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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Bcgovic, Miroslav Ghrayeb, Ali Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States Hamad Bin Khalifa University Information and Computing Technology Division Doha Qatar University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States

ISBN: (纸本)9798331541125

With the increasing complexity of power systems and their vulnerability to cyber-attacks, robust detection mechanisms are crucial for ensuring the grid's transient and steady-state stability and resiliency. This study introduces an efficient deep learning framework to detect false data injection attacks (FDIAs) in power systems. Leveraging the MATPOWER environment, we simulate a realistic power grid scenario to generate datasets comprising normal operations and various FDIA scenarios. Utilizing advanced deep learning techniques, including convolutional neural networks enhanced with channel attention mechanisms and transformer encoders, our model processes sequential power system data to discern subtle attack signatures from normal fluctuations. The proposed methodology employs a stratified k-fold cross-validation approach on resampled datasets to address class imbalance, ensuring robust model evaluation. The model's performance is meticulously assessed using accuracy metrics and confusion matrices, demonstrating its capability to effectively identify and differentiate between normal and compromised states in power systems. Results on the IEEE 39-bus and 300-bus systems highlight that this approach significantly enhances detection capabilities, achieving high accuracy and practical detection times of less than 2 milliseconds for the IEEE 39-bus system, which addresses the real-time requirements. These promising results suggest that such models can be pivotal for preemptive security measures and real-time monitoring of power grids. © 2025 IEEE.

关键词： Convolutional neural networks

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Advancing Federated Learning: A Comprehensive Solution for Model Aggregation, Heterogeneity, Privacy, and Security

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SN computer Science 2025年第5期6卷 1-36页

作者： Bari, Bifta Sama Yelamarthi, Kumar Department of Electrical and Computer Engineering College of Engineering Tennessee Tech University Cookeville 38505 TN United States

Secure vehicular communication is one of the challenges that is crucial for Intelligent Transportation Systems (ITS). Although Federated Learning (FL) enhances data privacy compared to Centralized Learning (CL), it faces challenges with data heterogeneity and resource allocation. To address these challenges, the Hybrid Federated-Centralized Learning (HFCL) framework improves efficiency and reduces communication overhead but still lacks comprehensive solutions to existing key issues including FL model aggregation, client heterogeneity, privacy-preserving communication, and enhanced security protocols. This paper proposes a unified HFCL framework incorporating the existing key issues that aims to develop and analyze model aggregation ensuring equitable client contributions, secure communications, and protect against various security threats. The implementation with FedAvg aggregation algorithm on diverse clients and datasets ensures robust and efficient communication, while Differential Privacy (DP) in HFCL enhances security and resilience against adversaries including poisoning attacks. Performance analysis reveals that HFCL maintains high accuracy and stability across varying learning rates and client configurations. Notably, the proposed unified privacy preserving HFCL achieves nearly 99.15% accuracy on the MNIST dataset and 68.4% on the CIFAR10 dataset, demonstrating its efficacy in different scenarios. By addressing the existing critical challenges, the proposed framework enhances the scalability, performance, and security of distributed ML systems, with a primary focus on vehicular communication. It highlights the significant advantages of HFCL for data processing and decision-making in applications like collision avoidance and autonomous driving. Additionally, the framework’s applicability could be extended to healthcare and finance, enabling secure and efficient analysis of sensitive data. © The Author(s), under exclusive licence to Springer Nature Singapore P

关键词： Centralized machine learning Convolutional neural networks Edge computing Federated learning Vehicular communication

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Kernel-Based Anomaly Detection Using Generalized Hyperbolic Processes

Kernel-Based Anomaly Detection Using Generalized Hyperbolic ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bourigault, Pauline Mandic, Danilo P. Department of Computing Imperial College London United Kingdom Department of Electronic and Electrical Engineering Imperial College London United Kingdom

ISBN: (纸本)9798350368741

We present a novel approach to anomaly detection by integrating Generalized Hyperbolic (GH) processes into kernel-based methods. The GH distribution, known for its flexibility in modeling skewness, heavy tails, and kurtosis, helps to capture complex patterns in data that deviate from Gaussian assumptions. We propose a GH-based kernel function and utilize it within Kernel Density Estimation (KDE) and One-Class Support Vector Machines (OCSVM) to develop anomaly detection frameworks. Theoretical results confirmed the positive semi-definiteness and consistency of the GH-based kernel, ensuring its suitability for machine learning applications. Empirical evaluation on synthetic and real-world datasets showed that our method improves detection performance in scenarios involving heavy-tailed and asymmetric or imbalanced distributions. https://***/paulinebourigault/GHKernelAnomalyDetect. © 2025 IEEE.

