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Output Tracking Control of a Nonlinear System Based on Takagi-Sugeno Fuzzy Model: Generalized Partial Eigenstructure Assignment Approach

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IEEE Access 2024年 12卷 18520-18535页

作者： Soltanian, Farzad Shasadeghi, Mokhtar Mobayen, Saleh Skruch, Pawel University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2M9 Canada Shiraz University of Technology Electrical Engineering Faculty Shiraz71557-13876 Iran National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan AGH University of Science and Technology Department of Automatic Control and Robotics Kraków30-059 Poland

This paper introduces an innovative optimal control approach to achieve output tracking while incorporating H2-performance specifications in a specific class of nonlinear dynamics modeled by the Takagi-Sugeno fuzzy model (TSFM). The primary innovation lies in extending partial eigenstructure assignment to TSFM-based nonlinear systems within the framework of sliding mode control (SMC). We propose a two-step methodology for designing optimal sliding surface gains. Initially, optimal state feedback gains are computed for each rule consequence containing a linear subsystem, adhering to predetermined eigenvalues and satisfying H2-performance criteria. Subsequently, using a convex combination, the overall state feedback gain is calculated and utilized to design sliding matrix gains. The sliding matrix gains are then determined by strategically combining previously calculated state-feedback gains in a convex optimization problem. We reframe the output tracking strategy as a stabilization problem using a virtual control input and reformulate the optimization task concerning tracking state errors. This process yields state feedback gains, sliding gains, and the formulation of the virtual control input. The effectiveness of our approach is verified through comprehensive simulations, emphasizing its capability in addressing output tracking challenges within nonlinear systems. © 2013 IEEE.

关键词： Transient analysis

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Nanogenerator-based bidirectional pressure sensor array and its demonstration in underwater invasive species detection

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Nano Research 2023年第9期16卷 11822-11831页

作者： Yunqi Cao Hongyang Shi Xiaobo Tan Nelson Sepúlveda State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichigan 48824USA

Lakes areas,which cause catastrophic damages in both commercial fishery and ecological ***,current assessment strategies may pose challenges for lake-wide abundance estimation and non-target anadromous species ***,we demonstrate an efficacious species-specific non-destructive sensing system based on porous ferroelectret nanogenerator for in-situ monitoring of lamprey spawning migration using their unique suction *** show that the porous structure enables a redistribution of surface charges under bidirectional deformations,which allows the detection of both positive and negative *** quasi-piezoelectric effect is further validated by quantitative analysis in a wide pressure range of−50 to 60 kPa,providing detailed insights into transduction working *** reliable lamprey detection,a 4×4-pixel sensor array is developed and integrated with a complementary metal-oxide-semiconductor(CMOS)based signal processing array thus constituting a sensing panel capable of recording oral suction patterns in an underwater environment.

关键词： bidirectional pressure sensing nanogenerators self-powered sensor array ferroelectret invasive species detection

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A 935-953-MHz Q-Enhanced CMOS Tunable Bandpass Filter With Ultra-Narrowband and Ultralow Power for RFID Applications

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IEEE Transactions on Microwave Theory and Techniques 2024年第10期72卷 6120-6129页

作者： Hou, Yaru Li, Kun Liu, Bin Zeng, Tiantian Liu, Yuanan Chi, Pei-Ling Yang, Tao University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu Sichuan610054 China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing100876 China National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A Q-enhanced CMOS tunable bandpass filter (BPF) with ultra-narrow bandwidth and ultralow dc power consumption for radio frequency identification (RFID) devices is proposed using 55-nm bulk CMOS technology in this article. The proposed filter is constructed by a capacitively coupled LC resonator (LCC), which consists of a 16-nH on-chip integrated inductor, fixed capacitors, and tunable varactors. nMOS cross-coupled transistors are used to compensate for the loss of the resonator and improve the loaded Q of the resonator. To achieve low power consumption, an inductor with an ultralarge value is exploited to reduce the loss of the resonator. Besides, an improved dual varactor inverse (DVI) and degenerated resistors at the source of the cross-coupled transistors with linearity and resistance adjusting, respectively, are utilized to optimize the linearity under center frequency tuning. Besides, the active circuits, the fixed capacitance, and varactors are placed inside the inductor to improve the integration. With all the proposed techniques, the presented BPF achieves a 935-953-MHz center frequency tuning range with a constant narrow bandwidth of 180 kHz, a low power consumption of 85-μW , and a core area of 0.43\times0.43 mm2, demonstrating a high-performance tunable on-chip filter using bulk CMOS technology for low-power and narrowband Internet of Things (NBIoT) applications. © 1963-2012 IEEE.

