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Entanglement-Assisted Covert Communication via Qubit Depolarizing Channels

IEEE Transactions on Information Theory 2025年第5期71卷 3693-3706页

作者： Zlotnick, Elyakim Bash, Boulat A. Pereg, Uzi Technion—Israel Institute of Technology Faculty of Electrical and Computer Engineering Helen Diller Quantum Center Haifa3200003 Israel Nvidia Tel Aviv Israel The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

We consider entanglement-assisted communication over the qubit depolarizing channel under the security requirement of covert communication, where the transmission itself must be concealed from detection by an adversary. Previous work showed that (Formula presented) information bits can be reliably and covertly transmitted in n channel uses without entanglement assistance. However, Gagatsos et al. (2020) showed that entanglement assistance can increase this scaling to (Formula presented) for continuous-variable bosonic channels. Here, we present a finite-dimensional parallel, and show that (Formula presented) covert bits can be transmitted reliably over n uses of a qubit depolarizing channel. The coding scheme employs "weakly" entangled states such that their squared amplitude scales as (Formula presented). © 1963-2012 IEEE.

关键词： Qubits

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Peer-to-Peer Energy Trading Framework Using Markov Chain for a Resilient Smart Grid

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IEEE Transactions on Industry Applications 2025年第3期61卷 4499-4514页

作者： Arora, Mayank Vishwanath, Gururaj Mirle Sharma, Ankush Chilamkurti, Naveen Department of Electrical Engineering IIT Kanpur Kanpur India Department of Computer Science and Information Technology La Trobe University - Melbourne Campus Melbourne Australia

This paper presents a resilience-driven framework leveraging advanced control technologies, particularly a Markov chain approach, to enhance the robustness of peer-to-peer (P2P) energy trading networks under Low Probability High Impact Events (LPHIE) such as climate change, cyberattacks, and natural disasters. The proposed framework addresses critical challenges in maintaining energy system stability by integrating renewable energy sources, optimizing energy exchange, and quantifying system resilience through novel metrics. A key contribution lies in the development of a multi-layered strategy that incorporates fuzzy logic for dynamic fault mitigation and a three-tier energy management system combining solar PV, battery storage, and electric vehicles. Additionally, the framework utilizes blockchain technology to ensure transparency, security, and fairness in energy trading, while promoting inclusivity and equity among diverse prosumer groups. By advancing the state-of-the-art in resilience modeling, this study provides a robust foundation for sustainable energy management in decentralized systems, offering practical insights into mitigating the impacts of LPHIE and advancing the democratization of energy resources. © 1972-2012 IEEE.

关键词： Smart power grids

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Extensive Numerical Analysis of PDF Parameters for Wind Energy in Brazil: A Study Across 27 Cities for 60 Years Wind Speed

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IEEE Access 2025年 13卷 84555-84568页

作者： Badawi, Ahmed Souza, Amaury Elzein, I.M. Ali, Hassan Zyoud, Alhareth College of Engineering Technology University of Doha for Science and Technology Qatar Federal University of Mato Grosso do Sul Brazil Birzeit University Faculty of Engineering Department of Electrical and Computer Engineering Birzeit Ramallah Palestine

This research conducts a comprehensive numerical assessment of wind energy potential across Brazil, leveraging six decades of wind speed data collected from 27 cities to analyze Probability Density Function (PDF) parameters and enhance wind resource mapping. The study employs the Weibull distribution, defined by its shape factor k and scale factor c, to represent variations in wind speed. A total of nine numerical practices and methods including the Maximum Likelihood Method (MLM), Method of Moments (MM), Empirical Method (EM), Standard Deviation Method (STDM), Modified Maximum Likelihood Method (MMLM), Second Modified Maximum Likelihood Method (SMMLM), Graphical Method (GM), Least Mean Square Method (LSM), and Energy Pattern Factor Method (EPF) were utilized to achieve optimal parameter estimation. Statistical analysis was performed using various metrics such as the Index of Agreement (IA), Root Mean Square Error (RMSE), Chi-square test (X2), Mean Absolute Percentage Error (MAPE), and Relative Root Mean Square Error (RRMSE). The findings indicated that MLM was the most precise method, followed by STDM, EM, MM, and EPF, while GM and LSM demonstrated lower effectiveness. The analysis determined a scale factor range of 2.17-2.36 m/s and a shape factor of 2.5-2.78, resulting in a bell-shaped Gaussian PDF. The Mean Wind Speed (MWS) was recorded at 2.06 m/s, which, despite being categorized as low, indicates the potential for effective deployment of small-scale wind turbines for energy production. This study offers significant insights for wind energy development in Brazil, presenting a methodological approach for optimizing the placement of wind turbines and minimizing electricity generation costs. © 2013 IEEE.

