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arXiv 2022年

作者： Huang, Yan Sun, Ying Zhu, Zehan Yan, Changzhi Xu, Jinming College of Control Science and Engineering Zhejiang University Hangzhou China School of Electrical Engineering and Computer Science The Pennsylvania State University PA16802 United States

We develop a general framework unifying several gradient-based stochastic optimization methods for empirical risk minimization problems both in centralized and distributed scenarios. The framework hinges on the introduction of an augmented graph consisting of nodes modeling the samples and edges modeling both the inter-device communication and intra-device stochastic gradient computation. By designing properly the topology of the augmented graph, we are able to recover as special cases the renowned Local-SGD and DSGD algorithms, and provide a unified perspective for variance-reduction (VR) and gradient-tracking (GT) methods such as SAGA, Local-SVRG and GT-SAGA. We also provide a unified convergence analysis for smooth and (strongly) convex objectives relying on a proper structured Lyapunov function, and the obtained rate can recover the best known results for many existing algorithms. The rate results further reveal that VR and GT methods can effectively eliminate data heterogeneity within and across devices, respectively, enabling the exact convergence of the algorithm to the optimal solution. Numerical experiments confirm the findings in this paper. Copyright © 2022, The Authors. All rights reserved.

关键词： Lyapunov functions

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Reliability and controllability of the System Consisting of a Robot and Its Associated Safety Mechanism

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Journal of Mathematical Research with Applications 2020年第4期40卷 342-350页

作者： Li ZHOU Liansuo WEI College of Science Qiqihar University Computer and Control Engineering Qiqihar University

This paper mainly studies the system composed of robot and its safety *** using functional analysis method, the partial differential equation of the original system is transformed into the abstract Cauchy problem in Banach space. The transient and steady-state availability of the system can be obtained by algebraic theory and Csemi-group theory, and the system is proved to be reliable and zero-state controllable by using transformation ***, Maple software is used to simulate the system transient reliability and steady-state availability.

关键词： Cauchy problem transient reliability steady availability numerical simulation

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A Non-Iterative LMI-Based Design of Static Output Feedback control with $\mathcal{L}_{2}$ Gain Performance for Linear Systems

A Non-Iterative LMI-Based Design of Static Output Feedback C...

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International Conference on Electrical, control and Instrumentation engineering (ICECIE)

作者： Hidetoshi Oya Daiki Asada Shunya Nagai Kazushi Nakano Department of Computer Science Tokyo City University Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

In this paper, we present a new LMI-based non-iterative design strategy for static output feedback controllers with $\mathcal{L}_{2}$ Gain Performance for linear systems. In our approach, on the basis of the well-known necessary and sufficient condition for the existence of the static output feedback control for linear systems, the non-iterative LMI-based design procedure for the proposed static output feedback controller is derived. In this paper, we show the LMI-based non-iterative design procedure for static output feedback controllers with $\mathcal{L}_{2}$ Gain Performance and the effectiveness of the proposed static output feedback control is presented through a simple illustrative example.

关键词： Linear systems Sufficient conditions Instruments Interconnected systems control systems Output feedback Gain

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Cooperative Global K-exponential Tracking control of Multiple Mobile Robots-Extended Version

arXiv

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arXiv 2024年

作者： Xu, Liang Su, Youfeng Cai, He College of Computer and Data Science Fuzhou University Fuzhou350116 China Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen518055 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China

This paper studies the cooperative tracking control problem for multiple mobile robots over a directed communication network. First, it is shown that the closed-loop system is uniformly globally asymptotically stable under the proposed distributed continuous feedback control law, where an explicit strict Lyapunov function is constructed. Then, by investigating the convergence rate, it is further proven that the closed-loop system is globally K-exponentially stable. Moreover, to make the proposed control law more practical, the distributed continuous feedback control law is generalized to a distributed sampled-data feedback control law using the emulation approach, based on the strong integral input-to-state stable Lyapunov function. Numerical simulations are presented to validate the effectiveness of the proposed control methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lyapunov functions

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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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An Adaptive Attitude controller for A Quadrotor Transporting the Payload With Unknown Mass

An Adaptive Attitude Controller for A Quadrotor Transporting...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Shihao Qiu Longlong Chen Shuang Li Zongyang Lv Huayi Tang School of Computer Science and Technology Dalian University of Technology Dalian China School of Control Science and Engineering Dalian University of Technology Dalian China College of Information Science and Engineering Henan University of Technology Zhengzhou China School of Mechanical Engineering Dalian University of Technology Dalian China

An adaptive control approach is presented in this article to deal with the impacts of carrying a payload with unknown mass by a quadrotor. This approach can effectively control the quadrotor’s attitude stability. The paper commences by presenting the dynamic model of the quadrotor, followed by the development of a controller to manage its attitude control. Through Lyapunov analysis, the stability of the controller is validated. Ultimately, the proposed controller and a backstepping controller are used to controlling the attitude of the quadrotor, respectively, and experimental outcomes substantiate the efficiency and feasibility of the introduced controller.

