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Impulsive Consensus of MASs With Input Saturation and DoS Attacks

IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 414-424页

作者： Xuyang Wang Dengxiu Yu Xiaodi Li the Unmanned System Research Institute Northwestern Polytechnical University IEEE the School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University the School of Mathematics and Statistics Shandong Normal University the Center for Control and Engineering Computation Shandong Normal University

This paper investigates the secure impulsive consensus of Lipschitz-type nonlinear multi-agent systems(MASs) with input saturation. According to the coupling of input saturation and denial of service(DoS) attacks, impulsive control for MASs becomes extremely challenging. Considering general DoS attacks,this paper provides the sufficient conditions for the almost sure consensus of the MASs with input saturation, where the error system can achieve almost sure local exponential *** linear matrix inequalities(LMIs), the relation between the trajectory boundary and DoS attacks is characterized, and the trajectory boundary is estimated. Furthermore, an optimization method of the domain of attraction is proposed to maximize the size. And a non-conservative and practical boundary is proposed to characterize the effect of DoS attacks on MASs. Finally, considering a multi-agent system with typical Chua's circuit dynamic model, an example is provided to illustrate the theorems' correctness.

关键词： Almost sure consensus denial of service (DoS) attacks impulsive control input saturation

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The Price of Simplicity: Analyzing Decoupled Policies for Multi-Location Inventory control

arXiv

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arXiv 2025年

作者： John, Yohan Shah, Vade Preiss, James A. Alizadeh, Mahnoosh Marden, Jason R. Center for Control Dynamical Systems and Computation The University of California Santa BarbaraCA United States

What is the performance cost of using simple, decoupled control policies in inherently coupled systems? Motivated by industrial refrigeration systems, where centralized compressors exhibit economies of scale yet traditional control employs decoupled room-by-room temperature regulation, we address this question through the lens of multi-location inventory control. Here, a planner manages multiple inventories to meet stochastic demand while minimizing costs that are coupled through nonlinear ordering functions reflecting economies of scale. Our main contributions are: (i) a surprising equivalence result showing that optimal stationary base-stock levels for individual locations remain unchanged despite the coupling when restricting attention to decoupled strategies;(ii) tight performance bounds for simple decoupled policies relative to optimal coupled policies, revealing that the worst-case ratio depends primarily on the degree of nonlinearity in the cost function and scales with the number of locations for systems with fixed costs;and (iii) analysis of practical online algorithms that achieve competitive performance without solving complex dynamic programs. Numerical simulations demonstrate that while decoupled policies significantly outperform their worst-case guarantees in typical scenarios, they still exhibit meaningful suboptimality compared to fully coordinated strategies. These results provide actionable guidance for system operators navigating the trade-off between control complexity and operational efficiency in coupled systems. © 2025, CC BY.

关键词： Stochastic systems

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Contractivity Analysis and control Design for Lur’e Systems: Lipschitz, Incrementally Sector Bounded, and Monotone Nonlinearities

arXiv

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arXiv 2025年

作者： Shima, Ryotaro Davydov, Alexander Bullo, Francesco Center for Control Dynamical Systems and Computation University of California at Santa Barbara Santa BarbaraCA93106 United States

In this paper, we study the contractivity of Lur’e dynamical systems whose nonlinearity is either Lipschitz, incrementally sector bounded, or monotone. We consider both the discrete- and continuous-time settings. In each case, we provide state-independent linear matrix inequalities (LMIs) which are necessary and sufficient for contractivity. Additionally, we provide LMIs for the design of controller gains such that the closed-loop system is contracting. Finally, we provide a numerical example for control design. Copyright © 2025, The Authors. All rights reserved.

关键词： Continuous time systems

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Physical Intrusion Attack Detection in Fieldbus Network With Passive Fail-Safe Biasing

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14008-14019页

作者： Wang, Xiangming Liu, Yang Yu, Nanpeng Liu, Pengfei Deng, Nanyi Li, Yanan Liu, Ting Xi’an Jiaotong University School of Cyber Science and Engineering MoE KLINNS Laboratory Xi’an710049 China University of California at Riverside Department of Electrical and Computer Engineering RiversideCA92521 United States Xi’an Thermal Power Research Institute Company Ltd. Power Infrastructure Cyber Security Research Center Xi’an710054 China Electric Power Dispatch and Control Center of Guangdong Power Grid Company Ltd. Guangzhou510699 China

