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arXiv 2022年

作者： Pan, Xiao Chen, Boli Dai, Li Timotheou, Stelios Evangelou, Simos A. The Dept. of Electrical and Electronic Engineering Imperial College London London United Kingdom The Dept. of Electronic and Electrical Engineering University College London London United Kingdom The School of Automation Beijing Institute of Technology Beijing China The Dept. of Electrical and Computer Engineering The KIOS Research and Innovation Center of Excellence University of Cyprus Cyprus Cyprus

The development of connected and automated vehicles is the key to improving urban mobility safety and efficiency. This paper focuses on cooperative vehicle management at a signal-free intersection with consideration of vehicle modeling uncertainties and sensor measurement disturbances. The problem is approached by a hierarchical robust control strategy in a decentralized traffic coordination framework where optimal control and tube-based robust model predictive control methods are designed to hierarchically solve the optimal crossing order and the velocity trajectories of a group of CAVs in terms of energy consumption and throughput. To capture the energy consumption of each vehicle, their powertrain system is modeled in line with an electric drive system. With a suitable relaxation and spatial modeling approach, the optimization problems in the proposed strategy can be formulated as convex second-order cone programs, which provide a unique and computationally efficient solution. A rigorous proof of the equivalence between the convexified and the original problems is also provided. Simulation results illustrate the effectiveness and robustness of the proposed strategy and reveal the impact of traffic density on the control solution. The study of the Pareto optimal solutions for the energy-time objective shows that a minor reduction in journey time can considerably reduce energy consumption, which emphasizes the necessity of optimizing their trade-off. Finally, the numerical comparisons carried out for different prediction horizons and sampling intervals provide insight into the control design. Copyright © 2022, The Authors. All rights reserved.

关键词： Robust control

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Robust Charging Solutions for 400V EV Batteries: Implementing a Quasi Single-Stage Current-Fed Resonant Converter

Robust Charging Solutions for 400V EV Batteries: Implementin...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Muhammad Furqan Waqar Uddin Kamran Zeb Muhammad Ashar Ayaz Irfan Khan Muhammad Khalid School of Electrical Engineering and Computer Science (SEECS) National University of Science and Technology (NUST) Islamabad Pakistan Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia IRC-SES KFUPM Dhahran Saudi Arabia Dept. of Electrical and Computer Engneering Texas A and M University College Station TX USA Dept. of Elec. and Comp. Engineering Texas A and M University College Station TX USA Electrical Engineering Department KFUPM and IRC-SES KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

This work presents a high-performance AC-DC quasi single-stage current-fed resonant converter based electric vehicles (EVs) charger. A non-linear control technique is applied which has two main objectives i.e; Power factor correction (PFC) and regulation of DC output voltage. For this purpose, super twisting sliding mode control (ST-SMC) is suggested in this study for 400V batteries of EVs. To reduce switching losses and improve efficiency, the secondary side switch shows turn OFF with zero voltage switching (ZVS) while the primary ones exhibit ZVS at turn ON instant. Hardware-in-loop (HIL) experimental validation based on the Delfino F28369D dual-core microcontroller is used to verify the performance of the controller and converter simulated on PSIM and MATLAB/Simulink.

关键词： Microcontrollers Switching loss Switches Resonant converters Zero voltage switching Regulation Batteries Safety Voltage control Sliding mode control

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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Cybersecurity Based on Converged Form of Blockchain, Internet-of-Things and Machine Learning in Smart Micro-Grid

Cybersecurity Based on Converged Form of Blockchain, Interne...

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Computing, Internet of Things and Microwave Systems (ICCIMS), International Conference on

作者： Dhaou Said Mubashir Husain Rehmani Idir Mellal Aziz Oukaira Ahmed Lakhssass Dept. Electrical and Computer Engineering Uuniversity of Sherbrooke Sherbrooke QC Canada Munster Technological University Bishopstown Cork Ireland Temerty Center for AI Research and Education in Medicine University of Toronto Toronto Ontario Canada University of Quebec in Outaouais Gatineau QC Canada

ISBN: (数字)9798350351736

ISBN: (纸本)9798350351743

This paper explains the main cybersecurity problems facing SMG systems and the role of IoT, ML and Blockchain technologies in addressing them. It highlights how IoT, ML, and Blockchain can converge to meet SMG data privacy preservation and cyber-attack protection. Finally, Blockchain, standards and regulations are discussed as open issues and challenges of the perfect fitting of technologies in the SMG cybersecurity.

