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FLCSDet: Federated learning-driven Cross-Spatial Vessel Detection for Maritime Surveillance With Privacy Preservation

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年第1期26卷 1177-1192页

作者： Huang, Yanhong Liu, Wen Lin, Yijing Kang, Jiawen Zhu, Fenghua Wang, Fei-Yue Wuhan Univ Technol Hubei Key Lab Inland Shipping Technol Sch Nav Wuhan 430063 Peoples R China State Key Lab Maritime Technol & Safety Wuhan 430063 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China

Maritime surveillance plays a vital role in reducing maritime accidents and improving maritime safety. To enhance situational awareness for maritime movements, deep learning-based visual object detection has become an important part of maritime surveillance. However, the detection results are highly dependent on the training datasets collected from different departments (i.e., clients). If the sub-datasets from departments are sensitive and private in cross-department maritime surveillance, it will be intractable to directly combine these sub-datasets to train the learning-based object detection method. To solve this issue, we propose a federated learning-driven cross-spatial vessel detection model, called FLCSDet, for maritime surveillance with privacy preservation. In particular, an efficient multi-scale attention module is integrated into our FLCSDet to achieve local cross-spatial feature learning. To improve the federated-learning aggregation method, we propose an optimized algorithm based on the proportion of valid data on departments to adaptively select the allocating weights and preserve the specific characteristics of client data. In addition, we employ transfer learning to further improve the robustness and convergence of our FLCSDet under different experimental scenarios. Compared with several representative federated learning-based detection methods, our FLCSDet could achieve superior detection performance in terms of both quantitative and qualitative results. Moreover, comprehensive experiments conducted on real datasets from both inland waterways and open seas demonstrate the robustness and generalization of our method in intelligent transportation systems. The source code is available at https://***/huangyanh/FLCSDet.

关键词： Surveillance data privacy data models Visualization Training Adaptation models Safety Accuracy Traffic control Federated learning Maritime surveillance vessel detection federated learning privacy preservation

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Finite-Time Vehicle Platoon control for CPVS Under DoS Attacks

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年

作者： Guo, Zhiyuan Fan, Sha Fu, Yukai Zhang, Dan Dong, Lu Deng, Chao Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210003 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Key Lab Networked Control Syst Shenyang 110016 Peoples R China Zhejiang Univ Technol Dept Automat Hangzhou 310023 Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210023 Peoples R China

This paper addresses the problem of vehicle platoon control for third-order nonlinear cyber physical vehicle systems (CPVSs) within finite-time under denial-of-service (DoS) attacks. Unlike existing approaches that assume known systematic matrix of the leader vehicle, this study proposes a data-driven learning algorithm to learn unknown systematic matrix of the leader vehicle. Additionally, a finite-time distributed observer is introduced, thereby enabling follower vehicles to achieve finite-time state observation of the leader vehicle under DoS attacks. Moreover, a novel low-pass filter chain is designed to construct a new variable with high-order derivatives. Utilizing the new variable, a finite-time resilient decentralized controller is formulated, incorporating fuzzy adaptive methods and backstepping techniques to achieve finite-time vehicle platoon control under DoS attacks. Finally, simulation experiments validate the effectiveness of the proposed method.

关键词： Observers Denial-of-service attack Vehicle dynamics Vectors Low-pass filters Asymptotic stability Stability analysis Resilience Protocols Heuristic algorithms Vehicle platoon DoS attacks data-driven learning algorithm finite-time resilient decentralized controller

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RF Challenge: The data-driven Radio Frequency Signal Separation Challenge

IEEE Open Journal of the Communications Society

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ieee Open Journal of the Communications Society 2025年 6卷 4083-4100页

作者： Lancho, Alejandro Weiss, Amir Lee, Gary C.F Jayashankar, Tejas Kurien, Binoy G. Polyanskiy, Yury Wornell, Gregory W. Universidad Carlos III de Madrid Leganés28911 Spain Gregorio Marañón Health Research Institute Madrid28007 Spain Bar-Ilan University Ramat Gan Israel Institute for Infocomm Research 138632 Singapore Massachusetts Institute of Technology CambridgeMA02139 United States MIT Lincoln Laboratory LexingtonMA02421 United States

