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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Islam, Riadul Kabir, Shaharier Shufian, Abu Rabbi, Md Shahiduzzaman Akteruzzaman, Md Lamar University Dept. of Electrical and Computer Engineering Texas United States American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798331541125

The integration of renewable energy sources into the power grid presents significant challenges due to their inherent variability and unpredictability. Efficient management of these sources and the effective implementation of demand response strategies are crucial for maintaining grid stability and optimizing energy usage. This study presents a comprehensive evaluation of the performance of Ant Colony Optimization (ACO) in optimizing microgrid operations compared to other modern algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The primary metrics of analysis include energy cost, renewable energy utilization, grid stability, and demand response effectiveness. Through a detailed dataset analysis and algorithmic implementation, the results indicate that ACO consistently outperforms its counterparts across all key performance indicators. ACO achieved the lowest energy cost, maximized renewable energy utilization, and maintained superior grid stability and demand response effectiveness. These findings highlight the potential of ACO as a robust optimization technique for enhancing the efficiency and sustainability of microgrid systems. The study underscores the importance of selecting appropriate algorithms to meet the complex and dynamic challenges of modern energy systems, promoting a transition towards greener and more reliable energy infrastructure. © 2025 IEEE.

关键词： Demand response

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Real-Time Gaze Tracking for Online Examination

Real-Time Gaze Tracking for Online Examination

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bisht, Preyanshu Kumar, Suresh Netaji Subhas University of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798331508685

The exponential growth in online education has increased the demand for automated systems to ensure academic integrity during online examinations. A real-time proctoring system addresses this need by monitoring a student's eye gaze and head movements during an exam. This system leverages Dlib's pre-trained frontal face detector and 68-point facial landmark predictor to detect facial features and track the direction of eye movement and head position. By analyzing these metrics, the system can flag behaviors associated with potential cheating, such as looking away from the screen for extended periods. This paper discusses the design and implementation of the proctoring system, details the detection algorithms, and evaluates its effectiveness for real-time monitoring. © 2025 IEEE.

关键词： Online systems

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An electromagnetic indirect-driving scanning mirror for wide-field coaxial LiDAR applications 24

An electromagnetic indirect-driving scanning mirror for wide...

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MOEMS and Miniaturized Systems XXIV 2025

作者： Li, Shuangliang Wang, Di Song, Dezhen Zou, Jun Dept. of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States Dept. of Computer Science and Engineering Texas A&M University College StationTX77843 United States

ISBN: (数字)9781510685130

ISBN: (纸本)9781510685123

This paper reports an electromagnetic indirect-driving scanning mirror with an enlarged mirror plate (17mm × 17mm) supported by high-strength polymer hinges for wide-field coaxial LiDAR (Light Detection and Ranging) applications. An indirect-driving mechanism was developed to achieve large tilting angle through mechanical amplification, while maintaining a relatively high resonance frequency of the enlarged mirror plate. A prototype mirror was designed, fabricated, and tested. A Hall scan position sensor was integrated to monitor the pose of the mirror in real time. The testing results show a coupled resonance frequency of 54.9 Hz with an optical tilting angle of ±60°, corresponding to a field of view (FoV) of 120°. A wide-field coaxial LiDAR setup was also built based on the indirect-driving scanning mirror, and 2D imaging was demonstrated. © 2025 SPIE.

关键词： Mirrors

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Alehdaghi, Mahdi Shamsolmoali, Pourya Cruz, Rafael M. O. Granger, Eric Livia Ills Dept. of Systems Engineering Ets Montreal Canada University of York Dept. of Computer Science United Kingdom

ISBN: (纸本)9798331510831

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments11Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance. © 2025 IEEE.

关键词： Multi-task learning

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: electrical engineering and computer science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Optimizing EV Charging Infrastructure through Behavioral Analysis, Smart Charging, and V2G Integration

Optimizing EV Charging Infrastructure through Behavioral Ana...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Hossain, Md Ismail Anonto, Hasanur Zaman Hossain Emon, Md Mukter Nandi, Anup Shahriyar, Azad Shufian, Abu American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Lamar University Dept. of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331541125

The rapid adoption of electric vehicles (EVs) underscores the pressing need for optimized charging infrastructure to meet growing demands. This study investigates the integration of behavioral analysis, innovative charging strategies, and vehicle-to-grid (V2G) technology to improve EV infrastructure efficiency and grid stability. This research identifies key factors that influence charging demand and usage by examining user-specific charging patterns, including differences between private and fleet users. Smart charging and dynamic pricing models are explored to alleviate peak load stress, while V2G solutions enhance energy distribution during demand fluctuations. The findings emphasize the significance of flexible infrastructure planning, which includes Level 1, Level 2, and DC fast chargers strategically deployed to cater to diverse EV user needs. This approach offers a pathway to a sustainable, scalable charging ecosystem, promoting both user convenience and grid resilience. © 2025 IEEE.

