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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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Learning to Learn Transferable Generative Attack for Person Re-Identification

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IEEE Transactions on Image processing 2025年 PP卷 PP页

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning-based person re-identification (reid) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based crossmodel&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 20.0% and 11.3% on mean mAP drop rate, respectively. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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Deep reinforcement learning for path planning of autonomous mobile robots in complicated environments

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Complex and intelligent Systems 2025年第6期11卷 1-15页

作者： Zhang, Zhijie Fu, Hao Yang, Juan Lin, Yunhan School of Computer Science and Technology Wuhan University of Science and Technology No. 947 Heping Avenue Hubei Wuhan 430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System No. 947 Heping Avenue Hubei Wuhan 430081 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology No. 947 Heping Avenue Hubei Wuhan 430081 China

In complicated environments, which include dynamic and narrow areas, the path planning of Autonomous Mobile Robots (AMRs) encounters challenges, like slow model convergence and limited representational capabilities, often resulting in the robot taking longer, less efficient paths or even colliding with obstacles. To tackle these challenges, the Gated Attention Prioritized Experience Replay Soft Actor-Critic (GAP_ SAC) algorithm is proposed. key improvements include expanding the state space for better perception, designing a dynamic heuristic reward function to more effectively guide the AMR in achieving its path planning objectives and integrating Prioritized Experience Replay (PER) to improve sample efficiency and accelerate convergence. Additionally, a gated attention mechanism is also introduced to focus on critical environmental features, enhancing the models’ perception capability. Comparative experiments validate that the proposed GAP_SAC algorithm outperforms TD3, SAC and SAC’s variant, demonstrating superior robustness and generalization in complicated environments. © The Author(s) 2025.

关键词： Autonomous mobile robot (AMR) Deep reinforcement learning (DRL) Dynamic and narrow areas Gated attention mechanism Path planning

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Ultrahigh capacitive energy storage through dendritic nanopolar design

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Science (New York, N.Y.) 2025年第6743期388卷 211-216页

作者： Liu, Yajing Zhang, Yang Wang, Jing Yang, Chao Wang, Hongguang MacManus-Driscoll, Judith L. Yang, Hao van Aken, Peter A. Li, Weiwei Nan, Ce-Wen College of Physics MIIT Key Laboratory of Aerospace Information Materials and Physics State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and Astronautics Nanjing China College of Integrated Circuits MIIT Key Laboratory of Aerospace Integrated Circuits and Microsystem Nanjing University of Aeronautics and Astronautics Nanjing China College of Aerospace Engineering State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and Astronautics Nanjing China Max Planck Institute for Solid State Research Stuttgart Germany Department of Materials Science and Metallurgy University of Cambridge Cambridge United Kingdom State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing China

Electrostatic dielectric capacitors with ultrahigh power densities are sought after for advanced electronic and electrical systems owing to their ultrafast charge-discharge capability. However, low energy density resulting from low breakdown strength and suppressed polarization still remains a daunting challenge for practical applications. We propose a microstructural strategy with dendritic nanopolar (DNP) regions self-assembled into an insulator, which simultaneously enhances breakdown strength and high-field polarizability and minimizes energy loss and thus markedly improves energy storage performance and stability. For illustration, in this study, we achieved a high energy density of 215.8 joules per cubic centimeter with an efficiency of 80.7% at a high electric field of 7.4 megavolts per centimeter in a DNP structure-designed PbZr0.53Ti0.47O3-MgO film. The proposed strategy is generally applicable for development of high-performance dielectric microcapacitors.

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Underwater Image Restoration Based on Improved SwinIR Model

Underwater Image Restoration Based on Improved SwinIR Model

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International Conference on intelligent control and information processing (ICICIP)

作者： Yanke Lu Fang Li Xiaonan Luo Guangxi Key Laboratory of Image and and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Image restoration is a classic foundational visual task, aimed at recovering damaged images, such as those affected by compression, blurring, or noise, to high-definition clarity. Although current image enhancement techniques largely rely on convolutional neural networks, the efficient capabilities of Transformers demonstrated in complex visual tasks have not yet been extensively applied in the field of image restoration. This paper presents an optimized image restoration strategy based on the SwinlR model, which is applied to the task of underwater image restoration. In the shallow feature extraction phase, deep separable convolution technology is employed; while in the deep feature extraction phase, a Spatial Gated Feedforward Network (SGFN) is utilized. The experimental data suggests that the novel approach surpasses conventional techniques in effectiveness and has effectively minimized both the model's parameter quantity and computational demands.