We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution of this paper is the introduction of the RF Challenge, which is a publicly available, diverse RF signal dataset for data-driven analyses of RF signal problems. Specifically, we adopt a simplified signal model for developing and analyzing interference rejection algorithms. For this signal model, we introduce a set of carefully chosen deep learning architectures, incorporating key domain-informed modifications alongside traditional benchmark solutions to establish baseline performance metrics for this intricate, ubiquitous problem. Through extensive simulations involving eight different signal mixture types, we demonstrate the superior performance (in some cases, by two orders of magnitude) of architectures such as UNet and WaveNet over traditional methods like matched filtering and linear minimum mean square error estimation. Our findings suggest that the data-driven approach can yield scalable solutions, in the sense that the same architectures may be similarly trained and deployed for different types of signals. Moreover, these findings further corroborate the promising potential of deep learning algorithms for enhancing communication systems, particularly via interference mitigation. This work also includes results from an open competition based on the RF Challenge, hosted at the 2024 ieee International conference on Acoustics, Speech, and Signal Processing (ICASSP’24). © 2020 ieee.

关键词： Matched filters

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A Distributed ADMM-Based Deep learning Approach for Thermal control in Multi-Zone Buildings Under Demand Response Events

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 5994-6008页

作者： Taboga, Vincent Dagdougui, Hanane Polytech Montreal Dept Math & Ind Engn Montreal PQ H3T 1J4 Canada Quebec Artificial Intelligence Inst Montreal PQ H2S 3H1 Canada Grp Etud & Rech Anal Decis GERAD Montreal PQ H3T 2A7 Canada

The increasing electricity use and reliance on intermittent renewable energy sources challenge power grid management during peak demand, making Demand Response programs and energy conservation measures essential. This research combines distributed optimization using ADMM with deep learning models to plan indoor temperature setpoints effectively. A two-layer hierarchical structure is used, with a central building coordinator at the upper layer and local controllers at the thermal zone layer. The coordinator must limit the building's maximum power by translating the building's total power to local power targets for each zone. Local controllers can modify the temperature setpoints to meet the local power targets. While most algorithms are either centralized or require prior knowledge about the building's structure, our approach is distributed and fully data-driven. The proposed algorithm, called Distributed Planning Networks, is designed to be both adaptable and scalable to many types of buildings, tackling two of the main challenges in the development of such systems. The proposed approach is tested on an 18-zone building modeled in EnergyPlus. The algorithm successfully manages Demand Response peak events. Note to Practitioners-This article addresses the problems faced by multizone buildings in maintaining thermal comfort while participating in Demand Response events. A centralized control approach is nonscalable, raises privacy concerns, and requires huge communication bandwidth. This work introduces a distributed algorithm that adapts the temperature setpoints of multi-zone buildings to enhance Demand Response participation and energy conservation. The proposed approach is fully data-driven and only requires historical data on weather, indoor temperature, and HVAC power consumption. The blueprint of the building and details about the HVAC system's architecture are not needed. Energy savings will be spread across all the zones depending on the user's choice of comfo

关键词： Deep learning distributed optimization ADMM demand response buildings Deep learning distributed optimization ADMM demand response buildings

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Estimating Lyapunov Region of Attraction for Robust Model-Based Reinforcement learning USV

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 8898-8911页

作者： Xia, Lei Cui, Yunduan Yi, Zhengkun Li, Huiyun Wu, Xinyu Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Hong Kong Macao Joint Lab Human Machine Hong Kong 518071 Guangdong Peoples R China Shenzhen Univ Adv Technol Fac Computil Microelect Shenzhen 518172 Peoples R China

This article addresses the robustness of unmanned surface vehicles (USV) using model-based reinforcement learning (MBRL). A novel MBRL approach, Lyapunov probabilistic model predictive control (LPMPC) is proposed to simultaneously learn both the probabilistic model of a USV and its corresponding estimated Lyapunov region of attraction (ROA) under one reinforcement learning framework. Unlike the existing MBRL USV systems with less consideration of robustness and safety, our method naturally learns a general indicator of system stability based on the probabilistic model's belief and employs it to guide its policy. Evaluated by different navigation tasks in a simulation driven by real boat data, LPMPC demonstrated significant advantages in both control robustness and task completion against various levels of environmental disturbances compared with the baseline approach without Lyapunov ROA's guidance.