Fieldbus is widely used for real-time distributed control in Industrial control Systems (ICSs) due to its simplicity and stability. The real-world fieldbus network contains hundreds of interconnected devices, presenting a widespread network layout. Attackers can attach external intrusion devices to these communication lines to launch various attacks. In this paper, we model the fieldbus network’s channel fingerprint based on the signal’s amplitude and propose a detection method to identify potential attackers (silent intrusion devices that are eavesdropping) via channel fingerprint differences. Leveraging the passive fail-safe biasing voltage in the fieldbus network such as RS485, we can still detect the intrusion device when the fieldbus is idle (i.e., no devices are transmitting commands), which can significantly reduce the detection delay with lower sampling costs. Moreover, our method can adapt to environmental changes with little computational overhead by generating dynamic thresholds. Using a monitoring unit with stored channel fingerprints, our method can be easily deployed in fieldbus networks without occupying communication resources. The effectiveness and robustness of the proposed method have been demonstrated via extensive experiments on two real-world scenarios and one simulation scenario, where we can achieve 100% accuracy and 0% false alarm rates against various intrusion devices. © 2004-2012 IEEE.

关键词： Network intrusion

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Policy for Optimal Avoidance of Multiple Engagement Zones

Policy for Optimal Avoidance of Multiple Engagement Zones

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Milutinović, Dejan Von Moll, Alexander Weintraub, Isaac E. Casbeer, David Department of Electrical and Computer Engineering University of California Santa Cruz Santa CruzCA95064 United States Control Science Center Air Force Research Laboratory WPAFB OH45433 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This work formulates the feedback control strategies for vehicles to reach a goal point amongst a field of dynamic risk regions. Whereas previous work has considered deterministic versions of this problem, we consider the scenario in the context of uncertainty. This uncertainty could be due to unknown wind or other external disturbances. The risk regions’ dynamics are tightly coupled to the state of the vehicle and as a result the task of navigating through a field dynamically changes as the vehicle traverses through it. Rather than using a nonlinear program solver, the approach taken here is one of formulating the problem as a stochastic optimal control problem wherein a feedback control law is derived. This feedback control law allows a single or multiple vehicles to reach the desired goal location efficiently and with minimal risk incursion. When inside a risk region, there is a probability rate of the vehicle being neutralized. The stochastic dynamical system is modeled as a hybrid system, i.e., regime switching diffusion. The hybrid system is comprised of continuous spatial dynamics and discrete operational states corresponding to "normal" and "neutralized". The optimal control problem is discretized into a field of vehicle states and control. Using Value Iteration, the control signal at each discrete location is optimized, providing a feedback control strategy over the field. Following a discussion of the methods and approaches, this problem is formulated, simulations are performed, and conclusions and remarks are made © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Stochastic systems

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Meaningful human control of partially automated driving systems: Insights from interviews with Tesla users

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Transportation Research Part F: Traffic Psychology and Behaviour 2025年 113卷 213-236页

作者： Suryana, Lucas Elbert Nordhoff, Sina Calvert, Simeon Zgonnikov, Arkady van Arem, Bart Department of Transport and Planning Faculty of Civil Engineering and Geosciences Delft University of Technology Delft Netherlands Electric Vehicle Research Center Institute of Transportation Studies University of California Davis United States Department of Cognitive Robotics Faculty of Mechanical Engineering Delft University of Technology Delft Netherlands Centre for Meaningful Human Control Delft University of Technology Delft Netherlands

Partially automated driving systems are designed to perform specific driving tasks—such as steering, accelerating, and braking—while still requiring human drivers to monitor the environment and intervene when necessary. This shift of driving responsibilities from human drivers to automated systems raises concerns about accountability, particularly in scenarios involving unexpected events. To address these concerns, the concept of meaningful human control (MHC) has been proposed. MHC emphasises the importance of humans retaining oversight and responsibility for decisions made by automated systems. Despite extensive theoretical discussion of MHC in driving automation, there is limited empirical research on how real-world partially automated systems align with MHC principles. This study offers two main contributions: (1) an empirical evaluation of MHC in partially automated driving, based on 103 semi-structured interviews with users of Tesla's Autopilot and Full Self-Driving (FSD) Beta systems;and (2) a methodological framework for assessing MHC through qualitative interview data. We operationalise the previously proposed tracking and tracing conditions of MHC using a set of evaluation criteria to determine whether these systems support meaningful human control in practice. Our findings indicate that several factors influence the degree to which MHC is achieved. Failures in tracking—where drivers' expectations regarding system safety are not adequately met—arise from technological limitations, susceptibility to environmental conditions (e.g., adverse weather or inadequate infrastructure), and discrepancies between technical performance and user satisfaction. Tracing performance—the ability to clearly assign responsibility—is affected by inconsistent adherence to safety protocols, varying levels of driver confidence, and the specific driving mode in use (e.g., Autopilot versus FSD Beta). These findings contribute to ongoing efforts to design partially automated driving

