检索结果-内蒙古大学图书馆

Design of Distributed Nonlinear Observers Without Using Global Inputs

IEEE Transactions on Automatic Control 2025年

作者： Cao, Ganghui Wang, Jinzhi Polycarpou, Marios M. Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China University of Cyprus KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering Nicosia1678 Cyprus

This paper investigates the design of distributed observers for a class of nonlinear systems. The designed distributed observers reside in a network of sensor nodes. The communication links in the network enable each sensor node to estimate all states of the nonlinear system, even though each one can only collect a small portion of the system's outputs. However, all inputs of the system are still needed at each node to carry out a correct state estimation. For this problem, the paper further proposes a distributed nonlinear observer without using global inputs, in which each sensor node only needs partial input information of the nonlinear system. © 1963-2012 IEEE.

关键词： Nonlinear simulations

来源：评论

学校读者我要写书评

暂无评论

Digital Twin and TD3-Enabled Optimization of xEV Energy Management in Vehicle-to-Grid Networks

引用

IEEE Access 2025年 13卷 92495-92506页

作者： Irum Saba Abdulraheem H. Alobaidi Sultan Alghamdi Muhammad Tariq Smart Technologies and Applied Intelligence Research Lab School of Engineering National University of Computer and Emerging Sciences Islamabad Pakistan Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia

The rapid expansion of extended electric vehicle (xEV) adoption necessitates optimizing energy storage systems (ESS) management for enhanced performance, longevity, and reliability. However, traditional ESS management approaches struggle with real-time state estimation, energy optimization, and predictive maintenance, leading to inefficiencies in battery utilization and sustainability. This paper addresses these challenges by proposing an advanced ESS framework that integrates digital twin (DT) technology with the twin-delayed deep deterministic policy gradient (TD3) algorithm, a state-of-the-art reinforcement learning method derived from the deep deterministic policy gradient (DDPG). This integration enables precise real-time estimation of critical ESS states, including state of charge (SoC), state of health (SoH), state of energy (SoE), and remaining useful life (RUL), thereby enhancing predictive maintenance and operational efficiency. The proposed framework facilitates proactive battery health monitoring, generates early warnings for potential failures, and enables intelligent battery swapping via DT-driven ESS control. By dynamically adapting ESS control strategies, the TD3 algorithm optimizes energy distribution, reduces energy consumption, and improves overall vehicle performance. With a prediction accuracy of 99.8% for SoC, SoH, and SoE, the proposed approach effectively addresses key challenges in xEV ESS management like cell balancing, dynamic charging rate adjustment, battery swapping decisions, and energy optimization, contributing to a more sustainable and efficient EV ecosystem.

关键词： Batteries Real-time systems Vehicle-to-grid Optimization Prediction algorithms Digital twins Charging stations Accuracy Predictive models Monitoring

来源：评论

学校读者我要写书评

暂无评论

A COMPREHENSIVE, AUTOMATED APPROACH TO DETERMINING SEA-ICE THICKNESS FROM SAR DATA

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1995年第1期33卷 46-57页

作者： HAVERKAMP, D SOH, LK TSATSOULIS, C Center for Excellence in Computer Aided Systems Engineering Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS

This paper documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic, The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems, The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions, Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal charges and is not subject to limitations caused by using predefined parameters,

关键词： Synthetic aperture radar Local threshold Ice thickness Sea Ice Ice Heuristics Automation intensity distribution Regional Expert System Rescue Seasonal Variation Specific absorption rate

来源：评论

学校读者我要写书评

暂无评论

Fault-tolerant Mobile Service Offloading in Mobile Edge Computing

引用

IEEE Transactions on Automation Science and engineering 2025年

作者： Long, Tingyan Xia, Yunni Zhou, Mengchu Li, Jianqi Ma, Yong Ai-Turki, Yusuf Guizhou University School of Computer Science and Technology Guiyang550025 China Chongqing University College of Computer Science Chongqing400030 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Global energy interconnection research institute Co. Ltd Beijing102209 China Jiangxi Normal University School of Computer and Information Engineering Nanchang China King Abdulaziz University K. A. Care Energy Research and Innovation Center The Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia

Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods. © 2004-2012 IEEE.

