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A time-varying image encryption algorithm driven by neural network

OPTICS AND LASER TECHNOLOGY 2025年 186卷

作者： Zhang, Hangming Hu, Hanping Ding, Weiping Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China

Nowadays, the application of neural networks, genetic operations, and chaos to image encryption algorithms has become a focused area of frontier research. The neuronal mechanism and deep neural network have good nonlinear characteristics and meet the anti-aggression demands for cryptosystem security. What cannot be ignored is that due to multiplications between matrices in the computation of neuronal mechanisms and deep neural networks, the activation function cannot precisely recover the image, and the direct application of image encryption by neurons and deep neural networks cannot obtain secure results. Moreover, the performance of the adopted chaotic systems also determines the safety level of the cryptosystem. The fixed coupling structures in current research make the dynamic behavior easy to predict, bringing vulnerability to cryptosystems. Considering the aforesaid flaws, an image encryption algorithm using the neural-network pattern, genetic algorithm, and time-varying coupling structure of the chaotic system is proposed, helping improve the security and efficiency of our cryptosystem. Firstly, a time-varying coupled chaotic system is proposed, and highly stochastic sequences are used as keystreams. Then, the keystreams are processed using the neural-network mode and genetic operation to complete scrambling and diffusing. Ultimately, the simulations and security analyses indicate that our method reaches a highly safe level, which shows high efficiency and strong security, and meets the demands of various security applications.

关键词： Neural network Nerve cell Genetic operation Convolution lattice Chaos image encryption

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Fixed-time synchronization in p-th moment for stochastic multi-layer neural networks: An adaptive graph-theoretic Lyapunov functional approach

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NEUROCOMPUTING 2025年 636卷

作者： Yu, Guan-Nan Liu, Xiao-Kang Lei, Yan 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 Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China

In this paper, the p-th moment synchronization problem for a class of stochastic multi-layer neural networks with intra-layer and inter-layer connections is investigated. Due to the multiple connections with delays and stochastic noise, the typical methodologies that build a canonical linear or expanded matrix model to analyze its stability by constraining eigenvalues in the left-half plane, such as the Kronecker product method, linear matrix inequality and M-matrix approach are tough to tackle the problem. Consequently, a graph-theory-based Lyapunov functional is constructed by combining multiplicative principles and a graph-theoretic approach to help examine the effect of inter- and intra-layer connectivity on a unified framework. With the proposed adaptive fixed-time controller, sufficient conditions for the p-th moment synchronization in a fixed time are derived in terms of algebraic inequality. A corollary, together with a constant-gain fixed-time controller, is presented in case there is no delay. Finally, a confirmatory and two comparative simulations show the effectiveness and convenient implementation of the proposed control strategy.

关键词： Multi-layer network Neural networks Synchronization Fixed-time Adaptive control

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Real-Time Price-Based Demand Response for Industrial Manufacturing Process via Safe Reinforcement Learning

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第4期21卷 2937-2946页

作者： Ye, Xueyu Liu, Zhi-Wei Chi, Ming Ye, Lintao Li, Chaojie Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Industrial manufacturing processes present unique challenges in implementing demand response due to their complex equipment interactions, diverse operation modes, and safety constraints. Conventional model-based optimization methods often struggle in this context, requiring complete mathematical models and accurate uncertainty distributions. In this article, we propose a model-free safe deep reinforcement learning approach for real-time scheduling of industrial manufacturing systems, ensuring constraint adherence and accommodating diverse equipment operation modes. Specifically, the approach formulates the industrial demand response problem as a constrained Markov decision process with hybrid action space, capturing the intricate interplay between variable-speed equipment and discrete actuators, while considering safety constraints. To enhance exploration and robustness, the proposed method combines Lagrange multipliers based on soft actor-critic to satisfy the constraints. The cross-attention mechanism is utilized to find the association rules between hybrid actions to solve the challenges posed by the spatial gradient of hybrid actions. The proposed approach is trained and tested on a real-world dataset, demonstrating its superior performance in achieving significant cost reductions for manufacturing while satisfying operational constraints. Furthermore, sensitivity analysis underpins robustness against the variable real-time prices, showcasing its industrial applicability.

关键词： Production Safety Real-time systems Costs Manufacturing processes Job shop scheduling Electricity Demand response Uncertainty Training Hybrid action space industrial demand response (DR) industrial manufacturing process (IMP) safe deep reinforcement learning (DRL) scheduling

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Dual-active Li‖Te-Bi liquid metal batteries with enhanced coulombic efficiency and cycling stability

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CHEMICAL COMMUNICATIONS 2025年第31期61卷 5750-5753页

作者： Guo, Yewei Li, Zehang Cu, Qiao Feng, Shaoming Zhang, Weixin Wang, Kangli Li, Haomiao Jiang, Kai Huazhong Univ Sci & Technol Sch Mat Sci & Engn Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Technol Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China

Tellurium (Te), a chalcogenide with higher electrical conductivity than sulfur (S) and selenium (Se), can be made into liquid metal batteries with high energy density. The Te-Bi alloy positive electrode inhibits Te dissolution and enhances lithium diffusion, which further improves the coulombic efficiency and cycling stability of lithium batteries. Due to the dual-activity mechanism, Li & Vert;Te5Bi5 achieves similar to 100% capacity utilization. Our results highlight a viable strategy for advancing high-voltage LMBs, providing an option for achieving long-life stationary energy storage solutions.

