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Real-time dynamic medical image encryption based on extended multi-scroll memristive Hopfield neural network

NONLINEAR DYNAMICS 2025年第12期113卷 15517-15536页

作者： Leng, Xiangxin Wang, Xiaoping Du, Baoxiang Ren, Fangmin Zeng, Zhigang Educ Minist China Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol Hubei Key Lab Brain inspired Intelligent Syst Wuhan 430074 Peoples R China Heilongjiang Univ Elect Engn Coll Harbin 150080 Peoples R China

The memristor's inherent memristive and nonlinear properties make it particularly well-suited for simulating synaptic connections in neural networks, inducing rich dynamical behaviors that are essential for understanding brain mechanisms and brain-like learning. In this paper, a new locally active non-volatile trigonometric memristor is constructed and coupled into the Hopfield neural network. The motion state of the memristive Hopfield neural network (MHNN) is influenced by the coupling strength, allowing it to exhibit periodic initial offset boosting behavior. Furthermore, the MHNN demonstrates unique multi-scroll attractor extension behaviors. The number of attractor scrolls increases continuously with parameters variations at fixed time intervals, although there is an upper limit. However, as simulation time extends, the number of attractor scrolls can grow indefinitely, with newly formed scrolls extending monotonically in both directions under different conditions. The MHNN is implemented using both analog circuits and the DSP platform. Eventually, a real-time image encryption scheme aimed at protecting medical image privacy is designed, supported by practical test. In particular, the scheme can be further applied to remote video medical protection, which can encrypt the treatment content.

关键词： Memristive hopfield neural network Multi-scroll attractor Initial offset boosting Real-time image encryption

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Backstepping-Based Composite Prescribed-Time control for Two-Time-Scale Systems

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第8期35卷 3116-3127页

作者： Zeng, Ze-Hong Wang, Yan-Wu Liu, Xiao-Kang Liu, Zhi-Wei Huazhong Univ Sci & Technol Sch Artif Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

This paper explores the design of a composite prescribed-time controller for two-time-scale systems (TTSSs). First, due to numerical issues caused by the small perturbation parameter, standard control design techniques for prescribed-time stabilization of regular linear systems are typically unsuitable for TTSSs. To address this, we use time-scale separation to decouple the system into slow and fast subsystems. To avoid strict requirements on the input matrix, we apply a state transformation to convert the state equations of the slow and fast subsystems into block forms. Next, to prevent input saturation and achieve a directly and arbitrarily adjustable upper bound on the convergence time, we use time-varying scaling functions to design prescribed-time controllers for both subsystems. The composite prescribed-time controller ensures the prescribed-time convergence of the TTSS. Finally, two comparative examples and one RC ladder circuit system example are presented to demonstrate the effectiveness and advantages of the proposed approach.

关键词： backstepping control prescribed-time stability time-varying scaling function two-time-scale system

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Roll Torque Vibration Isolation control on Parallel Active Suspension System via Impedance-Based MPC for Vehicular Terminal

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13253-13268页

作者： Xue, Junfeng Chen, Zhihua Wang, Shoukun Wang, Junzheng Xu, Yongkang Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing Peoples R China Nanchang Hangkong Univ Key Lab Jiangxi Prov Image Proc & Pattern Recognit Nanchang 330063 Peoples R China Nanchang Hangkong Univ Key Lab Nondestruct Testing Technol MOE Nanchang 330063 Peoples R China

To improve the stability of vehicle body dynamics on rugged roads, the majority of researchers have studied vertical force control at the terminal of the vehicle utilizing an active suspension system. However, terminal disturbance roll torque can also have a negative impact on the stability of the vehicle's state. In this paper, a terminal roll torque control strategy (TRTCS) is proposed to enhance the stability of body attitude by controlling terminal roll torque in wheeled motion. The strategy includes impedance-based model predictive control (IMPC) and terminal posture return controller (TPRC). Firstly, IMPC analyzes the single-leg dynamic model embedded with torque impedance at the terminal. Then, the controller iterates through quadratic optimization to obtain an optimal impedance target torque that can efficiently mitigate terminal disturbance roll torque while stabilizing the terminal roll velocity, acceleration, and force on each electric cylinder. Then, TPRC generates additional smooth return speed for the terminal roll angle to the initial value through the Bezier curve and event-trigger mechanisms to estimate the terminal contact state to the ground. TPRC aims to compensate for low angular return speed due to insufficient measured torque on the suspending terminal during the downhill process. Finally, through simulation and experiment, TRTCS significantly diminishes the deviation of terminal roll torque and body attitude from target value to 25.2% and 35.8% respectively when a vehicle crosses multi-slopes compared with traditional torque *** to Practitioners-The motivation for this work is that the vibration isolation control on vehicles or wheeled robots often neglects the disturbance of the roll torque on their feet on rough roads. Unfortunately, this can damage the leg actuators of vehicles or robots, and even have a negative impact on the steadiness of the body posture. Therefore, this paper proposes a TRTCS, which controls the terminal rol

