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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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LSTM for Modelling and Predictive control of Multivariable Processes 44th

LSTM for Modelling and Predictive Control of Multivariabl...

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44th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, SGAI 2024

作者： Zarzycki, Krzysztof Lawryńczuk, Maciej Institute of Control and Computation Engineering Warsaw University of Technology ul. Nowowiejska 15/19 Warsaw00-665 Poland

ISBN: (纸本)9783031779145

This paper presents the use of Long Short-Term Memory (LSTM) networks as models for prediction in the Model Predictive control (MPC) algorithm. LSTMs are recurrent neural networks often used to model dynamical processes. MPC is an advanced control technique using a process model to calculate online predictions. MPC sets the control policy for the process by solving an optimisation problem to minimise the prediction error and not violate the constraints. This paper compares two LSTM model architectures for MPC in a process with Multiple Inputs and Multiple Outputs (MIMO): a single LSTM model with multiple inputs and multiple outputs (LSTM MIMO) and several parallel LSTM models, each with Multiple Inputs and Single Output (LSTM MISO). The quality of modelling using these two architectures is analysed. Next, the selected MIMO and MISO LSTM models are implemented in the MPC algorithm. Finally, the control quality and execution time are investigated. It is concluded that both MIMO and MISO approaches offer distinct advantages;however, for the benchmark neutralisation reactor, MIMO models provide a more efficient solution for MPC implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Long short-term memory

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Decentralized Federated Learning for Wireless Traffic Prediction

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IEEE Communications Letters 2025年第5期29卷 1057-1061页

作者： Zhang, Haochang Huang, Sirui Zhou, Xiaotian Zhang, Chuanting Jia, Junrong Shandong University School of Control Science and Engineering China Shandong University Shandong Key Lab. of Intelligent Communications and Sensing-Computation Integration China Shandong University Institute of Intelligent Communication Technologies China Shandong University School of Software China

Wireless traffic prediction is indispensable for future intelligent cellular networks, as it can guide the resource allocation smartly to boost the usage efficiency. While the deep learning based methods have been reported to have promising performance, they encounter issues such as data privacy and data heterogeneity. To overcome these, in this letter we design a decentralized federated learning based network (DFLNet) for wireless traffic prediction, where a two layered federated learning framework is proposed. In the proposed algorithm, the base stations are divided into clusters, where the intra-cluster parameter aggregation is achieved through attention mechanism and that of inter-cluster is realized by reinforcement learning. The proposed approach enables the collaborative model updates to be carried out among the most spatial correlated clients, without involving the adversarial information provided by the geometrical remote clients. Simulations confirm the improved accuracy of the proposed algorithm compared to the benchmark schemes. © 1997-2012 IEEE.

关键词： Federated learning

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Motion planning for robotics:A review for sampling-based planners

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Biomimetic Intelligence & Robotics 2025年第1期5卷 15-34页

作者： Liding Zhang Kuanqi Cai Zewei Sun Zhenshan Bing Chaoqun Wang Luis Figueredo Sami Haddadin Alois Knoll School of Computation Information and Technology(CIT)Technical University of MunichMunich 85748Germany Munich Institute of Robotics and Machine Intelligence(MIRMI) Technical University of MunichMunich 80992Germany School of Control Science and Engineering Shandong UniversityJinan 250061China School of Computer Science University of NottinghamNottingham 315199United Kingdom

Recent advancements in robotics have transformed industries such as manufacturing,logistics,surgery,and planetary exploration.A key challenge is developing efficient motion planning algorithms that allow robots to navigate complex environments while avoiding collisions and optimizing metrics like path length,sweep area,execution time,and energy *** the available algorithms,sampling-based methods have gained the most traction in both research and industry due to their ability to handle complex environments,explore free space,and offer probabilistic completeness along with other formal *** their widespread application,significant challenges still *** advance future planning algorithms,it is essential to review the current state-of-the-art solutions and their *** this context,this work aims to shed light on these challenges and assess the development and applicability of sampling-based ***,we aim to provide an in-depth analysis of the design and evaluation of ten of the most popular planners across various *** findings highlight the strides made in sampling-based methods while underscoring persistent *** work offers an overview of the important ongoing research in robotic motion planning.

关键词： Robotics Motion planning Sampling-based algorithms

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The Fredholm Determinants Approach to the computations of Quantum Entanglement 15th

The Fredholm Determinants Approach to the Computations of...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Gielerak, Roman Wiśniewska, Joanna Sawerwain, Marek Institute of Control and Computation Engineering University of Zielona Góra Licealna 9 Zielona Góra65-417 Poland Institute of Information Systems Faculty of Cybernetics Military University of Technology Gen. S. Kaliskiego 2 Warszaw00-908 Poland

ISBN: (纸本)9783031856990

The paper discusses several mathematical issues related to the numerical calculation of entanglement amount present in states describing bipartite quantum systems. The novel aspect of the presented material is the use of the Fredholm determinants theory methods for computing the amount of entanglement through the use of several, entropy-based entanglement measures. In particular, the continuous variable systems case [30, 31] is covered properly by the obtained here results, including presentation of a suitable renormalisation procedure of extracting the finite part of entanglement (often arises a situation in which the value of the corresponding entropy is infinite). Particular attention has been paid to the application of so-called unified entropy variants known as Rényi, resp. Tsallis entropies in the region of parameter α values in the interval (0, 1), where, in the case of infinitely dimensional systems, the infinite value of the corresponding unified entropy is very typical, even for frequently encountered quantum states. A library of functions supporting entropies and the corresponding entanglement calculations are also presented as an extension of the constructed by us EntDetector package for the Python language platform. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum entanglement