关键词： Probabilistic logic Stability analysis Thermal stability Robustness Safety Predictive models Reinforcement learning Predictive control Numerical stability Lyapunov methods Gaussian processes predictive models marine vehicles

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Vehicle Trajectory Prediction by Integrating data-driven and Knowledge-Guided Technique

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2025年第5期26卷 5888-5898页

作者： Guo, Jinghua He, Zhifei Wang, Huinian Wang, Jingyao Li, Keqiang Xiamen Univ Pen Tung Sah Inst Micronano Sci & Technol Xiamen 361005 Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen 361005 Peoples R China Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

In the context of autonomous driving, acquiring the trajectories of surrounding vehicles in advance by autonomous vehicles is a crucial factor in ensuring high-level road safety. While trajectory prediction methods based on deep learning have achieved promising results, these data-driven models lack interpretability and transparency, making their reliable use a significant challenge. In this paper, firstly, an intention-aware spatial-temporal attention network-based trajectory prediction model is constructed, which considers the coupling of driving intention and the interaction with surrounding vehicles, extracts important feature information of vehicles in both temporal and spatial dimensions. Secondly, a vehicle trajectory prediction method via the integration of data-driven and knowledge-guided is proposed, considering both hard and soft constraints. A hard constraint of vehicle kinematics is incorporated into the intention-aware spatial-temporal attention network prediction model to generate physically feasible predicted trajectories and to make this part of the network structure have a human-understandable physical meaning. In addition, by leveraging knowledge related to traffic rules, an auxiliary loss function based on knowledge constraint penalties is designed as a soft constraint to optimize the training of the model and improve the interpretability of the training process. Finally, the proposed model is experimentally evaluated on the datasets and the prediction results are analyzed in terms of reliability and accuracy. The experimental results demonstrate that knowledge guidance effectively enhance the reliability and interpretability of the prediction, and improve the accuracy of long-term trajectory prediction.

关键词： Trajectory Predictive models Vectors Training Long short term memory Kinematics Feature extraction Autonomous vehicles Accuracy data mining Trajectory prediction knowledge-guided data-driven attention mechanism deep learning

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Federated learning-Based Offset-Free Distributed control of Nonlinear Networked systems With Application to IIoT

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ieee Transactions on Network Science and Engineering 2025年第3期12卷 1859-1871页

作者： Xu, Zeyuan Wang, Yujia Wu, Zhe Zheng, Wei Xing Hu, Cheng National University of Singapore Department of Chemical and Biomolecular Engineering 117585 Singapore Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China

Preserving data privacy in data-driven modeling for the Industrial Internet of Things (IIoT) has become critically important due to the susceptibility of communication data from numerous devices to cyber-attacks. Given its multi-subsystem integration, nonlinear interactions, and networking characteristics, IIoT can be modeled as nonlinear networked systems (NNSs). This paper presents a federated learning-based offset-free distributed control (FL-OFDC) method for NNSs with multiple subsystems to preserve data privacy and achieve offset-free control, with potential applications to IIoT. First, a novel FL algorithm with personalized optimization (FLPO) is proposed to simultaneously obtain global and local models using a simple algorithm framework, which can preserve data privacy and address the heterogeneity issue among subsystems. Subsequently, a novel information-theoretic bound for the generalization error of the FLPO algorithm with iteration properties is constructed using individual sample mutual information. Next, an FL-OFDC scheme for NNSs under external disturbances is developed to eliminate the offset, and its closed-loop stability criteria are derived. Finally, a chemical process network, that is, a specific case of IIoT, is employed to demonstrate the practicality of the FLPO and FL-OFDC methods. © 2013 ieee.