关键词： Active suspension system vibration isolation control impedance control impedance control torque control torque control parallel structure parallel structure parallel structure

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Event denoising for dynamic vision sensor using residual graph neural network with density-based spatial clustering☆

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NEUROCOMPUTING 2025年 636卷

作者： Feng, Weibin Wang, Xiaoping Zhan, Xin Huang, Hongzhi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol 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

The bio-inspired emerging dynamic vision sensor (DVS), characterized by its exceptional high temporal resolution and immediate response, possesses an innate advantage in capturing rapidly changing scenes. Nevertheless, it is also susceptible to severe noise interference, especially in challenging conditions like low illumination and high exposure. Notably, the existing noise processing approaches tend to oversimplify data into 2-dimensional (2D) patterns, disregarding the sparse and irregular crucial event structure information that the DVS intrinsically provides via its asynchronous output. Aiming at these problems, we propose a residual graph neural network (RGNN) framework based on density spatial clustering for event denoising, called DBRGNN. Leveraging the temporal window rule, we extract non-overlapping event segments from the DVS event stream and adopt a density-based spatial clustering algorithm to obtain event groups with spatial correlations. To fully exploit the inherent sparsity and plentiful spatiotemporal information of the raw event stream, we transform each event group as compact graph representations via directed edges and feed them into a graph coding module composed of a series of graph convolutional and pooling layers to learn robust geometric features from event sequences. Importantly, our approach effectively reduces noise levels without compromising the spatial structure and temporal coherence of spike events. Compared with other baseline methods, our DBRGNN achieves competitive performance by quantitative and qualitative evaluations on publicly available datasets under varying lighting conditions and noise ratios.

关键词： DVS Event denoising Temporal window Density-based clustering Graph learning for visual processing

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Zero-Sum Game-Based Distributed Secondary control for DC Microgrids Against Stealthy Attacks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第2期55卷 864-875页

作者： Cheng, Fabin Lai, Jingang Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This article investigates the voltage restoration problem of direct current (DC) microgrids (MGs) under stealth attacks. The fact is that secondary control requires information exchange through sparse communication networks, which makes cybersecurity the key to achieving voltage restoration. Compared with the attack signals launched by nonintelligent attackers, stealthy attack signals are more difficult to capture. Similarly, since MGs actually operate in a closed environment, malicious attackers have limited knowledge of the MG's structural information. Therefore, we reformulate the problem to a two-person zero-sum game between the attacker and defender and then seek the optimal voltage restoration control strategy. In the process of solving the optimal problem, both robustness and convergence of parameter estimation are considered in this article to enhance the feasibility of adaptive dynamic programming schemes. Additionally, while ensuring the voltage restoration performance of DC MGs and considering the constraints of actual network communication, a novel dynamic event-triggered mechanism is designed to reduce information transmission pressure. The effectiveness of the established control scheme is verified through real-time testing equipment built on OPAL-RT and comparison results.

关键词： Voltage control Games Game theory Event detection Robustness Communication networks Resists Resistance Process control Microgrids Distributed secondary control dynamic event-triggered control stealthy attack voltage restoration zero-sum game

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Differentially Private Distributed Algorithms for Aggregative Games With Directed Communication Graphs

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IEEE TRANSACTIONS ON AUTOMATIC control 2025年第4期70卷 2652-2658页

作者： Guo, Kai-Yuan Wang, Yan-Wu Luo, Yun-Feng Xiao, Jiang-Wen Liu, Xiao-Kang 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

Due to the transmission of information during seeking the Nash equilibrium and the possible leaking of sensitive information deduced from the transmitted information, it is urgent to propose privacy-preserving seeking algorithms for aggregative games. This article proposes two & varepsilon;-differentially private distributed Nash equilibrium seeking algorithms for aggregative games under directed communication graphs with row- and column-stochastic adjacency matrices, respectively. By utilizing the diameters of players' strategy sets, Laplacian noise free from the uniformly upper bound information of gradients is proposed to achieve & varepsilon;-differential privacy and guarantee the algorithms being fully distributed. To avoid the noise accumulating in the estimate of the aggregate strategy, a noise deduction mechanism is employed to ensure the accuracy of the algorithms. The tradeoff between accuracy and privacy level is investigated. Simulation examples and comparisons with existing result are carried out to verify the effectiveness of our algorithms and theorems.

