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Encrypted Finite-Horizon Energy-to-Peak State Estimation for Time-Varying Systems Under Eavesdropping Attacks: Tackling Secrecy Capacity

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 985-996页

作者： Lei Zou Zidong Wang Bo Shen Hongli Dong Guoping Lu College of Information Science and Technology Donghua UniversityShanghai 201620 Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesex UB83PHUK Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318 Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper is concerned with the problem of finitehorizon energy-to-peak state estimation for a class of networked linear time-varying *** to the inherent vulnerability of network-based communication,the measurement signals transmitted over a communication network might be intercepted by potential *** avoid information leakage,by resorting to an artificial-noise-assisted method,we develop a novel encryption-decryption scheme to ensure that the transmitted signal is composed of the raw measurement and an artificial-noise term.A special evaluation index named secrecy capacity is employed to assess the information security of signal transmissions under the developed encryption-decryption *** purpose of the addressed problem is to design an encryptiondecryption scheme and a state estimator such that:1)the desired secrecy capacity is ensured;and 2)the required finite-horizon–l_(2)-l_(∞)performance is *** conditions are established on the existence of the encryption-decryption mechanism and the finite-horizon state ***,simulation results are proposed to show the effectiveness of our proposed encryption-decryption-based state estimation scheme.

关键词： Artificial-noise-assisted technique eavesdropping encryption-decryption scheme energy-to-peak state estimation finitehorizon state estimation

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Perimeter Defense Using a Turret with Finite Range and Startup Time

Perimeter Defense Using a Turret with Finite Range and Start...

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American control Conference (ACC)

作者： Shivam Bajaj Shaunak D. Bopardikar Alexander Von Moll Eric Torng David W. Casbeer Department of Electrical and Computer Engineering Michigan State University Control Science Center Air Force Research Laboratory Department of Computer Science and Engineering Michigan State University

We consider a perimeter defense problem in a planar conical environment comprising a turret that has a finite range and non-zero startup time. The turret seeks to defend a concentric perimeter against N ≥ 2 intruders. Upon release, each intruder moves radially towards the perimeter with a fixed speed. To capture an intruder, the turret's angle must be aligned with that of the intruder's angle and must spend a specified startup time at that orientation. We address offline and online versions of this optimization problem. Specifically, in the offline version, we establish that in general parameter regimes, this problem is equivalent to solving a Travelling Repairperson Problem with Time Windows (TRP-TW). We then identify specific parameter regimes in which there is a polynomial time algorithm that maximizes the number of intruders captured. In the online version, we present a competitive analysis technique in which we establish a fundamental guarantee on the existence of at best (N – 1)-competitive algorithms. We also design two online algorithms that are provably 1 and 2-competitive in specific parameter regimes.

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ONLINE FEEDBACK OPTIMIZATION AND SINGULAR PERTURBATION VIA CONTRACTION THEORY∗

arXiv

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arXiv 2023年

作者： Cothren, Liliaokeawawa Bullo, Francesco Dall’Anese, Emiliano Department of Electrical Computer and Energy Engineering University of Colorado Boulder BoulderCO United States Center for Control Dynamical Systems and Computation UC Santa Barbara Santa BarbaraCA93101 United States Department of Electrical and Computer Engineering Boston University BostonMA02215 United States

In this paper, we provide a novel contraction-theoretic approach to analyze two-time scale systems, including those commonly encountered in Online Feedback Optimization (OFO). Our framework endows these systems with several robustness properties, enabling a more comprehensive characterization of their behaviors. The primary assumptions are the contractivity of the fast subsystem and the reduced model, along with an explicit upper bound on the time-scale parameter. For two-time scale systems subject to disturbances, we show that the distance between solutions of the nominal system and solutions of its reduced model is uniformly upper bounded by a function of contraction rates, Lipschitz constants, the time-scale parameter, and the variability of the disturbances over time. Applying these general results to the OFO context, we establish new individual tracking error bounds, showing that solutions converge to their time-varying optimizer, provided the plant and steady-state feedback controller exhibit contractivity and the controller gain is suitably bounded. Finally, we explore two special cases: for autonomous nonlinear systems, we derive sharper bounds than those in the general results, and for linear time-invariant systems, we present novel bounds based on induced matrix norms and induced matrix log norms. Copyright © 2023, The Authors. All rights reserved.

关键词： State feedback

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Cardiac Ischemia Detection Using Self-Organizing Map Feature Selection Method

Cardiac Ischemia Detection Using Self-Organizing Map Feature...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Ramin Aghili Karampour Amir Arshia Bakhtiar Kiarash Hajizamani Alireza Fallahi Hamid Soltanian Zadeh Biomedical Engineering Department Hamedan University of Technology Hamedan Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

Cardiac ischemia, a prevalent cause of heart failure, remains the leading cause of death in Iran. Early diagnosis of this condition is crucial, and electrocardiogram (ECG) signal processing techniques offer valuable insights. By analyzing ECG signals from ischemic patients and comparing them to those of normal individuals, we can extract relevant features for classification. Our study extracted some features from ECG signals, including statistical, time-based, frequency, and time-frequency features. We employed the self-organizing maps method (SOM) to identify the most informative features. Subsequently, we evaluated the classification accuracy using several machine learning algorithms, including support vector machines (SVM), Naive Bayes, Linear Discriminant Analysis (LDA), and Logistic Regression. The results demonstrated the superiority of the SOM method in selecting the best features compared to PCA, with accuracies of $\mathbf{9 1. 2 \%}, \mathbf{8 4. 3 \%}, \mathbf{7 2. 5 \%}$, and $88.9 \%$ for SVM, LDA, NB, and LR methods, respectively. Our findings contribute to the early detection and management of cardiac ischemia, potentially improving patient outcomes.