关键词： Bandpass filters

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A spatiotemporal graph neural network with multi granularity for air quality prediction 3

A spatiotemporal graph neural network with multi granularity...

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3rd International Workshop on Spatio-Temporal Reasoning and Learning, STRL 2024

作者： Liao, Haibin Yuan, Li Wu, Mou School of Electronic and Electrical Engineering Wuhan Textile University Wuhan430200 China School of Computer Science and Technology Hubei University of Science and Technology Xianning437100 China

Air quality prediction is a complex system engineering. How to fully consider the impact of meteorological, spatial and temporal factors on air quality is the core problem. To address this central conundrum, in an elaborate encoder-decoder architecture, we propose a new air quality prediction method based on multi-granularity spatiotemporal graph network. At the encoder, firstly, we use multi granularity graph and the well-known HYSPLIT model to build spatial relationship and dynamic edge relationship between nodes, respectively, while meteorological, temporal and topographic characteristics are used to build node features and LSTM (Long Short Term Memory) is used to learn the time-series relationship of pollutant concentration. At the decoder, secondly, we use the attention mechanism LSTM for decoding and forecasting of pollutant concentration. The proposed model is capable of tracking different influences on prediction resulting from the changes of air quality. On a project-based dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in pollutant process. We also compare the proposed model with the state-of-the-art air quality forecasting methods on the dataset of Yangtze River Delta city group, the experimental results show the appealing performance of our model over competitive baselines. © 2024 Copyright for this paper by its authors.

关键词： Graph neural networks

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Interpreting Multiclass Lung Cancer from CT Scans using Grad-CAM on Lightweight CNN Layers 6

Interpreting Multiclass Lung Cancer from CT Scans using Grad...

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6th International Conference on electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Muntasir, Fahim Datta, Ayon Mahmud, Shakib Brac University Department of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Bangladesh University of Engineering and Technology Department of Biomedical Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Lung cancer is one of the most prevalent and deadly cancer kinds. CT scan imaging has made it easier to observe in-depth at different lung cancer conditions. Convolutional Neural Networks have already been utilized for detecting objects in images. This research implements a lightweight CNN architecture in classifying two types of cancerous images and distinguishing them from healthy ones. AlexNet, ResNet101 V2, DenseNet201, MobileNet V2, and other sizable deep learning and transfer learning models were tested against this unique architecture with global max pooling. The proposed CNN model overlaps in performance with much bigger models whilst using about eight times fewer parameters, thus being efficient. Apart from making the architecture smaller, Grad-CAM was used to explain the convolutional flow over different convolution layers. This integration of the Grad-CAM technique into the analysis adds a crucial layer of transparency to the deep-learning models. By vi-sualizing the regions within lung cancer images that significantly influence predictions, we aim to enhance the interpretability of our model, fostering trust and better understanding among healthcare professionals. © 2024 IEEE.

关键词： Convolutional neural networks

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Can blockchain link the future?

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Digital Communications and Networks 2022年第5期8卷 687-694页

作者： Liang Qiao Shuping Dang Basem Shihada Mohamed-Slim Alouini Robert Nowak Zhihan Lv College of Computer Science&Technology Qingdao UniversityQingdao266071China Computer Electrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Institute of Computer Science Warsaw University of TechnologyNowowiejska 15/1900-665WarsawPoland

Recently,decentralization has been extensively explored by ***,as a representation of decentralized technology,has attracted attention with its unique characteristics,such as irrevocability and ***,herein,we introduce cutting-edge blockchain technologies from four directions:blockchain system,consensus algorithms,smart contract,and ***,we analyze the current lack of consensus mechanism,fault tolerance,and block capacity of the blockchain,and the integration of blockchain into 5G/6G mobile ***,we discuss the possible applications of blockchain in intellectual property protection,the Internet of Things,digital twins,standardization,and epidemic prevention and ***,explore the impacts and solutions of blockchain on human society beyond technology.

