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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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Stability and bifurcation analysis of delayed tri-trophic food chain model in poisoned environment with fear effect and additional food

Stability and bifurcation analysis of delayed tri-trophic fo...

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作者： Ramasamy, Sivasamy Banjerdpongchai, David Park, PooGyeon Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of

One of the most important goals of theoretical ecologists is to find a strategy for controlling the chaos in ecological models to maintain healthy ecosystems. We investigate the influence of fear and the supply of additional food to predators in a delayed tri-trophic food chain model, where interference among the species is determined by Holling type-II functional response. The proposed model incorporates the fact that the growth rates of prey and middle predators are decreased due to the fear of middle and special predators, respectively. The additional food is supplied to both predators by either nature or external agencies. Furthermore, the poisoned prey in the predator's environment is taken into account. In the proposed model, we use two time delays: one for the growth term of the middle predator and another for the gestation delay of the special predator. We determine the conditions for the existence of ecologically feasible equilibrium points and their local and global stability. In addition, we establish the conditions for the existence of Hopf bifurcation around interior equilibrium to seek periodic behavior in nondelayed and delayed models. Numerical investigations are performed to justify the proposed theoretical findings through phase portraits, time series of solutions, and bifurcation diagrams. We observe that the chaotic dynamics of a tri-trophic food chain model can be controlled by the proper choice of the fear effect and by supplying additional food parameters. As a result, our findings provide more ecological insights into the dynamics of the delayed tri-trophic food chain model with the fear effect and additional food supply. © 2023 John Wiley & Sons, Ltd.

关键词： Hopf bifurcation

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A Novel Optimal Distributed Nonlinear Filter for Simultaneous State and Unknown Input Estimation in Multi-Sensor Networks

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IEEE Signal Processing Letters 2025年 32卷 1745-1749页

作者： Ding, Bo Wei, Yuanchu Zhang, Enze Fang, Huajing Yangzhou University College of Information Engineering Yangzhou225127 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China

This letter investigates the problem of simultaneous state and unknown input estimation in a nonlinear system within multi-sensor networks. To avoid the linearization errors caused by existing methods, such as statistical linear regression techniques and first-order Taylor approximation, a novel optimal distributed nonlinear filter is proposed based on the unscented transformation and unbiased minimum variance criterion. First, the unscented transformation is applied to generate predicted estimates and the covariance matrix. Second, input and state estimation are carried out in an unbiased minimum variance manner using measurements from the nonlinear system. Third, a distributed strategy leveraging average consensus is employed to incorporate local estimates from neighboring sensors. Finally, simulation results confirm that the proposed method achieves substantial improvements in estimation accuracy compared to existing methods. © 1994-2012 IEEE.

关键词： Covariance matrix

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Frequency-domain analysis for cooling load prediction: A Multi-Scale FourierGNN Crossformer approach

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International Journal of Refrigeration 2025年 176卷 268-283页

作者： Cen, Jian Zeng, Linzhe Liu, Xi Yang, Jianming Li, Xinyao Deng, Feiqi School of Automation Guangdong Polytechnic Normal University Guangzhou510665 China Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory GuangZhou510665 China School of Automation Science and Engineering South China University of Technology GuangZhou510640 China

With the rise in energy consumption in buildings, particularly for air conditioning systems, intelligent air conditioning control has become more important. Accurate prediction of central air conditioning system cooling load is crucial to optimizing energy efficiency. This paper proposes a Multi-Scale FourierGNN Crossformer (MFGformer) model based on Crossformer, which integrates Fourier Graph Neural Networks (FourierGNN) and Multi-scale Cross-axis Attention (MCA) mechanisms for multivariate cooling load time series forecasting of central air conditioning systems. The complexity of dynamic characteristics of multivariable time series data is often misunderstood by current forecasting models, which this model takes into account. The FourierGNN module maps time series data into the frequency domain through the Discrete Fourier Transform, effectively capturing periodic and trend features of the data. The MCA mechanism captures multi-scale characteristics and local detail information in time series data through dual cross-attention computation, enhancing the ability to capture dependencies between different variables. Validation on two real case datasets shows that the MFGformer model performs well in predicting the cooling loads of central air conditioning systems, especially when outputting 24-hour predictions at a 96-hour input time step, the model's Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Arctangent Absolute Percentage Error (MAAPE), and Coefficient of Variation of Root Mean Square Error (CV-RMSE) are 130.7765 kW, 322.2957 kW, 25.2611% and 56.6846%. Compared with eight other typical models, the MAE, RMSE, MAAPE and CV-RMSE of the MFGformer model were reduced by 22.8869-156.1751 kW, 59.6766-336.4538 kW, 0.9422-51.109% and 8.6393-32.6965%, respectively, for different output step sizes. These results demonstrate the model's ability to provide accurate predictions when dealing with data characterized by volatility and nonlinearity, while maintain

