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Chimera-inspired dynamics: When higher-order interactions are expressed differently

Physical Review E 2024年第4期110卷 044204页

作者： Xinrui Ji Xiang Li Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems The Frontiers Science Center for Intelligent Autonomous Systems and The State key laboratory of Intelligent Autonomous Systems Department of Control Science and Engineering College of Electronics and Information Engineering

The exploration of chimera-inspired dynamics in nonlocally coupled networks of Kuramoto oscillators with higher-order interactions is still in its nascent stages. Concurrently, the investigation of collective phenomena in higher-order interaction networks is gaining attraction. Here, we observe that hypergraph networks tend to synchronize through lower-order interactions, whereas simplicial complex networks exhibit a preference for higher-order interactions. This observation suggests that higher-order representations manifest substantial differences in chimera-inspired synchronization regions. Moreover, we introduce an explicit expression for identifying the chimera state. With a comprehensive basin stability analysis and the interplay of pairwise and higher-order interaction strengths, the emergence of the chimera state is inherent in high-order interaction networks. Our findings contribute to the understanding of chimera-inspired dynamics in higher-order interaction networks.

关键词： Chimera states Continuous phase transition Coupled oscillators Synchronization Synchronization transition

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Detection of Termite Nests in Dams Using Ground Penetrating Radar Based on Deep Learning 20

Detection of Termite Nests in Dams Using Ground Penetrating ...

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20th International Conference on Ground Penetrating Radar, GPR, 2024

作者： Peng, Jiao Xue, Wei Wang, Wenkai Song, Junlei Zhou, Feng Huang, Pengchun Zhang, Zhixuan Zhang, Feng Zhang, Xu Chen, Longjia Sun, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China Dam Safety Monitoring and Termite Control Center of Hubei Provincial Department of Water Resources China Key Laboratory of Termite Control Ministry of Water Resources China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

Termites have always been one of the major hidden dangers to the operational safety of dams. Traditional detection methods such as manual inspection and termite baiting have limitations in terms of efficiency and accuracy. To solve the problems, paper proposes a termite nest detection method based on ground penetrating radar (GPR) and deep learning. First, the simulated GPR data are generated using gprMax software, and the real data are collected using a GPR at several dams. Subsequently, the YOLOv8 deep neural network is employed for termite nest recognition in GPR images. The experimental results show that the proposed model can achieve the mean average precision (mAP) of 0.96 and a detection speed of 53.76 frames per second (FPS). © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Opportunities and Challenges in Deep Compressed Sensing Techniques for Multichannel ECG Data Compression

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SN Computer Science 2024年第8期5卷 1147页

作者： Kumar, Sushant Pachori, Ram Bilas Deka, Bhabesh Datta, Sumit Center for Mechatronics Indian Institute of Technology Kanpur UP Kanpur 208016 India Department of Electrical Engineering Indian Institute of Technology Indore MP Indore 453552 India Department of Electronics and Communication Engineering Tezpur University Assam Tezpur 784028 India School of Electronic Systems and Automation Digital University Kerala (Formerly IIITM Kerala) Kerala Thiruvananthapuram 695317 India

Energy consumption involved in wireless transmission poses a major challenge in the implementation of wireless body area networks (WBAN). Compressed sensing (CS)-based multichannel electrocardiogram (ECG) compression is a new paradigm in signal acquisition and reconstruction;a viable alternative for traditional wavelet-based signal reconstruction. However, several challenges must be addressed to achieve efficient and reliable ECG compression in real-world wireless healthcare systems. This paper presents a comprehensive review focused on the evolution of compressed sensing-based energy-efficient single-channel (S-) and multi-channel (M-) ECG data compression techniques. It is observed that the performance of different compression techniques depends on several diagnostic or non-diagnostic test parameters. The present study could be useful for researchers to analyze the state-of-the-art compression techniques in e-healthcare applications. We discuss the challenges associated with implementing ECG compression in wireless healthcare systems, such as signal quality, interoperability, and privacy concerns. We also explore the potential future directions for research in this area, including the development of novel algorithms for compressed sensing-based ECG compression, the integration of artificial intelligence and deep learning techniques, and the exploration of new application areas for wireless healthcare systems. This paper will serve as a good reference for the researcher interested in the area of wireless transmission for WBAN applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Compressed sensing Compression Deep learning Electrocardiogram (ECG) Wireless body area networks

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EMHDRL: Adversarial Attacks on Graph Neural Networks via Hierarchical Reinforcement Learning 42

EMHDRL: Adversarial Attacks on Graph Neural Networks via Hie...

