American Journal of Electrical Engineering and Technology
type: channel
description: The American Journal of Electrical and Electronic Engineering is an open-access, peer-reviewed journal dedicated to publishing high-quality research in the fields of electrical and electronic engineering. It serves as a platform for scholars and professionals to share innovative methodologies, applications, and case studies that advance the understanding and practice of these disciplines.
publisher: INTERNATIONAL STUDY COUNSELOR
language: en
publication name: American Journal of Electrical Engineering and Technology
issn: 2688-1802
copyright: Articles in the American Journal of Electrical Engineering and Technology (AJEET) are distributed under the CC BY 4.0 license, allowing users to share and modify the material for any purpose, including commercial use. Proper attribution, a license link, and indication of changes are required, and no additional restrictions may be applied. Authors agree to these open-access terms upon submission.
items: Sparse View CT Reconstruction with Projection Consistency Constraints
items: Security Knowledge Distillation for Enhancing LLM Safety in Code Generation
items: Joint Energy-Delay Optimization of WuR-Assisted MAC Protocols in Energy Harvesting IoT
items: Multidisciplinary Monte Carlo Robust Optimization for AUVs under Polar Ice Multi-Source Uncertainties
items: Diffusion Models for Automated Standard Cell Layout Generation in Advanced Node Design
items: Cascading Failure Modeling and Rapid Recovery of Coupled WaterPower Networks under Earthquakes
items: Multi Sensor Pedestrian Re Identification with Uncertainty Guided Fusion of Vision Language and LiDAR
items: Design, Modeling, and Performance Analysis of an Enhanced Doubly Fed Reluctance Generator (DFRG) for Efficient and Reliable Renewable Energy Systems
items: Selective Modality Weighting for Robust Anomaly Identification in Noisy Observability Environments
items: A Dynamic Policy Engine for Access Control in Zero Trust Architecture
items: Fairness-Aware Policy Gradient Methods for Mitigating Demographic Bias in AI-Driven Student Performance Prediction
items: Non-Equilibrium Dynamics in Optical Matter Systems under Variable Light Fields: A Computational Fluid Dynamics Perspective
items: IoT-Based Real-Time Monitoring Systems for Electrical Distribution Networks: Enhancing Reliability and Operational Efficiency
items: Advanced Control Algorithms for Microgrids and Smart Grids: Optimization, Stability, and Integration Challenges
items: The Future of Electrical Engineering in the Aerospace Industry
items: Wireless Power Transfer in Electric Vehicles: A Comprehensive Review
items: The Role of Electrical Engineering in Artificial Intelligence Hardware
items: A Transformer-based Approach to Real-Time Task Scheduling in Latency-Critical Edge Systems
items: Modern Techniques for Power System Optimization and Control
items: Artificial Intelligence in Predictive Fault Diagnosis for Electrical Networks
items: Design Considerations for Smart Cities’ Electrical Power Infrastructure
items: Impact of High-Efficiency Electrical Machines in Industry 4.0
items: Developments in Electrical Engineering for Energy Efficiency in Manufacturing
items: The Impact of Energy Storage on Electrical System Stability
items: Power System Management Using Distributed Ledger Technologies
items: Advanced Electrical Materials for High-Temperature Superconducting Applications
items: Electrical System Monitoring with Big Data and Artificial Intelligence
items: Energy Efficiency Improvements in Large-Scale Electrical Power Plants
items: Renewable Energy Systems: Design and Optimization for Urban Environments
items: Advanced Control Techniques for Electric Power Distribution Systems

Sparse View CT Reconstruction with Projection Consistency Constraints
type: item
description: Sparse-view CT reduces radiation exposure but increases streak artifacts due to insufficient projection angles. Motivated by convolution–self-attention denoising architectures such as CTLformer, this paper proposes a reconstruction framework that uses convolution–attention priors to regularize image updates while enforcing projection-domain consistency through an unrolled optimization scheme. The model alternates between a learned denoising block and a data-consistency step based on the forward projection operator. Experiments are conducted on two sparse-view CT benchmarks totaling 36,000 paired slices, with view counts ranging from 30 to 90. Comparisons against FBP, TV-regularized iterative reconstruction, RED-CNN post-processing, and transformerbased sparse-view models show PSNR gains of 1.2–2.0 dB and SSIM improvements of 0.015–0.028 under 60-view settings, with reduced streak intensity near high-contrast boundaries.
subject: Articles
  topic: section
subject: Sparse-view CT
  topic: keywords
subject: unrolled reconstruction
  topic: keywords
subject: data consistency
  topic: keywords
subject: convolution–attention priors
  topic: keywords
subject: medical imaging
  topic: keywords
creator: James R. Walker
creator: Emily K. Thompson
creator: Daniel M. Chen
rights: Copyright (c) 2026 James R. Walker, Emily K. Thompson, Daniel M. Chen (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 95
ending page: 101
doi: 10.71465/ajeet3631

Security Knowledge Distillation for Enhancing LLM Safety in Code Generation
type: item
description: We propose a distillation framework that transfers secure coding expertise into a compact model trained using expert demonstrations, static-analysis labels, and secure patterns. The distilled model (7B parameters) achieves 83% security compliance, nearly matching GPT-4’s 89% performance while requiring significantly less compute. Evaluated across CWE-20, CWE-78, CWE-89, the distilled model reduces injection vulnerabilities by 62% relative to baseline models. This work demonstrates that targeted distillation can teach smaller models to respect security best practices.
subject: Articles
  topic: section
subject: knowledge distillation
  topic: keywords
subject: secure coding
  topic: keywords
subject: vulnerability categories
  topic: keywords
subject: LLM compression
  topic: keywords
subject: CWE mitigation
  topic: keywords
creator: Daniel Thompson
creator: Sarah Mitchell
creator: Jason Li
rights: Copyright (c) 2026 Daniel Thompson, Sarah Mitchell, Jason Li (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 88
ending page: 94
doi: 10.71465/ajeet3630