关键词： Visualization Convolution Computational modeling Noise information processing Logic gates Feature extraction Transformers Image restoration intelligent control

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Divide-and-Conquer: Dual-Hierarchical Optimization for Semantic 4D Gaussian Spatting

arXiv

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arXiv 2025年

作者： Yan, Zhiying Liang, Yiyuan Cai, Shilv Zhang, Tao Zhong, Sheng Yan, Luxin Zou, Xu Huazhong University of Science and Technology China National Key Laboratory of Multispectral Information Intelligent Processing Technology China Nanyang Technological University Singapore

Semantic 4D Gaussians can be used for reconstructing and understanding dynamic scenes, with temporal variations than static scenes. Directly applying static methods to understand dynamic scenes will fail to capture the temporal features. Few works focus on dynamic scene understanding based on Gaussian Splatting, since once the same update strategy is employed for both dynamic and static parts, regardless of the distinction and interaction between Gaussians, significant artifacts and noise appear. We propose Dual-Hierarchical Optimization (DHO), which consists of Hierarchical Gaussian Flow and Hierarchical Gaussian Guidance in a divide-and-conquer manner. The former implements effective division of static and dynamic rendering and features. The latter helps to mitigate the issue of dynamic foreground rendering distortion in textured complex scenes. Extensive experiments show that our method consistently outperforms the baselines on both synthetic and real-world datasets, and supports various downstream tasks. Project Page: https://***/DHO. Copyright © 2025, The Authors. All rights reserved.

关键词： 3D reconstruction

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Morphotectonic Analyses of LiDAR-Derived DEMs:Insights into Tectonic Activity of the Xinhua Fault within the Three Gorges Area(Central China)

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Journal of Earth Science 2025年第2期36卷 823-829页

作者： Xiaofeng Wang Xiaohan Yin Tonghui Liu Xuan Li Hongming Wang Yaqi Zhong Gang Rao School of Computer and the Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences(Wuhan)Wuhan 430078China School of Geoscience and Technology Southwest Petroleum UniversityChengdu 610500China School of Earth Sciences China University of Geosciences(Wuhan)Wuhan 430074China Hubei Communications Planning and Design Institute Co. Ltd.Wuhan 430051China School of Economics and Management Shandong Jiaotong UniversityJinan 250357China

0 *** global availability of digital elevation model(DEM)data,such as 90-m Shuttle Radar Topography Mission(SRTM)DEM and 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model(ASTER GDEM),has been extensively utilized in morphotectonic analyses(e.g.,Wang et al.,2024;Cheng et al.,2018;Pérez-Pe?a et al.,2010;El Hamdouni et al.,2008).

关键词： xinhua fault morphotectonic analyses egwang three gorges area digital elevation model aster lidar derived dems tectonic activity morphotectonic analyses digital elevation model dem datasuch

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M2-Net: Multi-Task Learning-Based Multi-Band Signal Recognition Network

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IEEE Internet of Things Journal 2025年

作者： Zhang, Xingjian Wang, Pengxu Ma, Yuan Jiao, Jian Wu, Shaohua Zhang, Qinyu Harbin Institute of Technology Shenzhen Guangdong Provincial Key Laboratory of ACNT Shenzhen518055 China Pengcheng Laboratory Shenzhen518055 China Guangdong Provincial Key Laboratory of ACNT Shenzhen518055 China Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Guangdong Key Laboratory of Intelligent Information Processing the College of Electronic and Information Engineering Shenzhen518060 China

Traditional signal recognition requires the design of multiple different deep neural networks to handle different signal recognition tasks, which not only fails to take into account the correlation among different subtasks, but also leads to large model size and higher computational complexity. In this work, we propose a multi-task learning-based multi-band signal recognition network (M2-Net) to simultaneously recognize the location of occupied frequency bands, modulation types, and signal types. The proposed M2-Net consists of two main parts: shared feature extraction network (SFEN) and multi-task classification header (MCH). In SFEN, a plug-and-play multi-task feature extraction convolution and an adaptive threshold denoising module are introduced to provide better shared feature extraction and denoising performance. In MCH, the shared features obtained from SFEN are further processed for different recognition tasks. Furthermore, during the multi-task model training, homoscedastic uncertainty is introduced as a task-dependent weight to adaptively balance the training loss of different tasks. To evaluate the recognition performance of the proposed method, we construct a multi-band signal dataset and compare M2-Net with several state-of-the-art models in signal recognition field. Experiment results show that the proposed M2-Net has significant performance improvements in terms of recognition accuracy and model complexity, especially under low signal-to-noise ratio conditions. © 2025 IEEE.

关键词： Deep neural networks

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Hierarchical finite-time optimization regulation of multiple autonomous surface vehicles for time-varying target cooperative tracking

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Kongzhi yu Juece/control and Decision 2025年第1期40卷 279-284页

作者： Ge, Ming-Feng Li, Yi-Fan Liu, Zhi-Wei Gu, Zhi-Wei Chen, Qian School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Imaging Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China

We propose a novel hierarchical finite-time optimization regulation algorithm for multiple autonomous surface vehicles (ASVs) to achieve cooperative tracking of a time-varying target. An objective function reflecting the energy consumption of the multiple ASVs system in the tracking process is designed, and a finite-time optimal estimator (FTOE) is developed to minimize the objective function in the case that ASVs form a specified formation while following the desired trajectory. Meanwhile, the target trajectory can be variable instead of constant. In the finite-time local controller (FTLC), the sailing states of the multiple ASVs system can be stabilized to the vicinity of the multiple various predefined time-varying trajectories in finite time in a way that minimizes the objective function by tracking the FTOE. The proof of the finite-time stability is provided and the settling time is calculated. Finally, simulation results demonstrate the effectiveness of the proposed hierarchical algorithm. © 2025 Northeast University. All rights reserved.

关键词： Target tracking

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