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42nd Chinese control Conference, CCC 2023

作者： Sun, Guowei Ding, Jie Li, Xiang School of Information Science and Engineering Fudan University Adaptive Networks and Control Lab Department of Electronic Engineering Shanghai200433 China Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China

ISBN: (纸本)9789887581543

Graph neural networks (GNNs), with its powerful ability to explore the topological structure of graph data, have achieved great success in various downstream tasks. However, recent studies have shown that due to the lack of robustness, GNNs are easily fooled by perturbations. For node classification tasks, the current attack methods mainly focus on manipulating edges, modifying features and adding fake nodes. Although these attacks are effective, the changes to the original graph data are easy to be detected. Therefore, to misclassify the target nodes, we propose an Edge Manipulation method via Hierarchical Deep Reinforcement Learning (EMHDRL) by taking accounting to the imperceptibility of perturbation on graph data. Through modeling the topology perturbation as Finite Horizon Markov Decision Process, EMHDRL adopts a hierarchical Q-function network to manipulate edges of graph, while trying to ensure that the degree of the attacked node and the total number of edges of the whole graph remain unchanged. Our experimental results show that the proposed method can effectively reduce the performance of GNNs in classifying the target nodes and has strong transferability. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Reinforcement learning

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Data Informativity for Quadratic Stabilization under Data Perturbation

arXiv

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arXiv 2024年

作者： Kaminaga, Taira Sasahara, Hampei Department of Systems and Control Engineering Graduate School of Engineering Institute of Science Tokyo Tokyo Japan

Assessing data informativity, determining whether the measured data contains sufficient information for a specific control objective, is a fundamental challenge in data-driven control. In noisy scenarios, existing studies deal with system noise and measurement noise separately, using quadratic matrix inequalities. Moreover, the analysis of measurement noise requires restrictive assumptions on noise properties. To provide a unified framework without any restrictions, this study introduces data perturbation, a novel notion that encompasses both existing noise models. It is observed that the admissible system set with data perturbation does not meet preconditions necessary for applying the key lemma in the matrix S-procedure. Our analysis overcomes this limitation by developing an extended version of this lemma, making it applicable to data perturbation. Our results unify the existing analyses while eliminating the need for restrictive assumptions made in the measurement noise scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Velocity measurement

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Combined Nonlinear control System for Non-Affine Multi-Loop Plant with control and State Delays 4

Combined Nonlinear Control System for Non-Affine Multi-Loop ...

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4th International Conference on control systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2022

作者： Eremin, Eugenie L. Nikiforova, Larisa V. Shelenok, Evgeniy A. Amur State University Information and Control Systems Department Blagoveshchensk Russia Pacific National University Automation and System Engineering Department Khabarovsk Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

The paper considers the synthesis of a combined nonlinear controller of the control multiply-connected system for an input-non-affine plant with delay at the input signal and state. Dynamics of the plant changes in stages. The plant under consideration operates in conditions of a parametric, functional and structural uncertainties, in the presence of a permanent external noise and measurement only the controlled variable (output signal). The structure of a multiply connected control system includes an implicit reference model, predictor-compensator and correction filters. The authors use the hyperstability criterion and method for constructing L-dissipative dynamic systems to synthesize the nonlinear algorithms for combined control system. The structural synthesis is performed by defining the special estimates which guarantee the validity of the V.M. Popov's integral inequality. Operability of the developed hyperstable control system with structural perturbation is ensured with the help of the special L-dissipativity conditions. At the final section of the article with the help of simulation the authors show the quality of the obtained nonlinear control system. The results of the system simulation show the fulfillment of the control goal with a sufficiently high quality. The obtained results may be useful for construction the control systems for technical plants which dynamics can be described as non-affine MIMO-system. For example, the robot-manipulators and some plants of heat power engineering has the mathematical description like multi-connected systems. © 2022 IEEE.

关键词： MIMO systems

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Adversarial Attack using Projected Gradient Method to Direct Data-Driven control

Adversarial Attack using Projected Gradient Method to Direct...

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control Technology and Applications (CCTA),

作者： Taira Kaminaga Hampei Sasahara Department of Systems and Control Engineering Graduate School of Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350370942

ISBN: (纸本)9798350370959

In this paper, we propose a new algorithm for adversarial attacks to direct data-driven control. In direct data-driven control, the controller is directly designed from input-output data of the system to be controlled without system identification. Previous research has shown that direct data-driven control is vulnerable to adversarial attacks which add small perturbations to the data, and has proposed the directed gradient sign method (DGSM) as a method to generate severe attacks. We introduce a new method, referred to as the iterative directed gradient sign method (IDGSM), based on DGSM and the projected gradient method. Several numerical examples with comparison of IDGSM and DGSM demonstrate that IDGSM generates more sophisticated adversarial attacks than DGSM. Furthermore, we identify the role of the regularization terms through stability analysis using the Lyapunov equation. The regularization term are defense mechanisms against disturbances, and their effectiveness against adversarial attacks has been numerically validated. We provide a theoretical explanation for their effectiveness against attacks.