关键词： Mobile applications

来源：评论

学校读者我要写书评

暂无评论

Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification 3

Empowering Leather Quality Assurance: Leveraging Convolution...

引用

3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Logeswaran, K. Suresh, P. Savitha, S. Sah, Swati Kr, Prasanna Kumar Rajadevi, R. Vasugi, M. Sujit, B. Akilesh, T. Kongu Engineering College Center of Excellence in Real-Time Analytic Department of Artificial Intelligence Tamilnaud Erode India Vellore Institute of Technology Department of Database Systems Tamil Nadu Vellore India K.S.R College of Engineering Department of Computer Science and Engineering Tamil Nadu Tiruchengode India Sharda University School of Engineering and Technology Uttar Pradesh Greater Noida India Kongu Engineering College Department of Information Technology Tamilnaud Erode India Kongu Engineering College Department of Artificial Intelligence Tamilnaud Erode India

ISBN: (纸本)9798331523893

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed. © 2025 IEEE.

关键词： Fault tolerance

来源：评论

学校读者我要写书评

暂无评论

Asymptotic Leader-Following With Continuous Control Inputs in Networked Euler-Lagrangian systems: The Case of Directed Topologies

引用

IEEE Transactions on Control of Network systems 2025年

作者： Rezaee, Hamed Parisini, Thomas Polycarpou, Marios M. Imperial College London Department of Mechanical Engineering LondonSW7 2AZ United Kingdom Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy University of Cyprus KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering NicosiaCY-1678 Cyprus

In most practical dynamic leader-following scenarios, the control input or dynamical model of the neighboring agents are not known to the followers. In such scenarios, asymptotic leader-following requires employing discontinuous robust mechanisms to cope with unknown parts of the neighbors trajectories, leading to chattering in the followers inputs. Few studies already addressed asymptotic leader-following with continuous control inputs;however, they require the communication topology to be undirected, which is a restrictive assumption in leader-following. In this paper, asymptotic dynamic leader-following in Euler-Lagrangian multiagent systems is addressed. We assume that the leader control input is determined by the leader locally and is unknown to the followers. The contribution of this paper is that whereas the unknown input and model of the neighboring agents are compensated by a robust mechanism in the followers control strategy, the continuity in the followers control inputs is guaranteed and the interaction in the network is under directed topologies. Simulation results for a network of manipulator robots illustrate the performance of the proposed control strategy. © 2014 IEEE.

关键词： Lagrange multipliers

来源：评论

学校读者我要写书评

暂无评论

Adaptive Control for Shunt Active Power Filter under Stochastic Solar Photovoltaics Behavior

引用

IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Prakash, Surya Alkhatib, Mohamed Sindi, Hatem F. Alghamdi, Sultan Behera, Ranjan Kumar Muduli, Utkal Ranjan Nit Jamshedpur Dept. of Electrical Engg Jharkhand Jamshedpur India Uae University Al Ain Dept. of Electrical and Communication Engg Coe Abu Dhabi15551 United Arab Emirates King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia Indian Institute of Technology Patna Dept. of Electrical Engineering Bihta801103 India Khalifa University Apec Dept. of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates

The paper discusses the challenges in controlling a two-stage photovoltaic (PV)-based shunt active power filter (SAPF) under distorted grid conditions and stochastic PV system behavior. To address this, the paper proposes an adaptive proportional integral (API) regulator to regulate the DC-link voltage, which adapts its parameters based on the DC-link error voltage to provide low bandwidth for disturbance rejection and high bandwidth for dynamic performance enhancement. The paper also proposes an estimation method for the reference current using the sliding discrete Fourier transform and the instantaneous symmetrical component theory, which is suitable for non-ideal grid or load conditions. The accuracy of the proposed reference current estimation method is analyzed using time- and frequency-domain analysis. The performance of the PV-SAPF system with the proposed control algorithms is illustrated through simulation and real-time hardware validation under various grid conditions. Despite distorted grid conditions, the total harmonic distortion of the grid current is substantially within IEEE Std 519-2014. The proposed API regulator and reference current estimation method show promising results in improving the performance of the PV-SAPF system in the presence of internal and external disturbances. © 2013 IEEE.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Local wafer temperature non-uniformity correction with laser irradiation 26

Local wafer temperature non-uniformity correction with laser...

