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AI-Driven Proportionate Power Sharing in Virtual Synchronous Generators for Optimizing the Source Conditions and Efficiency 50

AI-Driven Proportionate Power Sharing in Virtual Synchronous...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Asif, Uzair D’silva, Silvanus Shadmand, Mohammad B. Bayhan, Sertac Abu-Rub, Haitham Dept. of Electrical & Computer Engineering University of Illinois Chicago IL United States Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Dept. of Electrical & Computer Engineering Texas A&M University at Qatar Doha Qatar

ISBN: (纸本)9781665464543

Virtual impedance-based power sharing between several interconnected grid-forming inverters (GFMI)s in a power electronics-dominated grid (PEDG) does not consider the available power reserves. This non-optimal power-sharing may result in the overloading of nearby inverter-based resources (IBR)s exhibiting lower effective line impedances in the network. This paper proposes an optimal and controllable power-sharing scheme based on a modular artificial neural network (ANN) that can consider various factors like power reserve, line losses, incentives from the grid, etc., and increases the system's scalability. An optimum power allocator (OPA) mechanism that monitors the output active powers and line losses of all the IBRs is proposed to address the issue of uncontrolled power sharing. The OPA dynamically adjusts power allocation weights using linear programming to optimize the overall loss minimization cost function. Moreover, an artificial neural network-based virtual impedance optimizer (ANN-VIO) is also proposed, which estimates the corresponding virtual impedance (VI) value for each GFMI unit in the PEDG. The primary controller of GFMI uses these estimated VI values to adjust its point of common coupling (PCC) voltage reference and attain optimum proportionate power sharing within the overall PEDG. Multiple case studies are performed to validate the effectiveness of the proposed GFMIs power-sharing scheme. © 2024 IEEE.

关键词： Cost functions

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Enhancing 3D Modeling Quality Through Orthorectified Image Accumulation

Enhancing 3D Modeling Quality Through Orthorectified Image A...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kwag, Cheolsu Kim, Euntaek Lee, Yongsuk Jung, Seungmin Hwang, Sung Soo Ryu, Hwangryol Moon, Youngmin Handong Global University Dept. of Computer Science and Electrical Engineering Pohang Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of Research Institute of Industrial Science and Technology Smart Engineering Research Laboratory Pohang Korea Republic of

ISBN: (纸本)9798331510756

This study introduces a novel methodology designed to facilitate the capture of comprehensive image datasets, crucial for accurate 3D modeling of expansive indoor spaces. Leveraging orthophotos generated from panoramic camera data and visualizing image coverage density through heatmaps, our approach effectively identifies under-captured and unvisited areas. This enables targeted image acquisition, minimizes redundant reshoots, and enhances the overall quality and fidelity of 3D models. © 2025 IEEE.

关键词： Digital elevation model

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Power and Rate Allocation for Energy-Efficient Rate-Splitting Multiple Access via Deep Q-Learning

Power and Rate Allocation for Energy-Efficient Rate-Splittin...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Diamanti, Maria Kapsalis, Georgios Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350370218

Rate-Splitting Multiple Access (RSMA) has recently emerged as an effective technique for increasing network capacity by smartly controlling the tradeoff between decoding and treating interference as noise. In this paper, aligned with the need for sustainable wireless networks, we study the energy-efficient power and rate allocation of the common and private messages in the downlink of a network where rate-splitting is adopted. The corresponding energy efficiency maximization problem is transformed into a multi-agent Deep Reinforcement Learning (DRL) problem, based on which each private stream transmitted in the downlink constitutes a different DRL agent. The formulated DRL problem is solved by using Deep Q-Learning (DQL) algorithm and training a single Deep Q-Network (DQN) from the cumulative experiences gained from the DRL agents and their exploration of the environment, i.e., the exploration of different private-message power allocations. Numerical results obtained via modeling and simulation verify the effectiveness of the proposed DQL algorithm, demonstrating that it concludes solutions that outperform existing approaches from the literature in the achieved energy efficiency. © 2023 IEEE.

关键词： Deep Reinforcement Learning (DRL) Energy Efficiency Maximization Rate-Splitting Multiple Access (RSMA)

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Grid Voltage Sensorless Model Predictive Control for Single-phase Five-level ANPC-FC Rectifier 50

Grid Voltage Sensorless Model Predictive Control for Single-...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bayindir, Abdullah Berkay Sharida, Ali Bayhan, Sertac Abu-Rub, Haitham Dept. of Electrical & Computer Engineering Texas A&M University College StationTX77840 United States Dept. of Electrical & Computer Engineering Texas A&M University at Qatar Doha23874 Qatar Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Dept. of Electrical & Computer Engineering College of Science & Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665464543

