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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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ℋ︁∞output feedback control design for discrete-time fuzzy systems

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Optimal control Applications and Methods 2008年第4期30卷

作者： Jinhui Zhang Yuanqing Xia Peng Shi Jiqing Qiu Department of Automatic Control Beijing Institute of Technology Beijing 100081 China Department of Computing and Mathematical Sciences University of Glamorgan Pontypridd CF37 1DL U.K. ILSCM School of Computer Science and Mathematics Victoria University Melbourne Australia School of Mathematics and Statistics University of South Australia Adelaide Australia College of Sciences Hebei University of Science and Technology Shijiazhuang 050018 China

This paper investigates the relaxed non-quadratic stability conditions, fuzzy observer designs and ℋ︁ ∞ controller designs for discrete-time Takagi-Sugeno fuzzy systems based on a relaxed approach in which fuzzy Lyapunov functions are used. First, a new relaxed condition of non-quadratic stability is presented, which is shown to be useful in designing fuzzy controller and observer. Second, new fuzzy observers based on the relaxed non-quadratic stability conditions have been proposed. Then, a sufficient linear matrix inequality (LMI)-type condition is proposed to guarantee the existence of the ℋ︁ ∞ controllers based on the fuzzy observers designed. It is shown that the controller and observer parameters can be obtained by solving a set of LMIs that are numerically feasible with commercially available software. Finally, the effectiveness and less conservativeness of the proposed approach are demonstrated by two examples. Copyright © 2008 John Wiley & Sons, Ltd.

关键词： T-S fuzzy systems fuzzy observer linear matrix inequalities (LMIs) ℋ︁∞ control

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