关键词： Bode diagrams

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Empowering Lab Education: Integrating a Vision-Based Monitoring System with Small-Scale Self-Driving Experiment Platforms

Empowering Lab Education: Integrating a Vision-Based Monitor...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Ren, Junru Fisker-Bodker, Nis Guldenring, Ronja Chang, Jin Hyun Vegge, Tejs Ravn, Ole Nalpantidis, Lazaros Technical University of Denmark Automation and Control Group Department of Electrical and Photonics Engineering Denmark Technical University of Denmark Department of Energy Conversion and Storage Denmark

ISBN: (纸本)9798331531614

The rise of self-driving laboratories has seen significant growth across various research domains, particularly in chemistry, materials science and life science. However, a major challenge persists - the majority of self-driving systems are costly due to the use of highly precise lab equipment, robotic platforms, and case-specific algorithms, rendering these systems less accessible for educational purposes. This paper takes a multidisciplinary approach;we first introduce a small-scale self-driving experiment platform tailored for educational use, focusing on liquid materials mixing tasks commonly seen in chemistry and life sciences. To understand the operational status in real-time while maintaining self-driving capability and efficiency, we propose a novel system concept: employing a mobile robot as the lab supervisor to monitor the experiment process across multiple identical self-driving platforms. Specifically, this paper focuses on implementing a vision-based monitoring system. A deep learning architecture with a new training strategy is presented to jointly address two tasks: (a) vessel and content material segmentation and (b) volume estimation. The two tasks can be trained independently but can be inferred end-to-end by integrating them into the Mask R-CNN framework. Through evaluating the monitoring module on a real dataset, the results showcase promising detection capabilities, good real-time performance, and compatibility with the self-driving platform, indicating the feasibility of our proposed system. © 2025 IEEE.

关键词： Crystalline materials

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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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Multi-User Cooperative Receive Scheme for Air-to-Ground Vortex Wave Communications

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China Communications 2025年第3期22卷 76-85页

作者： Zhang Beibei Chen Ruirui Lin Jinyang Li Qi Liang Liping Sun Yanjing School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Jiangsu Automation Research Institute Lianyungang 222061China State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an 710071China

Orbital angular momentum(OAM)can achieve multifold increase of spectrum efficiency,but the hollow divergence characteristic and Line-of-Sight(LoS)path requirement impose the crucial challenges for vortex wave *** air-to-ground vortex wave communications,where there exists the LoS path,this paper proposes a multi-user cooperative receive(MUCR)scheme to break through the communication distance limitation caused by the characteristic of vortex wave hollow *** particular,we derive the optimal radial position corresponding to the maximum intensity,which is used to adjust the waist *** on the waist radius and energy ring,the cooperative ground users having the minimum angular square difference are ***,the signal compensation scheme is proposed to decompose OAM signals in air-to-ground vortex wave *** results are presented to verify the superiority of our proposed MUCR scheme.

关键词： air-to-ground capacity cooperative receive multi-user vortex wave communications

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Predefined-Time Distributed Fault-Tolerant control for Nonlinear Multiagent Systems Suffering From Nonaffine Faults

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3300-3311页

作者： Cheng, Shuai Xin, Bin Chen, Jie Deng, Fang Qingdao University School of Automation Qingdao266071 China Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing100081 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Department of Control Science and Engineering Shanghai200092 China

This article investigates the distributed predefined-time (PT) fault-tolerant control for nonlinear multiagent systems (NNMSs) with nonaffine faults. A novel distributed PT control scheme is proposed based on a new PT theorem. The switching functions are introduced into distributed control signals to avoid the singularity problem. The second-order filter technology is designed to solve "explosion of complexity" issues. The newly designed PT compensation signals are utilized to eliminate filter errors. The proposed distributed PT controller based on local information can guarantee that all signals of the closed-loop system are PT bounded, and consensus errors of NNMSs can be enforced to a small region around the origin within a predefined time. Compared with the finite/fixed time, the predefined time is an exact value given in advance, which remains constant regardless of initial states and control parameters. Finally, two comparative simulations are provided to demonstrate the presented strategy. © 2013 IEEE.

