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Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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Third-Order Low-Pass Filters for Limiting the Signal Spectrum at the Differential Input of the Analog/Digital Converters

Third-Order Low-Pass Filters for Limiting the Signal Spectru...

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International Conference for Young Scientists on Issues of Physics and Technology in Science, Power Industry and Medicine 2019, IPTSPIM 2019

作者： Yu Denisenko, D. Prokopenko, N.N. Butyrlagin, N.V. Department of Information System and Radio Engineering Don State Technical University Gagarin Sq. 1 Rostov-on-Don344000 Russia Department of Automatic Control Systems Southern Federal University Bolshaya Sadovaya St. 33 Rostov-on-Don344000 Russia Institute for Design Problems in Microelectronics of Russian Academy of Sciences Institute for Design Problems in Microelectronics Ras Sovetskaya St. 3 Zelenograd124681 Russia

Two new architectures of the third-order low-pass filters (LPF) based on differential (Op-Amp) and differential difference amplifiers (DDA) with a paraphase output are compared. This architectures designed for practical use as anti-aliasing filters in analog-to-digital converters of sensor signals of various physical quantities. The basic equations of the developed LPF modifications that make possible to perform their parametric synthesis are given. The computer simulation results are presented, which show that the LPFs under consideration provide the guaranteed frequency response (FR) attenuation outside the bandwidth being implemented on Op-Amps by Analog Device (AD8132) and Texas Instruments (THS4131). © Published under licence by IOP Publishing Ltd.

关键词： Low pass filters

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2-D GCANet Applied to Denoise 1-D EEG Signals in Online Remote Teaching Scenarios

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1289-1302页

作者： Huang, Ting-Qing Wang, Chuan-Sheng Chen, Zhao-Qi Zhang, Fu-Quan Meng, Xiang-Long Grau, Antoni Chen, Yang Huang, Jing-Wei College of Computer and Control Engineering Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulong Jiangbei Avenue Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music No. 2 Wannianchang Street Chenghua District Sichuan Province Chengdu City China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town NO. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 599 Quanxiu Road Juyuanzhou Ecological Civilization District Fujian Province Fuzhou City China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fujian Province Fuzhou City China

When EEG signals are used to assess the level of student engagement in online teaching tasks, they are often interfered by noise. It is a challenge to effectively remove these noises. Currently, deep learning methods have been applied to the field of EEG denoising. However, existing methods still have some problems. The denoised signals still have obvious noise residue, or the original EEG signals are damaged, and the model fitting speed is too slow. Image dehazing, as a typical denoising task in the field of image enhancement, has achieved great success in recent years. Therefore, inspired by advanced models in this field, we introduce CNN into EEG denoising tasks. In this paper, we take GCANet, an excellent image enhancement model, as an example. The dilated convolutions and gate fusion subnetworks included in GCANet enable more efficient EEG signal denoising. The results demonstrate that the proposed model effectively reduces noise while preserving essential features. Furthermore, in comparison to other state-of-the-art models, our proposed model exhibits enhanced robustness and faster convergence, as evidenced by achieving lower loss values after five epochs. Its good performance provides a new development idea for the field of EEG denoising. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Image enhancement

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Detection and classification of turn fault and high-resistance connection fault in inverter-fed permanent magnet machines based on high-frequency signals

Detection and classification of turn fault and high-resistan...

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作者： Hu, Rongguang Wang, Jiabin Mills, Andrew Chong, Ellis Sun, Zhigang Department of Electronics and Electrical Engineering University of Sheffield Sheffield United Kingdom Department of Automatic Control and System Engineering University of Sheffield Sheffield United Kingdom Electrical Capability Group Rolls-Royce plc Derby United Kingdom

Winding turn fault and high-resistance connection (HRC) fault will lead to different consequences and require different mitigation actions. In this study, the differentiating features between a turn fault and HRC fault are analysed and compared in a three-phase surface-mounted permanent magnet machine fed by the inverter with pulse-width-modulation voltages. The resultant high-frequency components in both voltages and currents are utilised for the fault detection and classification based on the high-frequency impedance and ripple current, without requiring modifications to the machine or interface design. Extensive simulations show that this method is capable of fault detection and classification in both transient and steady-state operations. © 2019 Institution of engineering and Technology. All rights reserved.

