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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guo, Bicheng Zhou, Conghao Liu, Haoyu He, Shibo Chen, Jiming Shen, Xuemin Zhejiang University College of Control Science and Engineering China University of Waterloo Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350351255

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： Adversarial machine learning

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Designing an Optimal Integrated Energy System Including Sustainable Resources for Real-Time electrical and Thermal Loads Supplying, Considering Grid Interaction 28

Designing an Optimal Integrated Energy System Including Sust...

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28th International electrical Power Distribution Conference, EPDC 2024

作者： Kiani, Hossein Nazari, Mohammad Hassan Khorsandi, Amir Abyaneh, Hossein Askarian Hosseinian, Seyed Hossein Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Smart Control Systems Department Tehran Iran

ISBN: (纸本)9798350385502

Recently, there has been significant attention directed towards renewable energy sources, driven by advancements in power electronics, cost reductions, and escalating environmental concerns. The utilization of entirely renewable sources for fulfilling energy requirements presents advantages such as reduced energy costs, sustainable expenses, and local accessibility. This research assesses various scenarios encompassing diverse configurations to minimize greenhouse gas (GHG) emissions, cost of energy (COE), and annual net present costs (NPC). The evaluation considers factors like investment, replacement, decommissioning, operation, and maintenance costs. The study conducts an optimal power planning and techno-economic-environmental analysis of a grid-connected hybrid renewable energy system, incorporating elements such as wind turbines (WT), photovoltaic systems (PVs), diesel generators (DG), boilers, converters, thermal load controllers (TLC), and battery energy storage systems (BESS). The system is designed to cater to both electrical and thermal loads concurrently. The proposed methodology is tested using HOMER software on a small-scale system situated in Manjil, Iran, with a focus on its efficacy in meeting economic and sustainable electrical and thermal requirements within a hybrid renewable energy framework. © 2024 IEEE.

关键词： Renewable energy

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Reinforcement Learning-based Receding Horizon control using Adaptive control Barrier Functions for Safety-Critical systems 63

Reinforcement Learning-based Receding Horizon Control using ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Sabouni, Ehsan Sabbir Ahmad, H.M. Giammarino, Vittorio Cassandras, Christos G. Paschalidis, Ioannis Ch. Li, Wenchao Boston University Division of Systems Engineering BostonMA02215 United States Boston University Department of Electrical and Computer Engineering The Division of Systems Engineering BostonMA02215 United States Boston University Department of Electrical and Computer Engineering Division of Systems Engineering Department of Biomedical Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

Optimal control methods provide solutions to safety-critical problems but easily become intractable. control Barrier Functions (CBFs) have emerged as a popular technique that facilitates their solution by provably guaranteeing safety, through their forward invariance property, at the expense of some performance loss. This approach involves defining a performance objective alongside CBF-based safety constraints that must always be enforced with both performance and solution feasibility significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness. To address these challenges, we propose a Reinforcement Learning (RL)-based Receding Horizon control (RHC) approach leveraging Model Predictive control (MPC) with CBFs (MPC-CBF). In particular, we parameterize our controller and use bilevel optimization, where RL is used to learn the optimal parameters while MPC computes the optimal control input. We validate our method by applying it to the challenging automated merging control problem for Connected and Automated Vehicles (CAVs) at conflicting roadways. Results demonstrate improved performance and a significant reduction in the number of infeasible cases compared to traditional heuristic approaches used for tuning CBF-based controllers, showcasing the effectiveness of the proposed method. In order to guarantee reproducibility, our code is provided here: ***/EhsanSabouni/CDC2024_RL_adpative_MPC_CBF/tree/main © 2024 IEEE.

关键词： Adaptive control systems

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Comparison of Long-term Energy Demand Forecasting in Developing and Developed Countries Using Machine Learning-based Algorithms 9

Comparison of Long-term Energy Demand Forecasting in Develop...