Joint Energy-Delay Optimization of WuR-Assisted MAC Protocols in Energy Harvesting IoT
type: item
description: The proliferation of the Internet of Things has precipitated a critical need for sustainable energy management strategies, particularly in devices deployed in remote or inaccessible environments. Energy Harvesting has emerged as a promising solution to extend theoperational lifetime of such networks indefinitely; however, the stochastic nature of environmental energy sources introduces significant instability. Simultaneously, Wake-up Radio technology offers a pathway to minimize idle listening, which is the primary source of energy wastage in traditional Medium Access Control protocols. This paper presents a comprehensive theoretical and simulation-based analysis of a novel Medium Access Control protocol that integrates Wake-up Radio hardware with Energy Harvesting capabilities. We formulate a joint optimization problem that dynamically adjusts the wake-up intervals and transmission parameters based on the instantaneous energy state and data buffer occupancy. The primary objective is to balance the conflicting requirements of minimizing packet delivery latency and maximizing energy neutrality. Through rigorous system modeling and extensive simulations, we demonstrate that the proposed approach significantly outperforms static dutycycling mechanisms. The results indicate that our adaptive protocol achieves a superior Pareto optimal frontier, ensuring reliable data delivery while maintaining the energy neutrality of the sensor nodes under variable harvesting conditions.
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: Energy Harvesting
  topic: keywords
subject: Medium Access Control
  topic: keywords
subject: Wake-up Radio
  topic: keywords
creator: Liang Zhao
creator: Christopher Bennett
rights: Copyright (c) 2026 Liang Zhao, Christopher Bennett (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 79
ending page: 87
doi: 10.71465/ajeet3604

Multidisciplinary Monte Carlo Robust Optimization for AUVs under Polar Ice Multi-Source Uncertainties
type: item
description: The exploration of polar regions has become increasingly critical due to rapid climate change and the strategic significance of Arctic resources. Autonomous Underwater Vehicles (AUVs) serve as the primary modality for data acquisition in these harsh, ice-covered environments. However, the operational reliability of AUVs is severely compromised by multi-source uncertainties, including complex under-ice hydrodynamics, acoustic communication failures, variable sea ice draft, and stochastic sensor errors. Traditional deterministic optimization methods often fail to account for these variabilities, leading to designs that are optimal in theory but fragile in practice. This paper proposes a comprehensive Multidisciplinary Design Optimization (MDO) framework integrated with Monte Carlo simulations to enhance the robustness of AUVs operating under polar ice. We define a stochastic coupling mechanism that links hydrodynamic, structural, energy, and navigation subsystems. By embedding Monte Carlo sampling within the optimization loop, we evaluate the probability of mission success and enforce reliability constraints against environmental and systemic uncertainties. The results demonstrate that the proposed robust optimization approach significantly reduces the variance in mission performance metrics compared to deterministic baselines. We provide a detailed analysis of the trade-offs between optimality and reliability, offering a theoretical foundation for the next generation of polar-class AUVs.
subject: Articles
  topic: section
subject: Autonomous Underwater Vehicles
  topic: keywords
subject: Robust Optimization
  topic: keywords
subject: Monte Carlo Simulation
  topic: keywords
subject: Polar Engineering
  topic: keywords
creator: John Smith
creator: Emily Davis
creator: Robert Johnson
creator: Sarah Wilson
rights: Copyright (c) 2026 John Smith, Emily Davis, Robert Johnson, Sarah Wilson (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 69
ending page: 78
doi: 10.71465/ajeet3603

Diffusion Models for Automated Standard Cell Layout Generation in Advanced Node Design
type: item
description: Standard cell layout generation in advanced semiconductor nodes presents escalating complexity due to stringent design rule constraints, multi-patterning lithographic requirements, and severely diminishing layout flexibility at sub-5nm feature sizes. This paper proposes a diffusion model-based generative framework for automating the synthesis of standard cell layouts at 5nm and below technology nodes, addressing a critical bottleneck in modern electronic design automation (EDA) flows. The proposed approach employs a denoising diffusion probabilistic model (DDPM) conditioned on cell functionalspecifications, parasitic extraction targets, pin accessibility requirements, and design rule check (DRC) parameter vectors extracted directly from process design kits (PDKs). Inspired by the forward-reverse diffusion process originally demonstrated on structured 2D data distributions — wherein a learned reverse Markov chain reconstructs complex geometric patterns from Gaussian noise — the framework applies this principle to recover DRC-compliant multi-layer layout geometries through an iterative denoising trajectory guided by cell-specific conditioning signals. A U-Net denoising backbone with multi-scale encoder-decoder structure and cross-attention conditioning injection captures both local geometric precision and global layout topology simultaneously,enabling the generative model to produce diverse yet constraint-compliantlayout solutions. A DRC-aware auxiliary training loss explicitly penalizes constraint violations at each denoising step, directly embedding rule compliance into the learned score function. Experimental evaluation on an industry-representative benchmark suite of 48 standard cell types atthe 5nm technology node demonstrates that the proposed method achieves a DRC violation rate reduction of 34.2% compared to baseline GAN approaches, generates valid layouts for 98.3% of benchmark cells, and produces performance-power-area (PPA) metrics within 8.3% of reference …
subject: Articles
  topic: section
subject: diffusion models
  topic: keywords
subject: standard cell layout
  topic: keywords
subject: advanced node design
  topic: keywords
subject: electronic design automation
  topic: keywords
subject: denoising diffusion probabilistic models
  topic: keywords
subject: design rule check
  topic: keywords
creator: Yutong Shen
rights: Copyright (c) 2026 Yutong Shen (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 55
ending page: 68
doi: 10.71465/ajeet3593