关键词： Permanent magnets

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Nash optimality based distributed model predictive control for vehicle platoon ⁎

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IFAC-PapersOnLine 2020年第2期53卷 6610-6615页

作者： Shuyou Yu Hao Chen Yangyang Feng Yajing Zhang Yongfu Li Christian Ebenbauer Hong Chen Department of Control Science and Engineering Jilin University Changchun 130012 P.R. China College of Automation Chongqing University of Posts and Telecommunications Chongqing 400065 P.R. China Institute for System Theory and Automatic Control University of Stuttgart Stuttgart 70550 Germany Clean Energy Automotive Engineering Tongji University Shanghai 201804 P. R. China

In this paper, a distributed model predictive control algorithm (DMPC) based on Nash optimality is proposed for automated vehicle platoon control. The optimization decision of vehicle platoon is decomposed into the decentralized optimization of single vehicles, in which the Nash optimality algorithm is adopted to solve the decentralized optimization problem. Thus, each vehicle can reach the local optimal target and the whole team can reach its Nash equilibrium. The methodology employs neighborhood information of the entire platoon through on-board sensors and V2V communication to achieve coordination of the entire platoon. The ability of the methods in terms of robustness to disturbances and cyber-physical interaction is demonstrated with simulation case studies.

关键词： Vehicle platoon Distributed control Model predictive control Nash optimality

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Hierarchical control with Guaranteed Fault Diagnosability

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IFAC-PapersOnLine 2018年第24期51卷 1105-1110页

作者： Stoican, Florin Petzke, Felix Prodan, Ionela Streif, Stefan University POLITEHNICA of Bucharest Automatic Control and Systems Engineering Department 313 Splaiul Independentei Bucharest Romania Technische Universität Chemnitz Automatic Control and System Dynamics Lab Chemnitz09107 Germany GrenobleINP France

A multiple time-scale hierarchical control approach with guarantees for active fault diagnosability (detection and isolation) is presented. Robust positive invariance notions are used to provide a set-membership test for FDI validation at the low level and sufficient constraints for guaranteeing FDI at the high level. Due to the computational burden of the involved bilinear optimization problem, the high-level reference governor operates at a slower sampling time than the controller of the low-level system. Therefore, corner cutting avoidance constraints are imposed such that the faster-sampled lower level is still guaranteed to function properly. A case study illustrates the presented theoretical notions. © 2018

关键词： Fault detection Edge detection Bilinear optimizations Computational burden Corner cutting Fault diagnosability Hierarchical control isolation Positive invariance Reference governors

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Distributed Nonconvex Optimization: Gradient-free Iterations and ϵ-Globally Optimal Solution

arXiv

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arXiv 2020年

作者： He, Zhiyu He, Jianping Chen, Cailian Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Automatic Control Laboratory ETH Zurich Zurich8092 Switzerland

Distributed optimization utilizes local computation and communication to realize a global aim of optimizing the sum of local objective functions. This article addresses a class of constrained distributed nonconvex optimization problems involving univariate objectives, aiming to achieve global optimization without requiring local evaluations of gradients at every iteration. We propose a novel algorithm named CPCA, exploiting the notion of combining Chebyshev polynomial approximation, average consensus, and polynomial optimization. The proposed algorithm is i) able to obtain ϵ-globally optimal solutions for any arbitrarily small given accuracy ϵ, ii) efficient in both zeroth-order queries (i.e., evaluations of function values) and inter-agent communication, and iii) distributed terminable when the specified precision requirement is met. The key insight is to use polynomial approximations to substitute for general local objectives, distribute these approximations via average consensus, and solve an easier approximate version of the original problem. Due to the nice analytic properties of polynomials, this approximation not only facilitates efficient global optimization, but also allows the design of gradient-free iterations to reduce cumulative costs of queries and achieve geometric convergence for solving nonconvex problems. We provide a comprehensive analysis of the accuracy and complexities of the proposed algorithm. © 2020, CC BY-NC-SA.