引用

26th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)

作者： Rao, Preetham P. Computer Aided Engineering Center of Excellence Applied Materials Bangalore India

ISBN: (纸本)9781479999309

The application of correcting small temperature non-uniformity on Silicon wafers using local irradiation with spatially scanning laser beams was analyzed. The objective of the study was to understand the specifications of such a laser beam to elevate the temperature of a wafer locally by 1 to 5 degrees C. A detailed analytical model has been developed for predicting power level, exposure time, scanning speed, and the beam characteristics. The model has been derived by solving the three dimensional transient heat equation using Green's function approach. Various wafer characteristics, such as the surface reflectivity, material absorption coefficient, and thermal properties have been built into the formulation as parameters, so that several what-if scenarios can be evaluated with ease and accuracy. Existing analytical methods in literature for prediction of laser irradiated substrate temperatures assume infinite thickness of the substrate. In this study, it has been found that this approximation could result in significant errors particularly for the present application of interest, where the wafer thickness is finite and the focus is on relatively small local temperature rise in short exposure durations. Numerical models were also developed to mimic some particular cases using a commercial finite volume method solver. The numerical and analytical results show an excellent agreement. The analytical model allows for a more diverse range of variables than the finite volume numerical models.

关键词： Laser heating of wafer scanning laser wafer temperature uniformity

来源：评论

学校读者我要写书评

暂无评论

Deficient Excitation in Parameter Learning

arXiv

引用

arXiv 2025年

作者： Cao, Ganghui Wang, Shimin Guay, Martin Wang, Jinzhi Duan, Zhisheng Polycarpou, Marios M. State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Massachusetts Institute of Technology CambridgeMA02139 United States Queen’s University KingstonONK7L 3N6 Canada KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

This paper investigates parameter learning problems under deficient excitation (DE). The DE condition is a rank-deficient, and therefore, a more general evolution of the well-known persistent excitation condition. Under the DE condition, a proposed online algorithm is able to calculate the identifiable and non-identifiable subspaces, and finally give an optimal parameter estimate in the sense of least squares. In particular, the learning error within the identifiable subspace exponentially converges to zero in the noise-free case, even without persistent excitation. The DE condition also provides a new perspective for solving distributed parameter learning problems, where the challenge is posed by local regressors that are often insufficiently excited. To improve knowledge of the unknown parameters, a cooperative learning protocol is proposed for a group of estimators that collect measured information under complementary DE conditions. This protocol allows each local estimator to operate locally in its identifiable subspace, and reach a consensus with neighbours in its non-identifiable subspace. As a result, the task of estimating unknown parameters can be achieved in a distributed way using cooperative local estimators. Application examples in system identification are given to demonstrate the effectiveness of the theoretical results developed in this paper. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification

Empowering Leather Quality Assurance: Leveraging Convolution...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Logeswaran K Suresh P Savitha S Swati Sah Prasanna Kumar Kr Rajadevi R Vasugi M Sujit B Akilesh T Department of Artificial Intelligence Center of Excellence in Real-Time Analytic Kongu Engineering College Erode Tamilnaud India Department of Database Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode Tamil Nadu India School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Information Technology Kongu Engineering College Erode Tamilnaud India Department of Artificial Intelligence Kongu Engineering College Erode Tamilnaud India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

关键词： Deep learning Visualization Quality assurance Accuracy Quality control Product design Quality assessment Convolutional neural networks Reliability Defect detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：