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Coupled Urban Risks: A Complex Systems Perspective with a People-Centric Focus

ENGINEERING 2025年第1期44卷 44-50页

作者： Ouyang, Min Cheng, Zekai Ma, Jiaxin Wang, Hongwei Mitoulis, Stergios Aristoteles 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 Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Univ Birmingham Sch Engn Birmingham B15 2TT England

The complexity of coupled risks, which refer to the compounded effects of interacting uncertainties across multiple interdependent objectives, is inherent to cities functioning as dynamic, interdependent systems. A disruption in one domain ripples across various urban systems, often with unforeseen consequences. Central to this complexity are people, whose behaviors, needs, and vulnerabilities shape risk evolution and response effectiveness. Realizing cities as complex systems centered on human needs and behaviors is essential to understanding the complexities of coupled urban risks. This paper adopts a complex systems perspective to examine the intricacies of coupled urban risks, emphasizing the critical role of human decisions and behavior in shaping these dynamics. We focus on two key dimensions: cascading hazards in urban environments and cascading failures across interdependent exposed systems in cities. Existing risk assessment models often fail to capture the complexity of these processes, particularly when factoring in human decision-making. To tackle these challenges, we advocate for a standardized taxonomy of cascading hazards, urban components, and their interactions. At its core is a people-centric perspective, emphasizing the bidirectional interactions between people and the systems that serve them. Building on this foundation, we argue the need for an integrated, people-centric risk assessment framework that evaluates event impacts in relation to the hierarchical needs of people and incorporates their preparedness and response capacities. By leveraging real-time data, advanced simulations, and innovative validation methods, this framework aims to enhance the accuracy of coupled urban risk modeling. To effectively manage coupled urban risks, cities can draw from proven strategies in real complex systems. However, given the escalating uncertainties and complexities associated with climate change, prioritizing people-centric strategies is crucial. This ap

关键词： Coupled urban risks People-centric Risk management

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Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces

arXiv

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arXiv 2025年

作者： Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China

Due to the non-stationarity and large individual differences of EEG signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/session, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 10 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits the EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding. © 2025, CC BY.

关键词： Calibration

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Robust Semi-Global Output Regulation of Uncertain Two-Time-Scale Systems With Input Saturation

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第9期35卷 3484-3495页

作者： Lei, Yan Wang, Yan-Wu Morarescu, Irinel-Constantin Astolfi, Daniele 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 Peoples R China Southwest Univ Sch Elect & Informat Engn Chongqing Peoples R China Univ Lorraine CNRS CRAN Nancy France Univ Claude Bernard Lyon 1 Univ Lyon CNRS Villeurbanne France

This paper investigates the robust semi-global output regulation of uncertain linear systems with input saturation. In this setup, one cannot apply off-the-shelf techniques to reject structural uncertainties due to numerical issues caused by the two time scales but also due to technical issues generated by the input saturation. To solve this problem, we combine internal model principle with low gain technique and singular perturbation theory. Explicitly, the regulator design is based on the computation of the solutions of a Sylvester equation, which can be ill-conditioned in the two-time scales setting. To overcome this, we propose an easily solvable equation, and the existence and uniqueness of its solution are guaranteed under some standard assumptions. Accordingly, an internal model-based controller is designed such that the semi-global robust output regulation problem can be solved. In order to cope with the input saturation, we impose that the ultimate upper bound of the infinity norm of the exosystem state is limited and satisfies certain constraints. Finally, three examples are provided to illustrate the effectiveness of the results.

关键词： input saturation internal model robust output regulation two-time-scale

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VSLM: Virtual Signal Large Model for Few-Shot Wideband Signal Detection and Recognition

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 909-925页

作者： Hao, Xiaoyang Yang, Shuyuan Liu, Ruoyu Feng, Zhixi Peng, Tongqing Huang, Bincheng Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China AVIC Xian Flight Automat Control Res Inst AVIC FAC Software Engn Dept Xian 710076 Peoples R China MeiTuan Grocery Retail Dept Beijing 100029 Peoples R China China Elect Technol Grp Corp Inst Informat Sci & Technol Beijing 100036 Peoples R China

Most existing wideband signal detection and recognition (WSDR) methods rely on diverse, large-scale, and well-labeled training data, which are often difficult to obtain in practical application scenarios such as non-cooperative environments and novel signaling regimes. In this article, we propose a method for constructing a virtual signal large model (VSLM) and applying it to tackle the WSDR challenge under few-shot or even cross-domain few-shot scenarios. Firstly, we design two plug-and-play modules, virtual sample generation (VSG) and virtual category generation (VCG), for VSLM, respectively. VSG simulates the local and overall relationship between the burst signal and the constant signal, which is mainly completed by extracting time-frequency meta-block and data enhancement. Based on VSG and the multi-label concept, we further create virtual novel categories by injecting customizable semantic information into meta-blocks. Then, we further propose a dual decoupled network (DDN) to train the VSLM. DDN enhances signal details by decoupling low gray values (DLGV) in time-frequency representation, and alleviates conflicts during multi-task joint optimization by decoupling spectrum localization and signal classification. Finally, based on the wideband spectrogram dataset, extensive experiments have validated that our proposed methods can significantly improve the performance of WSDR under few-shot conditions.