Cascading Failure Modeling and Rapid Recovery of Coupled WaterPower Networks under Earthquakes
type: item
description: The interdependence between critical infrastructure systems, particularly power grids and water distribution networks, has created a complex landscape of vulnerability where failures can propagate across domain boundaries. This paper presents a comprehensive modeling framework for analyzing cascading failures in coupled water-power networks subjected to seismic hazards and proposes a rapid recovery strategy to mitigate systemic losses. We first establish a mathematicallyrigorous interdependent network model that accounts for the physical and functional coupling between electric power substations and water pumping facilities. Unlike traditional topological approaches, this study integrates hydraulic and power flow constraints to realistically simulate the propagation of failure following an earthquake. Subsequently, we introduce a resilience-oriented recovery optimization algorithm designed to prioritize the restoration of critical nodes that facilitate the reactivation of the largest connected components. Simulation results demonstrate that the proposed recovery strategy significantly outperforms random and static repair protocols, reducing the duration of service outages and minimizing unserved demand. The findings underscore the critical importance of synchronized restoration planning in managing modern infrastructure resilience.
subject: Articles
  topic: section
subject: Interdependent Networks
  topic: keywords
subject: Cascading Failures
  topic: keywords
subject: Seismic Resilience
  topic: keywords
subject: Infrastructure Recovery
  topic: keywords
creator: Zainab Mahmoud
creator: Sarah Mitchell
rights: Copyright (c) 2026 Zainab Mahmoud, Sarah Mitchell (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 47
ending page: 54
doi: 10.71465/ajeet3592

Multi Sensor Pedestrian Re Identification with Uncertainty Guided Fusion of Vision Language and LiDAR
type: item
description: In autonomous driving, pedestrian appearance cues from cameras can be unreliable under glare, fog, or low illumination, while LiDAR offers complementary geometry signals. Building on uncertainty-aware CLIP-based modal modeling, this work introduces a fusion framework that integrates vision–language embeddings with LiDAR-derived shape descriptors using uncertainty gated feature mixing. The gating module increases reliance on LiDAR when visual uncertainty rises and maintains vision–language dominance in clear conditions. Experiments are conducted on multi sensor datasets covering 210,000 paired camera–LiDAR observations and 26,000 identities. Baselines include camera-only ReID (OSNet, TransReID), CLIP-based ReID, and naive concatenation fusion. The proposed method improves overall mAP by 3.3%–4.8% and yields larger gains of 6.0%–7.5% in low-light subsets, while adding less than 8% inference latency compared with camera-only models.
subject: Articles
  topic: section
subject: Multi-sensor fusion
  topic: keywords
subject: LiDAR
  topic: keywords
subject: pedestrian re-identification
  topic: keywords
subject: uncertainty gating
  topic: keywords
subject: autonomous driving
  topic: keywords
creator: Carlos Martínez
creator: Lucía Fernández
creator: Javier Ruiz
rights: Copyright (c) 2026 Carlos Martínez, Lucía Fernández, Javier Ruiz (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 40
ending page: 46
doi: 10.71465/ajeet3573

Design, Modeling, and Performance Analysis of an Enhanced Doubly Fed Reluctance Generator (DFRG) for Efficient and Reliable Renewable Energy Systems
type: item
description: This study investigates the performance and control strategies of an Enhanced Doubly Fed Reluctance Generator (DFRG) system, with the aim of improving torque generation, efficiency, and voltage stability in renewable energy applications. The motivation stems from limitations in traditional generators such as the Permanent Magnet Synchronous Generator (PMSG) and Doubly Fed Induction Generator (DFIG), including higher costs, reduced reliability under transient loads, and limited efficiency across operating ranges. The study addresses these challenges by employing analytical modeling and simulation to analyze the magnetic and dynamic behavior of the DFRG system. A comprehensive methodology was implemented using MATLAB/Simulink to simulate and evaluate the inductance variation, torque production mechanisms, stator voltage dynamics, vector and direct torque control strategies, and battery state-of-charge under hybrid scenarios. The study also conducted a comparative assessment of efficiency, power quality (THD), cost, reliability, and voltage regulation under diverse load conditions. Quantitatively, the DFRG system demonstrated a smooth inductance variation between 0.05 H and 0.15 H, producing torque peaks up to ±884 Nm with stator current at 420 A. Under vector control, torque reached 45 Nm, while DTC yielded 38 Nm, showing robust response. Voltage peaks were observed near 220 V, with flux linkage around 63 Wb. Efficiency under rated load was 92%, and up to 97% under high-speed, full-load conditions, surpassing the PMSG’s 96% peak. In fault conditions, efficiency dropped to 0%, indicating critical reliability issues in abnormal operations. The DFRG also maintained a THD of 2.1% under rated load, rising to 28.7% during faults. Voltage regulation was within 4.17%, confirming voltage stability. The hybrid system simulation showed effective energy cycling with a battery SOC increase from 50% to nearly 100%, and a discharge down to 10% within a 24-hour period. Cost and relia…
subject: Articles
  topic: section
subject: Doubly Fed Reluctance Generator (DFRG)
  topic: keywords
subject: Efficiency
  topic: keywords
subject: Load
  topic: keywords
subject: Renewable Energy Systems
  topic: keywords
subject: Vector Control
  topic: keywords
subject: Direct Torque Control
  topic: keywords
creator: Emmanuel C. Obuah
creator: B. A. Wokoma
creator: Oto-Obong John
creator: Peace Biragbara
creator: Ubong J. Joseph
rights: Copyright (c) 2026 Emmanuel C. Obuah, B. A. Wokoma, Oto-Obong John, Peace Biragbara, Ubong J. Joseph (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 40
ending page: 57
doi: 10.71465/ajeet3567

