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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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DISTANCE BETWEEN SPECIES BY CONFUSION OPERATORS OF MULTI-CLASS CLASSIFIERS

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期2025卷 171-182页

作者： Tarbă, Nicolae Irimescu, Ionela N. Pleavă, Ana M. Scarlat, Eugen N. Mihăilescu, Mona Doctoral School Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology POLITEHNICA Bucharest Romania Applied Sciences Doctoral School National University of Science and Technology POLITEHNICA Bucharest Romania CAMPUS Research Center National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept National University of Science and Technology POLITEHNICA Bucharest Romania Physics Dept Research Center for Applied Sciences in Engineering National University of Science and Technology POLITEHNICA Bucharest Romania

We introduce a method to evaluate the similarities between classes of objects based on the confusion matrices coming from the multi-class machine learning (ML) predictors that operate in the vector space generated by the classes. Operation in the confusion domain allows us to use the distance as evaluation metric of the closeness of the relevant properties carried by the classes. We applied the method to semiquantitatively evaluate the effects of X-ray and carbon ion radiation on cultured SW1353 chondrosarcoma cells with respect to the non-irradiated reference. We prove that it is possible to draw useful information on the strength of cells nuclei changes. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Radiation effects

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Application of phononic crystals in modern engineering vibration and noise control, a review

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Noise and Vibration Worldwide 2025年第4-5期56卷 224-241页

作者： Huang, Wei Xu, Jian Yao, Yi Zhang, Yongliang Wang, Xin Han, Pengbo SINOMACH Academy of Science and Technology Co. Ltd SINOMACH Research Center of Engineering Vibration Control Technology Beijing100080 China Beijing China CAS Key Laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics University of Science and Technology of China Anhui China Key Laboratory of Vibration Control for Key Process Equipment of Major Scientific Infrastructure in the Machinery Industry Beijing China

Phononic crystals are composite materials consisting of periodically distributed inclusions, and this structure enables wave band gaps under specific conditions. This characteristic allows for the suppression of elastic waves within certain frequency ranges, offering a new approach to vibration reduction and noise control. In this study, phononic crystals, with their periodic structure, tunable bandgap properties, topology optimization, and additive manufacturing technology, are reviewed. Some key hot issues, such as low-frequency vibration and noise control, intelligent materials, and tunable bandgap design, are discussed. In the future, phononic crystals will play a significant role in barrier vibration isolation, rail transit vibration isolation, power equipment vibration isolation, precision equipment vibration isolation, noise control, and underground protection engineering. This study constitutes a systematic summary and outlook of phononic crystals in modern engineering vibration and noise control. © The Author(s) 2025.

关键词： Precision engineering

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MM-UKAN++: A Novel Kolmogorov–Arnold Network-Based U-Shaped Network for Ultrasound Image Segmentation

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IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency control 2025年第4期72卷 498-514页

作者： Zhang, Boheng Huang, Haorui Shen, Yi Sun, Mingjian Harbin Institute of Technology Department of Control Science and Engineering Heilongjiang Harbin150001 China Pohang University of Science and Technology Department of Convergence IT Engineering Pohang37673 Korea Republic of Harbin Institute of Technology Suzhou Research Institute Jiangsu Suzhou215000 China Harbin Institute of Technology Department of Information Science and Engineering Shandong Weihai264200 China Harbin Institute of Technology Shandong Weihai264200 China Shandong Laboratory of Advanced Biomaterials and Medical Devices Shandong Weihai264209 China