关键词： Wideband Signal detection Time-frequency analysis Feature extraction Wireless communication Modulation Narrowband Computational modeling Accuracy Spectrogram Wideband signal detection and recognition (WSDR) few-shot dual decoupling network (DDN) virtual sample generation (VSG) virtual category generation (VCG)

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Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年第4期133卷 1711-1726页

作者： Shao, Zhiwen Zhu, Hancheng Zhou, Yong Xiang, Xiang Liu, Bing Yao, Rui Ma, Lizhuang China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MoE Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Mine Digitizat Engn Res Ctr Xuzhou 221116 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

Facial action unit (AU) detection remains a challenging task, due to the subtlety, dynamics, and diversity of AUs. Recently, the prevailing techniques of self-attention and causal inference have been introduced to AU detection. However, most existing methods directly learn self-attention guided by AU detection, or employ common patterns for all AUs during causal intervention. The former often captures irrelevant information in a global range, and the latter ignores the specific causal characteristic of each AU. In this paper, we propose a novel AU detection framework called AC2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{AC}<^>{2}$$\end{document}D by adaptively constraining self-attention weight distribution and causally deconfounding the sample confounder. Specifically, we explore the mechanism of self-attention weight distribution, in which the self-attention weight distribution of each AU is regarded as spatial distribution and is adaptively learned under the constraint of location-predefined attention and the guidance of AU detection. Moreover, we propose a causal intervention module for each AU, in which the bias caused by training samples and the interference from irrelevant AUs are both suppressed. Extensive experiments show that our method achieves competitive performance compared to state-of-the-art AU detection approaches on challenging benchmarks, including BP4D, DISFA, GFT, and BP4D+ in constrained scenarios and Aff-Wild2 in unconstrained scenarios.

关键词： Adaptively constraining self-attention Causal intervention Sample confounder Facial AU detection

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Single-Loop Deadbeat Predictive control Framework for Uncertain SPMSM Drives With Disturbance Observation

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IEEE Transactions on Transportation Electrification 2025年

作者： Jiang, Fuxi Wang, Siqi Guo, Yuhao Cheng, Zhe Ye, Jie Cheng, Shanmei Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology School of Optical and Electronic Information Wuhan430074 China

To mitigate the detrimental effects of parameter perturbations and external load torque on the accuracy of speed and current tracking control in surface-mounted permanent magnet synchronous motor (SPMSM) drive systems, while avoiding the complexities inherent in traditional speed-current series control architectures, this paper presents a single-loop deadbeat predictive control (SDPC) framework that incorporates online disturbance observation. The control input voltage vector is directly computed using an SDPC controller in conjunction with a set of discrete-time disturbance observers (DDOBs). This proposed methodology offers two notable advantages over existing research: (i) it substantially reduces the number of adjustable controller parameters, requiring only the tuning of the observer bandwidth and the speed scaling gain, which enhances its practicality for engineering applications;and (ii) the disturbance estimation and compensation mechanism utilizing DDOBs significantly improves the robustness of the SPMSM system. Furthermore, the SDPC framework ensures that the system maintains satisfactory transient and steady-state tracking performance in response to reference signals. Experimental comparisons validate both the effectiveness and superiority of the proposed method. © 2015 IEEE.

关键词： Permanent magnets

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Stability Enhancement of Inverter-Current Feedback Grid-Following Inverters Using a Passivity-Based Voltage controller

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IEEE Transactions on Energy Conversion 2025年

作者： Huang, Yukai Xu, Jinbang Ye, Jie Huang, Songtao Ren, Ziao Shen, Anwen Hu, Yihua Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China King's College London Department of Engineering LondonWC2R 2LS United Kingdom

Grid-following inverters remain the primary interface for modern renewable energy systems due to their strong power output performance. As the global demand for clean and efficient energy conversion increases, optimizing the stability of inverter systems is crucial for integrating renewable sources into the power grid. In certain energy conversion scenarios, current sensors are placed on the inverter side due to equipment constraints and space limitations, which can impact the stability of the inverter. Although the stability of inverter-side current feedback (ICF) configurations has been studied, methods to further enhance their stability remain underexplored. In this paper, the stability of ICF grid-following inverters is analyzed using a passivity-based design approach, focusing on a point of common coupling voltage controller. The proposed controller is thoroughly analyzed, and a detailed design strategy is presented to ensure stability under varying grid impedance conditions. Experimental results validate the theoretical analysis, demonstrating the effectiveness of the proposed design in enhancing the stability of ICF grid-following inverters. These advancements contribute to improving energy conversion efficiency and reliability. © 2025 IEEE.