Selective Modality Weighting for Robust Anomaly Identification in Noisy Observability Environments
type: item
description: Anomaly detection in multimodal sensor environments presents significant challenges when data quality varies across different modalities due to environmental noise, sensor degradation, or communication disruptions. This paper proposes a Selective Modality Weighting (SMW) framework that dynamically adjusts the contribution of each sensory modality based on its reliability and information quality for robust anomaly identification. The framework integrates an adaptive attention mechanism that learns modality-specific confidence scores during training and applies selective weighting during inference to suppress unreliable data streams while amplifying trustworthy signals. Our methodology combines deep autoencoder architectures with cross-modal consistency validation to establish baseline normality patterns across heterogeneous sensor types.Experimental evaluations on industrial monitoring datasets demonstrate that SMW achieves superior anomaly detection accuracy compared to static fusion approaches, particularly in scenarios with simulated sensor noise levels ranging from 10% to 40%. Results indicate a 12.7% improvement in F1-score over baseline multimodal methods when operating under high-noise conditions. The proposed framework offers a practical solution for deployment in real-world environments where sensor reliability cannot be guaranteed, contributing to more resilient anomalydetection systems for critical infrastructure monitoring, industrial quality control, and autonomous system safety.
subject: Articles
  topic: section
subject: Multimodal anomaly detection
  topic: keywords
subject: selective modality weighting
  topic: keywords
subject: sensor fusion
  topic: keywords
subject: adaptive attention mechanism
  topic: keywords
subject: robust detection
  topic: keywords
subject: noisy environments
  topic: keywords
creator: Haolin Zheng
creator: Mingrui Cao
creator: Andrew Keller
rights: Copyright (c) 2026 Haolin Zheng, Mingrui Cao, Andrew Keller (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 28
ending page: 39
doi: 10.71465/ajeet3566

A Dynamic Policy Engine for Access Control in Zero Trust Architecture
type: item
description: Over-the-air (OTA) updates in aviation and industrial systems need reliable and secure access control to stop unauthorized actions during software updates. This study developed a zero-trust OTA access control framework using the Open Policy Agent (OPA) and a context-based verification module. The system checked each access request in real time using user, device, and session data. Tests were done in both aviation and industrial cloud setups with more than 10,000 access requests under different load conditions. The results showed an average policy delay of 4.3 ms, a false rejection rate of 0.6%, and an 85% decrease in unauthorized access compared with a standard role-based control model. These results show that the proposed zero-trust system improves OTA security while keeping low delay and steady operation. The framework can be used in secure software updates for safety-critical and industrial systems, but further large-scale and long-term testing is needed to confirm its stability and performance.
subject: Articles
  topic: section
subject: zero trust
  topic: keywords
subject: access control
  topic: keywords
subject: continuous verification
  topic: keywords
subject: OTA security
  topic: keywords
subject: Open Policy Agent
  topic: keywords
subject: industrial network
  topic: keywords
subject: aviation system
  topic: keywords
creator: Yuna Kim1
creator: Carlos M. Ortega2
creator: Sophie L. Nguyen3
creator: Elena V. Petrova4
rights: Copyright (c) 2026 Yuna Kim1, Carlos M. Ortega2, Sophie L. Nguyen3, Elena V. Petrova4 (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 21
ending page: 27
doi: 10.71465/ajeet3553

Fairness-Aware Policy Gradient Methods for Mitigating Demographic Bias in AI-Driven Student Performance Prediction
type: item
description: The proliferation of artificial intelligence systems in educational contexts has raised critical concerns regarding algorithmic fairness and demographic bias in student performance prediction. Traditional machine learning models, while achieving high predictive accuracy, often perpetuate or amplify existing disparities across gender, ethnicity, and socioeconomic backgrounds. This paper introduces a novel fairness-aware policy gradient framework that integrates demographic parity constraints into reinforcement learning architectures for student performance forecasting. Our approach employs constrained policy optimization to balance prediction accuracy with fairness metrics, utilizing a dual-objective reward function that penalizes discriminatory predictions while maintaining educational relevance. Through comprehensive experiments on diverse student datasets, we demonstrate that policy gradient methods with fairness constraints achieve superior bias mitigation compared to conventional supervised learning approaches, reducing demographic disparity by an average of 37.4% while preserving predictive performance within 2.8% of baseline models. The proposed framework incorporates adaptive fairness thresholds that dynamically adjust based on observed demographic distributions, enabling robust performance across heterogeneous educational environments. Our findings suggest that reinforcement learning-based fairness mechanisms provide a viable pathway for developing equitable AI systems in education while maintaining operational effectiveness for academic intervention strategies.
subject: Articles
  topic: section
subject: Fairness-aware machine learning
  topic: keywords
subject: Policy gradient methods
  topic: keywords
subject: Demographic bias
  topic: keywords
subject: Student performance prediction
  topic: keywords
subject: Educational data mining
  topic: keywords
subject: Algorithmic fairness
  topic: keywords
subject: Reinforcement learning
  topic: keywords
creator: Ingrid Solheim
creator: Marco De Santis
rights: Copyright (c) 2026 Ingrid Solheim, Marco De Santis (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 8
ending page: 20
doi: 10.71465/ajeet3552

