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Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

arXiv

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arXiv 2023年

作者： Qu, Jiaqi Wei, Lu Sun, Qiang Zareipour, Hamidreza Qian, Zheng School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability. Copyright © 2023, The Authors. All rights reserved.

关键词： Failure analysis

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

arXiv

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arXiv 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan The Beijing Key Laboratory of Network System Architecture and Convergence The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing China Singapore The Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Department of Industrial and Systems Engineering University of Miami Coral GablesFL33146 United States The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Computer Science North Carolina State University RaleighNC27695 United States The Beijing Key Laboratory of Network System Architecture and Convergence China The Beijing Advanced Information Network Laboratory Beijing University of Posts and Telecommunications Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025, CC BY-NC-SA.

关键词： Markov processes

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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The Approach of Establishing the High-Resolution Shading Occupant Behavior Model in the Office Building

SSRN

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SSRN 2023年

作者： Chen, Gaoxiang Lu, Jun Zhang, Zhiang Deng, Wu Bie, Jing Yao, Jian Yan, Lei Guan, Rui Xia, Bowei Zhang, Jialu Yang, Rouyao Department of Architecture and Built Environment University of Nottingham Ningbo China Ningbo315100 China Department of Architecture Ningbo University Ningbo China Department of Financial Engineering Ningbo University Ningbo China Department of Information Management and Information System Ningbo University Ningbo China Department of Computer Science University of Nottingham Ningbo China Ningbo315100 China Ningbo University China

The environmental behavior of building occupants has become the key factor in determining indoor thermal comfort and building energy performance. The diversity and randomness of occupant behavior may lead to variations in building energy consumption. Early studies on shading occupant behavior have increasingly involved mathematical tools to establish a mathematical model for correlating shading behavior patterns and energy consumption. However, the existing model does not consider the multiple states of shading (only fully open, fully closed, or half-open state, which has a large deviation from the actual situation), accessibility of shading behavior (ASB), room configuration (single people room and multi-people room), occupancy rate and other factors, resulting in low accuracy of the model. This study proposed an index of the ASB to analyze and explore the characteristics of blind occupant adjustment behavior (named shading behavior afterwards). It also promotes an occupant-centric consideration in occupant behavior studying. By using the accessibility classification of shading behavior with building parameters, blinds can be quickly divided into four types: high ASB, medium ASB, general ASB, and low ASB. Based on the results of shading classification and integrating the data mining techniques such as data feature selection, soft voting, and Markov method was integrated, a high-resolution shading occupant behavior (HRSOB) model is proposed. Using the measured data of office buildings on a case study campus, the model selects the optimal algorithm and evaluates and verifies the HRSOB model. The results show that the predictive performance of the HRSOB model is improved by 10.92% compared with the typical SOB model. Finally, the advantages and disadvantages of the model are discussed. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Performance Optimization for Multicast Mm Wave MIMO Networks with Mobile Users

Performance Optimization for Multicast Mm Wave MIMO Networks...

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IEEE International Conference on Communications Workshops, ICC

作者： Songling Zhang Mingzhe Chen Wenjing Zhang Zhaohui Yang Danpu Liu Zhilong Zhang Kai-Kit Wong Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Zhejiang Lab Hangzhou China Zhejiang University Hangzhou China Department of Electronic and Electrical Engineering University College London London UK

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, the problem of maximizing the sum rate of mobile users in a multi-base station (BS) cooperative millimeter-wave (mmWave) multicast communication system is studied. In the considered model, due to the real-time mobility of users, the users being served by a given BS and beamforming of BSs and users are dynamic. Multiple BSs must cooperate to serve dynamic requests of multiple mobile users. This problem is posed as an optimization framework whose goal is to maximize the sum rate of all mobile users by jointly optimizing the number of users served by all BSs and beamforming matrices of both BSs and users. To solve this non-convex optimization problem, we first introduce a value decomposition based reinforcement learning (VD- RL) algorithm to determine the users to be served by each BS. Then, we use the block diagonalization method to obtain the fully digital transmit beamforming matrices of all BSs as well as the receive beamforming matrices of the users. Finally, a fast optimization algorithm is used to optimize the hybrid beamforming matrices of both BSs and users. Simulation results show that, the proposed algorithm can achieve up to 51 % gain in terms of the sum rate of all mobile users compared to baseline multi-agent algorithms.