关键词： Human engineering

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A Two-Stage Solution to Quantum Process Tomography: Error Analysis and Optimal Design

引用

IEEE Transactions on Information Theory 2025年第3期71卷 1803-1823页

作者： Xiao, Shuixin Wang, Yuanlong Zhang, Jun Dong, Daoyi Mooney, Gary J. Petersen, Ian R. Yonezawa, Hidehiro University of New South Wales School of Engineering and Technology CanberraACT2600 Australia University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai200240 China The Australian National University School of Engineering CanberraACT2601 Australia Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China The Australian National University CIICADA Laboratory School of Engineering CanberraACT2601 Australia The University of Melbourne School of Physics ParkvilleVIC3010 Australia RIKEN Center for Quantum Computing Saitama351-0198 Japan Centre for Quantum Computation and Communication Technology Australian Research Council CanberraACT2600 Australia

Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage solution for both trace-preserving and non-trace-preserving quantum process tomography. Utilizing a tensor structure, our algorithm exhibits a computational complexity of O(MLd2) where d is the dimension of the quantum system and M, L(M≥ d2, L≥ d2) represent the numbers of different input states and measurement operators, respectively. We establish an analytical error upper bound and then design the optimal input states and the optimal measurement operators, which are both based on minimizing the error upper bound and maximizing the robustness characterized by the condition number. Numerical examples and testing on IBM quantum devices are presented to demonstrate the performance and efficiency of our algorithm. © 1963-2012 IEEE.

关键词： Error analysis

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Tumor Cellularity Assessment Using Artificial Intelligence Trained on Immunohistochemistry-Restained Slides Improves Selection of Lung Adenocarcinoma Samples for Molecular Testing

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American Journal of Pathology 2025年第5期195卷 907-922页

作者： Gertych, Arkadiusz Zurek, Natalia Piaseczna, Natalia Szkaradnik, Kamil Cui, Yujie Zhang, Yi Nurzynska, Karolina Pyciński, Bartłomiej Paul, Piotr Bartczak, Artur Chmielik, Ewa Walts, Ann E. Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Department of Pathology and Laboratory Medicine Cedars-Sinai Medical Center Los Angeles California United States Department of Surgery Cedars-Sinai Medical Center Los Angeles California United States F. Widjaja Inflammatory Bowel Disease Institute Cedars-Sinai Medical Center Los Angeles California United States Biostatistics Shared Resource Cedars-Sinai Medical Center Los Angeles California United States Biobank and Research Pathology Resource Cedars-Sinai Medical Center Los Angeles California United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Tumor Pathology Department Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch Gliwice Poland Hospital for Lung Diseases “Rebirth ” Zakopane Poland

Tumor cellularity (TC) in lung adenocarcinoma slides submitted for molecular testing is important in identifying actionable mutations, but lack of best practice guidelines results in high interobserver variability in TC assessments. An artificial intelligence (AI)–based pipeline developed to assess TC in hematoxylin and eosin (H&E) whole slide images (WSIs) and in tumor areas (TAs) within WSIs includes a new model (CaBeSt-Net) trained to mask cancer cells, benign epithelial cells, stroma in H&E WSIs using immunohistochemistry-restained slides, and a model to detect all cell nuclei. High masking accuracy (>91%) by CaBeSt-Net computed using 1024 H&E regions of interest and intraclass correlation coefficient >0.97 assessing TC assessments reliability by one pathologist and AI in 20 test regions of interest supported the pipeline's applicability to TC assessment in 50 study H&E WSIs. Using the pipeline, TCs assessed in TAs and WSIs were compared with those by three pathologists. Reliabilities of these ratings by the pathologists supported by the pipeline were good (intraclass correlation coefficient >0.82, P < 0.0001). The consistency of sample categorizations as inadequate or adequate (TC ≤ 20% cut point) for molecular testing among the pathologists assessing TCs without AI support was moderate in TAs (κ = 0.410, P < 0.0001) and slight in WSIs (κ = 0.132, nonsignificant). With AI support, the consistency was substantial in both WSIs (κ = 0.602, P < 0.0001) and TAs (κ = 0.704, P < 0.0001). By visualizing cancer and measuring TC in the sample, this novel AI-based pipeline assists pathologists in selecting samples for molecular testing. © 2025 American Society for Investigative Pathology

关键词： artificial intelligence computational pathology immunohistochemical staining lung adenocarcinoma restained slides tumor burden tumor cellularity assessment

