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An Improved Perturb and Observe Maximum Power Point Tracking Algorithm for Photovoltaic Power Systems

Journal of Modern Power Systems and Clean Energy 2023年第4期11卷 1165-1175页

作者： Rasool Kahani Mohsin Jamil M.Tariq Iqbal Department of Electrical and Computer Engineering Memorial University of NewfoundlandSt.John’sA1B 3X5Canada

This paper aims to improve the performance of the conventional perturb and observe(P&O)maximum power point tracking(MPPT)*** the oscillation around the maximum power point(MPP)is the main disadvantage of this technique,we introduce a modified P&O algorithm to conquer this *** new algorithm recognizes approaching the peak of the photovoltaic(PV)array power curve and prevents the oscillation around the *** key to achieve this goal is testing the change of output power in each cycle and comparing it with the change in array terminal power of the previous *** a decrease in array terminal power is observed after an increase in the previous cycle or in the opposite direction,an increase in array terminal power is observed after a decrease in the previous cycle;it means we are at the peak of the power curve,so the duty cycle of the boost converter should remain the same as the previous ***,an optimized duty cycle is introduced,which is adjusted based on the operating point of PV ***,a DC-DC boost converter powered by a PV array simulator is used to test the proposed *** the irradiance changes,the proposed algorithm produces an averageηMPPT of nearly 3.1.greater than that of the conventional P&O algorithm and the incremental conductance(In C)*** addition,under strong partial shading conditions and drift avoidance tests,the proposed algorithm produces an averageηMPPT of nearly 9%and 8%greater than that of the conventional algorithms,respectively.

关键词： Photovoltaic system maximum power point tracking(MPPT) perturb and observe(P&O) boost converter steady-state performance

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A-Gamba: An Adaptive Graph-Mamba Model for Traffic Prediction in Wireless Cellular Networks

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IEEE Wireless Communications Letters 2025年第6期14卷 1801-1805页

作者： Mehrabian, Ali Wong, Vincent W.S The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Accurate traffic prediction is essential for optimizing network resource allocation in wireless cellular networks. In this letter, we propose an adaptive graph-Mamba (A-Gamba) model for traffic prediction. The proposed model utilizes a bidirectional-Mamba block and a selective scan algorithm to capture the temporal dependencies by prioritizing relevant historical data. We develop an adaptive graph convolutional block that captures the spatial dependencies between cells by learning a graph structure without requiring the location information. Results show that our proposed A-Gamba model outperforms three state-of-the-art baselines, achieving a lower root mean squared error and mean absolute error with faster training time. © 201. IEEE.

关键词： Prediction models

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Wave Propagation in Composite Metal Foams Investigated by Finite Element Methods in Two Dimensional Case 7

Wave Propagation in Composite Metal Foams Investigated by Fi...

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7th International Conference on Material Strength and Applied Mechanics, MSAM 2024

作者： Kovács, Márió Pere, Balázs Department of Applied Mechanics Faculty of Mechanical Computer and Electrical Engineering Széchenyi István University Egyetem tér 1. Gyor9026 Hungary

ISBN: (纸本)9781643685472

The presented work is based on the wave propagation properties of composite metal foams in a two-dimensional model with a focus on the energy absorption. The energy flux method was used for the study and it was shown that composite metal foams have a significant energy absorption capacity. Hence, they can be used with high efficiency, for example, as a sound insulation layer. A pair of materials commonly used in syntactic metal foams, iron shell and aluminium matrix material, was used in the finite element model. Damping is included in the calculations to avoid oscillations. © The Author(s), 2024.