关键词： Bismuth alloys

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Statistical Feasibility Robust Optimization With Polyhedron Uncertainty Set for Hydrogen-Data Center Microgrid Operations

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9884-9902页

作者： Duan, Juntao Li, Yuanzheng Li, Yang Yang, Pu Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132000 Peoples R China Tencent Technol Shenzhen Co Ltd Shenzhen 518057 Guangdong Peoples R China

Robust optimization (RO) has been widely used in the hydrogen-data-center microgrid (H-2- DCMG) optimal operations. However, the operation results based on RO are too conservative. Statistical feasibility can be introduced into RO to reduce conservatism. Therefore, statistical feasibility-based RO is adopted in the H-2- DCMG operations optimization. In this study, a data-driven statistical-feasibility-based robust rolling optimization framework is constructed for the optimal operations of the c-DCMG. In this framework, DC temperature is influenced by the uncertain outside temperature, and an uncertainty set is needed to express the uncertain outside temperature. Unfortunately, the existing studies that construct the uncertainty set satisfying statistical feasibility only constructs the ellipsoid uncertainty sets. The ellipsoid uncertainty sets will be converted into the second-order cone constraints, which will increase the complexity when solving. In this study, the Statistical-Guarantee-based Vertex Link (SGVL) algorithm is proposed to construct the polyhedron uncertainty sets, which are used to describe the uncertainty of the outside temperature. Moreover, statistical feasibility-based DC temperature bound is guaranteed by the optimal operations obtained based on these uncertainty sets. Compared with the traditional ellipsoid uncertainty set, the polyhedron uncertainty set can reduce the complexity of the optimization problem and improve the efficiency of the solution process. Case studies based on the real-world temperature dataset are processed. The results show that the introduction of statistical feasibility can reduce the total operation cost by 0.29%similar to 0.64%. The average solving time of the optimization problems based on the polyhedron uncertainty sets constructed using the SGVL algorithm also reduces by 7%similar to 13%. The cases also verify that other uncertainty parameters and different kinds of forecasters do not influence the performance and e

关键词： Uncertainty Optimization Temperature distribution Upper bound Servers Microgrids Heating systems Data centers Hydrogen Greenhouse gases Hydrogen energy storage system data center microgrid statistical feasibility robust optimization

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A large-scale group SLIM considering expert credibility under social network to estimate human error probabilities in the railway driving process

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 255卷

作者： Zhou, Jian-Lan Zhou, Ya-Lun Xiao, Ren-Bin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Shenzhen Huazhong Univ Sci Technol Res Inst Shenzhen 518057 Peoples R China

The Large-scale Group Success Likelihood Index Method (LG-SLIM) can eliminate bias caused by a single expert in human error assessment. The traditional LG-SLIM uses trust degrees to cluster and reach a consensus. However, the existing clustering algorithms do not consider the trust degrees between a given pair of experts to be multiple and vary according to different evaluated tasks. Besides, the existing consensus models do not consider various combinations of the evaluated tasks and trusted experts' professions when managing trust degrees and self-confidence. Therefore, the similarity-trust-based clustering algorithm is improved using the comprehensive trust degree integrated from diverse trust degrees concerning all evaluated tasks. Moreover, expert credibility is proposed to reflect the quality of the expert's evaluation results, determined by self-confidence and trust degree simultaneously according to various combinations of the expert profession and target task. Accordingly, under the social network derived from expert credibility, the incompatible outliers change their opinions by referring to the views of those with the highest expert credibility. Finally, the sensitivity experiment and comparative analysis verify the effectiveness of the proposed model. The proposed LG-SLIM model is useful for human error assessment when critical operations need many experts to obtain reliable and accurate results.

关键词： Railway operation Success likelihood index method Human error analysis Expert credibility Social network Large-scale group decision-making

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Adaptive Neural Finite-Time Deployment of Heterogeneous Multi-agent Systems via a Cross-Species Bionic PDE-ODE Approach

Artificial Intelligence Science and Engineering

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Artificial Intelligence Science and Engineering 2025年第1期1卷 52-63页

作者： Jingtao MAN Zhigang ZENG Jun LI School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China

For large-scale heterogeneous multi-agent systems (MASs) with characteristics of dense-sparse mixed distribution, this paper investigates the practical finite-time deployment problem by establishing a novel cross-species bionic analytical framework based on the partial differential equation-ordinary differential equation (PDE-ODE) approach. Specifically, by designing a specialized network communication protocol and employing the spatial continuum method for densely distributed agents, this paper models the tracking errors of densely distributed agents as a PDE equivalent to a human disease transmission model, and that of sparsely distributed agents as several ODEs equivalent to the predator population models. The coupling relationship between the PDE and ODE models is established through boundary conditions of the PDE, thereby forming a PDE-ODE-based tracking error model for the considered MASs. Furthermore, by integrating adaptive neural control scheme with the aforementioned biological models, a “Flexible Neural Network” endowed with adaptive and self-stabilized capabilities is constructed, which acts upon the considered MASs, enabling their practical finite-time deployment. Finally, effectiveness of the developed approach is illustrated through a numerical example.

