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$H_{\infty }$ PID Control for Discrete-Time Fuzzy Systems With Infinite-Distributed Delays Under Round-Robin Communication Protocol

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IEEE Transactions on Fuzzy Systems 2021年第6期30卷 1875-1888页

作者： Yezheng Wang Zidong Wang Lei Zou Hongli Dong College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Computer Science Brunel University London Uxbridge U.K. Institute of Complex Systems and Advanced Control and the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University Daqing China

This article is concerned with the $H_{\infty }$ proportional–integral–derivative (PID) control problem for class of discrete-time Takagi–Sugeno fuzzy systems subject to infinite-distributed time delays and round-robin (RR) protocol scheduling effects. The information exchange between the sensors and the controller is conducted through a shared communication network. For the purpose of alleviating possible data collision, the well-known RR communication protocol is deployed to schedule the data transmissions. To stabilize the target system with guaranteed $H_{\infty }$ performance index, a novel yet easy-to-implement fuzzy PID controller is developed whose integral term is calculated based on the past measurements defined in a limited time window with hope to improve computational efficiency and reduce accumulation error. Based on the Lyapunov stability theory and the convex optimization technique, sufficient conditions are derived to ensure the exponential stability as well as the $H_{\infty }$ disturbance attenuation/rejection capacity of the underlying system. Furthermore, by utilizing the cone complementarity linearization algorithm, the nonconvex controller design problem is transformed into an iterative optimization one that facilitates the controller implementation. Finally, simulation examples are given to show the effectiveness and correctness of the developed control method.

关键词： PD control Protocols PI control Control systems Symmetric matrices Communication networks Optimization

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Challenges of online learning for children with special educational needs and disabilities during the COVID-19 pandemic: A scoping review

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Australian Journal of Developmental Disabilities 2023年第2期48卷 105-116页

作者： Indre Bakaniene Martyna Dominiak-Świgoń Miguel Augusto Meneses da Silva Santos Dimitris Pantazatos Mary Grammatikou Marco Montanari Irene Virgili Giovanni Galeoto Paolo Flocco Laura Bernabei Audrone Prasauskiene a Department of Children's Rehabilitation Lithuanian University of Health Sciences Kaunas Lithuania b Poznan Supercomputing and Networking Center Institute of Bioorganic Chemistry Polish Academy of Sciences Poznan Poland c Centre for Research and Innovation in Education School of Education Polytechnic Institute of Porto Porto Portugal d School of Electrical & Computer Engineering National Technical University of Athens Zografou Greece e ISO Department Sapienza University of Rome Rome Italy f Universita degli Studi Roma Tre Centro di Ateneo per lo Studio di Roma Casa Santa Rosa Rehabilitation Center Roma Italy g Department of Human Neurosciences Sapienza University of Rome Rome Italy h Department of Human Neuroscience Sapienza University of Rome Rome Italy i Mental Health Department Sapienza University Rome Italy

ABSTRACTABSTRACTBackgroundThe COVID-19 pandemic has affected education at all levels in various ways. This paper provides a review of the literature on the challenges of online learning for children with special educational needs and disabilities (SEND).MethodIn total, 17 studies from nine countries were analysed. The challenges of online learning for children with SEND reported by teachers and parents and the strategies applied to overcome the challenges were *** challenges of online learning were the need of parental support, routine change, inequities of resources and access to technology, lack of accommodations, and social isolation. There was a considerable lack of interventions aimed at promoting the educational outcomes of children with *** findings emphasise the importance of parent-teacher collaboration and communication. Suggestions for strategies to address challenges as well as for further research are also discussed.

关键词： Children COVID-19 pandemic disabilities online learning special educational needs

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RETHINKING THE IMPLEMENTATION TRICKS AND MONOTONICITY CONSTRAINT IN COOPERATIVE MULTI-AGENT REINFORCEMENT LEARNING

arXiv

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arXiv 2021年

作者： Hu, Jian Jiang, Siyang Harding, Seth Austin Liao, Shih-Wei Wu, Haibin Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taipei Taiwan Department of Computer Science National Taiwan University Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