关键词： anomaly detection generalized hyperbolic processes kernel

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VITMST++: Efficient Hyperspectral Reconstruction Through Vision Transformer-Based Spatial Compression

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2025年 6卷 398-404页

作者： Silveira, Ana C. Caznok do Carmo, Diedre S. Ueda, Lucas H. Fantinato, Denis G. Costa, Paula D. P. Rittner, Leticia Universidade Estadual de Campinas Department of Computer Engineering and Automation School of Electrical and Computer Engineering Campinas13083-852 Brazil Universidade Estadual de Campinas CPQD Recod.AI Institute of Computing Campinas13083-852 Brazil

Hyperspectralchannel reconstruction transforms a subsampled multispectral image into hyperspectral imaging, providing higher spectral resolution without a dedicated acquisition hardware and camera. Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (MST++) is a state-of-the-art channel reconstruction technique, but it faces memory limitations for high spatial-resolution images. In this context, we introduced VITMST++, a novel architecture incorporating Vision Transformer embeddings for spatial compression, multi-resolution image context, and a custom channel-weighted loss. Developed for the ICASSP 2024 HyperSkin Challenge, VITMST++ outperforms the state-of-the-art MST++ in both performance and computational efficiency in channel reconstruction. In this work, we perform a deeper analysis on the main aspects of VITMST++ efficiency, quantitative performance, and generalization to other datasets. Results show that VITMST++ achieves similar values of SAM and SSIM hyperspectral reconstruction metrics when compared to state-of-the-art methods, while consuming up to three fold less memory and needing up to 10 times fewer multiply-add operations. © 2020 IEEE.

关键词： Image compression

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Efficient Path Selection Design for Large Scale LEO Satellite Constellations Using Graph Embedding-Based Reinforcement Learning

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IEEE Transactions on Network Science and engineering 2025年第3期12卷 2007-2020页

作者： Kang, Yuhan Zhu, Yifei Wang, Dan Han, Zhu University of Houston Electrical and Computer Engineering Department HoustonTX77004 United States Shanghai Jiao Tong University UM-SJTU Joint Institute Shanghai200240 China The Hong Kong Polytechnic University Department of Computing Hong Kong University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The rapid expansion of large-scale Low Earth Orbit (LEO) satellite constellations marks a new phase in global connectivity and communication. However, efficient path selection among numerous satellites connected by inter-satellite links (ISL) in such dynamic networks poses substantial challenges. This paper introduces a novel Path Selection Mechanism (PSM-LEO) utilizing Graph Embedding-Based Reinforcement Learning (GERL). Our GERL-based PSM-LEO method employs Graph Embedding (GE) to model the satellite network in a simplified low-dimensional space, simplifying the analysis of complex satellite relationships. Combining Reinforcement Learning (RL) with GE allows for dynamic adaptation of path choices in response to dynamic network conditions and communication needs. To the best of our knowledge, we are the first to propose GERL for PSM-LEO. We evaluate our method's performances through simulations in the Ansys Systems Tool Kit (STK), focusing on diverse LEO scenarios. Our results reveal that our approach surpasses several benchmarks in convergence speed, end-to-end latency, and energy consumption, demonstrating enhanced data transfer efficiency, reduced latency, and improved reliability. Additionally, we assess the scalability of the proposed method by analyzing its performance with increasing satellite constellation sizes. Our results confirm the framework's high scalability, demonstrating its suitability for addressing path selection challenges in future larger satellite networks. © 2013 IEEE.

关键词： Graph embeddings

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Dual AAV Cluster-Assisted Maritime Physical-Layer Secure Communications via Collaborative Beamforming

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IEEE Internet of Things Journal 2025年第9期12卷 12589-12607页

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Du, Hongyang Niyato, Dusit Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong

Autonomous aerial vehicles (AAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of AAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the AAV relay for remote maritime communications. However, due to the open nature of AAV channels, security issue requires special consideration. This article proposes a dual AAV cluster-assisted system via CB to achieve physical-layer security in maritime wireless communications. Specifically, one AAV cluster forms a maritime AAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other AAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multiobjective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of AAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method. © 2014 IEEE.

关键词： Multiobjective optimization

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Diamagnetically Levitated and Trapped Graphite Mechanical Resonators 38

Diamagnetically Levitated and Trapped Graphite Mechanical Re...