关键词： Multicast algorithms Array signal processing Heuristic algorithms Simulation Conferences Multicast communication Real-time systems

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An Automatic Ridge Estimation Method for Tacholess Order Tracking of Wind Turbine Gearbox Under Varying-Speed Operation

SSRN

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SSRN 2023年

作者： Liu, Changliang Liu, Shaokang Wu, Yingjie Liu, Weiliang Liu, Shuai Control and Computer Engineering Department University of North China Electric Power Beijing102200 China Baoding key Laboratory of State Detection and Optimization Regulation for Integrated Energy System Baoding071000 China Automation Engineering Department University of Northeast Electric Power Jinlin132000 China

The instantaneous shaft speed estimation method of wind turbine gearbox based on time-frequency ridge estimation is the most widely used method, but it requires parameter adjustment and expert knowledge to ensure the accuracy of ridge estimation, which significantly reduces the effectiveness of such methods in practice. In this paper, an end-to-end automated ridge estimating (ARE) method is proposed. ARE uses the inherent harmonic relationship in the vibration signal of wind turbine (WT) gearbox to construct a selection index for automatically selecting the target ridge with clear physical meaning. Then, a sub-region division strategy is proposed to avoid the interference of other components and improve computational efficiency. Finally, a parameter-free objective function considering both the amplitude and smoothness of the ridge is proposed to achieve target ridge estimation. The vibration signals of different WT gearboxes collected on-site were applied to verify the performance of ARE. The result shows that ARE realizes the automatic estimation of a ridge. It outperforms other methods in terms of accuracy and computational efficiency. © 2023, The Authors. All rights reserved.

关键词： Computational efficiency

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Metrology for Underwater Acoustics: Rethinking Measurement Strategies for the Assessment of the Environmental Status

Metrology for Underwater Acoustics: Rethinking Measurement S...

引用

IEEE International Workshop on Metrology for the Sea;Learning to Measure Sea Health Parameters (MetroSea)

作者： Pier Francesco Moretti Angela Pomaro Silvano Buogo Department of Earth System Sciences and Environmental Technologies National Research Council of Italy Rome Italy Department of Computer Control and Management Engineering A. Ruberti Sapienza University of Rome Rome Italy Institute of Marine Sciences National Research Council of Italy Venice Italy Institute of Marine Engineering) National Research Council of Italy Rome Italy

ISBN: (数字)9798350379006

ISBN: (纸本)9798350379013

Underwater acoustics is a discipline that is facing an increased political attention due to the impacts of anthropogenic activities on the environment. Metrology for underwater acoustics is not yet linked to an established common frame, introducing uncertainties in the interpretation of signals aiming to provide clues on the status of the marine environmental status. Challenges in adopting technologies and procedures for the calibration and use of hydrophones are reported. An observation strategy that can be considered appropriate for the investigation of physical processes underlying the dynamics of the marine ecosystems is suggested.

关键词： Uncertainty Conferences Sea measurements Metrology Acoustic measurements Marine ecosystems Calibration Underwater acoustics

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset 26

A Horizontal Federated Learning Approach to IoT Malware Traf...

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Do, Phuc Hao Le, Tran Duc Vishnevsky, Vladimir Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia University of Science and Technology The University of Danang Da Nang Viet Nam The University of Danang University of Science and Technology Information Technology Faculty Danang Viet Nam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Bonch Bruevich Saint Petersburg State University of Telecommunications Department of Programming Engineering and Computer Science Russia

ISBN: (纸本)9791188428120

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy. © 2024 Global IT Research Institute - GIRI.

关键词： Malware

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Some Use Cases of Security Constrained Economic Dispatch Problem for Outage Work Planning Collaboration system

Some Use Cases of Security Constrained Economic Dispatch Pro...

引用

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Ichiro Toyoshima Takashi Tatekoji Ryota Mizutani Hiroaki Sato Infrastructure Service Development & Design Department Toshiba Energy Systems & Solutions Corporation Tokyo Japan Power Grid System Solutions Engineering Depattment Toshiba Energy Systems & Solutions Corporation Kawasaki Japan Power Control Computer Systems Department Toshiba Energy Systems & Solutions Corporation Tokyo Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Outage work planning has been essential to maintain the power grid system. For the operators, the planning is troublesome. It is caused by too complex process. The past few years, we have been developing Outage Work planning Collaboration system (OWCS) with some mathematical optimization techniques. Especially, Security Constrained Economic Dispatch(SCED) is extened in some ways to handle various power grid situations.

关键词： Electric potential Costs Key performance indicator Collaboration Power grids Planning Security Optimization Consumer electronics

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Advanced IOT Traffic Light control system

Advanced IOT Traffic Light Control System

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International Conference on Advances in Computational Tools for engineering Applications, ACTEA

作者： Martin Abou Hamad Antonio El Hajj Roy Abi Zeid Daou Ali Hayek Gaby Abou Haidar Biomedical Technologies Department Faculty of Public Health Lebanese German University Lebanon MART Learning Education and Research Center Lebanon Department of Computer Architecture and System Programming University of Kassel Kassel Germany Engineering Faculty American University of Science and Technology Zahle Lebanon

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy.

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