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9th IEEE International Conference on Power and Energy, PECon 2022

作者： Ahmed, Sajib Mekhilef, Saad Mubin, Marizan Tey, Kok Soon Rawa, Muhyaddin University of Malaya Power Electronics and Renewable Energy Research Laboratory Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah21589 Saudi Arabia

ISBN: (数字)9781665409902

ISBN: (纸本)9781665409902

Solar energy is considered one of the best essential energy sources to solve the present power crisis. The solar photovoltaic system has a low conversion efficiency that inflicts maximum power extractions. The non-linear relationship between the voltage and current insists on using a maximum power point tracking (MPPT) controller. Perturb and observe (P&O) MPPT controller is popular because of its simple design and cost-effectiveness features;nonetheless, it has drawbacks while operating under partial shading conditions (PSC). The maximum power trapezium (MPT) method modified the P&O algorithm by incorporating an appropriate scanning mechanism for PSC. Despite having good PSC performance, this method's tracking time is much longer than the conventional algorithm in both uniform irradiance conditions (UIC) and PSC. The algorithm proposed in this paper modifies the MPT method with an extra strategy to distinguish between the UIC and PSC. If the pattern is recognized as UIC, the proposed method utilizes the conventional P&O algorithm to track the MPP;otherwise, scanning techniques are being used. The proposed algorithm's operation was tested on simulation using distinct UIC and PSC patterns and worked effectively. © 2022 IEEE.

关键词： Cost effectiveness

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Variational Bayesian Learning Based Localization and Channel Reconstruction in RIS-aided systems

arXiv

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arXiv 2024年

作者： Li, Yunfei Luo, Yiting Wu, Xianda Shi, Zheng Ma, Shaodan Yang, Guanghua The Department of Electrical Engineering Anhui Polytechnic University Wuhu City China The School of Electronics and Information Engineering South China Normal University Foshan528000 China The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The State Key Laboratory of Internet of Things for Smart City the Department of Electrical and Computer Engineering University of Macau Taipa China

The emerging immersive and autonomous services have posed stringent requirements on both communications and localization. By considering the great potential of reconfigurable intelligent surface (RIS), this paper focuses on the joint channel estimation and localization for RIS-aided wireless systems. As opposed to existing works that treat channel estimation and localization independently, this paper exploits the intrinsic coupling and nonlinear relationships between the channel parameters and user location for enhancement of both localization and channel reconstruction. By noticing the non-convex, nonlinear objective function and the sparser angle pattern, a variational Bayesian learning-based framework is developed to jointly estimate the channel parameters and user location through leveraging an effective approximation of the posterior distribution. The proposed framework is capable of unifying near-field and far-field scenarios owing to exploitation of sparsity of the angular domain. Since the joint channel and location estimation problem has a closed-form solution in each iteration, our proposed iterative algorithm performs better than the conventional particle swarm optimization (PSO) and maximum likelihood (ML) based ones in terms of computational complexity. Simulations demonstrate that the proposed algorithm almost reaches the Bayesian Cramer-Rao bound (BCRB) and achieves a superior estimation accuracy by comparing to the PSO and the ML algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Distributed Optimal Load Frequency Control with Stochastic Wind Power Generation

Distributed Optimal Load Frequency Control with Stochastic W...

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2021 European Control Conference, ECC 2021

作者： Silani, Amirreza Cucuzzella, Michele Scherpen, Jacquelien M. A. Javad Yazdanpanah, Mohammad University of Groningen Jan C. Willems Center For Systems and Control ENTEG Faculty of Science and Engineering Groningen Netherlands University of Tehran Control Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Tehran Iran Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

ISBN: (纸本)9789463842365

Motivated by the inadequacy of conventional control methods for power networks with a large share of renewable generation, in this paper we study the (stochastic) passivity property of wind turbines based on the Doubly Fed Induction Generator (DFIG). Differently from the majority of the results in the literature, where renewable generation is ignored or assumed to be constant, we model wind power generation as a stochastic process, where wind speed is described by a class of stochastic differential equations. Then, we design a distributed control scheme that achieves load frequency control and economic dispatch, ensuring the stochastic stability of the controlled network. © 2021 EUCA.