Non-Equilibrium Dynamics in Optical Matter Systems under Variable Light Fields: A Computational Fluid Dynamics Perspective
type: item
description: The self-organization of nanoparticles into ordered arrays mediated by optical binding forces, known as optical matter, represents a frontier in soft matter physics and photonics. While static optical binding has been extensively characterized, the behavior of these systems under time-varying light fields remains less understood, particularly regarding the role of hydrodynamic interactions. This study employs a coupled Computational Fluid Dynamics (CFD) and electrodynamics simulation framework to investigate the non-equilibrium dynamics of optical matter in aqueous environments. By explicitly resolving the solvent velocity field using the Navier-Stokes equations alongside the Langevin dynamics of the particles, we elucidate the complex interplay between optical scattering forces and fluid-mediated coupling. Our results demonstrate that under variable illumination—specifically oscillating and rotating optical traps—hydrodynamic interactions significantly alter the trajectory and stability of particle arrays compared to predictions based solely on conservative optical forces. We observe emergent collective motion and fluid pumping effects that suggest new mechanisms for controlling transport at the mesoscale. This work underscores the necessity of full hydrodynamic treatments in modeling dynamic optical matter systems and offers a predictive platform for designing optically reconfigurable colloidal machines.
subject: Articles
  topic: section
subject: Optical Matter
  topic: keywords
subject: Computational Fluid Dynamics
  topic: keywords
subject: Hydrodynamic Coupling
  topic: keywords
subject: Non Equilibrium Statistical Mechanics
  topic: keywords
creator: Austra Lian
rights: Copyright (c) 2026 Austra Lian (Author) https://creativecommons.org/licenses/by/4.0https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 1
ending page: 7
doi: 10.71465/ajeet3547

IoT-Based Real-Time Monitoring Systems for Electrical Distribution Networks: Enhancing Reliability and Operational Efficiency
type: item
description: The increasing complexity and demand on electrical distribution networks have emphasized the need for real-time, scalable, and intelligent monitoring solutions. Internet of Things (IoT)-based technologies offer a transformative approach to enhance network reliability, detect faults early, and optimize load distribution. This paper explores the architecture, components, applications, and challenges of IoT-based real-time monitoring systems in electrical distribution networks. Case studies from Pakistan are presented to highlight local implementations, technological gaps, and future directions for smart grid integration. The findings underscore the potential of IoT to reshape traditional power systems into data-driven, self-diagnostic infrastructures.
subject: Articles
  topic: section
subject: Smart Grid
  topic: keywords
subject: IoT Sensors
  topic: keywords
subject: Fault Detection
  topic: keywords
subject: Distribution Automation
  topic: keywords
creator: Anna Kim
rights: Copyright (c) 2025 Anna Kim (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 11
ending page: 16
doi: 10.71465/ajeet3520

Advanced Control Algorithms for Microgrids and Smart Grids: Optimization, Stability, and Integration Challenges
type: item
description: The evolution of power distribution systems into smart grids and microgrids necessitates advanced control mechanisms to ensure reliability, stability, and optimization. This paper explores cutting-edge control algorithms, including model predictive control (MPC), adaptive neuro-fuzzy inference systems (ANFIS), distributed control schemes, and AI-driven controllers for efficient energy management in microgrids. Emphasis is placed on their implementation in renewable energy-integrated systems, real-time optimization, and fault-tolerant operations. The study also presents case scenarios and simulation-based analyses to highlight performance improvements across various parameters. These advanced algorithms signify the shift from centralized control to intelligent, decentralized frameworks, which are crucial for the future of sustainable energy networks.
subject: Articles
  topic: section
subject: Smart Grid
  topic: keywords
subject: Microgrid Control
  topic: keywords
subject: Model Predictive Control (MPC
  topic: keywords
subject: Renewable Energy Integration
  topic: keywords
creator: Sarah Williams
rights: Copyright (c) 2025 Sarah Williams (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 6
ending page: 10
doi: 10.71465/ajeet3519

The Future of Electrical Engineering in the Aerospace Industry
type: item
description: Electrical engineering continues to be a cornerstone of aerospace innovation, driving advances in avionics, propulsion systems, communication, and autonomous technologies. The future of electrical engineering in aerospace will be shaped by emerging trends such as electrification of aircraft, smart sensor networks, advanced power management, AI-enabled systems, and cyber-physical integration. These developments promise enhanced efficiency, safety, and sustainability in aerospace operations. This article explores key areas where electrical engineering will transform aerospace technology and discusses challenges and opportunities ahead.
subject: Articles
  topic: section
subject: Aerospace
  topic: keywords
subject: Electrical Engineering
  topic: keywords
subject: Electrification
  topic: keywords
subject: Avionics
  topic: keywords
subject: Autonomous Systems
  topic: keywords
subject: Power Management
  topic: keywords
creator: Carlos Martinez
rights: Copyright (c) 2025 Carlos Martinez (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-31
publication date: 2025-10-31
starting page: 12
ending page: 16
doi: 10.71465/ajeet3516

Wireless Power Transfer in Electric Vehicles: A Comprehensive Review
type: item
description: Wireless Power Transfer (WPT) technology is emerging as a promising alternative to conventional plug-in charging methods for Electric Vehicles (EVs). It offers significant advantages such as convenience, safety, and enhanced user experience. This review comprehensively discusses the state-of-the-art WPT techniques, system architectures, coupling mechanisms, and standards relevant to EV charging. Various WPT methods including inductive coupling, resonant inductive coupling, capacitive coupling, and microwave power transfer are analyzed in terms of efficiency, range, power levels, and system complexity. The paper also addresses challenges such as alignment issues, electromagnetic interference, thermal management, and safety standards. Future trends including dynamic charging, integration with smart grids, and advances in materials and system design are examined. This review aims to provide researchers and industry practitioners with a detailed overview of WPT for EVs, highlighting current developments and directions for future research.
subject: Articles
  topic: section
subject: Wireless Power Transfer
  topic: keywords
subject: Electric Vehicles
  topic: keywords
subject: Inductive Charging
  topic: keywords
subject: Resonant Coupling
  topic: keywords
subject: Dynamic Charging
  topic: keywords
subject: Electromagnetic Compatibility
  topic: keywords
subject: Power Electronics
  topic: keywords
creator: Sophia Lee
rights: Copyright (c) 2025 Sophia Lee (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-31
publication date: 2025-10-31
starting page: 7
ending page: 11
doi: 10.71465/ajeet3515