关键词： Sound insulation

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Contrastive-Enhanced Domain Generalization With Federated Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1525-1532页

作者： Yu, Xinhui Wang, Dan McKeown, Martin J. Jane Wang, Z. University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Domain generalization (DG) aims to train a global model from different but related domains, which can be generalized to an unseen out-of-distribution domain. Most existing DG methods are based on the centralized learning paradigm, raising the privacy leakage concern. In this article, we propose a contrastive-enhanced domain generalization framework in the federated learning paradigm, where there are a server and multiple clients. Each client owns data from one domain and builds a local model consisting of a domain-invariant feature extractor and a classifier head. The server generates a global model through aggregating and broadcasting local models' parameters, thus achieving knowledge sharing and keeping data confidential. To enhance the discrimination and generalization ability of the local model, we build an improved instance normalization module that focuses on task-relevant features with less domain-specific information. Moreover, for better classwise alignment in the embedding space, we propose a prototype-based contrastive loss. Given the limited annotation budget in practice, we also extend the proposed framework into the semisupervised DG setting (i.e., only ten labeled samples per class). Experimental results on three benchmarks and different backbones show that the proposed framework yields promising performances for both DG and semisupervised DG in the federated learning paradigm. © 2020 IEEE.

关键词： Budget control

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Deep Reinforcement Learning Based Joint Optimization of Task Migration and Resource Allocation for Mobile Edge Computing

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IEEE Internet of Things Journal 2025年第13期12卷 24431-24440页

作者： Li, Juncai Jiang, Qi Leung, Victor C. M. Ma, Zhuo Abrokwa, Kofi Kwarteng Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Task migration and resource allocation are essential to integrate available resources for improving the efficiency of mobile edge computing to support various computation-intensive and delay-sensitive Internet of Things applications, which cooperative optimization has yet been well addressed to achieve desirable performance. In this paper, a deep reinforcement learning (DRL) based adaptive cooperative optimization strategy is presented to fill this gap. Task migration and resource allocation are jointly employed to adaptively integrate available resources within cooperative edge nodes for processing various randomly offloaded tasks. The policy optimization to minimize the system cost and task dropout rates is formulated as Markov decision processes. A DRL algorithm that enhances deep deterministic policy gradient with a dual experience pool is proposed to jointly optimize the task migration and resource allocation in unknown stochastic application environments. Simulation experiments have been conducted to evaluate the performance of the presented strategy, and the results illustrate that it increases system rewards by 1..9 61.5% and reduces task dropout rate by 5.2 31.3% comparing with benchmarks. © 2025 IEEE.

关键词： Deep reinforcement learning

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Class-conditional domain adaptation for semantic segmentation

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Computational Visual Media 2024年第5期10卷 1013-1030页

作者： Yue Wang Yuke Li James H.Elder Runmin Wu Huchuan Lu School of Information and Communication Engineering Dalian University of TechnologyDalian 116024China School of Computer Science Wuhan UniversityWuhan 430072China Department of Electrical Engineering and Computer Science York UniversityToronto M3J 1P3Canada Department of Computer Science the University of Hong KongHong Kong 999077China

Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.

关键词： domain adaptation generative adversarial networks semantic segmentation cityscapes

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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Highly Efficient Three-Level AC-AC Converter with Identical In-Phase and Antiphase Buck-Boost Operations

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IEEE Transactions on Power Electronics 2024年第8期39卷 9929-9942页

作者： Ahmed, Hafiz Furqan Khan, Ashraf Ali Al Zaabi, Omar Mousavi, Seyyed Mohammad Javad Babaei, Ebrahim National Sun Yat-Sen University Department of Electrical Engineering Kaohsiung804 Taiwan Memorial University of Newfoundland Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Khalifa University Advanced Power and Energy Center Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates University of Tabriz Faculty of Electrical and Computer Engineering Tabriz51666 Iran Near East University Engineering Faculty Nicosia99138 Turkey

In this article, a new coupled-inductors based three-level bipolar buck-boost ac-ac converter is proposed. The proposed converter can produce highly efficient and symmetric in-phase and antiphase buck and boost modes of operation with much lower component voltage and current stresses. This reduces the operation and design complexity, and helps to achieve a high-efficiency operation. The high-frequency modulating switches of the proposed converter are implemented with coupled-inductor-based dual-buck phase leg, producing three-level input and output voltages, and providing inherent protection from voltage-source short-circuit issue during switch transitions. The other salient features of the proposed converter are no-commutation issue, no need for RC snubbers or dedicated safe-commutation algorithms, provision of high-quality and continuous input/output currents, and support for nonresistive loads. An in-depth circuit analysis of the proposed converter is provided based on the proposed switch modulation strategies. The guidelines for component design/selection are discussed, followed by the comparisons with state-of-the-art three-level ac-ac converters. Finally, practical circuit verifications are performed on a laboratory-assembled prototype. © 1.86-201. IEEE.