关键词： Random processes

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A Novel F-SVM based on PSO for Gait Phase Recognition in Lower Limb Exoskeleton 18

A Novel F-SVM based on PSO for Gait Phase Recognition in Low...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Lv, Pingping Zhang, Chi Yi, Feng Yuan, Ting Li, Shupei Zhang, Meitong Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China

ISBN: (纸本)9798350312201

To meet the requirements of lower limb exoskeleton robots for lightweight equipment and accurate gait phase detection under different speeds, the kinematics and dynamics information are investigated. In this paper, we propose a novel radius-margin-based supporting vector machine (SVM) model with particle swarm optimization algorithm (PSO) in feature space called PSO-F-SVM for gait phase detection. The proposed method considers both the maximization of margin and the minimization of radius and uses PSO to optimize the parameter in F-SVM, which can enhance the performance of the SVM classifier. Plantar pressure insoles with flexible and elastic sensors are designed for gait feature measurement. We conduct comparative experiments between the proposed method and other variants of SVM under four speeds. The experimental result indicates that the proposed method improves the accuracy by 2.68% compared with the standard SVM. Moreover, more than 98% accuracy can be obtained at four speeds, which demonstrates that the proposed method outperforms the other variants of SVM. © 2023 IEEE.

关键词： Support vector machines

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Exploiting NOMA Transmissions in Multi-UAV-assisted Wireless Networks: From Aerial-RIS to Mode-switching UAVs

arXiv

引用

arXiv 2024年

作者： Zhao, Songhan Gong, Shimin Gu, Bo Li, Lanhua Lyu, Bin Hoang, Dinh Thai Yi, Changyan The School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University China The Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou510006 China The School of Communications and Information Engineering Nanjing University of Posts and Telecommunications China The School of Electrical and Data Engineering University of Technology Sydney Australia The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

In this paper, we consider an aerial reconfigurable intelligent surface (ARIS)-assisted wireless network, where multiple unmanned aerial vehicles (UAVs) collect data from ground users (GUs) by using the non-orthogonal multiple access (NOMA) method. The ARIS provides enhanced channel controllability to improve the NOMA transmissions and reduce the co-channel interference among UAVs. We also propose a novel dual-mode switching scheme, where each UAV equipped with both an ARIS and a radio frequency (RF) transceiver can adaptively perform passive reflection or active transmission. We aim to maximize the overall network throughput by jointly optimizing the UAVs’ trajectory planning and operating modes, the ARIS’s passive beamforming, and the GUs’ transmission control strategies. We propose an optimization-driven hierarchical deep reinforcement learning (O-HDRL) method to decompose it into a series of subproblems. Specifically, the multi-agent deep deterministic policy gradient (MADDPG) adjusts the UAVs’ trajectory planning and mode switching strategies, while the passive beamforming and transmission control strategies are tackled by the optimization methods. Numerical results reveal that the O-HDRL efficiently improves the learning stability and reward performance compared to the benchmark methods. Meanwhile, the dual-mode switching scheme is verified to achieve a higher throughput performance compared to the fixed ARIS scheme. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Quantum-Dot-Based Optical Fiber Temperature Sensor Fabricated by Two-Photon Polymerization

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激光与光电子学进展 2024年第23期61卷 431-437页

作者： Wang Chao Sun Shengzhi Qian Bin Peng Sida Feng Zeqi Qiu Jianrong Laboratory of Infrared Materials and Devices Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211ZhejiangChina Institute of Light+X Science and Technology Faculty of Electrical Engineering and Computer ScienceNingbo UniversityNingbo 315211ZhejiangChina State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027ZhejiangChina State Key Laboratory of Modern Optical Instrumentation Zhejiang UniversityHangzhou 310027ZhejiangChina

Temperature is a common and significant physical quantity,particularly under harsh conditions such as a high voltage or strong electric/magnetic *** this study,we developed an all-fiber-optic temperature sensor based on a fiber-end microstructure doped with quantum dots(QDs).The microstructure was fabricated on a fiber tip using twophoton polymerization *** results show that the fluorescence intensity of the QD microstructure is reduced with an increase in temperature,and the peak position of photoluminescence exhibits a linear change in the temperature range of 26-70℃.The developed sensor exhibits a high-temperature sensitivity of 135 pm/℃.The proposed method has enormous potential for the development of fiber-end temperature sensors.

关键词： two-photon polymerization femtosecond laser temperature sensors quantum dots optical fiber

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Guest Editorial Special Issue on Near-Field Communications (NFCs) in Internet of Everything

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IEEE Internet of Things Journal 2025年第12期12卷 18459-18460页

作者： Pin-Han Ho Miaowen Wen Zhiguo Ding Marco Di Renzo Wei Duan Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada School of Electronic and Information Engineering South China University of Technology Guangzhou China Department of Computer Communication Engineering Khalifa University Abu Dhabi UAE Intelligent Physical Communications group in the Laboratory of Signals and Systems (L2S) Paris-Saclay University Gif-sur-Yvette France School of Information Science and Technology Nantong University Nantong China