关键词： Blockchain Consensus algorithm Scalability Smart contract Internet of things Digital twins

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Sustainable Solutions for Organ Donation and Procurement via Regional Collaboration System 6

Sustainable Solutions for Organ Donation and Procurement via...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mily, Farzana Akter Arefin, Md Shamsul Department of Computer Science & Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Electrical & Electronic Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (纸本)9798331522667

The severe shortage of organs requires the efficient management and collaboration of practices. Therefore, this paper introduces the Regional Collaboration System (RCS) to deal with this critical issue. The system aims at enhancing organ donation and procurement with the perspective of coordination of regional healthcare organizations. The primary purpose is to assist patients in finding suitable hospitals, donors, and doctors, in their region to carry out a seamless transplantation process. This system has been developed using Visual Studio 2022, SQL Server 2019 Express, and C# for a robust and scalable solution. Key features include an administrative panel, advanced search options, and the user interface to make it easy for administrators, patients, donors, and doctors to use. Moreover, the system also provides a medical history management platform that enables the patients to maintain a record of their history for future use via the administrative panel. This comprehensive approach is designed to improve transplant outcomes, minimize waiting times, and enhance organ availability. By effectively addressing the crisis in organ shortages, RCS holds a great potential to bring revolutionary changes in the existing structure of organ donation and transplantation, thereby saving more lives and hence fostering equality in access to health resources. © 2025 IEEE.

关键词： Records management

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Enhancing Voltage and Frequency Control in Islanded VSC-Based Microgrids: A PSO-Driven Multilayer Perceptron Approach

Enhancing Voltage and Frequency Control in Islanded VSC-Base...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Beyene, Yared Bekele Worku, Getachew Biru Tjernberg, Lina Bertling Addis Ababa University School of Electrical and Computer Engineering Addis Ababa Ethiopia Kth Royal Institute of Technology School of Electrical and Computer Science Stockholm Sweden

ISBN: (纸本)9789531842976

This paper proposes a novel design approach using the particle swarm optimization-trained multilayer perceptron neural network (PSO-MLPNN) for inner current and voltage controllers in voltage source converter (VSC)-based islanded AC microgrids. Unlike conventional controllers that depend on line parameters, our intelligent controller structure operates independently of these parameters. The PSO-MLPNN controller effectively replaces the traditional Proportional-Integral (PI) controller, achieving accurate power-sharing and excellent dynamic performance across various load scenarios. Simulation results validate the effectiveness of our proposed controller. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Optimal Energy Forecasting Using Hybrid Recurrent Neural Networks

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Intelligent Automation & Soft Computing 2023年第4期36卷 249-265页

作者： Elumalaivasan Poongavanam Padmanathan Kasinathan Kulothungan Kanagasabai Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Electrical and Electronics Engineering Agni College of TechnologyChennai600130India Department of Information Science and Technology Anna UniversityChennai600025India

The nation deserves to learn what India’s future energy demand will be in order to plan and implement an energy *** energy demand will have to be fulﬁlled by an adequate mix of existing energy sources,considering the con-straints imposed by future economic and social changes in the direction of a more sustainable *** energy demand,on the other hand,is a tricky task because it is inﬂuenced by numerous *** a result,an macro model with only a few factors that may be predicted globally,rather than a detailed ana-lysis for each of these variables,is *** this work,a hybrid approach is proposed for identifying the optimal generation-based mix of electricity systems in *** approach is developed by combing Recalling-Enhanced Recurrent Neural Network(RERNN)and Giza Pyramids Construction(GPC).RERNN pos-sesses selective memory features whereas GPC is a meta-heuristic algorithm that deals with different sets of *** goal of this approach is to assess the present load requirements and production proﬁle to understand the current requirement and its production *** on regards to electricity load proﬁle involving the generation of all power plants,capacity factors and transmission limits were gathered from different sources including the Indian National Load Dispatch Centre,Central Electricity Authority database,annual reports of India and regional load dispatching *** proposal has introduced the RERNN technique to analyze the optimal time series whereas the GPC technique is involved in the optimization of RERNN *** present work is simu-lated in MATLAB and the performance assessed with different *** present technique gives an effective results in terms of accuracy,computational efﬁciency and feasibility.

关键词： Electricity generation demand RERNN GPC time series prediction

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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