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9th International Conference on Technology and Energy Management, ICTEM 2024

作者： Kiani, Hossein Golshaeian, Sajad Nazari, Mohammad Hassan Gharehpetian, G.B. Hosseinian, Seyed Hossein Sarbazi, Jafar Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Smart Control Systems Department Tehran Iran

ISBN: (纸本)9798350329797

As the world's population grows, many nations grapple with the task of supplying sufficient energy. One effective way to manage and plan for this demand is through energy demand forecasting. In this research, we adopt a method employing machine learning algorithms to predict energy demand in various countries, both developed and developing, up to the year 2050. For our long-term forecast covering 2020 to 2050, two types of historical data are utilized: (i) energy consumption (EEC), and (ii) socio-economic indicators, such as Gross domestic product (GDP), energy import and export, and population. An Artificial Neural Network (ANN) based machine learning is employed utilizing 30 years' worth of socio-economic data (1991-2020). In this manner, the utilization of the ANN facilitates the prediction of long-term energy consumption (EEC) for the planning period spanning from 2020 to 2050. Furthermore, we present an optimization model designed to enhance the precision of our predictions. The outcomes from machine learning algorithms serve as input for our comprehensive model, implemented through ANN across various sections. Ultimately, our forecasts indicate a projected increase in electric energy consumption by 130.1% for Iran, 37.4% for Portugal, and 58.6% for the United States in 2050 compared to 2020. Significant distinctions exist between developing and developed economies with regard to the anticipated trends in Energy Efficiency Compliance (EEC). © 2024 IEEE.

关键词： Adversarial machine learning

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An Innovative PID controller Optimization Algorithm for Two-area Power System Frequency control and Monitoring 9

An Innovative PID Controller Optimization Algorithm for Two-...

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9th International Conference on Technology and Energy Management, ICTEM 2024

作者： Golshaeian, Sajad Nazari, Mohammad Hassan Hosseinian, Seyed Hossein Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Smart Control Systems Department Tehran Iran

ISBN: (纸本)9798350329797

In this article, the frequency of a Two-Area power system is controlled using a proportional-integral-derivative (PID) controller that is adjusted with optimization algorithms. Classical controllers may not perform optimally under different conditions due to the natural variability of renewable energies and their uncertainties. Multiple algorithms are utilized to enhance the nominal value of the PID controller. parameters in these controllers, including particle swarm optimization (PSO), shuffled frog leaping algorithms (SFLA), and genetic algorithms (GA). By comparing their results, the most efficient method will be determined Using the integral time-absolute error (ITAE) method. MATLAB is employed for simulating the frequency control model of a Two-Area power system. By evaluating the results, it can be seen that strong and appropriate performance, in a wide range of system parameter changes and load changes, can be obtained with the proposed SFLA optimization method in system nonlinearity. Examining the proposed enhanced method in the field of frequency monitoring and control will show a significant improvement compared to the previous algorithms used. © 2024 IEEE.

关键词： Three term control systems

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A Novel Random Pulse Width Modulation for PMSM Drive System Based on Unified Voltage Modulation 6

A Novel Random Pulse Width Modulation for PMSM Drive System ...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Kan, Jinpeng Meng, Yongqing Shi, Yixin Li, Fengjiao Chen, Wenjie Li, Changyi Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

The electromagnetic interference (EMI) problem caused by power electronic switching devices and pulse width modulation (PWM) affects the normal operation of the motor drive system. In this paper, a random PWM based on unified modulation is proposed for permanent magnet synchronous motor (PMSM) drive system. The theoretical basis of unified modulation and the implementation scheme of random modulation are analyzed. Finally, the effectiveness of the proposed random PWM is verified by simulation. © 2024 IEEE.

关键词： Pulse width modulation

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Robust design of sliding mode control for airship trajectory tracking with uncertainty and disturbance estimation

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Journal of systems engineering and Electronics 2024年第1期35卷 242-258页

作者： WASIM Muhammad ALI Ahsan CHOUDHRY Mohammad Ahmad SHAIKH Inam Ul Hasan SALEEM Faisal Department of Aeronautics and Astronautics Engineering Institute of Space TechnologyIslamabad 44000Pakistan Department of Electrical Engineering University of Engineering and TechnologyTaxila 47080Pakistan Department of Measurements and Control Systems Silesian University of TechnologyGliwice 44-101Poland The Joint Doctoral School Silesian University of TechnologyGliwice 44-100Poland

The robotic airship can provide a promising aerostatic platform for many potential *** applications require a precise autonomous trajectory tracking control for *** has a nonlinear and uncertain *** is prone to wind disturbances that offer a challenge for a trajectory tracking control *** paper addresses the airship trajectory tracking problem having time varying reference path.A lumped parameter estimation approach under model uncertainties and wind disturbances is opted against distributed *** uses extended Kalman filter(EKF)for uncertainty and disturbance *** estimated parameters are used by sliding mode controller(SMC)for ultimate control of airship trajectory *** comprehensive algorithm,EKF based SMC(ESMC),is used as a robust solution to track airship *** proposed estimator provides the estimates of wind disturbances as well as model uncertainty due to the mass matrix variations and aerodynamic model *** stability and convergence of the proposed method are investigated using the Lyapunov stability *** simulation results show that the proposed method efficiently tracks the desired *** method solves the stability,convergence,and chattering problem of SMC under model uncertainties and wind disturbances.