关键词： Gradient methods Perturbation methods Noise Machine learning Stability analysis Mathematical models System identification

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Finite-time robust performance improvement for non-linear systems in the presence of input non-linearity and external disturbance

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IET control Theory and Applications 2023年第6期17卷 672-682页

作者： Wang, Haoping Ghaffari, Valiollah Mobayen, Saleh Bartoszewicz, Andrzej Fekih, Afef Assawinchaichote, Wudhichai School of Automation Nanjing University of Science and Technology Nanjing China Department of Electrical Engineering Persian Gulf University Bushehr Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan Institute of Automatic Control Lodz University of Technology Lodz Poland Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA United States Department of Electronic and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand

The main focus of this research is on improving the performance of dynamic systems with actuator non-linearities and time-varying disturbances. To this end, using the concept of finite-time stability, a novel observer is presented to estimate the disturbances in systems with input non-linearities. Then, introducing an innovative sliding manifold, a robust observer-based technique is derived to guarantee the finite-time stability of the sliding surface. Hence, the closed-loop system would be stable in the presence of hard non-linearities and time-varying disturbances. The performance of the proposed approach was assessed in using a simulation study of two numerical examples. The obtained results and comparison with a conventional method confirmed the benefits of the suggested approach. © 2022 The Authors. IET control Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Closed loop systems

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Black-Box Modeling of Synchronous Generators Using Feedforward Neural Networks 22

Black-Box Modeling of Synchronous Generators Using Feedforwa...

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22nd International Symposium on Power Electronics, Ee 2023

作者： Ivanovic, Luka Stojic, Dorde Veinovic, Slavko Joksimovic, Dusan Klasnic, Ilija Milic, Sasa D. Rakic, Aleksandar Electrical Institute Nikola Tesla Automation and Regulation Center Belgrade Serbia School of Electrical Engineering The Department of Signals and Systems Belgrade Serbia Electrical Institute Nikola Tesla Belgrade Serbia

ISBN: (纸本)9798350343175

This paper provides a black-box modeling approach for grid connected synchronous generators using a specific type of Artificial Neural Network (ANN) known as the Feedforward Neural Network (FNN). The FNN is utilized to establish connection between excitation voltage, active power, reactive power and terminal voltage values. Meanwhile, the weight coefficients of the FNN are determined using the Levenberg-Marquardt algorithm. The data generation required for the experiment was conducted using 297 MVA synchronous generator model in Simulink, and a graphical programming environment based on Matlab. Various experiments were conducted for validation purposes, involving different step disturbance values. The proposed FNN model is accurate and provides a very high degree of matching with the experimental results. Moreover, the test procedure and model are easily implementable, requiring no decoupling of the generator from the grid or additional equipment for realization. The only required measurements are active power, reactive power, excitation voltage and stator terminal voltage. Obtained models can be applied to various testing scenarios related to the excitation system. © 2023 IEEE.

关键词： Synchronous generators

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EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin Cancer Classification 27th

EDB-Net: An Edge-Guided Dual-Branch Neural Network for Skin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ray, Amartya Gayen, Soumyajit Kaplun, Dmitrii Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (纸本)9783031781032

Skin cancer poses a serious global health challenge, where timely and precise diagnosis is essential to improve patient outcomes. Recently, neural networks have proven to be highly effective tools for automated skin cancer classification, significantly advancing the field of dermatology. This paper introduces a novel approach to generate edge maps from dermoscopic images using a holistically nested edge detector model. These edge maps enhance the detection of shape and symmetry irregularities, which are key indicators of malignancy, and improve the focus on relevant regions of interest. We then propose an edge-guided dual-branch neural network, called EDB-Net, for the classification task. Branch 1 handles edge maps, while Branch 2 processes original dermoscopic images. To highlight significant regions and focus on specific lesion areas, we incorporate a novel channel-spatial synergistic attention block within Branch 2. Additionally, we introduce a unique strategy to modulate the generated attention maps using edge features extracted from the edge maps in Branch 1, creating edge-guided features that refine the overall feature representation. In the final stage, both edge-guided and attention-aided features are combined, producing more distinct and contextually relevant outputs, thereby significantly enhancing classification performance. Our model achieves accuracies of 0.927 and 0.848 on the challenging HAM10000 and ISIC 2016 datasets, respectively, without employing any data augmentation. The source code of the proposed model is available at: https://***/Cmatermedicalimageanalysis/EDB_Net. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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