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Distributed Prescribed-Time Observer for Nonlinear systems in Block-Triangular Form

IEEE Control systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs Control systems Vectors Linear systems Electronic mail

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Resonance broadening effects of weak turbulence on Earth's radiation belt electrons

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Physical Review E 2025年第3期111卷 L033201-L033201页

作者： Xiongdong Yu Zhigang Yuan Dedong Wang Oliver Allanson Samuel Hunter School of Earth and Space Science and Technology Wuhan University Wuhan 430072 China GFZ German Research Center for Geosciences Potsdam 14473 Germany Space Environment and Radio Engineering Electronic Electrical and Systems Engineering School of Engineering University of Birmingham Birmingham B15 2TT United Kingdom Environmental Mathematics Department of Earth and Environmental Sciences University of Exeter Penryn TR10 9FE United Kingdom Department of Mathematics University of Exeter Exeter EX4 4QJ United Kingdom

The quasilinear diffusion theory is generalized to include the resonance broadening effect of weak turbulence on relativistic electrons in Earth's radiation belts. In contrast with quasilinear resonant diffusion, in which cases the resonant condition should be rigorously satisfied, the second-order theory demonstrates that the wave-driven scattering of relativistic electrons could be broadened into an area of velocity space adjacent to the resonant region. It is shown that a finite departure time, resulted from the second-order wave-particle interaction, is the key factor controlling the occurrence and effectiveness of such a resonance broadening. The formula to solve the departure time has been proposed. Adopting the solved departure time, the resultant model can correctly reproduce results of test particle simulations. Our result indicates the importance of resonance broadening effect in the radiation belt modeling, and in understanding the weak turbulence dynamics in a magnetized plasma system.

关键词： Radiation effects

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IDSDeep-CCD: intelligent decision support system based on deep learning for concrete cracks detection

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Multimedia Tools and Applications 2025年第12期84卷 10013-10026页

作者： Abualigah, Sayel M. Al-Naimi, Ahmad F. Sachdeva, Gagan AlAmri, Omran Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Gagan Sachdeva Dubai United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied Science Research Center Applied Science Private University Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

In an era where Intelligent Decision Support systems (IDSS) are integral to managing the vast data from Internet of Everything (IoE) systems, this study introduces IDSDeep-CCD, a novel IDSS approach for detecting concrete cracks, a critical issue in civil infrastructure maintenance. Traditional visual inspection methods for crack detection are time-consuming and error-prone. To address this, IDSDeep-CCD employs deep learning, utilizing an open-source dataset of concrete crack images for enhanced accuracy. The system's two-stage approach, featuring feature extraction via pre-trained models and a fully connected network for classification, significantly outperforms existing methods. Our results demonstrate a remarkable 99.9% accuracy rate in crack detection without data augmentation, marking a notable advancement in automatic detection technology. This breakthrough offers practical benefits for the industry by enabling more precise and efficient identification of concrete cracks, paving the way for timely and accurate remedial actions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Encrypted Domain Secret Medical-Image Sharing with Secure Outsourcing Computation in IoT Environment

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IEEE Internet of Things Journal 2024年第2期11卷 2382-2393页

作者： Huang, Jingwang Cui, Qi Zhou, Zhili Yu, Keping Yang, Ching-Nung Choo, Kim-Kwang Raymond Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Nanjing University Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanyang Technological University School of Electrical and Electronic Engineering Jurong West639798 Singapore Guangzhou University Institute of Artificial Intelligence and Blockchain Guangzhou510006 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan The University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

In existing secret medical-image sharing (SMIS) schemes, to protect and manage secret medical-images (SMIs), the sharing and recovery of each SMI are implemented by local servers of medical institutions. However, since a lot of SMIs are produced by personal smart terminal devices in Internet of Things (IoT) environment, directly implementing the sharing and recovery processes will cause excessive communication and computing burden for those local servers, which makes the existing SMIS schemes not suitable for IoT environment. To address the above issue, we propose an encrypted domain SMIS (Enc-SMIS) scheme with secure outsourcing computation for protecting and managing medical images in IoT environment. In the proposed scheme, the medical images are first encrypted using fully homomorphic encryption (FHE) and then outsourced to a cloud server for generating a set of image shares. Subsequently, these shares are stored separately in different local servers of medical institutions. Furthermore, the recovery process is also outsourced to the cloud server when doctors need to observe the patients' medical images. Compared with the existing SMIS schemes, the proposed Enc-SMIS scheme alleviates the computing and communication burden on local servers significantly with secure outsourcing computation in the semi-honest model, and thus supports the storage and management of medical images well in IoT environment. © 2014 IEEE.

关键词： Internet of things

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QoS Prediction Model of Cloud Services Based on Deep Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

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A Review on Active Customers'Participation in Smart Grids

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Journal of Modern Power systems and Clean Energy 2023年第1期11卷 3-16页

作者： Konrad Schmitt Rabindra Bhatta Manohar Chamana Mahtab Murshed Ilham Osman Stephen Bayne Luciane Canha Electrical and Computer Engineering Department Texas Tech UniversityLubbockTX79409USA Wind Energy Program Texas Tech UniversityLubbockTX79409USA Electromechanical and Power Systems Department Federal University of Santa MariaSanta MariaRS97105-900Brazil Design Release Engineer at Electrification and Power conversion General MotorsGM Tech Center RoadWarrenMI48092USAl IEEE

Industrial,commercial,and residential facilities are progressively adopting automation and generation *** having flexible demand and renewable energy generation,traditional passive customers are becoming active participants in electric power system *** profound coordination among grid operators and active customers,the facilities’capability for demand response(DR)and distributed energy resource(DER)management will be valuable asset for ancillary services(ASs).To comply with the increasing demand and flexible energy,utilities urgently require standards,regulations,and programs to efficiently handle load-side resources without trading off stability and *** study reviews different types of customers’flexibilities for DR,highlighting their capabilities and limitations in performing local ancillary services(LASs),which should benefit the power grid by profiting from it through incentive *** financial incentives and techniques employed around the world are presented and *** potential barriers in technical and regulatory aspects are successfully identified and potential solutions along with future guidance are discussed.