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Enhanced Session-Based Recommendation Using Multi-Channel Hypergraph Network

Enhanced Session-Based Recommendation Using Multi-Channel Hy...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Abdur Rehman Xirong Xu Li Zhu Muhammad Qasim Wali Muhammad Usama Amin Xiaopeng Wei School of Computer Science and Technology Dalian University of Technology Dalian P.R. China School of Control Science and Technology Dalian University of Technology Dalian P.R. China Software Engineering Government College University Faisalabad Pakistan

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

In today’s digital landscape, the drive for supremely personalized content made Session-Based Recommendation (SBR) systems crucial. Traditional methods often overlook the complex high-order relationships among items during the recommendation process. Our paper introduces a Multi-Channel Hypergraph Convolutional Network to enhance SBR systems by addressing these gaps. Our framework first transforms session data into a hypergraph, facilitating many-to-many relationship modeling between items, beyond linear and pairwise associations. It employs hypergraph convolutional layers and a line graph channel to process these intricate relationships and capture inter-session connections. Additionally, it utilizes multi-channel convolution operations with both parallel and serial channels, varying kernel sizes, and filters designed to thoroughly process the encoded multidimensional data to extract essential features. To combat data sparsity, we apply self-supervised learning techniques to maximize mutual information between the learned session representations. This approach significantly improves the system’s accuracy and context-aware recommendations, as demonstrated by our model’s performance improvements on various datasets, including a precision at 10 (P@10) of 40.22% and a mean reciprocal rank at 20 (M@20) of 19.45% on the Diginetica dataset, reflecting substantial advancements over existing models.

关键词： Training Convolution Neural networks Self-supervised learning Transforms Feature extraction Convolutional neural networks

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Optimized Ensemble Algorithm for Predicting Metamaterial Antenna Parameters

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computers, Materials & Continua 2022年第6期71卷 4989-5003页

作者： El-Sayed M.El-kenawy Abdelhameed Ibrahim Seyedali Mirjalili Yu-Dong Zhang Shaima Elnazer Rokaia M.Zaki Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Nile Higher Institute for Engineering and Technology MansouraEgypt Computer and Information Technology College Taif UniversityTaifSaudi Arabia Higher Institute of Engineering and Technology Kafrelsheikh Department of Electrical Engineering Shoubra Faculty of EngineeringBenha UniversityEgypt

Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.

关键词： Metamaterial antenna machine learning ensemble model feature selection guided whale optimization support vector machines

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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A Multiple Surrogate-Assisted Hybrid Evolutionary Feature Selection Algorithm

SSRN

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SSRN 2024年

作者： Zhang, Wan-Qiu Hu, Ying Zhang, Yong Zheng, Zi-Wang Peng, Chao Song, Xianfang Gong, Dunwei School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer and Information Anhui Normal University Wuhu241000 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266100 China

Feature selection (FS) is an important data processing technology. However, existing FS methods based on evolutionary computation have still the problems of "curse of dimensionality" and high computational cost, with the increase of the number of feature and/or the size of instance. In view of this, the paper proposes a multiple surrogate-assisted hybrid evolutionary feature selection (MSa-HEFS). Two kinds of surrogates (i.e., objective regression surrogate and sample surrogate) and two kinds of FS methods (i.e., filter and wrapper) are integrated into MSa-HEFS to improve its performance. Firstly, an ensemble filter FS method is designed to reduce the search space of subsequent wrapper evolutionary FS method. Secondly, in the proposed evolutionary FS method, a dual-surrogate-assisted hierarchical individual evaluation mechanism is developed to reduce the evaluation cost on feature subsets, an online management and update strategy is used to adaptively choose appropriate surrogates for individuals. The proposed algorithm is applied to 12 typical datasets and compared with 4 state-of-the-art FS algorithms. Experimental results show that MSa-HEFS can obtain good feature subsets at the smallest computational cost on all datasets. © 2024, The Authors. All rights reserved.

关键词： Feature Selection

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