关键词： Adaptation models Uncertainty Biological system modeling Neural networks Stochastic processes Mathematical models Numerical models Topology Multi-agent systems Diseases

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Distributed predefined-time algorithms for optimal solution seeking in multi-agent systems subject to input disturbances

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AUTOMATICA 2025年 174卷

作者： Xu, Jing-Zhe Liu, Zhi-Wei Ge, Ming-Feng Yang, Tao Chi, Ming He, Dingxin 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 China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

This paper presents a novel incremental consensus-based algorithm for solving a class of distributed optimization problems in multi-agent systems, considering input disturbances, equality constraints, and box constraints. Traditional methods rely on average consensus to maintain the satisfaction of equality constraints throughout the entire evolution process. However, in practical applications, input disturbances can disrupt these equality constraints, rendering traditional methods ineffective. To address this challenge, the proposed algorithm combines integration sliding mode control technology with the observer methodology, creating a unified framework capable of handling input disturbances and preventing the system state from deviating beyond the solution space defined by the equality and box constraints. Moreover, the proposed algorithm offers the advantage of ensuring that all agents reach the optimal solution within a predefined time frame. This settling time can be directly adjusted by modifying one or more parameters. Finally, several numerical examples are validated to demonstrate the effectiveness and performance of the proposed algorithm. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Distributed optimization Disturbance rejection Predefined-time convergence Multi-agent systems

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Event-triggered integral sliding mode control of two-time-scale systems

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NONLINEAR DYNAMICS 2025年第2期113卷 1263-1277页

作者： Yu, Tian-Tian Wang, Yan-Wu Yang, Wu Lei, Yan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China GuangXi Univ Sch Elect Engn Nanning 530004 Peoples R China Southwest Univ Sch Elect & Informat Engn Chongqing Key Lab Gener Technol & Syst Serv Robot Chongqing 400715 Peoples R China

This paper investigates the H-infinity performance of two-time-scale systems with nonlinear uncertainty and external disturbance via the event-triggered integral sliding mode control (ETISMC) strategy. To facilitate the analysis of the sliding mode surface's reachability and the design of the event-triggered (ET) integral sliding mode controller, the epsilon-dependent integral sliding function with the event-triggered mechanism is constructed. Based on H-infinity control theory, the integral sliding mode controller gain and the ET parameter matrix are co-designed through the solution of a set of linear matrix inequalities. The proposed event-triggered mechanism can avoid Zeno behavior. Furthermore, an adaptive ETISMC strategy is proposed to address the unknown upper bounds of uncertainty and external disturbance. Finally, the effectiveness, applicability, and advantages of the control strategies are verified by simulations and comparative analysis.

关键词： Two-time-scale system H-infinity performance Event-triggered mechanism Integral sliding mode control Adaptive control

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A Novel Sequence-to-Sequence-Based Deep Learning Model for Multistep Load Forecasting

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第1期36卷 638-652页

作者： Lu, Renzhi Bai, Ruichang Li, Ruidong Zhu, Lijun Sun, Mingyang Xiao, Feng Wang, Dong Wu, Huaming Ding, Yuemin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Shanghai Elect Grp Co Ltd Cent Acad Shanghai 200070 Peoples R China Kanazawa Univ Inst Sci & Engn Kakuma Kanazawa Ishikawa 9201192 Japan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Zhejiang Univ Dept Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China Univ Navarra Tecnun Sch Engn San Sebastian 20018 Spain

Load forecasting is critical to the task of energy management in power systems, for example, balancing supply and demand and minimizing energy transaction costs. There are many approaches used for load forecasting such as the support vector regression (SVR), the autoregressive integrated moving average (ARIMA), and neural networks, but most of these methods focus on single-step load forecasting, whereas multistep load forecasting can provide better insights for optimizing the energy resource allocation and assisting the decision-making process. In this work, a novel sequence-to-sequence (Seq2Seq)-based deep learning model based on a time series decomposition strategy for multistep load forecasting is proposed. The model consists of a series of basic blocks, each of which includes one encoder and two decoders;and all basic blocks are connected by residuals. In the inner of each basic block, the encoder is realized by temporal convolution network (TCN) for its benefit of parallel computing, and the decoder is implemented by long short-term memory (LSTM) neural network to predict and estimate time series. During the forecasting process, each basic block is forecasted individually. The final forecasted result is the aggregation of the predicted results in all basic blocks. Several cases within multiple real-world datasets are conducted to evaluate the performance of the proposed model. The results demonstrate that the proposed model achieves the best accuracy compared with several benchmark models.

关键词： Load modeling Predictive models Load forecasting Forecasting Autoregressive processes Data models Time series analysis Decomposition strategy long short-term memory (LSTM) neural network multistep load forecasting sequence-to-sequence (Seq2Seq) model temporal convolution network (TCN)

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