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A coherent approach-based fine-tuning of segment anything model plus watershed algorithm for instance segmentation of mitochondria in electron microscopy images

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IEEE Access 2025年 13卷 98088-98105页

作者： Faska, Zahra Khrissi, Lahbib Mountasser, Imadeddine Haddouch, Khalid Akkad, Nabil El Alshathri, Samah El-Shafai, Walid Sidi Mohamed Ben Abdellah University Laboratory of Engineering Systems and Applications ENSA Fez Morocco Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Information Technology P.O. Box 84428 Riyadh11671 Saudi Arabia Computer Science Department Riyadh11586 Saudi Arabia Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt

Increasingly available ultrastructural data from a continuously growing diversity of experimental conditions are driving new opportunities for fruitful neuroscientific hypotheses tested in intracellular compartments such as the nanoscale roles of, e.g., the mitochondria. Reliable morphological statistics are based on achieving highly accurate semantic segmentations of EM images. The state-of-the-art deep CNNs can be somewhat brittle;they tend to provide coarse and high-frequency-oscillatory solutions with discontinuities and false positives even for simple mitochondria segmentation. Historically, the current state-of-the-art in medical image segmentation would involve some variant of the encoder-decoder architecture, such as the U-Net architecture. The SAM does not perform as well, since it has not been explicitly trained for the task and does not demonstrate user-interactive, over one billion annotations mostly for natural images. However, the SAM may be applied to segment anything, including medical image segmentation challenging new datasets. This work is aimed at the difficult task of implementing domain adaptation in mitochondria segmentation within EM images obtained from various tissues and species, using deep learning. We do a systematic study to assess SAM's ability to perform segmentation in medical images, measure its performance on volumetric EM datasets, and show that it is powerful at segmenting instances even under challenging imaging conditions. We provide a fine-tuning SAM which can be naturally trained by SAM at an exemplary scale, benefiting from a diverse and large dataset over one million image masks in 11 modalities. This model would be able to perform precise segmentation for a wide range of targets under various imaging conditions, at the level of performance of specialized U-Net models, or even better. A visual comparison is shown between our fine-tuning SAM model and U-Net, along with an examination of different watershed post-processing st

关键词： Semantic Segmentation

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Low Voltage Ride Through Based On Hydrogen Energy Storage for Improving Performance of Wind Energy Conversion System Under Grid Faults

Low Voltage Ride Through Based On Hydrogen Energy Storage fo...

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Innovative Research in Applied science, engineering and Technology (IRASET),Conference on

作者： Mohamed Makhad Khalida Zazi Abdelilah Hilali Azeddine Loulijat Mouncef El Marghichi Department of Electrical Engineering ENSEM-Rabat Mohammed V university in Rabat Rabat Morocco Electronics Communication Systems and Energy Optimization Team Faculty of Sciences Moulay Ismail University Meknes Morocco Faculty of Sciences and Technology Hassan First University Settat Morocco Intelligent Systems Design Laboratory (ISDL) Faculty of science Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

With the rapid growth of renewable energy projects, leading to high integration of wind energy conversion systems (WECS) into the power grid, several challenges have emerged. In recent years, these issues have taken on high priority in order to guarantee the stability, reliability and security of the electrical grid. Thus, to address urgently these issues, almost all countries have updated their grid codes to include new requirements relating to grid-connected WECS. Low voltage ride through (LVRT) capability is one of the first specifications imposed to define the operating mode of wind farms during and in post-grid fault conditions. This paper presents a cost-effective hydrogen energy storage system (HESS) for regulating the DC-link voltage of WECS based on permanent magnet generator (PMSG) under grid faults. This hardware approach is combined with the modified control scheme of the grid-side converter (GSC) for reducing the second-order oscillations that occur in the event of an asymmetrical grid fault, and for injecting reactive power into the grid in accordance with the grid code requirements. To validate the proposed approach, the entire system is modeled in MATLAB/Simulink. The simulation results show that HESS is perhaps a future solution for improving the LVRT capability of PMSG-WECS.

关键词： Protons Low voltage Codes Hydrogen Wind farms Hardware Wind turbines

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Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints

arXiv

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arXiv 2023年

作者： Sun, Yuzhu Van, Mien McIlvanna, Stephen Nhat, Nguyen Minh McLoone, Seán Ceglarek, Dariusz Ge, Shuzhi Sam The Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Queen’s University Northern Ireland Belfast United Kingdom The Warwick Manufacturing Group University of Warwick Coventry United Kingdom The Department of Electrical and Computer Engineering Social Robotics Laboratory National University of Singapore Singapore

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Lyapunov functions

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