关键词： Polynomial approximation

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Constrained Extremum Seeking: a Modified-Barrier Function Approach ⁎

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IFAC-PapersOnLine 2019年第16期52卷 694-699页

作者： Christophe Labar Emanuele Garone Michel Kinnaert Christian Ebenbauer Department of Control Engineering and System Analysis Université libre de Bruxelles 1050 Brussels Belgium Institute for Systems Theory and Automatic Control University of Stuttgart 70569 Stuttgart Germany

In this paper, we address the problem of steering the input of a convex function to a value that minimizes the function under a convex constraint. We consider the case where the constraint cannot be violated of more than a user-defined value during the whole transient phase. The mathematical expression of both the cost function and the constraint are assumed to be unknown. The only information available are the on-line values of the cost and the constraint. To tackle this problem, an optimization law, based on a modified-barrier function, and involving the gradient of both the cost function and the constraint, is firstly designed. The Lie bracket formalism is then exploited to approximate this law, by combining time-periodic signals with the on-line measurements of both the cost and the constraint. The stability property of the resulting constrained extremum seeking system is proved, and its effectiveness is shown in simulation.

关键词： Constrained Optimization Extremum Seeking Singular Perturbations Data-based control Modified Barrier-Function

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A Hierarchical Architecture for the Coordination of an Ensemble of Steam Generators

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IFAC-PapersOnLine 2020年第2期53卷 11557-11562页

作者： Stefano Spinelli Elia Longoni Marcello Farina Felix Petzke Stefan Streif Andrea Ballarino Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Italy Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato Consiglio Nazionale delle Ricerche Department of Electrical Engineering and Information Technology Technische Universität Chemnitz Automatic Control and System Dynamics Lab

This work presents a hierarchical architecture for the optimal management of an ensemble of steam generators, which needs to jointly sustain a common load. The coordination of independent subsystems is provided by a multi-layer control scheme. A high-level optimizer computes the optimal shares of production to be allocated to single generators. At medium level, a robust tube-based model predictive control (MPC) is proposed to track the time-varying demand of the ensemble using a centralized, but aggregated model, whose order does not scale with the number of subsystems. At low level, decentralized controllers are in place to stabilize the internal boiler pressure. The control architecture enables the dynamic modification of the ensemble configuration and plug and play operations. Simulation results are reported to demonstrate the potentialities of the proposed approach.

关键词： Model predictive optimization-based control Optimization control of large-scale network systems Hierarchical multilevel multi-layer control Steam generator Energy systems

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Extremum seeking algorithms based on non-commutative maps 11

Extremum seeking algorithms based on non-commutative maps

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11th IFAC Symposium on Nonlinear control systems, NOLCOS 2019

作者： Feiling, Jan Labar, Christophe Grushkovskaya, Victoria Garone, Emanuele Kinnaert, Michel Ebenbauer, Christian Institute for Systems Theory and Automatic Control University of Stuttgart Germany Dr. Ing. h.c. F. Porsche AG Stuttgart Germany Department of Control Engineering and System Analysis Université libre de Bruxelles Belgium Institute for Mathematics Julius-Maximilians-University Würzburg Germany

In this work, discrete-time extremum seeking algorithms for unconstrained optimization problems are developed. A general class of non-commutative maps and one- and two point function evaluation polices are presented to approximate a gradient-descent algorithm, suitable for extremum seeking problems. Moreover, adaptive step size rules are discussed to achieve faster convergence and vanishing steady state oscillations. © 2019, IFAC (International Federation of automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Optimization

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