The Role of Electrical Engineering in Artificial Intelligence Hardware
type: item
description: Artificial Intelligence (AI) has rapidly transformed numerous industries, and its hardware backbone is critical for efficient AI processing. Electrical Engineering plays a pivotal role in designing, developing, and optimizing AI hardware components such as processors, memory units, sensors, and communication interfaces. This article explores the intersection of electrical engineering and AI hardware, emphasizing advancements in neuromorphic computing, ASICs, FPGAs, and system-on-chip (SoC) designs that accelerate AI tasks. Additionally, it discusses challenges related to power consumption, speed, and scalability, highlighting ongoing research in low-power architectures and hardware-software co-design. The article aims to provide a comprehensive overview of how electrical engineering innovations enable the deployment of AI across applications from autonomous vehicles to smart IoT devices
subject: Articles
  topic: section
subject: Artificial Intelligence Hardware
  topic: keywords
subject: Electrical Engineering
  topic: keywords
subject: Neuromorphic Computing
  topic: keywords
subject: ASIC
  topic: keywords
subject: FPGA
  topic: keywords
subject: Low-Power Design
  topic: keywords
subject: Hardware Acceleration
  topic: keywords
creator: David Johnson
rights: Copyright (c) 2025 David Johnson (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-10-31
publication date: 2025-10-31
starting page: 1
ending page: 6
doi: 10.71465/ajeet3514

A Transformer-based Approach to Real-Time Task Scheduling in Latency-Critical Edge Systems
type: item
description: Edge computing has emerged as a critical paradigm for supporting latency-sensitive applications by bringing computational resources closer to data sources. However, efficient task scheduling in edge systems remains a significant challenge due to the heterogeneous nature of resources, dynamic workload patterns, and stringent latency requirements. Traditional heuristic-based scheduling algorithms often struggle to adapt to the rapidly changing conditions inherent in edge environments. This paper presents a novel Transformer-based approach for real-time task scheduling in latencycritical edge systems. By leveraging the self-attention mechanism of Transformer architecture, our proposed method learns complex temporal dependencies and spatial relationships among tasks and resources, enabling intelligent scheduling decisions that minimize response latency while maintaining high resource utilization. The methodology incorporates a multi-head attention mechanism to capture diverse task characteristics including priority levels and deadline constraints, positional encoding to preserve temporal information, and a reinforcement learning framework to optimize long-term performance. Experimental results demonstrate that the proposed approach achieves up to 37% reduction in average task completion time and 42% improvement in deadline satisfaction rate compared to state-of-the-art baseline algorithms including completion time-based assignment with hierarchical weighted assignment and weighted round-robin scheduling methods. The system maintains deadline satisfaction rates above 94% for time-critical tasks even under high workload intensities, adapting effectively to resource heterogeneity across edge-cloud continuum architectures.
subject: Articles
  topic: section
subject: Edge computing
  topic: keywords
subject: Task scheduling
  topic: keywords
subject: Transformer architecture
  topic: keywords
subject: Real-time systems
  topic: keywords
subject: Latency optimization
  topic: keywords
subject: Self-attention mechanism
  topic: keywords
creator: Klara Vuković
creator: Alessandro Romano
rights: Copyright (c) 2025 Klara Vuković, Alessandro Romano (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 17
ending page: 34
doi: 10.71465/ajeet3453

Modern Techniques for Power System Optimization and Control
type: item
description: Power systems worldwide are evolving rapidly to accommodate the growing demand for energy, integration of renewable resources, and the increasing complexity of grid operations. Modern techniques for power system optimization and control play a vital role in enhancing system reliability, efficiency, and sustainability. This article reviews contemporary optimization methods including heuristic and metaheuristic algorithms, machine learning-based approaches, and advanced control strategies such as Model Predictive Control (MPC) and decentralized control. The integration of smart grid technologies and real-time data analytics further enriches system operation. We also discuss challenges and future directions in the context of renewable integration, cyber-physical security, and computational scalability.
subject: Articles
  topic: section
subject: Power system optimization
  topic: keywords
subject: Advanced control techniques
  topic: keywords
subject: Smart grids
  topic: keywords
subject: Renewable energy
  topic: keywords
subject: integration
  topic: keywords
subject: Machine learning in power systems
  topic: keywords
creator: Dr. Ayesha Malik
rights: Copyright (c) 2025 Dr. Ayesha Malik (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 11
ending page: 16
doi: 10.71465/ajeet1191

Artificial Intelligence in Predictive Fault Diagnosis for Electrical Networks
type: item
description: The integration of Artificial Intelligence (AI) in predictive fault diagnosis for electrical networks has revolutionized power system reliability and maintenance efficiency. This paper explores state-of-the-art AI techniques including machine learning, deep learning, and hybrid models applied to fault detection, classification, and prediction in electrical distribution and transmission networks. Emphasis is placed on data-driven approaches using real-time sensor data and historical fault records. Challenges such as data imbalance, noisy measurements, and computational constraints are discussed. A case study on the Pakistani power grid demonstrates the effectiveness of AI-driven fault diagnosis in reducing downtime and improving grid resilience.
subject: Articles
  topic: section
subject: Predictive Fault Diagnosis
  topic: keywords
subject: Electrical Networks
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Power System Reliability
  topic: keywords
creator: Dr. Ayesha Siddiqui
rights: Copyright (c) 2025 Dr. Ayesha Siddiqui (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 6
ending page: 10
doi: 10.71465/ajeet1186

