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Dynamic and Heterogeneous Identity-Based Cooperative Co-Evolution for Distributed Lot-Streaming Flowshop Scheduling Problem

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Complex System Modeling and Simulation 2025年第1期5卷 86-106页

作者： Wang, Juan Zhang, Guanghui Li, Xiaoling Feng, Yanxiang School of Information Science and Technology Hebei Agricultural University Baoding071001 China Xi'an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi'an710049 China School of Electronics and Control Engineering Chang'an University Xi'an710064 China School of Automation Science and Engineering Xi'an Jiaotong University Xi'an710049 China

In this research, a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm (DHICCA) is proposed for addressing the distributed lot-streaming flowshop scheduling problem (DLSFSP) with the objective to minimize the makespan. A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of DHICCA. In the evolution of DHICCA, population individuals are endowed with heterogeneous identities according to their quality, including superior individuals, ordinary individuals, and inferior individuals, which serve local exploitation, global exploration, and diversified restart, respectively. Because individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials, identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary way. This is important to use limited population resources to solve complex optimization problems. Specifically, exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood, destruction-construction, and gene targeting. Exploration is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover strategy. In addition, restart is performed on inferior individuals to introduce new evolutionary individuals to the population. After the cooperative co-evolution, all individuals with different identities are merged as a population again, and their identities are dynamically adjusted by new evaluation. The influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all algorithms. The results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving

关键词： Vector spaces

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Adaptive Path-following control of Underactuated Surface Vessels Subject to Unknown Disturbances With Uniform Bounded Lateral Motion

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International Journal of control, automation and Systems 2025年 1-11页

作者： Cao, Duc Sang Tran, Quoc Van Department of Electric Actuators & Automation Control Control Automation in Production and Improvement of Technology Institute (CAPITI) Hanoi Viet Nam Department of Mechatronics the School of Mechanical Engineering Hanoi University of Science and Technology (HUST) Hanoi Viet Nam

This article presents a unified framework for path-following and trajectory tracking of underactuated surface vessels (USVs) in the presence of unknown disturbances. To handle the underactuation of the vessel, the path-following of a hand point in the longitudinal axis ahead of the vessel’s mass center is considered. Two robust adaptive path-following control laws are subsequently proposed in the vessel-fixed coordinates to drive the vessel (specifically, the hand point) to the desired path asymptotically. The first controller employs a time-varying discontinuous signum term to attenuate unknown disturbances. By utilization of normalization technique, the second tracking controller is continuous, thus eliminating chattering effect in the control forces commonly occurred in sliding mode controllers. Global uniform asymptotic stability of the path tracking is established based on Lyapunov theory and Barbalat’s lemma. As another technical contribution of this article, the stability of the non-actuated lateral motion of the vessel is shown rigorously based on the square-integrability of the path-following error vector. © ICROS, KIEE and Springer 2025.

关键词： Internal dynamics path-following robust adaptive control stability analysis underactuated vehicles

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Deep Co-Training Partial Least Squares Model for Semi-Supervised Industrial Soft Sensing

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3363-3371页

作者： Zheng, Junhua Zhou, Le Ye, Lingjian Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction performance, it is highly restricted to the number of labeled training data, as well as large computational burden and cumbersome parameter tuning procedures. How to break through the bottleneck of data-drive models in terms of limited labeled data and high computational complexity should be one of the main recent focuses in the field of industrial soft sensing. In this article, a deep co-training PLS (deep CT-PLS) model is proposed to extend the ordinary PLS model to the semi-supervised deep form. While the deep model can efficiently extract inherent natures of process data, the co-training strategy makes lots of unlabeled data useful through a two-view cross training and annotation process. In this case, the performance restriction of the deep PLS model can be greatly relieved, with the incorporation of additional unlabeled data, while at the same time the designed model structure keeps in a low computational complexity. Based on the case study on a real industrial production process, the deep CT-PLS model can significantly improve the soft sensing performance. © 2013 IEEE.

关键词： Self-supervised learning

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Geometric attitude tracking control of quadrotor UAVs with adaptive extended state observers

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ISA Transactions 2025年 160卷 289-297页

作者： Wang, Liping Chen, Jiapeng Huang, Yuyuan Zhu, Yongbin Pei, Hailong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou510640 China

This paper studies a geometric attitude tracking control problem of quadrotor unmanned aerial vehicles (UAVs) with adaptive extended state observers (AESOs). Through coordinate transformation, the error dynamic of AESO can be transformed into a canonical form which is easier to analyze by linear time-varying theory. In the presence of unknown disturbances and system uncertainties, the estimated error and stability of AESO can be analyzed. Simultaneously, a geometric tracking controller is developed on SO(3) with AESOs which guarantees that the error exponentially converges to a bounded set related to the estimated error. Some examples and a flight test are given to verify the availability of the control scheme. © 2025

关键词： Unmanned aerial vehicles (UAV)

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Optimal Scheduling of an Independent Electro-Hydrogen System with Hybrid Energy Storage Using a Multi-Objective Standardization Fusion Method

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Energy engineering 2025年第1期122卷 63-84页

作者： Suliang Ma Zeqing Meng Mingxuan Chen Yuan Jiang School of Electrical and Control Engineering North China University of TechnologyBeijing100144China ChinaThree Gorges Technology Co. Ltd.Beijing100032China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China

In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is ***,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical *** deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s *** optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization *** proposed method better balances all optimization objective functions and reduces the impact of their *** the cost of BESS decreases by approximately 30%,its participation deepens by about 1 ***,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase *** study aims to provide a more reasonable approach to solving multi-objective optimization problems.