This paper presents a grid voltage sensorless model predictive control (MPC) technique for a single-phase five-level active neutral point clamped flying capacitor (ANPC-FC) rectifier. The control strategy utilizes MPC to regulate the grid current and ensure equilibrium in the voltages of the flying and DC side capacitors, without measuring the grid voltage. A state feedback observer (SFO) is responsible to provide the grid voltage estimation to the MPC. Achieving this involves integrating all controlled parameters into a finite control set MPC (FCS-MPC). Moreover, the reference grid current is generated to regulate the DC link voltage based on a power equilibrium technique supported by a feed-forward loop. The proposed controller is implemented and evaluated using MATLAB/Simulink. Simulation results are carried out to show that the proposed MPC technique effectively achieves control objectives, while ensuring accurate tracking, and resisting voltage distortions. © 2024 IEEE.

关键词： MATLAB

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Design and Analysis of Digital Twin Models for Dual Active Bridge 50

Design and Analysis of Digital Twin Models for Dual Active B...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Bayindir, Abdullah Berkay Al-Khateeb, Ahmad Sharida, Ali Alnuweiri, Hussein Bayhan, Sertac Abu-Rub, Haitham Dept. of Electrical & Computer Engineering Texas A&M University College StationTX77840 United States Dept. of Electrical & Computer Engineering Texas A&M University at Qatar Doha23874 Qatar Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Dept. of Electrical & Computer Engineering College of Science & Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9781665464543

Digital twins (DTs) are emerging as effective tools for power electronic converters, addressing important objectives such as real-time monitoring, fault detection and predictive maintenance. Among these power converters, the dual active bridge (DAB) stands out for DC-DC conversion applications. However, the non-linear dynamics of DABs pose considerable challenges to their accurate modeling. To address these challenges, this paper investigates the use of machine learning (ML) and deep learning (DL) models for DT implementation in DABs. Specifically, XGBoost and dense neural network (DNN) models are employed to construct DTs compatible with low-cost microcontrollers. These DTs are operated in parallel with the physical DAB converter to predict its output voltage in real-time. Experimental results demonstrate that both of the models perform well in terms of accuracy and applicability to low-cost microcontroller. However, the DNN is more reliable and the XGBoost is simpler. © 2024 IEEE.

关键词： Microcontrollers

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Effect of Ultra Violet Light on Planar Field Emitters 24

Effect of Ultra Violet Light on Planar Field Emitters

引用

24th International Vacuum Electronics Conference, IVEC 2023

作者： Bhattacharya, Ranajoy Turchetti, Marco Keithley, P. Donald Berggren, Karl K. Browning, Jim Boise State University Dept. of Electrical and Computer Engineering BoiseID United States Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science CambridgeMA United States Massachusetts Institute of Technology Research Laboratory of Electronics CambridgeMA United States Massachusetts Institute of Technology Laboratory of Electronics Dept. Of Electrical Engineering and Computer Science and Research CambridgeMA United States

ISBN: (纸本)9781665473217

Two different types of planar nano-scale (4-5 nm) field emitters, bowtie and diode devices, were characterized for use as electron sources for vacuum nano-transistors. I-V measurements were performed before and after Ultra-violet (UV) light exposure. Experiments showed that the field emission current was 2.7 nA before the UV exposure and was 1.8 nA after the exposure for the bow-die devices. Experiments also showed that the field emission current was 5.6 nA before the UV exposure and was 3.6 nA after the exposure for the diode devices. This reduction in current could be attributed to the removal of water vapor by UV exposure resulting in the reduction in the surface leakage current. A long-term stability tests conducted after the UV exposure showed both the bow-tie and diode devices emitted a stable current ( 1100 hrs. of continuous operation at a DC bias of 5 V. © 2023 IEEE.

关键词： Field emission

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Localization of Dengue Virus Likelihood Using Image Processing Technique 6

Localization of Dengue Virus Likelihood Using Image Processi...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Hosen, Md. Mahbub Islam, Md. Rahatul Tabassum, Faria Tonni, Syeda Thamina Noor, Malik Mohammad Nafiur Rahman, Shahid Northern University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Kyushu Institute of Technology Dept. of Human Intelligence Systems Wakamatsu Kitakyushu Japan Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Advance Robotics Research Center Human Intelligence Lab Dhaka Bangladesh Canadian University of Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306613

The Aedes mosquito, found in tropical areas, transmits, and causes dengue fever. The spread of the dengue fever virus from an infected Aedes mosquito bite typically takes between three and fifteen days to manifest itself clinically as dengue fever symptoms. Early and automated detection of this illness will reduce the occurrence of outbreaks and facilitate the digitization of the system. Our primary objective is to identify regions at high risk for dengue fever using limited image data through the utilization of sophisticated machine learning and image processing methods. This work showcases the application of transfer learning algorithms for the timely identification of regions at risk for dengue mosquito infestation, particularly in situations where obtaining early data on dengue outbreaks is challenging. The experiments included images of 2150 dengue-positive data sets and 1440 dengue-negative data sets. We used AI and image classifiers Yolo V4 Tin, Tensor Flow, Google Colab, etc. for the training data set, and the system makes predictions with a 99.63% accuracy rate. Our system has some unique attributes;it not only detects the percentage of dengue probability but also the specific dengue-prone location on the submitted image. © 2024 IEEE.