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The TechDebt Game - Enabling Discussions about Technical Debt

arXiv

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arXiv 2025年

作者： Wiese, Marion Heinrichs, Angelina Rusieshvili, Nino de Almeida, Rodrigo Rebouças Borowa, Klara Department of Informatics University of Hamburg Hamburg Germany Federal University of Paraíba Department of Exact Sciences PB Rio Tinto Brazil Warsaw University of Technology Institute of Control and Computation Engineering Warsaw Poland

Context. Technical Debt (TD), defined as software constructs that are beneficial in the short term but may hinder future change, is a frequently used term in software development practice. Nevertheless, practitioners do not always fully understand its definition and, in particular, conceptual model. Previous research highlights that communication about TD is challenging, especially with non-technical stakeholders. Discussions on this topic often cause conflicts due to misunderstandings related to other stakeholders’ perspectives. Goal. We designed a board game to emulate TD concepts to make them tangible to all stakeholders, including nontechnical ones. The game aims to encourage discussions about TD in an emulated and safe environment, thereby avoiding real-life conflicts. Method. To evaluate the game’s effectiveness, we surveyed 46 practitioners from diverse domains, positions, and experience levels who played the game in 13 sessions following extensive testing during its development. In addition to the players’ general feedback, we examined situations where players recognized new insights about TD or connected game scenarios to real-life experiences. Results. Overall, the feedback on the game and its enjoyment factor were highly positive. While developers and software architects often connected game situations to their real-world experiences, non-technical stakeholders, such as scrum masters, product owners, and less experienced developers, encountered multiple new insights on TD. Numerous players have shifted their attitudes toward TD and have outlined a plan to modify their behavior regarding TD management. Conclusions. Although the game may not lead to long-term behavior change among stakeholders, participants’ feedback provides evidence that it might serve as a valuable starting point for team discussions on technical debt management. Copyright © 2025, The Authors. All rights reserved.

关键词： Software design

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

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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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Debiasing Architectural Decision-Making: An Experiment With Students and Practitioners

arXiv

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arXiv 2025年

作者： Borowa, Klara de Almeida, Rodrigo Rebouças Wiese, Marion Warsaw University of Technology Institute of Control and Computation Engineering Warsaw Poland Federal University of Paraíba Department of Exact Sciences PB Rio Tinto Brazil Universität Hamburg Department of Informatics Hamburg Germany

Cognitive biases are predictable, systematic errors in human reasoning. They influence decision-making in various areas, including architectural decision-making, where architects face many choices. For example, anchoring can cause architects to unconsciously prefer the first architectural solution that they came up with, without considering any solution alternatives. Prior research suggests that training individuals in debiasing techniques during a practical workshop can help reduce the impact of biases. The goal of this study was to design and evaluate a debiasing workshop with individuals at various stages of their professional careers. To test the workshop’s effectiveness, we performed an experiment with 16 students and 20 practitioners, split into control and workshop group pairs. We recorded and analyzed their think-aloud discussions about improving the architectures of systems they collaborated on. The workshop improved the participants’ argumentation when discussing architectural decisions and increased the use of debiasing techniques taught during the workshop. This led to the successful reduction of the researched biases’ occurrences. In particular, anchoring and optimism bias occurrences decreased significantly. We also found that practitioners were more susceptible to cognitive biases than students, so the workshop had a more substantial impact on practitioners. We assume that the practitioners’ attachment to their systems may be the cause of their susceptibility to biases. Finally, we identified factors that may reduce the effectiveness of the debiasing workshop. On that basis, we prepared a set of teaching suggestions for educators. Overall, we recommend using this workshop to educate both students and experienced practitioners about the typical harmful influences of cognitive bias on architectural decisions and how to avoid them. Copyright © 2025, The Authors. All rights reserved.

关键词： Students

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Debiasing Architectural Decision-Making: An Experiment With Students and Practitioners

Debiasing Architectural Decision-Making: An Experiment With ...

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Working IEEE/IFIP Conference on Software Architecture

作者： Klara Borowa Rodrigo Rebouças de Almeida Marion Wiese Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland Department of Exact Sciences Federal University of Paraíba Rio Tinto/PB Brazil Department of Informatics Universität Hamburg Hamburg Germany

ISBN: (数字)9798331520908

ISBN: (纸本)9798331520915

Cognitive biases are predictable, systematic errors in human reasoning. They influence decision-making in various areas, including architectural decision-making, where architects face many choices. For example, anchoring can cause architects to unconsciously prefer the first architectural solution that they came up with, without considering any solution alternatives. Prior research suggests that training individuals in debiasing techniques during a practical workshop can help reduce the impact of biases. The goal of this study was to design and evaluate a debiasing workshop with individuals at various stages of their professional careers. To test the workshop's effectiveness, we performed an experiment with 16 students and 20 practitioners, split into control and workshop group pairs. We recorded and analyzed their think-aloud discussions about improving the architectures of systems they collaborated on. The workshop improved the participants' argumentation when discussing architectural decisions and increased the use of debiasing techniques taught during the workshop. This led to the successful reduction of the researched biases' occurrences. In particular, anchoring and optimism bias occurrences decreased significantly. We also found that practitioners were more susceptible to cognitive biases than students, so the workshop had a more substantial impact on practitioners. We assume that the practitioners' attachment to their systems may be the cause of their susceptibility to biases. Finally, we identified factors that may reduce the effectiveness of the debiasing workshop. On that basis, we prepared a set of teaching suggestions for educators. Overall, we recommend using this workshop to educate both students and experienced practitioners about the typical harmful influences of cognitive bias on architectural decisions and how to avoid them.

关键词： Training Systematics Protocols Software architecture engineering profession Conferences Decision making Cognition Faces

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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

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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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