关键词： Charging stations

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Protecting Battery Health During Charge and Discharge for Electric Vehicles

Protecting Battery Health During Charge and Discharge for El...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Anonto, Hasanur Zaman Hossain Emon, Md Mukter Nandi, Anup Islam, Sashyni Hossain, Md Ismail Shufian, Abu American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Lamar University Dept. of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798331541125

Lithium-ion (Li-ion) batteries are pivotal to electric vehicles performance, efficiency, and sustainability (EVs), offering high energy density, lightweight design, and the ability to endure hundreds of charge-discharge cycles. However, they are susceptible to suboptimal operating conditions, including overcharging, under-discharging, and exposure to extreme temperatures. These can significantly degrade battery capacity, shorten lifespan, and pose safety hazards such as thermal runaway. This paper investigates advanced battery management systems (BMS) and protection mechanisms to mitigate these risks. Through optimized charge/discharge strategies, real-time monitoring, and precise temperature regulation within safe operating limits (15°C to 45°C), the proposed methods demonstrate the potential to reduce battery degradation by up to 30% and extend overall battery life by 20%. Additionally, integrating solid-state battery technology is explored as a promising solution to enhance battery safety, energy density, and reliability. These advancements improve the long-term performance of EV batteries and contribute to the broader goal of making electric transportation systems safer, more sustainable, and more efficient for future mobility. © 2025 IEEE.

关键词： Solid-State Batteries

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Diversity Seeking Techniques for Red-Teaming Large Language Models

Diversity Seeking Techniques for Red-Teaming Large Language ...

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

作者： Lee, Seokhan Ku, Bonhwa Ko, Hanseok Dept. Computer Science and Engineering Korea University Seoul Korea Republic of Machine Learning Big Data Institute Korea University Seoul Korea Republic of Dept. Electrical and Computer Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798350368741

In this paper, we present new techniques for increasing the diversity of red-teaming prompts generated by automated machine learning-based methods, thereby enabling the discovery of more vulnerabilities in large language models. Using reinforcement learning to train models to output effective prompts for this task results in the models converging deterministically to a single output. Our first technique, which we term Defender, acts by blocking the reward signal for prompts that have already been discovered, thus making what was a stationary problem into a non-stationary problem that compels the reward maximizing algorithm to continually seek new prompts. Our second technique, Teamplay, trains two prompt generation models in tandem and adds the KL divergence between them to the reward in order to make them search in disparate regions of the space of prompts. Our techniques are shown experimentally to increase the effectiveness and diversity of prompts generated by existing reinforcement learning baselines. © 2025 IEEE.

关键词： AI safety large language models red-teaming reinforcement learning toxicity

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Exploring the Potential of MANETs: A Study on Raspberry Pi and UAV Integration 3

Exploring the Potential of MANETs: A Study on Raspberry Pi a...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Archana, B. Mahalakshmi, A. Revathi, K. Ashok Kumar, K. Hema, A.B. Dept. of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Dept. of Information Technology SNS College of Engineering Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

This research investigates the feasibility of utilizing Mobile Ad Hoc Networks (MANETs) in conjunction with Raspberry Pi-equipped Unmanned Aerial Vehicles (UAV) swarms. The primary objective is to overcome the limitations of traditional communication infrastructures in dynamic and decentralized environments. Specifically, we address the challenge of establishing reliable and efficient communication links within a swarm of UAVs engaged in various applications, including disaster response, military reconnaissance, and environmental monitoring. By integrating Raspberry Pi devices with UAVs, we aim to create a self-organizing communication network that enables seamless data transmission, coordinated swarm operations, and enhanced mission effectiveness. The research explores different synchronization strategies, including master-slave and decentralized approaches, to optimize swarm coordination. Furthermore, we investigate the potential of incorporating advanced technologies such as machine learning, computer vision, and swarm intelligence to further enhance the autonomy, adaptability, and overall performance of the UAV swarm. © 2025 IEEE.

关键词： Mobile ad hoc networks

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Advanced Modeling Technique of Class-E Inverter Considering Low Ron of eGaN FETs and Different Design Procedures 14

Advanced Modeling Technique of Class-E Inverter Considering ...

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

作者： Palmal, Manas Choi, Jungwon University of Washington Dept. of Electrical and Computer Engineering Seattle United States

ISBN: (纸本)9798331516116

Class-E inverters have been used extensively in high-frequency applications with eGaN (enhancement-mode Gallium Nitride) devices due to their high switching speed and low on-state resistance (in the order of a few mΩ). However, the developed generalized averaging model to control the class-E inverter in prior literature works only for a class-E inverter with a high-Q resonant output network and high on-state resistance (Ron ≥ 0.5Ω) switches due to the presence of Ron in the denominator in their state equations. Therefore, this paper first eliminates Ron from the denominator of the state equations by incorporating u(t) appropriately in the state equations, where u(t) is the complementary function of the gate drive signal. Subsequently, leveraging the developed state equations, we propose a model suitable for a class-E inverter with a high-Q resonant output network. Lastly, due to the high distortion of the output current and the voltage across the eGaN FET, we develop another model for the class-E inverter with a DC-blocking output capacitor by adding more harmonic components in state variables without increasing complexity. Our simulation results from PLECS and experimental results with the 5MHz eGaN FET-based class-E inverter are provided to validate the efficacy of the proposed models. © 2025 IEEE.

关键词： Equations of state

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