关键词： Adaptive control systems

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Quality-Relevant Modeling and Monitoring of Industrial Cyber-Physical Systems: The Semi-Supervised Dynamic Latent Variable Models

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 39-47页

作者： Zhou, Le Wang, Yaoxin Wu, Yuanqing He, Shenghuang Song, Zhihuan Zhejiang University of Science and Technology Department of Automation and Electrical Engineering Hangzhou310023 China Guangdong University of Technology Department of Automation Guangzhou510006 China Dongguan University of Technology Department of Computer Science and Technology Dongguan523808 China Ningbo Industrial Internet Institute Ningbo315012 China Zhejiang University Department of Control Science and Engineering Hangzhou310027 China

In modern industrial cyber-physical systems, a mass of process variables has been obtained by the high-sampling online sensors. Meanwhile, the key quality indexes are usually obtained infrequently from the laboratory. Hence, these quality variables are with low sampling rate. To avail of the complete process and quality variables with various sampling rates in the dynamic processes, a set of semi-supervised dynamic latent variable models are proposed for dynamic modeling and quality-relevant monitoring. The proposed models have built a unified structure to consider both the auto-correlations and cross-correlations between the process and quality variables with unbalanced sampling sizes. Hence, the feature extraction of the time series data is dynamically adjusted under the guidance of the quality variables. Then, the quality-relevant monitoring schemes are proposed, which is validated by a numerical case and an actual wastewater treatment process. © 2024 IEEE. All rights reserved.

关键词： Indexes Probabilistic logic Kalman filters Noise Cyber-physical systems Analytical models Parameter estimation Time series analysis Process monitoring Numerical models

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An FE-S-BiLSTM and Heatmap-Based State-of-Health Estimation Method for Lithium-Ion Batteries

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IEEE Sensors Journal 2025年第10期25卷 17727-17738页

作者： Shao, Kui Zhai, Chao Zhang, Chaolong He, Yigang Du, Bolun Wu, Ji China University of Geosciences School of Future Technology Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China Jinling Institute of Technology College of Intelligent Science and Control Engineering Nanjing211169 China China Electric Power Research Institute Wuhan430074 China Wuhan University School of Electrical Engineering and Automation Wuhan430072 China Hefei University of Technology School of Automotive and Transportation Engineering Anhui Hefei230009 China

Accurate battery state-of-health (SOH) estimation can improve battery reliability and ensure its safe and efficient operation. Therefore, this study proposes a novel method for battery SOH estimation. First, a thermocouple temperature sensor monitors the battery’s operating temperature and provides feedback to the thermostat for precise temperature control during experiments. The battery’s charging voltage and current are measured using voltage transmitters and Hall current sensors, respectively, and the two are fused to obtain the battery’s charging power. Next, 1-D charging power data are converted into 2-D heatmaps using image encoding techniques. The heatmap corresponding to the first cycle is selected as the reference image, and the difference between the heatmaps of subsequent cycles and the reference image is quantified using structural similarity (SSIM). The final results serve as an indicator for battery health. In addition, this study proposes a novel battery SOH estimation model, the feature enhancement-simplification-bidirectional long short-term memory (FE-S-BiLSTM). The feature enhancement layer in the FE-S-BiLSTM model enriches global static features through enhancement learning. Based on the model’s bidirectional long short-term memory (BiLSTM) layer and simplification layer, dynamic features in the time-space domain are double captured. Finally, this study utilizes six batteries and designed a variety of experiments to validate the effectiveness of the proposed method. The experimental design comprises three tasks: SOH estimation based on full-charging data, SOH estimation based on random SOC interval charge data, and cross-battery SOH estimation. The experimental results demonstrate that the proposed SOH estimation method for batteries exhibits significant potential for practical applications. © 2001-2012 IEEE.

关键词： Lithium-ion batteries

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