Ultrasound (US) imaging is an important and commonly used medical imaging modality. Accurate and fast automatic segmentation of regions of interest (ROIs) in US images is essential for enhancing the efficiency of clinical and robot-assisted diagnosis. However, US images suffer from low contrast, fuzzy boundaries, and significant scale variations in ROIs. Existing convolutional neural network (CNN)-based and transformer-based methods struggle with model efficiency and explainability. To address these challenges, we introduce MM-UKAN++, a novel U-shaped network based on Kolmogorov-Arnold networks (KANs). MM-UKAN++ leverages multilevel KAN layers as the encoder and decoder within the U-network architecture and incorporates an innovative multidimensional attention mechanism to refine skip connections by weighting features from frequency-channel and spatial perspectives. In addition, the network effectively integrates multiscale information, fusing outputs from different scale decoders to generate precise segmentation predictions. MM-UKAN++ achieves higher segmentation accuracy with lower computational cost and outperforms other mainstream methods on several open-source datasets for US image segmentation tasks, including achieving 69.42% IoU, 81.30% Dice, and 3.31 mm HD in the BUSI dataset with 3.17 G floating point of operations (FLOPs) and 9.90 M parameters. The excellent performance on our automatic carotid artery US scanning and diagnostic system further proves the speed and accuracy of MM-UKAN++. Besides, the good performance in other medical image segmentation tasks reveals the promising applications of MM-UKAN++. The code is available on GitHub. © 1986-2012 IEEE.

关键词： Image segmentation

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Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1223-1240页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Department of Control Science and Engineering Harbin Institute of Technology Shenzhen Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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Predictive Vehicle control at Intersections with Look-Ahead Driving Approach

Predictive Vehicle Control at Intersections with Look-Ahead ...

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2025 SICE International Symposium on control Systems, SICE ISCS 2025

作者： Magzhan, Atykhan Hossain, Md Delowar Bakibillah, A.S.M. Kamal, Md Abdus Samad Yamada, Kou Graduate School of Science and Technology Gunma University Kiryu Japan Institute of Science Tokyo Department of Systems and Control Engineering Tokyo Japan

ISBN: (纸本)9784907764852

This paper presents a look-ahead vehicle control scheme for efficient traffic management at signalized intersections. The proposed scheme predicts future driving states with a look-ahead approach as an extended adaptive cruise control (ACC) model. It maintains car-following behavior similar to human driving while predicting future states of traffic flow to minimize intersection clearing time and aggressive braking independently, similar to model predictive control (MPC) approaches. In contrast to MPC models that are computationally demanding and have limited practical applications due to real-time constraints, the proposed scheme uses a modified Intelligent Driver Model (IDM) with predictive adjustments. The efficiency of the control scheme is evaluated through simulations with multiple vehicles in intersection traffic scenarios, focusing on maintaining safe time headway within a traffic flow and ensuring efficient flows with higher intersection throughput. Comparative results indicate improvements in intersection clearance, human-like vehicle headway management, and enhanced traffic flow performance. © 2025 The Society of Instrument and control Engineers.

关键词： Adaptive cruise control

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Cubature Kalman Fusion Filtering Under Amplify-and-Forward Relays With Randomly Varying Channel Parameters

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 356-368页

作者： Jiaxing Li Zidong Wang Jun Hu Hongli Dong Hongjian Liu the Department of Applied Mathematics Harbin University of Science and Technology IEEE the Department of Computer Science Brunel University London the School of Automation Harbin University of Science and Technology the Artificial Intelligence Energy Research Institute Northeast Petroleum University the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University the National Key Laboratory of Continental Shale Oil of China Northeast Petroleum University the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of Education Anhui Polytechnic University the School of Mathematics and Physics Anhui Polytechnic University

In this paper, the problem of cubature Kalman fusion filtering(CKFF) is addressed for multi-sensor systems under amplify-and-forward(AaF) relays. For the purpose of facilitating data transmission, AaF relays are utilized to regulate signal communication between sensors and filters. Here, the randomly varying channel parameters are represented by a set of stochastic variables whose occurring probabilities are permitted to exhibit bounded uncertainty. Employing the spherical-radial cubature principle, a local filter under AaF relays is initially constructed. This construction ensures and minimizes an upper bound of the filtering error covariance by designing an appropriate filter gain. Subsequently, the local filters are fused through the application of the covariance intersection fusion rule. Furthermore, the uniform boundedness of the filtering error covariance's upper bound is investigated through establishing certain sufficient conditions. The effectiveness of the proposed CKFF scheme is ultimately validated via a simulation experiment concentrating on a three-phase induction machine.