关键词： Active distribution network(ADN) ancillary services energy management flexible demand resiliency

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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Unlocking the potential of unlabeled data:Self-supervised machine learning for battery aging diagnosis with real-world field data

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Journal of Energy Chemistry 2024年第12期99卷 681-691页

作者： Qiao Wang Min Ye Sehriban Celik Zhongwei Deng Bin Li Dirk Uwe Sauer Weihan Li National Engineering Research Center for Highway Maintenance Equipment Chang'an UniversityXi'an 710064ShanxiChina Key Laboratory of Road Construction Technology and Equipment of MOE Chang'an UniversityXi'an 710064ShanxiChina School of Automobile Chang'an UniversityXi'an 710064ShanxiChina Chair for Electrochemical Energy Conversion and Storage Systems Institute for Power Electronics and Electrical Drives(ISEA)RWTH Aachen University52074 AachenGermany Center for Ageing Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems(CARL)RWTH Aachen University52074 AachenGermany Jülich Aachen Research Alliance JARA-Energy52074 AachenGermany Helholtz Institute Münster(HI MS) Forschungszentrum Jülich52425 JülichGermany School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina

Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric *** significant advancements achieved by data-driven methods,diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled *** paper presents a framework utilizing self-supervised machine learning to harness the potential of unlabeled data for diagnosing battery aging in electric vehicles during field *** validate our method using battery degradation datasets collected over more than two years from twenty real-world electric *** analysis comprehensively addresses cell inconsistencies,physical interpretations,and charging uncertainties in real-world *** is achieved through self-supervised feature extraction using random short charging sequences in the main peak of incremental capacity *** leveraging inexpensive unlabeled data in a self-supervised approach,our method demonstrates improvements in average root mean square errors of 74.54%and 60.50%in the best and worst cases,respectively,compared to the supervised *** work underscores the potential of employing low-cost unlabeled data with self-supervised machine learning for effective battery health and safety management in realworld scenarios.

关键词： Lithium-ion battery Aging diagnosis Self-supervised Machine learning Unlabeled data

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Modeling and Analysis of Transient Overvoltage due to Reclosing of Distributed Energy Resources 27

Modeling and Analysis of Transient Overvoltage due to Reclos...

引用

27th International Conference Electronics, ELECTRONICS 2023

作者： Ozcelik, Mehmet Ali Clark, Taite Ruppert, Michael Sun, Jian Gaziantep University Technical Science Department Electrical and Energy Gaziantep Turkey Center for Future Energy Systems Rensselaer Polytechnic Institute TroyNY United States Jem Engineering Services PoughkeepsieNY United States

ISBN: (纸本)9798350322552

With the inclusion of renewable energy sources in the electricity grid, micro-grid and distributed generation (DG) concepts have come to the fore in feeding local loads. Inverter technologies are used to convert the direct current (DC) produced by energy sources such as photovoltaic (PV) to alternating current (AC) and to provide grid integration. However, sometimes inverters (because of their non-detection zones, speeds, etc.) cannot detect the island mode during reclosing at the breaker. Such a situation could damage utility equipment and the distributed generator itself. Therefore, in the control hardware in the loop (CHIL) simulation study, the effect of 180-degree out-of-phase closure on utility and customer equipment was investigated according to line distance and location of the load. The simulation study was carried out in real-time. © 2023 IEEE.

关键词： Distributed power generation

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Deep Learning in Remote Sensing for Climate-Induced Disaster Resilience: A Comprehensive Interdisciplinary Approach

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Remote Sensing in Earth systems sciences 2024年第1期8卷 145-160页

作者： Padmaja, S.M. Naveenkumar, R. Kumari, N. P. Lavanya Pimo, Er. S. John Bindhu, M. Konduri, Bhagavan Jangir, Pradeep Department of Electrical and Electronics Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department of Computer Science and Systems Engineering AUCE Andhra University Visakhapatnam India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of ECE Sri Venkateswara College of Engineering Sriperumbudur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Climate-induced disasters pose significant threats to human lives, infrastructure, and ecosystems. Deep learning techniques, combined with remote sensing, offer powerful tools for predicting, detecting, and mitigating the impacts of such disasters. This study presents a comprehensive interdisciplinary approach using a hybrid cascaded CNN-RNN model to perform spatio-temporal analysis, enhanced image segmentation with U-Net and SegNet architectures, and a hybrid CNN-LSTM-XGBoost ensemble for multi-hazard prediction. The proposed CNN-RNN model achieves an accuracy of 96.78%, significantly outperforming traditional models in predicting flood-prone areas, wildfires, and droughts. Key metrics such as Intersection over Union (IoU), precision, recall, and F1-scores are used to evaluate model performance, highlighting the model’s ability to accurately identify disaster-prone areas and improve disaster resilience. This research not only demonstrates the efficacy of deep learning for climate-induced disaster prediction but also paves the way for future integration of socio-economic data to refine disaster risk assessment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate-induced disasters CNN-RNN Deep learning Disaster resilience Hybrid ensemble Multi-hazard prediction Remote sensing SegNet Spatio-temporal analysis U-Net

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