关键词： Buck-Boost converter

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RIS-Aided MIMO Downlink Transmission for Ultradense LEO Satellite-Terrestrial Networks

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IEEE Internet of Things Journal 2025年第11期12卷 15304-15318页

作者： Zhang, Xin Qin, Xiaohan Zhang, Zitian Cai, Lin X. Zhou, Haibo Zhuang, Weihua Nanjing University School of Electronic Science and Engineering Nanjing210023 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou310018 China Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Ultradense low-Earth orbit (LEO) satellite-terrestrial network (ULSN) has evolved as a new paradigm to provide ubiquitous and high-capacity communications in next generation wireless networks. However, the direct LEO satellite broadband connectivity faces significant challenges in urban environments due to the masking effect, which limits the reliability and availability of communication links in ULSNs. To address this, reconfigurable intelligent surface (RIS) is emerging as a promising solution in ULSNs. In this article, we investigate RIS-aided downlink data transmission in urban environments of multiusers in ULSNs. We set up a mixed-integer programming (MIP) model for maximizing the sum rate of terrestrial users in ULSNs. To solve the complex MIP problem, we propose a two-phase joint optimization algorithm with a deep learning phase and an alternative optimization (AO) phase. In the deep learning phase, a deep neural network (DNN) algorithm is employed to obtain the optimal user association matrix based on the positions of terrestrial users and LEO satellites. Then in the AO phase, successive convex approximation is utilized to transform the nonconvex subproblems of beamforming and RIS phase design into convex formulations and iteratively solve them. Simulation results demonstrate that the proposed algorithm outperforms other baseline algorithms. © 201. IEEE.

关键词： Integer programming

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Optimal deep neural network architecture design with improved generalization for data-driven cooling load estimation problem

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Neural Computing and Applications 2025年第19期37卷 13597-13616页

作者： Alagoz, Baris Baykant Keles, Cemal Ates, Abdullah Özdemir, Edanur Alkhulaifi, Nasser Department of Computer Engineering Inonu University Malatya Turkey Department of Electrical and Electronics Engineering Inonu University Malatya Turkey Lab University of Nottingham NottinghamNG8 1BB United Kingdom

Decision of the model complexity is a significant challenge in contemporary data-driven modeling applications. Designing neural architecture involves the process of determining the optimal model complexity for deep neural networks (DNNs) models in order to uncover relationships in real-world data patterns. Consequently, optimizing DNN architectures is crucial for enhancing the practical approximation performance of DNN models to real-world data. This study implements a data-driven neuroevolution scheme for the optimal neural architecture search (NAS) and demonstrates a data-driven engineering application for cooling load estimation. The proposed neuroevolution scheme aims at evolving to the best generalizing DNN model that well suits the modeling complexity requirements of the dataset. To this end, the objective function for the neural architecture optimization process is simplified to the mean square error of the test dataset, which enables to reduce the risk of insufficient generalization during NAS. By employing this objective function for evolution field optimization (EFO), the proposed neuroevolution process can automatically achieve the optimal model complexity, preventing overfitting and underfitting cases, and thereby attaining almost the best generalization for the dataset. For this purpose, this approach combines parametric learning with the backpropagation algorithm and structural learning with EFO-based neural architecture search to address data-driven, optimal complexity DNN model generation problems. Effectiveness of the method is demonstrated in the cooling load estimation problem of residential buildings, and performances of the optimal DNN models with four objective functions are analyzed. The design of objective function for the best generalizing model is also elaborated. © The Author(s) 2025.

关键词： Deep neural networks

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