关键词： Amplify-and-forward (AaF) relays covariance intersection fusion cubature Kalman filtering multi-sensor systems uniform boundedness

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Trust-Based Identity Privacy for Edge Computing 24

Trust-Based Identity Privacy for Edge Computing

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14th International Conference on Communication and Network Security, ICCNS 2024

作者： Dong, Jie Wen Kang, Xi Nai Peng, Jin Jiang, Xuan Ming Zhu, Tao Jun Information Technology Department National Key Laboratory of Seience and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute BeiJing China School of Computer Science Beijing University of Technology BeiJing China

ISBN: (纸本)9798400717994

College of Computer science Beijing University of technology, Beijing 100124, China, 1374622525@*** This paper proposes a trust collaboration technology for edge computing, addressing trust isolation and security issues through a unified trust value calculation method and blockchain technology. The designed trust management model evaluates the identity, capability, and behavior of terminal nodes using static and dynamic trust measurements. A consortium blockchain stores trust information to ensure immutability and efficient sharing. Simulation experiments validate the scheme's effectiveness in countering malicious node attacks, improving task completion rates, and reducing system risks. This solution offers an efficient and secure approach to trust management in edge computing environments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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Trust Collaboration for Edge Computing 24

Trust Collaboration for Edge Computing

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14th International Conference on Communication and Network Security, ICCNS 2024

ISBN: (纸本)9798400717994

As edge computing becomes an increasingly important computing model, trust management and security issues are becoming more severe. Problems such as malicious node attacks and trust isolation pose threats to the security and stability of edge computing environments. Therefore, designing an efficient and secure trust management mechanism is imperative. This paper proposes a trust collaboration technology for edge computing, addressing trust isolation and security issues through a unified trust value calculation method and blockchain technology. The designed trust management model evaluates the identity, capability, and behavior of terminal nodes using static and dynamic trust measurements. A consortium blockchain stores trust information to ensure immutability and efficient sharing. Simulation experiments validate the scheme's effectiveness in countering malicious node attacks, improving task completion rates, and reducing system risks. This solution offers an efficient and secure approach to trust management in edge computing environments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Mobile edge computing

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An interaction-fair semi-decentralized trajectory planner for connected and autonomous vehicles

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Autonomous Intelligent Systems 2025年第1期5卷 1-20页

作者： Liu, Zhengqin Lei, Jinlong Yi, Peng Hong, Yiguang Department of Control Science and Engineering Tongji University Shanghai201804 China The Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201804 China

Lately, there has been a lot of interest in game-theoretic approaches to the trajectory planning of autonomous vehicles (AVs). But most methods solve the game independently for each AV while lacking coordination mechanisms, and hence result in redundant computation and fail to converge to the same equilibrium, which presents challenges in computational efficiency and safety. Moreover, most studies rely on the strong assumption of knowing the intentions of all other AVs. This paper designs a novel autonomous vehicle trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated trajectory planning by exploiting vehicle-to-everything (V2X) technology. Firstly, the trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define the interaction fairness of the planned trajectories and prove that interaction-fair trajectories correspond to the variational equilibrium (VE) of this game. Subsequently, we propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, in which each CAV optimizes its individual trajectory based on neighboring CAVs’ information shared through V2X, and the roadside unit takes the role of updating multipliers for collision avoidance constraints. The approach can significantly improve computational efficiency through parallel computing among CAVs, and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, we conduct Monte Carlo experiments in multiple situations at an intersection, where the empirical results show the advantages of SVEP, including the fast computation speed, a small communication payload, high scalability, equilibrium concordance, and safety, making it a promising solution for trajectory planning in connected traffic scenarios. To the best of our knowledge, this is the first study to achieve semi-distributed solving of a game with coupled constr

关键词： Autonomous vehicles

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