关键词： Energy conversion efficiency

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Initial Offset-Boosted Coexisting Hidden Chaos and Firing Multistability in Memristive Ring Neural Network With Hardware Implementation

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年第2期72卷 2024-2033页

作者： Zhang, Sen Li, Yongxin Wang, Xiaoping Zeng, Zhigang Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing 210044 Jiangsu Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

Memristors can be introduced into neural networks for convenient chaos generation even combined with regulating firing activities. This article presents a no-equilibrium memristive ring neural network with dual memristors, one of which is used to mimic neural synapses and the other to describe the effects of external electromagnetic radiation. Initial conditions of different variables bring the chance to arise bistability and tristability. Initial offset-boosted coexisting hidden attractors are arranged by offset boosting, where homogeneous and heterogeneous multistabilities are captured simultaneously. When homogeneous attractors break barriers and connect together, the growth of attractors becomes apparent, allowing for flexible regulation over time and effective self-reproduction through varying initial values. Interestingly, abundant hidden firing patterns are well regulated by memristor-related parameters or the external current. Bursting and spiking firings are switched flexibly by proper knobs. In addition, infinitely many coexisting bursting and spiking firing patterns are also triggered by the initial values of the memristor acting as the coupled synapse or considered as an external electromagnetic radiation. Finally, a digital circuit based on CH32 is constructed to verify the characteristics of various coexisting hidden attractors and abundant firing patterns, thus confirming these new findings.

关键词： Firing multistability hardware implementation initial offset boosting memristive ring neural network memristor

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Adaptive Drive-Response Synchronization of Timescale-Type Neural Networks With Unbounded Time-Varying Delays

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第1期36卷 1056-1068页

作者： Wan, Peng Zhou, Yufeng Zeng, Zhigang Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In recent years, adaptive drive-response synchronization (DRS) of two continuous-time delayed neural networks (NNs) has been investigated extensively. For two timescale-type NNs (TNNs), how to develop adaptive synchronization control schemes and demonstrate rigorously is still an open problem. This article concentrates on adaptive control design for synchronization of TNNs with unbounded time-varying delays. First, timescale-type Barbalat lemma and novel timescale-type inequality techniques are first proposed, which provides us practical methods to investigate timescale-type nonlinear systems. Second, using timescale-type calculus, novel timescale-type inequality, and timescale-type Barbalat lemma, we demonstrate that global asymptotic synchronization can be achieved via adaptive control under algebraic and matrix inequality criteria even if the time-varying delays are unbounded and nondifferentiable. Adaptive DRS is discussed for TNNs, which implies our control schemes are suitable for continuous-time NNs, their discrete-time counterparts, and any combination of them. Finally, numerical examples on TNNs and timescale-type chaotic Ikeda-like oscillator with unbounded time-varying delays are carried out to verify the adaptive control schemes.

关键词： Artificial neural networks Delays Adaptive control Synchronization Time-varying systems Mathematical models Behavioral sciences Adaptive control asymptotic stability synchronization timescale-type neural networks (TNNs) unbounded time-varying delays

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A novel adaptive event-triggered controller for exponential quasi-synchronization and synchronization problems to coupled inertial memristive neural networks with time delays☆

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2025年 460卷

作者： Jiang, Ping Chen, Jiejie Chen, Boshan Zeng, Zhigang Hubei PolyTech Univ Comp Sch Huangshi 435002 Peoples R China Hubei Normal Univ Coll Comp Sci & Informat Engn Huangshi 435002 Peoples R China 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 Hubei Normal Univ Coll Math & Stat Huangshi 435002 Peoples R China

In this paper, we study the quasi-synchronization and synchronization problems for a coupled inertial delayed memristive neural networks (CIDMNNs) via adaptive event-triggered control. An adaptive event-triggered controller is designed, and a new Lyapunov function method is used to prove that the quasi-synchronization of CIDMNN is realized under this controller. As a corollary, some sufficient conditions to ensure the exponential synchronization of CIDMNNs are obtained. Finally, Numerical results validate the theoretical analysis, showing significant improvements in synchronization performance, including reduced control input and enhanced system stability.

关键词： Inertial delayed memristive neural networks Adaptive technique Event-triggered strategy Exponential quasi-synchronization

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