关键词： aedes mosquito dengue-prone image processing yolo v4 tiny

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Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications 24

Resource Efficient Federated Learning for Deep Anomaly Detec...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Gkillas, Alexandros Lalos, Aris Industrial Systems Institute Athena Research Center Greece University of Patras Dept. of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350339598

Anomaly data constitute a thorny problem in numerous industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy and reconstruction is achieved with the utilization of large neural networks with more layers and nodes, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the very recent on-device privacy-preserving distributed machine learning paradigm, known as federated learning (FL). This paradigm allows edge devices to locally train and exchange models increasing also the communication cost. Thus, in order to deal with the increased communication, processing and storage challenges introduced by FL based deep anomaly detection NN pruning is expected to have significant benefits towards both reducing the processing, storage and communication complexity but also towards avoiding the over fitting problem. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that simultaneously fusses the local models broadcast by the edge devices and performs pruning generating a much more compressed model to be deployed at the edge devices with no accuracy loss even if the training is performed using a small subset of data instances, achieving compression rates greater than 99%. © 2023 IEEE.

关键词： Anomaly detection

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HERA: A Novel Heuristic Resource Allocator for Multi-SDM Multi-User Settings

HERA: A Novel Heuristic Resource Allocator for Multi-SDM Mul...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Antoniadis, Christos Katsalis, Kostas Tyrovolas, Dimitrios Tegos, Sotiris A. Ioannidis, Sotiris Diamantoulakis, Panagiotis D. Karagiannidis, George K. Liaskos, Christos DOCOMO Euro-Labs Munich Germany Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Technical University of Crete Dept. of Electrical and Computer Engineering Chania Greece Artificial Intelligence & Cyber Systems Research Center Lebanon University of Ioannina Computer Science Engineering Department Ioannina Greece Institute of Computer Science Foundation for Research and Technology Hellas Greece

ISBN: (纸本)9798331507428

The advent of Programmable Wireless Environments (PWEs) has transformed the wireless propagation phenomenon into a software-defined resource, leveraging Software-defined metasurfaces (SDMs). These new technologies have shown that wireless waves can be routed within a space, contrary to the regular, chaotic wireless propagation, yielding considerable benefits to communication efficiency, and even completely new applications. A new topic has risen, in the context of modeling these capabilities as network resources, and allocating them to users. In this context, the present paper contributes HERA, a heuristic resource allocator that receives a setup with users and SDMs as input, and produces the necessary configuration of the latter, for efficient shared performance. Simulation results demonstrate considerable efficiency in this task, while the heuristic nature of HERA sets the basis for a flexible definition of complex user objectives in the future. © 2024 IEEE.

关键词： Resource allocation

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Resource Allocation as a Market: A Case Study on Multi-Server Multi-Model Federated Learning 20

Resource Allocation as a Market: A Case Study on Multi-Serve...

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20th Wireless On-Demand Network systems and Services Conference, WONS 2025

作者： Diamanti, Maria Rahman, Aisha B. Charatsaris, Panagiotis Tsiropoulou, Eirini Eleni Papavassiliou, Symeon National Technical University of Athens Institute of Communication and Computer Systems School of Electrical and Computer Engineering Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering Albuquerque NM United States

ISBN: (纸本)9783903176713

6G networks envision a seamless integration of data and AI into their core operations, marking a new era where distributed client devices actively engage in intelligent processes, supported by a diverse ecosystem of AI service providers. This shift necessitates approaching traditional resource allocation problems from a market perspective, transparently reflecting the gains and losses of clients and providers within the ecosystem. In this paper, we use a multi-server, multi-model Federated Learning (FL) network as a case study to propose a market-based resource allocation framework. The framework models the joint problem of client resource allocation and pricing as a Fisher market, where servers compete to attract clients with private data and local computing resources for distributed model training. The joint problem is formulated as a convex program, aiming to maximize the total server utility related to their model's accuracy, while incorporating clients' total computing resource constraints and servers' budget constraints. An analytical solution to the convex program is derived concluding the so-called Market Equilibrium (ME) point, where market clearance is achieved. The effectiveness of the proposed market-based resource allocation framework is validated through comparisons with established resource allocation and sharing schemes from the literature. © 2025 IFIP.

关键词： Fisher market Market Equilibrium Multi-server multi-model federated learning pricing resource allocation

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