Many complex multi-agent systems such as robot swarms control and autonomous vehicle coordination can be modeled as Multi-Agent Reinforcement Learning (MARL) tasks. QMIX, a widely popular MARL algorithm, has been used as a baseline for the benchmark environments, e.g., Starcraft Multi-Agent Challenge (SMAC), Difficulty-Enhanced Predator-Prey (DEPP). Recent variants of QMIX target relaxing the monotonicity constraint of QMIX, allowing for performance improvement in SMAC. In this paper, we investigate the code-level optimizations of these variants and the monotonicity constraint. (1) We find that such improvements of the variants are significantly affected by various code-level optimizations. (2) The experiment results show that QMIX with normalized optimizations outperforms other works in SMAC;(3) beyond the common wisdom from these works, the monotonicity constraint can improve sample efficiency in SMAC and DEPP. We also discuss why monotonicity constraints work well in purely cooperative tasks with a theoretical analysis. We open-source the code at https://***/hijkzzz/pymarl2. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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A C-V2X platform using transportation data and spectrum-aware sidelink access

arXiv

引用

arXiv 2021年

作者： Lin, Chia-Hung Lin, Shih-Chun Wang, Chien-Yuan Chase, Thomas Intelligent Wireless Networking Laboratory Department of Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States Institute for Transportation Research and Education North Carolina State University RaleighNC27695 United States

Intelligent transportation systems and autonomous vehicles are expected to bring new experiences with enhanced efficiency and safety to road users in the near future. However, an efficient and robust vehicular communication system should act as a strong backbone to offer the needed infrastructure connectivity. Deep learning (DL)-based algorithms are widely adopted recently in various vehicular communication applications due to their achieved low latency and fast reconfiguration properties. Yet, collecting actual and sufficient transportation data to train DL-based vehicular communication models is costly and complex. This paper introduces a cellular vehicle-to-everything (C-V2X) verification platform based on an actual traffic simulator and spectrum-aware access. This integrated platform can generate realistic transportation and communication data, benefiting the development and adaptivity of DL-based solutions. Accordingly, vehicular spectrum recognition and management are further investigated to demonstrate the potentials of dynamic slidelink access. Numerical results show that our platform can effectively train and realize DL-based C-V2X algorithms. The developed slidelink communication can adopt different operating bands with remarkable spectrum detection performance, validating its practicality in real-world vehicular environments. Copyright © 2021, The Authors. All rights reserved.

关键词： Vehicle to Everything

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Bagged Tree And ResNet Based Joint End-to-End Fast CTU Partition Decision Algorithm For Video Intra Coding

Research Square

引用

Research Square 2021年

作者： Li, Yixiao Li, Lixiang Fang, Yuan Peng, Haipeng Ling, Nam Information Security Center State Key Laboratory of Networking and Switching Technology National Engineering Laboratory for Disaster Backup and Recovery Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science and Engineering Santa Clara University Santa ClaraCA95053 United States

In the development of video coding standards, advanced ones have greatly improved the bit rate compared with those of previous generation, but also brought a huge increase in coding complexity. Coding standards, such as high efficiency video coding (HEVC), versatile video coding (VVC) and AOMedia video 2 (AV2), get the optimal encoding performance by traversing all possible combinations of coding unit (CU) partition and selecting the combination with minimum coding cost. This process of searching for the best makes up a large part of encoding complexity. To reduce the complexity of coding block partition for many video coding standards, this paper proposes an end-to-end fast algorithm for partition structure decision of coding tree unit (CTU) in intra coding. It can be extended to various coding standards with fine tuning, and is applied to the intra coding of HEVC reference software HM16.7 as an example. In the proposed method, the splitting decision of a CTU is made by a well designed bagged tree model firstly. Then, the partition problem of a 32×32 sized CU is modeled as a 17-output classification task and solved by a well trained residual network (ResNet). Jointly using bagged tree and ResNet, the proposed fast CTU partition algorithm is able to generate the partition quad-tree structure of a CTU through an end-to-end prediction process, instead of multiple decision making procedures at depth level. Besides, several effective and representative datasets are also conducted in this paper to lay the foundation of high prediction accuracy. Compared with the original HM16.7 encoder, experimental results show that the proposed algorithm can reduce the encoding time by 59.79% on average, while the BD-rate loss is as less as 2.02%, which outperforms the results of most of state-of-the-art approaches in the fast intra CU partition area. © 2021, CC BY.

关键词： Decision making

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Retraction Note to: Secure storage allocation scheme using fuzzy based heuristic algorithm for cloud

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Journal of Ambient Intelligence and Humanized Computing 2022年第1期14卷 325-325页

作者： Sivaram, M. Kaliappan, M. Shobana, S. Jeya Prakash, M. Viju Porkodi, V. Vijayalakshmi, K. Vimal, S. Suresh, A. Department of Computer Networking College of Engineering and Computer Science Lebanese French University Erbil Iraq Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam India Department of Computer Science College of Science Knowledge University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Erbil Iraq Department of Information Technology National Engineering College Kovilpatti India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India

