in different scenarios. The proposed KRadViz is compared with three high-dimensional visualization methods. The experimental results show that KRadViz can effectively display the high-dimensional PF shape, and give the optimization performance information of different solutions. The selection preferences of the three methods are also analyzed, and the effectiveness of the assisted decision process is verified. For the DTLZ2 and real-world MaOPs, the individual hypervolume (HV) contribution of preferred solutions increased by 9.98 % and 10.95 %, respectively.

关键词： Many-objective optimization High-dimensional visualization Knee points Preferred information RadViz

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Lagrange Stability of Fuzzy Memristive Neural Networks on Time Scales With Discrete Time Varying and Infinite Distributed Delays

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第8期30卷 3138-3151页

作者： Wan, Peng Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

The existing results of Lagrange stability for neural networks with distributed time delays are scale-free, which introduces conservativeness naturally. A class of Takagi-Sugeno fuzzy memristive neural networks (FMNNs) on time scales with discrete time-varying and infinite distributed delays is brought in this article. First, a new scale-limited Halanay inequality is demonstrated by timescale theory. Next, on the basis of inequality techniques on time scales, some new scale-limited algebraic criteria and linear matrix inequality criteria of Lagrange stability are obtained by comparison strategy and generalized Halanay inequality. All scale-limited sufficient criteria of Lagrange stability for FMNNs not only apply to continuous-time FMNNs and their discrete-time analogs, but also could deal with the arbitrary combination of them. Finally, two numerical simulations are given to verify the validity of the obtained theoretical results.

关键词： Neural networks Stability criteria Delays Takagi-Sugeno model Numerical stability control theory Synchronization Infinite distributed delays Lagrange stability memristive neural networks Takagi-Sugeno fuzzy logics time scales

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A thermophysical mechanism exploration of the brain: Motor cortex modeling with canonical ensemble theory

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NEUROCOMPUTING 2024年 610卷

作者： Li, Wei Zhou, Chenxi Chen, Xi Mao, Haodong He, Jiping Li, Qiang Zhang, Peng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Britton Chance Ctr Biomed Photon Wuhan Natl Lab Optoelect Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Collaborat Innovat Ctr Biomed Engn Sch Engn Sci MoE Key Lab Biomed Photon Wuhan Hubei Peoples R China Beijing Inst Technol Adv Innovat Ctr Intelligent Robots & Syst Beijing Peoples R China

The brain, recognized as one of the most intricate systems globally, has been a focal point for scientific exploration. Researchers have made efforts to construct models of the brain based on neural dynamics and complex networks to gain insights into its workings. It is crucial to investigate the brain's working principles from various perspectives. This study presents a novel thermophysical model of the motor cortex and examines its potential thermodynamic properties. Utilizing canonical ensemble theory, we constructed the thermophysical model using spike and local field potential (LFP) signals obtained from intracortical brain-machine-interfaces (iBMIs) in two monkeys. The parameters derived from this model-namely internal energy, free energy, and entropy-were employed to assess the thermodynamic properties and observe alterations in these properties during reaching and grasping movements. Furthermore, this proposed model was applied to movement pattern decoding, highlighting its potential in neural decoding tasks. In both LFP- and spike-based thermodynamic models, there was an increase in internal energy and free energy, coupled with a decrease in entropy when the motor cortex was activated across various movement tasks. This suggests that the neural system adheres to the principles of a thermophysical system. Notably, the thermodynamic features demonstrated superior performance in decoding movement intentions compared to traditional LFP and spike features. This study represents the first construction of a comprehensive thermodynamic model of the motor cortex based on LFP and spike signals. The model exhibits remarkable stationarity and holds promise for long-term and stable evaluations of motor cortex functions.

关键词： Thermodynamic model Motor cortex Intracortical brain-machine interface Local field potential Spike

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Dynamic Bilinear Fusion Network for Synthetic Aperture Radar image Change Detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Dong, Huihui Du, Xinyu Li, Zhijie Li, Xiaohuan Ma, Zongfang Gao, Feng Jiao, Licheng Xian Univ Architecture & Technol Sch Informat & Control Engn Xian 710055 Peoples R China Ocean Univ China Sch Comp Sci & Technol Qingdao 266100 Peoples R China Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Shaanxi Peoples R China

Change detection from synthetic aperture radar (SAR) imagery is critical in remote sensing research. Existing methods have made significant progress in the application of convolutional neural networks (CNNs) and attention mechanisms. However, traditional CNNs suffer the limitations in feature representation due to their depth and width constraints, and struggle to effectively capture complex interactions between image features. To address these issues, we propose a novel dynamic bilinear fusion network (DBFNet) for change detection in SAR imagery. First, to compensate for the lack of traditional convolutional representation capability, we design a dynamic shift convolution module that adaptively aggregates multiple convolution kernels and shifts pixels, enabling richer and more detailed features to be extracted. Second, a bilinear fusion module (BFM) is designed to generate the bilinear joint representation between parallel features by computing a matrix outer product of feature maps. The parallel features include both intraimage and interimage features, thereby effectively modeling the complex interactions and capturing the dependence relationship between spatiotemporal features. The experimental results on three real SAR datasets demonstrate the superior performance of DBFNet compared to existing state-of-the-art methods.

关键词： Convolution Feature extraction Radar polarimetry Logic gates Kernel Synthetic aperture radar Convolutional neural networks Adaptation models Transformers Rivers Bilinear pooling change detection dynamic convolution synthetic aperture radar (SAR) image

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