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Electroencephalogram and Event-Related Potential in Mild Cognitive Impairment: Recent Developments in Signal Processing, Machine Learning, and Deep Learning

IEEE Journal of Selected Areas in Sensors

引用

IEEE Journal of Selected Areas in Sensors 2025年 2卷 162-184页

作者： Azami, Hamed Mirjalili, Mina Rajji, Tarek K. Wu, Chien-Te Humeau-Heurtier, Anne Jung, Tzyy-Ping Wei, Chun-Shu Trinh, Thanh-Tung Liu, Yi-Hung University of Toronto Centre for Addiction and Mental Health TorontoONM5S 1A1 Canada University of Texas Southwestern Medical Center DallasTX75390 United States University of Florida Department of Occupational Therapy GainesvilleFL32611 United States University of Florida Center for Cognitive Aging and Memory McKnight Brain Institute GainesvilleFL32611 United States University of Angers LARIS SFR MATHSTIC AngersF-49000 France University of California Institute for Neural Computation Institute of Engineering in Medicine La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu City30010 Taiwan National Taipei University of Technology Graduate Institute of Manufacturing Technology College of Mechanical and Electrical Engineering Taipei10608 Taiwan Vietnam National University Department of Information and Communication Technology Faculty of Nontraditional Security HSB Hanoi720325 Viet Nam National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu City30010 Taiwan

Mild cognitive impairment (MCI) is an early stage of non-age-related cognitive decline with an increased risk of progressing to dementia. Early detection of MCI is essential for implementing preventative strategies that can delay or prevent the onset of dementia, ultimately improving patient outcomes and reducing healthcare costs. Electroencephalograms (EEGs) and event-related potentials (ERPs) have shown significant promise in detecting MCI due to their affordability, real-time monitoring capabilities, and noninvasiveness. EEG provides continuous brain activity data, while ERPs offer insights into specific cognitive processes by analyzing brain responses to stimuli. These methods can complement each other in MCI diagnosis by providing a comprehensive view of overall brain function and detailed information on specific cognitive processes. However, EEG and ERP are susceptible to noise and interindividual variability, which can hinder their reliability. In addition, applying machine learning models on EEG or ERP for MCI detection presents challenges such as the risk of overfitting and difficulties in interpreting the underlying decision-making process. This review emphasizes recent advancements in signal processing and feature extraction methods applied to EEG and ERP data and explores the use of machine learning and deep learning techniques to enhance diagnostic accuracy and interpretative depth. By integrating these methodologies, the review highlights how EEG and ERP can contribute to a more effective understanding and monitoring of cognitive changes associated with MCI, underscoring the importance of early diagnosis for timely intervention and improved patient care. Finally, the review focuses on future research directions, including the development of advanced analytical techniques and multimodal integration approaches involving EEG and ERP to further improve diagnostic accuracy and clinical application. © 2024 IEEE.

关键词： Deep learning

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Probing Rotational Decoherence with a Trapped-Ion Planar Rotor

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Physical Review Letters 2025年第3期134卷 033601-033601页

作者： Neil Glikin Benjamin A. Stickler Ryan Tollefsen Sara Mouradian Neha Yadav Erik Urban Klaus Hornberger Hartmut Häffner Department of Physics University of California Berkeley Berkeley California 94720 USA Challenge Institute for Quantum Computation University of California Berkeley Berkeley California 94720 USA Institute for Complex Quantum Systems and Center for Integrated Quantum Science and Technology Ulm University Albert-Einstein-Allee 11 89069 Ulm Germany Department of Electrical and Computer Engineering University of Washington Seattle Washington 98195 USA Present address: Exponent Inc. Electrical Engineering and Computer Science Practice Warrenville Illinois USA. University of Duisburg-Essen Faculty of Physics Lotharstraße 1 47057 Duisburg Germany

The quantum rotor is one of the simplest model systems in quantum mechanics, but only in recent years has theoretical work revealed general fundamental scaling laws for its decoherence. For example, a superposition of orientations decoheres at a rate proportional to the sine squared of the angle between them. Here, we observe scaling laws for rotational decoherence dynamics for the first time, using a 4 μm diameter planar rotor composed of two Paul-trapped ions. We prepare the rotational motion of the ion crystal into superpositions of angular momentum with well-defined differences ranging from 1−3ℏ, and measure the rate of decoherence. We also tune the system-environment interaction strength by introducing resonant electric field noise. The observed scaling relationships for decoherence are in excellent agreement with recent theoretical work, and are directly relevant to the growing development of rotor-based quantum applications.

关键词： Trapped ions

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