关键词： Weibull distribution

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A Data-Driven Adaptive Control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

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IEEE Transactions on Power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Design and Modeling of a Photonic Crystal Multiplexer Using Artificial Intelligence

Advanced Electromagnetics

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Advanced Electromagnetics 2025年第1期14卷 59-64页

作者： Karami, Pouya Yahya, Salah I. Palash, Behnam Chaudhary, Muhammad Akmal Assaad, Maher Parandin, Fariborz Roshani, Saeed Hazzazi, Fawwaz Roshani, Sobhan Department of Electrical Engineering Kermanshah Branch Islamic Azad University Kermanshah Iran Department of Communication and Computer Engineering Cihan University-Erbil Erbil44001 Iraq Department of Software Engineering Faculty of Engineering Koya University KOY45 Koya Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University 346 Ajman United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj11492 Saudi Arabia

In this paper, design and modeling of an all-optical 2×1 multiplexer based on 2D photonic crystals and artificial neural networks (ANNs) are presented. The proposed structure aims to maximize the difference between the output powers in logical states, which is critical for enhancing the system ability to distinguish between these states. In this study, an ANN model is employed to accurately predict the normalized output power of the designed photonic crystal multiplexer, providing a time-efficient alternative to conventional simulation methods for analyzing multiplexer behavior across various logical states. The results demonstrate significant improvements in signal separation and overall performance compared to previous works. Additionally, a detailed comparison of the normalized output power for different logic states is provided, highlighting the advantages of the proposed design. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Silicon photonics

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Mitigating Power System Oscillations in PMSGWind Integrated Grids Using Power Electronics and Inertia Control: A Comprehensive Review 16

Mitigating Power System Oscillations in PMSGWind Integrated ...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Sabo, Aliyu Jibia, Kabir Garba Shahinzadeh, Hossein Hayati, Mohammad Mohsen Abapour, Mehdi Gharehpetian, Gevork B. Department of Electrical and Electronic Engineering Malaysia Amirkabir University of Technology Tehran Polytechnic Department of Electrical Engineering Tabriz Iran Faculty of Electrical and Computer Engineering Tabriz Iran

ISBN: (纸本)9798331533946

The growing demand for reliable and sustainable energy has driven researchers and engineers to explore renewable energy sources to complement traditional power grids. Wind energy conversion systems (WECS), particularly those using permanent magnet synchronous generators (PMSG), are among the most widely adopted renewable energy solutions. Integrating WECS into traditional grids introduces challenges, including power system oscillations, especially during disturbances or faults. Inertia control and power electronics converters are pivotal in mitigating these oscillations and ensuring grid stability. This review investigates the design and optimization of damping controllers, emphasizing their integration with excitation systems to enhance the dynamic performance of PMSG-based wind integrated grids. Additionally, the role of power electronics in control strategies and inertia emulation for stable grid operation is discussed. The findings highlight the critical importance of advanced control measures in addressing the inherent uncertainties of renewable energy systems integrated with conventional grids. © 2025 IEEE.

关键词： Synchronous generators

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A Hybrid Switched Capacitor Converter Enabling Capacitive-Based Wireless Power Transfer for Battery Charging Applications 14

A Hybrid Switched Capacitor Converter Enabling Capacitive-Ba...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Sund, Jade Coday, Samantha Massachusetts Institute of Technology Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798331516116

This work investigates the feasibility of employing hybrid switched capacitor converters in wireless battery charging applications. A capacitively-isolated hybrid Dickson converter designed for use in low-power volume-constrained wireless power transfer applications is presented. The converter analysis, operation and design are detailed. Finally, a hardware prototype is presented and experimental results validate wireless power transfer, across varying load and gap distances. © 2025 IEEE.

关键词： Wireless charging

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Active and Reactive Power Flow Control of the Dual Active Bridge Converter 14

Active and Reactive Power Flow Control of the Dual Active Br...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Morris, Lauryn Francois, Thomas W. Saelens, Jonathan Oboreh-Snapps, Oroghene Fernandes, Arnold Uddarraju, Praneeth Strathman, Sophia A. Kimball, Jonathan W. Missouri University of Science and Technology Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331516116

The Dual Active Bridge (DAB) is a reliable and efficient converter capable of providing bi-directional power transfer and galvanic isolation. An ac-ac DAB can control both active and reactive power flow. The present work introduces a combined feedback/feed-forward current control system, utilizing the calculated and measured converter currents translated into the dq reference frame, to control the output power. The system was simulated in PLECS to demonstrate the control algorithm's ability to track the dq currents and provide the necessary output power. © 2025 IEEE.

关键词： Power electronics

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A Risk Identification Method for Power Operation Scenarios Using Image Caption and Semantic Text Similarity Analysis

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IEEE Transactions on Industrial Informatics 2025年第6期21卷 4488-4498页

作者： Li, Wei Ma, Fuqi Jia, Rong Wang, Bo Wang, Hongxia Alharbi, Abdullah M. Xi'an University of Technology School of Electrical Engineering Xi'an710048 China Wuhan University School of Electrical Engineering and Automation Wuhan430072 China University of Denver Department of Electrical and Computer Engineering DenverCO80208 United States Prince Sattam bin Abdulaziz University Department of Electrical Engineering College of Engineering Wadi ad-Dawasir11942 Saudi Arabia

Power production is a complex process that involves multiple interactions, which require rich semantic knowledge to categorize and evaluate. Utilizing high-level image understanding to accurately identify risks is significant in ensuring staff safety and grid stability. In this work, we propose image captioning and semantic text similarity analysis (IC-STSA), a risk identification method for complex power operation scenarios the employs image captioning and semantic text similarity analysis. First, image caption is used to transform an image at the site into the text that best describes the visual data. Next, a safe power production (SPP) knowledge base is established, after which semantic text similarity analysis is introduced to transform the risk identification into a text classification problem. By comparing the image caption information and the SPP knowledge base and identifying similarities, we achieve refined descriptions of operation scenarios and thereby risk identification. In the final step, actual images are used to verify the findings of the IC-STSA. Results show the proposed method performs well, generating accurate, targeted descriptions, and risk identification. © 2025 IEEE. All rights reserved.

关键词： Semantics Decoding Visualization Safety Production Feature extraction Electronic mail Vectors Long short term memory Knowledge based systems

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