关键词： Microwave technology Microwave integrated circuits Fitting Machine learning Regulation Blockchains Internet of Things Protection Microwave FET integrated circuits Standards

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Federated Learning for the Metaverse: A Short Survey

Federated Learning for the Metaverse: A Short Survey

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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Gokul Yenduri Dasaradharami Reddy K Gautam Srivastava Supriya Y Ramalingam M Thippa Reddy Gadekallu Feras M. Awaysheh Vellore Instituite of Technology Vellore Tamil Nadu India Dept. of Math and Computer Science Brandon University Brandon Canada Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Delta Research Center Institute of Computer Science Tartu University Tartu Estonia

The Metaverse, a 3-dimensional virtual realm mirroring real-world objects, promises transformative experiences. Its potential is tempered by data collection, confidentiality, and privacy concerns. In tandem with AI-enabled technologies, edge computing can provide a practical solution to overcoming many of these challenges. This paper argues that combining AI-enabled technologies with edge computing-specifically via federated learning (FL) can address these challenges. FL, as a privacy-centric distributed machine learning (ML) approach, enables knowledge sharing among Metaverse clients without compromising user data. However, while FL posits potential resolutions for the Metaverse, a discernible lacuna remains in the comprehensive study of its overarching consequences. The literature misses an extensive study of adopting these new deployment architectures, giving it significant research impact. This paper bridges this knowledge gap, exploring the symbiosis between the Metaverse and FL. We first introduce the foundational concepts of both domains and detail enabling technologies such as digital twins, the Internet of Things, brain-computer interfaces, blockchain, and extended reality. Next, we delve into practical applications of FL within the metaverse, spanning sectors like healthcare, education, e-commerce, gaming, and the military. Finally, the paper highlights the key challenges and future directions for integrating FL within the metaverse ecosystem.

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A smart ocean observation system for reliable real-time measurements

A smart ocean observation system for reliable real-time meas...

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OCEANS

作者： Camilla Saetre Astrid Marie Skålvik Kjell-Eivind Frøysa Marie Bueie Holstad Department of Physics and Technology University of Bergen Bergen Norway Dept. of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway NORCE Norwegian Research Center Bergen Norway

This paper presents a research and innovation centre for a smart ocean observation system. The main goal of the centre, SFI Smart Ocean, is to enable sustainable ocean management through real-time measurements from autonomous and smart sensors. As for smart systems on-land, a smart ocean observation system of sensors requires wireless communication and a software platform gathering all required data. To ensure usability in real-time, minimize maintenance costs, and time-consuming delayed mode data analysis, it is paramount to ensure high reliability of the system. This can be achieved by on-line quality control, self-diagnostics, and self-calibration at the sensors.

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Task Reverse Offloading with Deep Reinforcement Learning in Multi-Access Edge Computing

Task Reverse Offloading with Deep Reinforcement Learning in ...

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International Conference on computer and Communication engineering, ICCCE

作者： Mamoon M. Saeed Rashid A. Saeed Rania A. Mokhtar Othman O. Khalifa Zeinab E. Ahmed Mohammed Barakat Areeg Ali Elnaim Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif Saudi Arabia Department of Electrical and Computer Engineering International Islamic University Malaysia Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya Electrical and Computer Engineering Dept. International Islamic University Malaysia Malaysia Computer Engineering Dept. University of Gezira Sudan School of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan College of Engineering Alneelain University Khartoum Sudan

The Multi-access Edge Computing (MEC) technology’s quick development greatly benefits the Collaborative Mobile Infrastructure System (CMIS). To combine the data and produce tasks, crowd-sensing data will be transferred to the MEC server in CMIS. Nevertheless, if there are too many devices, it becomes extremely difficult for MEC to decide appropriately based on the data from the devices and infrastructure. This study builds a framework for reverse offloading that carefully balances the relationship between task completion time and user mobile energy consumption. Moreover, to decrease system use generally, an adaptive optimal reverse offloading method based on Deep Q-Network is created (DQN). The results of the simulations demonstrate that the suggested approach may successfully minimize energy consumption and work latency when compared to full local and fixed offloading techniques.