关键词： Electro-hydrogen system multi-objective optimization standardization method hybrid energy storage system

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High-Sensitivity and External RI-Independent Mode-Selective Michelson Interferometer Using a Single-Ended Helical Graded-Index Fiber

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Journal of Lightwave Technology 2025年第11期43卷 5336-5344页

作者： Zhu, Chengliang Meng, Xinyue Shi, Mengshuo Cheng, Tonglei Zhao, Yong Northeastern University College of Information Science and Engineering Shenyang110004 China Northeastern University School of Control Engineering Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China State Key Laboratory of Synthetical Automation for Process Industries Shenyang China Northeastern University School of Control Engineering Qinhuangdao066004 China

We have theoretically and experimentally proposed and demonstrated an all-in-fiber-core mode-selective Michelson interferometer (MS-MI) sensor utilizing a single-ended helical graded-index few-mode fiber (SE-HGIFMF). The SE-HGIFMF effectively eliminates multi-mode interference, remains insensitive to external refractive index (RI) variations, and significantly enhances sensing sensitivity due to the minimal disparity in group RI between the two guided core modes. We investigated the torsion, strain, and temperature characteristics of the sensor, achieving sensitivities of 1.785 nm/(rad/m) for directional torsion, 0.0316 nm/μϵ for strain, and 0.624 nm/°C for temperature. These sensitivities are competitive within the fiber optic sensor family and represent the highest values reported for mode-selective MI and Mach-Zehnder interferometer (MZI) sensors to our knowledge. The proposed sensor not only demonstrates exceptional sensitivity but also offers directionality identification for torsion, ease of use, flexible installation, low loss, and high reliability, showcasing robustness against environmental fluctuations. © 1983-2012 IEEE.

关键词： Michelson interferometers

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Aperiodically Intermittent control Approach to Finite-Time Synchronization of Delayed Inertial Memristive Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 1014-1023页

作者： Jiang, Yuxin Zhu, Song Shen, Mouquan Wen, Shiping Mu, Chaoxu School of Mathematics Xuzhou221116 China Nanjing Technology University College of Electrical Engineering and Control Science Nanjing211816 China University of Technology Sydney Centre for Artificial Intelligence UltimoNSW2007 Australia Tianjin University School of Electrical and Automation Engineering Tianjin300072 China

This article investigates the finite-time synchronization (FTS) for inertial memristive neural networks (IMNNs) with time-delays by the aperiodically intermittent control approach. Compared with the reduced-order method utilized in the existing literature, this article considers the FTS of delayed IMNNs directly without order reduction. First, the error IMNNs with time-delays is designed through the theories of set-valued mappings and differential inclusions, and its finite-time stability problem is discussed by applying the finite-time stability theorem. Furthermore, by constructing nonperiodic intermittent state-feedback controller and nonperiodic intermittent adaptive control strategy, the sufficient criteria to ensure the FTS of the master-slave delayed IMNNs are derived, and the settling times are explicitly estimated. Finally, a simulation to confirm the availability of results is provided. © 2020 IEEE.

关键词： Time delay

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Unformer: A Transformer-Based Approach for Adaptive Multiscale Feature Aggregation in Underwater Image Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 1024-1037页

作者： Qing, Yuhao Wang, Yueying Yan, Huaicheng Xie, Xiangpeng Wu, Zhengguang Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210023 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China

Underwater imaging is often compromised by light scattering and absorption, resulting in image degradation and distortion. This manifests as blurred details, color shifts, and diminished illumination and contrast, thereby hindering advancements in underwater research. To mitigate these issues, we propose Unformer, an innovative underwater image enhancement (UIE) technique that leverages a transformer-based architecture for multiscale adaptive feature aggregation. Our approach employs a multiscale feature fusion strategy that adaptively restores illumination and detail features. We reevaluate the relationship between convolution and transformer to develop a novel encoder structure. This structure effectively integrates both long-range and short-range dependencies, dynamically combines local and global features, and constructs a comprehensive global context. Furthermore, we propose a unique multibranch decoder architecture that enhances and efficiently extracts spatial context information through the transformer module. Extensive experiments on three datasets demonstrate that our proposed method outperforms other techniques in both subjective and objective evaluations. Compared with the latest methods, Unformer has improved the peak signal-to-noise ratio (PSNR) by 19.5% and 14.8% respectively on the LSUI and EUVP datasets. The code is available at: https://***/yhflq/Unformer. © 2024 IEEE.

关键词： Light scattering

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Coprime Factorization-Based Encryption and Attack Detection for Nonlinear Cyber-Physical Systems Using Deep Learning Approach

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IEEE Transactions on automation science and engineering 2025年 22卷 14020-14029页

作者： Wu, Shimeng Luo, Hao Zhang, Jiusi Qiao, Xinyu Tian, Jilun Jiang, Yuchen Harbin Institute of Technology Department of Control and Simulation Center Harbin150001 China National Key Laboratory ofComplex System Control and Intelligent Agent Cooperation Harbin150001 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China

This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep neural networks to realize the coprime factorization (CF) of nonlinear systems. The definition of the CF guides the network training and designing process, and the model’s topology is designed in the state-space form, which improves the interpretability of the data-driven CF. Based on the CF-aided neural networks, an encrypted transmission module is designed that projects information related to system dynamics into a perpendicular data space, which complements existing encryption methods from a control theory perspective. Subsequently, an anomaly detector are designed using the same CF pairs. This detector not only provides high-accuracy detection of attacks but also distinguishes between attacks and faults, thereby reducing the false positive rate and enhancing the reliability of the attack detection. The proposed method has been validated in a real CPS using a mecanum-wheeled vehicle as the physical plant, demonstrating its effectiveness and applicability. © 2004-2012 IEEE.

关键词： Factorization

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