关键词： Differential privacy

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A Novel data-driven Method of Real-Time Transient Stability Assessment for AC/DC Hybrid Power systems

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ieee Access 2025年 13卷 80748-80758页

作者： Li, Haifeng Wang, Zhiwei Jin, Tao Xu, Xian Shi, Lin State Grid Jiangsu Electric Power Company Ltd Jiangsu Nanjing324000 China

To effectively tackle the operational challenges facing today's bulk power systems with growing uncertainties, this paper presents a novel data-driven method for transient stability assessment method of the AC/DC hybrid power systems using auto-encoder-based algorithms for feature extraction and convolutional deep belief networks and Boltzmann machines for training accurate and robust models. First, a feature set is established from power grid operational states, and a stack-based noise reduction automatic encoder (SDAE) is used to extract key features that significantly affect the transient stability of the system. Next, transient stability assessment models are trained based on attention mechanisms, improved convolutional deep belief networks (CDBN), and multiple convolution-constrained Boltzmann machines to learn effective features from the input data adaptively. With such a design, the attention mechanism improves the model's attention to instability samples, and the high-precision assessment of the systems' transient stability is achieved. The experimental results conducted on the New England 39-bus system and the WECC 179-bus system show that the proposed method can accurately assess the transient stability of the system with multi-infeed DC by outperforming traditional supervised learning models considering various noise levels. © 2013 ieee.

关键词： Transient stability

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Effective data-driven Joint Friction Modeling and Compensation With Physical Consistency

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ieee Robotics and Automation Letters 2025年第6期10卷 5321-5328页

作者： Dai, Rui Rossini, Luca Laurenzi, Arturo Patrizi, Andrea Tsagarakis, Nikos Humanoids and Human-Centered Mechatronics Research Line Genova16163 Italy University of Genoa DIBRIS Genoa16145 Italy

The complex nonlinear nature of friction in real-world applications renders traditional physical models inadequate for accurately capturing its characteristics. While numerous learning-based approaches have addressed this challenge, they often lack interpretability and fail to uphold the physical guarantees essential for reliable modeling. Additionally, existing structured data-driven methods, despite their efficacy in handling nonlinear systems, seldom account for the specific traits of friction or ensure passivity. To overcome these limitations, we introduce a structured Gaussian Process (GP) model that adheres to the physical consistency of joint friction torque, enabling data-driven modeling in function space that accurately captures Coulomb and viscous friction characteristics while further guaranteeing passivity. We experimentally validate our approach by deploying the friction model on a two-degree-of-freedom (2-DoF) leg prototype. Our approach exhibits robust performance in the presence of non-passive and high-noise data. Experimental results demonstrate that our joint friction model achieves enhanced data efficiency, superior friction compensation performance, and improved trajectory tracking dynamics compared to other friction models. © 2016 ieee.

关键词： Friction

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Cooperative control of Power Grid Frequency Based on Expert-Guided Deep Deterministic Policy Gradient Algorithm

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ieee ACCESS 2025年 13卷 38502-38514页

作者： Shen, Tao Zhang, Jing He, Yu Yang, Shengsun Zhang, Demu Yang, Zhaorui Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China

In power systems, frequency control is essential to ensure stable grid operation. With the increasing share of renewable energy, frequency fluctuations have become more complex and unpredictable. To address this challenge, the paper proposes a cooperative control method for power grid frequency based on the Expert-Guided Deep Deterministic Policy Gradient (EGDDPG) algorithm. This method is data-driven and continuously interacts with the environment to adaptively optimize the control strategy. On the one hand, EGDDPG incorporates expert data during training to guide the agent, enabling faster learning of effective strategies and reducing training convergence time. On the other hand, random disturbances are introduced to simulate the uncertainties in the power system, encouraging the agent to learn control strategies across different environmental states. This approach avoids the overfitting issues associated with fixed disturbance training, enhancing the adaptability and robustness of the algorithm. Simulation results show that the proposed EGDDPG algorithm converges faster and demonstrates stronger control capability and adaptability across various scenarios. It effectively reduces frequency deviation fluctuations and overshoot.

关键词： Frequency control Power system stability Training Mathematical models Hydraulic turbines Fluctuations Differential equations Power system dynamics Uncertainty Robustness cooperative control expert-guided deep deterministic policy gradient

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