关键词： Games Noise Privacy Aggregates Accuracy Cost function Laplace equations Vectors Nash equilibrium Costs Aggregative games differentially privacy directed communication graphs

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Subjective-uncertainty-oriented dynamic renting framework for energy storage sharing

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APPLIED ENERGY 2025年 378卷

作者： He, Yan Xiao, Jiang-Wen Wang, Yan-Wu Liu, Zhi-Wei He, Shi-Yuan 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

In recent years, shared energy storage has gained significant attention for mitigating the supply and demand imbalance caused by the intermittency of distributed renewable energy. Considering the subjective perception of prosumers when facing uncertainty, this paper proposes anew dynamic competitive on-demand renting framework for energy storage capacity (ESC) sharing to increase energy storage utilization, increase energy storage operator (ESO) profits, and reduce prosumer costs. In this framework, a demand-based dynamic capacity pricing mechanism is introduced, modeling the relationship between ESO and prosumers as a Stackelberg game while establishing a generalized Nash equilibrium (GNE) problem among prosumers. ESO determines the dynamic capacity pricing mechanism, while prosumers determine the hourly renting capacity based on demand. In capacity sharing, prospect theory is introduced for the first time to describe the subjective perceptions of prosumers when facing the uncertainty of renewable energy. Moreover, the existence of SE and the uniqueness of GNE are analyzed, followed by a summary and proposal of a method to determine the existence of equilibrium in a nested generalized non-cooperative Stackelberg game. Simulations show the effectiveness of the proposed framework on improving the ESC utilization rate, the impact of subjective perceptions on prosumers' decision-making, and the profit favorability of the correct estimation of subjective perceptions on ESO. Specifically, the framework increases ESO utilization by 24.07% and profit by 13.73%.

关键词： Energy storage sharing Prospect theory Dynamic capacity price Stackelberg game Generalized Nash equilibrium problem Distributed optimization

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Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation

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APPLIED ENERGY 2025年 190卷

作者： Chen, Yuejiang Xiao, Jiang-Wen Wang, Yan-Wu Luo, Yunfeng 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

Probabilistic forecasting plays an important role in the safety, stability and operation of power system. The traditional quantile regression method of non-parametric probability forecasting has the problem of crossingquantile. Besides, current neural network methods for wind farm cluster power forecasting often overlook the spatio-temporal correlation among related wind farms. To solve these problems, a cluster power forecasting model (CFM) considering spatio-temporal correlation is proposed in this paper. A novel spatial pattern attention (SPA) combining the advantages of convolutional neural network and attention mechanism is used to extract the spatial information. An improved multi-horizon quantile recurrent neural network (IMQ-RNN) and an improved non-crossing quantile regression (INCQR) strategy are used as the output module of CFM to produce high quality forecasting results. Numerical simulations are conducted by using public real-world data from the Global Energy Forecasting Competition 2014. The results show that the proposed model has excellent performance in both deterministic forecasting and probabilistic forecasting.

关键词： Probabilistic forecasting Spatio-temporal correlation Non-crossing quantile regression Wind power

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Evolutionary Game-Based Regulatory Strategy Optimization for Information Transmission Prices in Integrated Energy Systems

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ENERGIES 2025年第6期18卷 1452-1452页

作者： Cui, Kun Chi, Ming Zhao, Yong Liu, Zhiwei 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

The rapid evolution of Integrated Energy Systems (IESs) demands robust management of information transmission, which is critical for real-time monitoring, coordination, and operational efficiency. However, the increasing complexity and costs associated with information exchange necessitate effective pricing mechanisms to ensure economic sustainability and optimal resource allocation. This paper presents an evolutionary game-theoretic framework to analyze regulatory strategies governing information transmission within IES. In the context of market dynamics, both market regulators and communication network operators are considered as actors with bounded rationality, emphasizing their strategic interplay within the system. The main contributions include formulating a model that treats communication network operators as independent entities, enhancing the understanding of IES member diversity and interactivity. This study introduces evolutionary game dynamics, providing new insights into optimizing regulatory policies. This paper also innovates by considering asset utilization in defining effective assets, potentially curbing excessive investment by communication network operators and preventing information transmission prices from soaring. A case study is provided to reveal the dynamics and equilibrium states of the regulatory game, offering theoretical support for refining regulatory strategies in IESs.

关键词： evolutionary game theory integrated energy system information transmission price regulatory strategies asset utilization

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Adaptive Event-Triggered Distributed Observer Under Communication Link Faults

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IEEE TRANSACTIONS ON AUTOMATIC control 2025年第5期70卷 2992-3007页

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

This article addresses the problem of event-based fully distributed state estimation for an unperturbed linear time-invariant system in the presence of communication link faults. In a networked system, each agent is capable of constructing its local observer adaptively and transmitting its estimation to neighbors through discrete communication, thereby achieving accurate estimation of the system state while saving communication resources effectively. Specifically, first, the target system state can be decomposed into detectable and undetectable parts through detectability decomposition. Based on this, an adaptive event-triggered distributed observer is proposed, employing event-based adaptive coupling gains without the necessity for any global information. Additionally, a dynamic event-triggered mechanism with a mixed event-triggered threshold is introduced to generate the event-triggered sequence. Subsequently, the effectiveness of the proposed observer is verified through the analysis of estimation error and the assessment of Zeno behavior. Finally, the theoretical results of this article are validated through some simulation examples.

关键词： Event detection Observers Directed graphs Adaptive systems Network topology Couplings Topology Symmetric matrices Estimation error Eigenvalues and eigenfunctions Adaptive scheme communication link faults distributed state estimation event-triggered strategy

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