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What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors

arXiv

引用

arXiv 2021年

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosrave, Abbas Nahavandi, Saeid The Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University VIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran K. N. Toosi University of Technology Tehran Iran The Department of Signal Theory Networking and Communications Universidad de Granada Spain The Department of Psychiatry University of Cambridge United Kingdom Deakin University VIC Australia The Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States

One of the most common problems encountered in human-computer interaction is automatic facial expression recognition. Although it is easy for human observer to recognize facial expressions, automatic recognition remains difficult for machines. One of the methods that machines can recognize facial expression is analyzing the changes in face during facial expression presentation. In this paper, optical flow algorithm was used to extract deformation or motion vectors created in the face because of facial expressions. Then, these extracted motion vectors are used to be analyzed. Their positions and directions were exploited for automatic facial expression recognition using different data mining techniques. It means that by employing motion vector features used as our data, facial expressions were recognized. Some of the most state-of-the-art classification algorithms such as C5.0, CRT, QUEST, CHAID, Deep Learning (DL), SVM and Discriminant algorithms were used to classify the extracted motion vectors. Using 10-fold cross validation, their performances were calculated. To compare their performance more precisely, the test was repeated 50 times. Meanwhile, the deformation of face was also analyzed in this research. For example, what exactly happened in each part of face when a person showed fear? Experimental results on Extended Cohen-Kanade (CK+) facial expression dataset demonstrated that the best methods were DL, SVM and C5.0, with the accuracy of 95.3%, 92.8% and 90.2% respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Data mining

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Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and transfer learning

arXiv

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arXiv 2021年

作者： Ghassemi, Navid Shoeibi, Afshin Khodatars, Marjane Heras, Jonathan Rahimi, Alireza Zare, Assef Pachori, Ram Bilas Gorriz, J. Manuel Clinical Studies Lab Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Ferdowsi University of Mashhad Computer Engineering Department Mashhad Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Department of Mathematics and Computer Science University of la Rioja La Rioja Spain Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Department of Electrical Engineering Indian Institute of Technology Indore Indore453552 India Department of Signal Theory Networking and Communications Universidad de Granada Spain

The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. © 2021, CC BY-NC-ND.

关键词： Diagnosis

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Reconfigurable Intelligent Surface (RIS)-aided Vehicular Networks: Their Protocols, Resource Allocation, and Performance

arXiv

引用

arXiv 2022年

作者： Chen, Yuanbin Wang, Ying Zhang, Jiayi Zhang, Ping Hanzo, Lajos State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The Department of Broadband Communication Peng Cheng Laboratory Shenzhen518055 China The Department of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Reconfigurable intelligent surfaces (RISs) assist in paving the way for the evolution of conventional vehicular networks to autonomous driving. Having said that, the 3rd Generation Partnership Project (3GPP) faces numerous open challenges concerning the RIS-aided vehicle-to-everything (V2X) solutions of the near future. To tackle these challenges and to stimulate future research, this article focuses on the prospective transmission design of RIS-aided V2X communications. In particular, two V2X sidelink modes are enhanced by exploiting RISs and their variants, followed by a customized transmission frame structure that partitions the transmission efforts into different phases. Next, effective channel tracking and resource allocation techniques are developed for attaining a high beamforming gain at low overhead and complexity. Finally, promising research topics are highlighted and future 3GPP standardization items are proposed for RIS-aided V2X systems. © 2022, CC BY-NC-ND.

关键词： Vehicle to Everything

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FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection

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IEEE Transactions on Mobile Computing 2025年

作者： Geng, Jiaxiang Li, Boyu Qin, Xiaoqi Li, Yixuan Li, Liang Hou, Yanzhao Pan, Miao Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Taiyuan University of Technology College of Electronic Information Engineering Taiyuan030024 China Peng Cheng Laboratory Department of Advanced Interdisciplinary Research 518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States

Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping local gradient transmission with continuous local computing, FL can remarkably reduce its training latency over homogeneous clients, yet encounter severe model staleness, model drifts, memory cost and straggler issues in heterogeneous environments. To unleash the full potential of overlapping, we propose, FedEx, a novel federated learning approach to expedite FL training over mobile devices under data, computing and wireless heterogeneity. FedEx redefines the overlapping procedure with staleness ceilings to constrain memory consumption and make overlapping compatible with participation selection (PS) designs. Then, FedEx characterizes the PS utility function by considering the latency reduced by overlapping, and provides a holistic PS solution to address the straggler issue. FedEx also introduces a simple but effective metric to trigger overlapping, in order to avoid model drifts. Experimental results show that compared with its peer designs, FedEx demonstrates substantial reductions in FL training latency over heterogeneous mobile devices with limited memory cost. © 2002-2012 IEEE.

关键词： Federated learning Heterogeneous mobile devices Overlapping computing and communications Participant selection

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