关键词： Self-organizing feature maps Support vector machines Time-frequency analysis Logistic regression Accuracy Machine learning algorithms Electrocardiography Signal processing Feature extraction Principal component analysis

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Q-Learning Based control for Swing-Up and Balancing of Inverted Pendulum

Q-Learning Based Control for Swing-Up and Balancing of Inver...

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Intermountain engineering, Technology and Computing (IETC)

作者： Antora Dev Kanan Roy Chowdhury Marco P. Schoen Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Measurement & Control Engineering Research Center (MCERC) Idaho State University Pocatello ID USA

ISBN: (数字)9798350373073

ISBN: (纸本)9798350373080

This study addresses the classic control problem of stabilizing an inverted pendulum on a moving cart, a challenge in control theory and robotics due to its inherent instability and highly nonlinear dynamics. We explore the application of Q-learning, a model-free reinforcement learning algorithm, and its efficacy in deriving an optimal control policy for the system without precise system models. Our approach utilizes Q-learning's capacity for stabilizing a pendulum in an upright position on the top of the horizontally moving cart within a certain boundary. Our strategy adapts to a dynamic environment while showcasing its robustness in developing control policies for complex systems. This research bridges classical control theory with reinforcement learning techniques, contributing to the domain by demonstrating the versatility and potential of machine learning in control tasks.

关键词： Adaptation models Q-learning Heuristic algorithms Optimal control Robustness control theory Nonlinear dynamical systems

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Interpretation of ADRC in $Q$-Structure

Interpretation of ADRC in $Q$-Structure

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Jiapeng Xu Xiang Chen Ying Tan Kemin Zhou Department of Electrical and Computer Engineering University of Windsor Windsor Canada Department of Mechanical Engineering The University of Melbourne Melbourne Australia Center for Advanced Control and Smart Operations Nanjing University Suzhou China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

This paper presents a control structure featuring an operator $Q$ driven by the residual signal, which indicates the difference between the measurement output and the estimated output from an observer. The form of this observer is very general, as long as it can generate the estimated state or relevant information that will be incorporated into this control structure. The operator $Q$ introduces an extra design freedom to address uncertainties, such that linear active disturbance rejection control (LADRC) can be interpreted as a special case in the Q-structure when a linear extended state observer is used.

关键词： Robust control Q measurement Uncertainty Simulation Observers Robustness Artificial intelligence

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State of the Art of Repetitive control in Power Electronics and Drive Applications

IEEE Open Journal of Industry Applications

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IEEE Open Journal of Industry Applications 2022年 3卷 13-29页

作者： Tang, Mi Di Benedetto, Marco Bifaretti, Stefano Lidozzi, Alessandro Zanchetta, Pericle Power Electronics Machine and Control Group University of Nottingham NottinghamNG7 2GT United Kingdom Roma Tre University C-PED Center for Power Electronics and Drives Rome00154 Italy Department of Industrial Engineering University of Rome 'Tor Vergata' Rome00133 Italy Department of Electrical and Electronic Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy

Power electronic systems present a non-linear behavior mainly due to their switching nature. This is often combined with their interaction with non-linear systems, such as other switching converters, diode rectifiers, motor drives, etc. and with possible non linearities of the power grid in the case of grid connected systems. The major effect of these non-linear interactions is the generation of harmonic distortion on voltages and currents (both in DC and AC), which needs to be compensated to achieve high power quality systems. The use of passive filters is often the simplest and most immediate solution;however, this decreases converter efficiency and increases its weight and volume. Thus, the use of a control strategy capable of tracking periodic signals, rejecting periodic disturbance and largely improving steady state behavior and harmonic distortion with a limited bandwidth is a very desirable feature. Repetitive control (RC) represents an extremely practical and efficient solution for the aforementioned issues, and it is widely employed in many different applications. This paper focuses on state of the art of RC used in power electronics and drives. RC basic concepts, different control structures, design methods, fixed and variable frequency operating conditions, etc. are investigated. Furthermore, many example applications and existing control approaches developed in recent years for power electronics and drive systems based on RC, have also been discussed in detail. © 2020 IEEE.

关键词： Electric drives

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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Incremental High Fuzzy Utility Itemset Mining 22

Incremental High Fuzzy Utility Itemset Mining

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9th Multidisciplinary International Social Networks Conference, MISNC 2022

作者： Hong, Tzung-Pei Hung, Wei-Teng Huang, Wei-Ming Tsai, Yu-Chuan Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Taiwan Department of Electrical and Control China Steel Inc. Taiwan Library and Information Center National University of Kaohsiung Kaohsiung811 Taiwan

ISBN: (纸本)9781450398435

In data mining, frequent-pattern mining methods are used for handling binary databases. Utility mining addresses this limitation by considering the item utilities and quantities when discovering the high utility itemsets. In addition, to make these high utility patterns more human-explainable, we use the fuzzy-set theory to the utility mining algorithms. However, real-world databases are usually dynamic. That is, new transactions may be intermittently added, and the corresponding mined knowledge needs to be updated. This paper uses the famous incremental strategy, fast update (FUP), to modify high fuzzy utility itemsets from new coming data. We implement the FUP strategy on the Apriori-based approach for maintaining up-to-date high fuzzy utility *** also conducted experiments to demonstrate that the incremental algorithm significantly outperforms the Apriori-based batch mining method. © 2022 ACM.

关键词： Data mining

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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