关键词： airship chattering extended Kalman filter(EKF) model uncertainties estimation sliding mode controller(SMC)

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A New Hybrid Model for Segmentation of the Skin Lesion Based on Residual Attention U-Net

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computers, Materials & Continua 2023年第6期75卷 5177-5192页

作者： Saleh Naif Almuayqil Reham Arnous Noha Sakr Magdy M.Fadel Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and Technology(DHIET)MansouraEgypt Computers Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt

Skin segmentation participates significantly in various biomedical applications,such as skin cancer identification and skin lesion *** paper presents a novel framework for segmenting the *** framework contains two main stages:The first stage is for removing different types of noises from the dermoscopic images,such as hair,speckle,and impulse noise,and the second stage is for segmentation of the dermoscopic images using an attention residual U-shaped Network(U-Net).The framework uses variational Autoencoders(VAEs)for removing the hair noises,the Generative Adversarial Denoising Network(DGAN-Net),the Denoising U-shaped U-Net(D-U-NET),and Batch Renormalization U-Net(Br-U-NET)for remov-ing the speckle noise,and the Laplacian Vector Median Filter(MLVMF)for removing the impulse *** the second main stage,the residual attention u-net was used for *** framework achieves(35.11,31.26,27.01,and 26.16),(36.34,33.23,31.32,and 28.65),and(36.33,32.21,28.54,and 27.11)for removing hair,speckle,and impulse noise,respectively,based on Peak Signal Noise Ratio(PSNR)at the level of(0.1,0.25,0.5,and 0.75)of *** framework also achieves an accuracy of nearly 94.26 in the dice score in the process of segmentation before removing noise and 95.22 after removing different types of *** experiments have shown the efficiency of the used model in removing noise according to the structural similarity index measure(SSIM)and PSNR and in the segmentation process as well.

关键词： Skin tumor speckle noise impulse noise hair noise deep learning segmentation

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A Novel Zero Common-Mode Voltage Modulation Strategy for Dual Three-Phase PWM Inverter Based on Unified Voltage Modulation 4

A Novel Zero Common-Mode Voltage Modulation Strategy for Dua...

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4th International Conference on Energy, Power and electrical engineering, EPEE 2024

作者： Kan, Jinpeng Meng, Yongqing Li, Changyi Chen, Wenjie Li, Fengjiao Shi, Yixin Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798331518066

Power electronic switching devices and pulse width modulation (PWM) not only improves the performance of motor drive systems, but also brings about common-mode voltage (CMV) issues, that challenging the normal operation of the system. This article proposes a zero common-mode voltage modulation strategy based on unified voltage modulation for a 60° Y-connected six-phase permanent magnet synchronous motor system driven by a dual three-phase inverter. The theoretical basis and zero common-mode implementation scheme of unified modulation are analyzed, and the effectiveness of the proposed zero common-mode voltage modulation strategy is finally verified through simulation. © 2024 IEEE.

关键词： Pulse width modulation

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Prediction of Radiated Electromagnetic Interference Based on Four-module Motor Drive System 6

Prediction of Radiated Electromagnetic Interference Based on...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Shi, Yixin Chen, Wenjie Li, Fengjiao Kan, Jinpeng Meng, Yongqing Yang, Guang Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

In recent years, wide bandgap semiconductor devices such as silicon carbide (SiC) and gallium nitride (GaN) have been increasingly applied in electric drive systems, effectively enhancing system power density. However, the high-frequency and high-speed characteristics of SiC devices exacerbate electromagnetic interference (EMI) issues within the system. Additionally, the cables in electric drive systems exhibit significant antenna effects, radiating conducted EMI into the surrounding space, thereby affecting the normal operation of other sensitive equipment. Therefore, it is essential to research motor drive system topologies that can actively suppress EMI and predict radiated interference in motor drive systems. The topology of a four-module motor paired with parallel inverters can achieve active suppression of EMI at the source. This paper conducts a simulation-based prediction of radiated interference for a parallel inverter-driven four-module motor system. It analyzes the coupling mechanisms of common mode and differential mode radiated interference and validates the radiated interference from both DC and AC cables through simulations, predicting the impact on surrounding sensitive equipment in practical systems. © 2024 IEEE.

关键词： Gallium nitride

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