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Wang, Yunong Gage, Alexander Lee, Jaesung Feng, Philip X.-L. University of Florida Herbert Wertheim College of Engineering Department of Electrical and Computer Engineering GainesvilleFL32611 United States University of Central Florida College of Engineering & Computer Science Department of Electrical and Computer Engineering OrlandoFL32816 United States

ISBN: (纸本)9798331508890

We report on studying diamagnetic levitation and rigid body resonances of millimeter- to centimeter-scale trapped graphite mechanical resonators, by combining theoretical analysis with experimental demonstrations. Harnessing the strong diamagnetic susceptibility of pyrolytic graphite, we demonstrate stable levitation of square graphite thin plates with varying dimensions and masses above permanent magnets, without the need of active control. The resonance motions are excited by dielectric gradient forces and detected via an ultrasensitive optical interferometry system at room temperature. We observe rigid body resonance modes with frequencies between 25 Hz and 50 Hz and with their quality (Q) factors from 30 to 70. The Q factors are primarily constrained by air damping and eddy current damping. This work represents an initial step toward developing high-stability, anchor-less levitating resonant systems with large masses, paving the way for high-performance resonant sensors with minimal energy dissipation and exceptional thermal and mechanical isolation with very small power consumption. © 2025 IEEE.

关键词： Damping

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Optimization of Power Control for Autonomous Hybrid Electric Vehicles With Flexible Power Demand

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Journal of Autonomous Vehicles and Systems 2025年第2期5卷 021002页

作者： Kargar, Mohammadali Song, Xingyong Department of Mechanical Engineering College of Engineering Texas A&M University College StationTX77843 United States Department of Engineering Technology and Industrial Distribution The Department of Mechanical Engineering The Department of Electrical and Computer Engineering College of Engineering Texas A&M University College StationTX77843 United States

Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics and powertrain dynamics, with one key aspect often overlooked. This aspect, known as flexible power demand, recognizes that the powertrain control system does not necessarily have to precisely match the power requested by the vehicle motion controller at all times. Leveraging this feature can lead to control designs achieving improved fuel economy by adding an extra degrees-of-freedom to the powertrain control while maintaining safety and drive comfort. The present research investigates the use of an approximate dynamic programming (ADP) approach to develop a powertrain controller, which takes into account the flexibility in power demand within the ADP framework. The concept of reachable sets is incorporated into the ADP framework to ensure safety, improve ride comfort, and enhance the accuracy of the optimization solution. The formulation is based on an autonomous hybrid electric vehicle, while the methodology can also be applied to other types of vehicles. It is also found that necessary customization of the ADP algorithm is needed for this particular control problem to prevent convergence issues. Finally, a case study is presented to evaluate the effectiveness of flexible power demand, as addressed by the ADP method. The experiment demonstrates a 14.1% improvement in fuel economy compared to a scenario without flexible power demand. © 2024 by ASME.

关键词： Dynamic programming

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An Efficient Framework for Multi-DER Integration in Distribution Networks With Time-Series Demand Flexibility

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IEEE Access 2025年 13卷 83828-83839页

作者： Massaoudi, Mohamed Davis, Katherine R. Haque, Khandaker Akramul Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station 77840 TX United States

The increasing penetration of renewable energy sources (RESs) has transformed power system operations. However, balancing supply and demand is more challenging due to the inherent variability of RESs. This paper presents an efficient framework that integrates demand flexibility, RESs, and energy storage in distribution systems to enhance distribution system performance. The study implements a detailed time-series power flow analysis to investigate the impact of distributed energy resources (DERs) on system performance over a 24-hour period. The simulations incorporate a modified IEEE 123-bus network with two PV systems, flexible loads, and a 300 kW/1200 kWh battery. Additionally, the IEEE 8500-node distribution feeder integrates higher-rated PV, wind generators, and a 500 kW/2000 kWh battery to evaluate grid performance under diverse operational conditions. The battery storage system provides essential grid support through strategic charging during high PV generation and discharging during peak demand periods. The simulation results demonstrate robust voltage regulation and effective demand response throughout the feeder despite varying generation and load conditions. The flexible loads effectively respond to system conditions, varying between 23 to 82 kVA. This study demonstrates the viability of coordinated DER operations and their impact on modern distribution networks. © 2013 IEEE.

关键词： Demand flexibility demand response distribution networks optimal power flow (OPF) photovoltaic systems

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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