Design Considerations for Smart Cities’ Electrical Power Infrastructure
type: item
description: Smart cities integrate advanced digital technologies with urban infrastructure to enhance sustainability, efficiency, and quality of life. Central to this transformation is the design of a resilient, flexible, and efficient electrical power infrastructure that supports the increasing demands of IoT devices, electric vehicles, renewable integration, and smart grid functionalities. This paper explores key design considerations for smart cities’ electrical power infrastructure, including grid modernization, distributed generation, energy storage, demand response, cybersecurity, and environmental impact. Challenges and future research directions are also discussed to guide planners, policymakers, and engineers in developing sustainable smart city power systems.
subject: Articles
  topic: section
subject: Smart Cities
  topic: keywords
subject: Electrical Power Infrastructure
  topic: keywords
subject: Smart Grid
  topic: keywords
subject: Distributed Generation
  topic: keywords
subject: Energy Storage
  topic: keywords
subject: Demand Response
  topic: keywords
creator: Dr. Ayesha Khan
rights: Copyright (c) 2025 Dr. Ayesha Khan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 1
ending page: 5
doi: 10.71465/ajeet1185

Impact of High-Efficiency Electrical Machines in Industry 4.0
type: item
description: Industry 4.0 represents the fourth industrial revolution characterized by digitalization, automation, and smart manufacturing. High-efficiency electrical machines play a pivotal role in enabling the energy-efficient and reliable operation of industrial processes in this new era. This paper explores the impact of high-efficiency electrical machines, including motors, generators, and actuators, within Industry 4.0 frameworks. We discuss advancements in machine design, control techniques, integration with IoT and AI, and their contribution to energy savings, operational reliability, and reduced carbon footprint. Furthermore, challenges and future research directions in implementing these machines in smart factories are outlined.
subject: Articles
  topic: section
subject: High-efficiency electrical machines
  topic: keywords
subject: Industry 4.0, smart manufacturing
  topic: keywords
subject: energy efficiency
  topic: keywords
subject: industrial automation
  topic: keywords
creator: Dr. Ayesha Tariq,
rights: Copyright (c) 2025 Dr. Ayesha Tariq, (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 6
ending page: 10
doi: 10.71465/ajeet3301

Developments in Electrical Engineering for Energy Efficiency in Manufacturing
type: item
description: The manufacturing sector is a significant consumer of electrical energy, prompting the need for improved energy efficiency to reduce operational costs and environmental impact. Electrical engineering innovations have played a pivotal role in enhancing energy efficiency in manufacturing processes by optimizing electrical systems, incorporating advanced control strategies, and deploying smart energy management solutions. This article reviews recent developments in electrical engineering techniques and technologies aimed at improving energy efficiency in manufacturing industries, including motor drives, power electronics, renewable integration, and smart grid applications. Case studies from Pakistani industries are also discussed, highlighting practical implementations and challenges.
subject: Articles
  topic: section
subject: Energy efficiency
  topic: keywords
subject: Electrical engineering
  topic: keywords
subject: Manufacturing
  topic: keywords
subject: Motor drives
  topic: keywords
subject: Smart grids
  topic: keywords
creator: Dr. Ayesha Khan
rights: Copyright (c) 2025 American Journal of Electrical Engineering and Technology https://creativecommons.org/licenses/by/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 1
ending page: 5
doi: 10.71465/ajeet3295

The Impact of Energy Storage on Electrical System Stability
type: item
description: The integration of energy storage systems (ESS) into electrical grids has become a critical factor in enhancing system stability amid increasing penetration of renewable energy sources and fluctuating loads. This paper investigates the role of various energy storage technologies in improving voltage stability, frequency regulation, and overall grid resilience. Through modeling and simulation studies, the impact of battery energy storage systems (BESS), flywheels, and supercapacitors on mitigating disturbances and maintaining system equilibrium is analyzed. Results indicate that appropriately sized and strategically located ESS can significantly reduce frequency deviations and improve transient response, thereby enhancing the reliability and robustness of modern electrical power systems.
subject: Articles
  topic: section
creator: Dr. Amina Khan
rights: Copyright (c) 2025 Dr. Amina Khan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 11
ending page: 15
doi: 10.71465/ajeet3293

Power System Management Using Distributed Ledger Technologies
type: item
description: The integration of Distributed Ledger Technologies (DLT), such as blockchain, in power system management is rapidly gaining attention for its potential to enhance grid transparency, security, and operational efficiency. This paper explores the role of DLT in power systems, focusing on decentralized energy trading, grid data integrity, and smart contract-based automation for demand response. It highlights the benefits of DLT in managing distributed energy resources (DERs), securing transactions, and improving trust among stakeholders. Challenges related to scalability, energy consumption, and regulatory compliance are also discussed. Case studies from pilot projects demonstrate promising results, underscoring DLT’s transformative potential in modernizing power grids.
subject: Articles
  topic: section
subject: Distributed Ledger Technology
  topic: keywords
subject: Power System Management
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Distributed Energy Resources
  topic: keywords
subject: Smart Grid
  topic: keywords
creator: Dr. Ayesha Khan
rights: Copyright (c) 2025 Dr. Ayesha Khan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 7
ending page: 11
doi: 10.71465/ajeet3287