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Design of receiver RF front end for mm-Wave 5G applications

Design of receiver RF front end for mm-Wave 5G applications

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Learning and Technology Conference (L&T)

作者： Aziza I. Hussein Nora Alamoudi Mirna Ali Ahmed Zakaria M. Mourad Mabrook College of Engineering Energy and Technology Research Center Effat University Saudi Arabia Electrical and Computer Engineering Department Effat University Jeddah Saudi Arabia Telecommunication Engineer Telecom Egypt Asyut Egypt Space Communication Dept. Faculty of Navigation Science & Space Technology Beni-Suef University Egypt

Recently, the research emphasis has shifted towards 5G due to its potential to accommodate the increasing demand for data traffic, extensive interconnectivity of devices, and the emergence of numerous novel applications. Given the inadequacy of speed provided by spectrum resources in the lower frequency bands of 5G, a novel sub-generation utilizing millimeter wave frequencies has been introduced. The new sub-generation is called mm-wave 5G and provides higher bandwidth for faster speed and higher capacity. RF system architecture, circuits, and antenna innovation will be required to provide the requisite speed and capacity. In this paper the design challenges and trade-offs in RF front-end circuits and receiver sub-systems are discussed. Moreover, the massive multiple-input multiple-output (MIMO) techniques are examined. In addition, design, and simulation of key components in 5G mm-wave receiver, including the design of linear phased array antenna receiver and an analog-to-digital converter (ADC) is presented. Sigma-Delta (ΣΔM) Converters are a type of ADC with a technique that involves oversampling and shaping quantization noise to achieve a higher resolution which make it a best choice to be used in the design of the mm wave 5G communication applications. The proposed ΣΔM uses multistage architecture to provide high-order noise shaping and high resolution. The simulation results shows that the designed Cascade 2-2 MASH ΣΔ achieve a signal to noise ratio of 110(resolution of 16 bits).

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Simulating The Effects of Low Intensity Focused Ultrasound in Parkinson's Disease

Simulating The Effects of Low Intensity Focused Ultrasound i...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Ítalo Karmann Aventurato Marcelo Ramos Romano Leonardo Abdala Elias Department of Neurology School of Medical Sciences University of Campinas Campinas SP Brazil Dept. of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas Campinas SP Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas Campinas SP Brazil

Invasiveness limits current neuromodulation approaches for treating Parkinson's disease (PD). Low-intensity focused ultrasound (LIFUS) is a viable non-invasive alternative to modulate the cortico-subcortical networks involved in PD. In this study, an intramembrane cavitation model was used to simulate the effects of ultrasonic stimulation to the subthalamic nucleus in a simplified validated model of the cortico-subcortical network. Simulation results from single neuron and network models show the ultrasonic stimulus was effective in producing neuronal excitation in the subthalamic nucleus as well as modulation of activities in other nuclei of the network model (e.g., globus pallidus and thalamus). Additionally, depending on LIFUS parameters (i.e., amplitude and duty cycle) there was a reduction in beta-band parkinsonian oscillations to values close to the healthy condition. Exploratory tests of the stimulus parametric space revealed different operating ranges that could be used to optimize the therapeutic response to LIFUS. In conclusion, the membrane cavitation framework provides reasons to believe that LIFUS would be a potential therapeutic intervention to modulate neuronal circuits involved in PD without surgical procedures. Also, the model employed in the present study would be helpful in exploring other neuromodulation effects of ultrasound waves in neuronal circuits in the brain. However, it is important to validate the results from the computer simulations using both experiments with animal models and clinical trials with humans.

关键词： Ultrasonic imaging Parkinson's disease Computational modeling Surgery Brain modeling Thalamus Acoustics

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Directional Metasurface Using Transistor-Loaded Circularly Polarized Antenna Elements

Directional Metasurface Using Transistor-Loaded Circularly P...

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Antennas and Propagation Society International Symposium

作者： David Chatzichristodoulou Photos Vryonides Dimitra Psychogiou Symeon Nikolaou Frederick Research Center Nicosia Cyprus RF and Microwave Solutions LTD Cyprus Dept. Electrical Computer Engineering and Informatics Frederick University Nicosia Cyprus School of Engineering University College Cork Cork Ireland Tyndall National Institute Cork Ireland

This paper discusses the RF design of a compact metasurface that enables directional propagation characteristics. It allows for circularly-polarized waves to penetrate through its volume in one direction of propagation whereas it absorbs them in the reverse one. Directionality is achieved by combining two transistors with two circularly-polarized patch antennas in each unit cell of the metasurface. A compact multi-layer integration scheme is proposed and is demonstrated through electromagnetic (EM) simulations at S-band. The proposed metasurface configuration exhibits directional characteristics with the following performance metrics. BW in the forward direction: 3.4-3.7 GHz, maximum Gain in the forward direction: 29 dB, isolation in the reversed direction> 50 dB.

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