Advanced Electrical Materials for High-Temperature Superconducting Applications
type: item
description: High-temperature superconductors (HTS) have revolutionized electrical engineering by enabling lossless power transmission, powerful electromagnets, and advanced electronic devices. This paper reviews recent advances in electrical materials tailored for HTS applications, focusing on novel fabrication techniques, material properties, and integration challenges. Emphasis is placed on ceramic-based cuprates, iron-based superconductors, and emerging composite materials that offer enhanced critical temperature (Tc), critical current density (Jc), and magnetic field tolerance. Additionally, the paper explores material engineering strategies to improve thermal stability and mechanical robustness for industrial deployment. Finally, the study highlights the ongoing research efforts in Pakistan and proposes future directions for material innovations in HTS applications.
subject: Articles
  topic: section
subject: High-temperature superconductors
  topic: keywords
subject: electrical materials
  topic: keywords
subject: critical current density
  topic: keywords
subject: cuprates
  topic: keywords
subject: iron-based superconductors
  topic: keywords
subject: material engineering
  topic: keywords
subject: thermal stability
  topic: keywords
creator: Dr. Saad Ahmed
rights: Copyright (c) 2025 Dr. Saad Ahmed (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-30
publication date: 2025-04-30
starting page: 1
ending page: 6
doi: 10.71465/ajeet3282

Electrical System Monitoring with Big Data and Artificial Intelligence
type: item
description: The integration of Big Data analytics and Artificial Intelligence (AI) into electrical system monitoring has transformed the traditional approaches to maintaining grid stability, fault detection, and predictive maintenance. This article reviews the state-of-the-art techniques that leverage massive datasets generated by smart meters, sensors, and grid components, combined with AI algorithms such as machine learning and deep learning, to enhance real-time monitoring and fault diagnosis in electrical power systems. We discuss challenges specific to the Pakistani power grid, including data heterogeneity, scalability, and cybersecurity concerns, and propose frameworks for AI-driven intelligent monitoring. Case studies demonstrate significant improvements in outage prediction, load forecasting, and system reliability. The article concludes with future research directions for AI and Big Data convergence in smart grid applications.
subject: Articles
  topic: section
subject: Electrical System Monitoring
  topic: keywords
subject: Big Data Analytics
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Smart Grids
  topic: keywords
subject: Fault Detection
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
creator: Dr. Ayesha Khan
rights: Copyright (c) 2025 Dr. Ayesha Khan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-02-28
publication date: 2025-02-28
starting page: 11
ending page: 15
doi: 10.71465/ajeet3280

Energy Efficiency Improvements in Large-Scale Electrical Power Plants
type: item
description: Energy efficiency in large-scale electrical power plants is a critical factor for sustainable energy production and environmental conservation. This article reviews key technological, operational, and management strategies to improve efficiency in thermal, hydro, and combined cycle power plants. Emphasis is given to advanced materials, combined heat and power (CHP) integration, digital monitoring systems, and retrofitting existing plants with modern equipment. The study also highlights challenges faced by Pakistani power plants and proposes actionable recommendations to enhance overall plant performance and reduce carbon emissions. Quantitative analyses demonstrate potential efficiency gains and environmental benefits, emphasizing the role of policy and investment in upgrading aging infrastructure.
subject: Articles
  topic: section
subject: Energy Efficiency
  topic: keywords
subject: Large-Scale Power Plants
  topic: keywords
subject: Combined Heat and Power (CHP)
  topic: keywords
subject: Digital Monitoring Systems
  topic: keywords
creator: Dr. Saima Khan
rights: Copyright (c) 2025 Dr. Saima Khan (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-02-28
publication date: 2025-02-28
starting page: 6
ending page: 10
doi: 10.71465/ajeet3278

Renewable Energy Systems: Design and Optimization for Urban Environments
type: item
description: The rapid urbanization worldwide has escalated the demand for sustainable and clean energy solutions within urban environments. Renewable energy systems (RES) designed for cities face unique challenges due to limited space, diverse load demands, and the need for integration with existing infrastructure. This paper reviews the design principles and optimization techniques for deploying solar photovoltaic (PV), wind, and hybrid renewable energy systems in urban settings. It emphasizes site-specific considerations, energy storage integration, and smart grid compatibility. Case studies from major Pakistani cities demonstrate the technical and economic feasibility of optimized renewable installations. Advanced optimization methods such as genetic algorithms, particle swarm optimization, and multi-objective optimization are explored to maximize energy yield, minimize costs, and reduce environmental impacts. The study concludes with recommendations for policy frameworks and urban planning to foster renewable energy adoption in Pakistan’s urban environments.
subject: Articles
  topic: section
subject: Renewable Energy Systems
  topic: keywords
subject: Urban Energy Optimization
  topic: keywords
subject: Solar Photovoltaics
  topic: keywords
subject: Hybrid Renewable Systems
  topic: keywords
creator: Dr. Ayesha Malik
rights: Copyright (c) 2025 Dr. Ayesha Malik (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-02-28
publication date: 2025-02-28
starting page: 1
ending page: 5
doi: 10.71465/ajeet3276

Advanced Control Techniques for Electric Power Distribution Systems
type: item
description: Electric power distribution systems are critical infrastructures facing challenges like increasing demand, integration of distributed energy resources (DERs), and maintaining reliability and power quality. This paper reviews and analyzes advanced control techniques applied to modern distribution systems, emphasizing adaptive, predictive, and decentralized control methods. The study evaluates their effectiveness in voltage regulation, fault detection, load balancing, and integration of renewable energy. Simulation results and practical case studies demonstrate significant improvements in system stability and operational efficiency. The paper also discusses challenges and future research directions in control strategies for smart grids.
subject: Articles
  topic: section
subject: Electric Power Distribution
  topic: keywords
subject: Advanced Control Techniques
  topic: keywords
subject: Smart Grid
  topic: keywords
subject: Voltage Regulation
  topic: keywords
subject: Distributed Energy Resources
  topic: keywords
creator: Dr. Saad Ahmed Khan
rights: Copyright (c) 2024 Dr. Saad Ahmed Khan (Author) https://creativecommons.org/licenses/by/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 11
ending page: 15
doi: 10.71465/ajeet3274