Hasil untuk "Electrical engineering. Electronics. Nuclear engineering"

Bilal Tasdemir, Svitlana Krüger, Pinank Sohagiya et al.

The growing demand for higher-energy lithium-ion batteries, encompassing consumer electronics, stationary grid storage, and electric mobility to specialized sectors like aerospace, medical devices, and industrial robotics, requires cathode materials that offer higher capacity while remaining cost-effective. This trend has intensified the development of nickel-rich LiNi_1−x−yMn_xCo_yO₂ (NMC) systems. However, high-Ni NMCs such as LiNi_0.9Mn_0.05Co_0.05O₂ (NMC90) suffer from limited thermal and cycling stability. Core–shell architectures using LiNi_0.6Mn_0.2Co_0.2O₂ (NMC622) as a shell can partially alleviate these drawbacks, but structural degradation caused by interdiffusion between the core and shell persists as a major challenge. This study investigates whether a tungsten oxide interlayer can act as a protective barrier that suppresses interdiffusion, stabilizes the crystal structure, and improves long-term electrochemical performance. In this work, NMC cathode powders were synthesized via a one-pot oxalate co-precipitation route, followed by structural characterization using X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and ion scattering spectroscopy (ISS). Electrochemical performance, including capacity retention, cycling stability, and internal resistance, was evaluated through galvanostatic charge–discharge (GCD) testing and electrochemical impedance spectroscopy (EIS). The core–shell configuration delivered higher specific discharge capacity compared to the individually synthesized core-only and shell-only reference materials, and the incorporation of a tungsten oxide interlayer resulted in a twofold increase in cycle life. These results demonstrate that tungsten oxide effectively enhances cycling stability by inhibiting core–shell interdiffusion, offering a promising pathway toward more durable high-Ni NMC cathodes.

Chadi Nohra, Bechara Nehme, Raymond Ghandour et al.

The integration of communication networks into modern power systems introduces variable time delays that degrade the performance of traditional Load Frequency Control (LFC), while the shift towards renewable energy sources increases system vulnerability through parametric uncertainties. Existing methods, predominantly based on Lyapunov-Krasovskii Functionals, involve a complexity&#x2013;conservatism trade-off and may not provide a unified and tractable solution for this multi-domain robustness challenge. This paper addresses this gap by proposing a novel control framework based on <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-analysis. The methodology models communication delays as structured uncertainties using a Pad&#x00E9; approximation and integrates them with parametric variations within a unified <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-synthesis design process. A detailed comparative analysis indicates that unlike Lyapunov-based approaches, which require guaranteeing system smoothness at every delay subinterval, the proposed method efficiently stabilizes the system under the worst-case conditions, quantified by the structured singular value. Simulation results demonstrate improved robustness compared to conventional H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> control under concurrent delay and parametric uncertainties. While the conventional H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> controller exhibits degraded stability margins when delays exceed 15 ms, the proposed <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-synthesis controller maintains stability and performance under extreme concurrent disturbances, including time-varying delays of 0.5&#x2013;5 s, 40% load changes, and over 80% variation in tie-line reactance and turbine-governor time constants. The proposed controller drives the Area Control Error (ACE) below 0.01 pu within two minutes for a 40% load change under these conditions. These results indicate that <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>-analysis provides a systematic framework for achieving multi-domain robustness in Load Frequency Control under large simultaneous uncertainties.

Tole Sutikno

This editorial introduces Volume 41, Number 2 (February 2026) of the Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), which presents a diverse collection of peer-reviewed articles reflecting recent advances in electrical engineering, electronics, and computer science. The issue highlights the convergence of power and energy systems, artificial intelligence, cybersecurity, the Internet of Things (IoT), and datadriven engineering methodologies in addressing contemporary technological and societal challenges, with key contributions focusing on renewable energy integration, intelligent control strategies, secure and trusted digital infrastructures, smart IoT-based systems, and AI-driven applications in healthcare, finance, industrial automation, and human-centered computing. Particular emphasis is placed on energy efficiency, system resilience, explainable and trustworthy artificial intelligence, and sustainable engineering practices. Collectively, the published works demonstrate how interdisciplinary research can bridge theory and real-world implementation while supporting the United Nations Sustainable Development Goals, including affordable and clean energy, good health and well-being, sustainable cities, responsible consumption, and strong digital institutions. By fostering innovation, cross-domain collaboration, and responsible technology development, this issue of IJEECS aims to advance secure, intelligent, and sustainable engineering solutions that respond to both current demands and future global challenges. This issue further reinforces the journal’s commitment to advancing engineering intelligence that is ethically grounded, environmentally responsible, and resilient by design.

A. Grishentsev, N. Korovkin, A. Korobeynikov

Hermite-Gauss wavelets are obtained as a result of the product of Hermite polynomials by the Gauss function. Hermite-Gauss wavelets form a basis in the space of real numbers and therefore allow decomposition of functions satisfying the Dirichlet condition. The set of orthonormal forms of Hermite-Gauss wavelets are eigenfunctions of the Fourier transform, such a feature determines the use of wavelets in a wide range of theoretical and practical engineering problems. An analysis of the literature shows that insufficient attention has been paid to the Fourier transform of various forms of Hermite-Gauss wavelets, their mutual expression through the Fourier transform and connections with other functions having the form of a product of polynomials by a Gauss function. Therefore, according to the authors, the research is relevant. The subject of research is to increase the efficiency of the Fourier transform of some types of polynomials based on the Hermite-Gauss wavelet decomposition. In the course of the research, five interrelated theorems have been formulated and proved, forming the basis of the work. Two types of decomposition are used to formulate and prove theorems. The first decomposition method is based on a generalized Fourier transform based on the Hermite-Gauss wavelet basis. The second method of decomposition of polynomials is based on sequential division with remainder. The main result of the work is the proposed mathematical apparatus in the form of formulated and proven theorems applicable to the Fourier transform of arbitrary polynomials of one variable multiplied by the Gauss function, and the transformation is performed analytically and without using the Fourier integral. According to the authors, the obtained result contributes to the development of the theory and practice of signal processing in the field of radio electronics, optoelectronics, electrical engineering and the theory of automatic control. In the final part of the paper, some examples of the use of the developed theory are presented and a method for synthesizing the responses of systems with a fractional-rational transfer function and a method for synthesizing radio signals based on Hermite-Gauss wavelets is proposed.

Gregorio Morales González, Jorge Gulín González, José Emilio Cuevas Chavez et al.

El objetivo de la investigación se enfoca en diseñar una guía de entrenamiento biomecánico correctivo para optimizar la técnica de pivoteo en la finalización, con el fin de reducir los factores de riesgo de lesión de rodilla en las jugadoras de futsal femenino. El presente estudio de caso toma 15 jugadoras de la posición específica de pívot provenientes de diferentes universidades de la capital cubana. Las investigaciones realizadas indican que los movimientos del pívot en la finalización y especialmente los giros rápidos de espalda a la portería y los lanzamientos en apoyo monopodal, exhiben patrones biomecánicos que los convierten en gestos de alto riesgo para la lesión del ligamento cruzado anterior y otras lesiones de rodilla, identificados con un valgo dinámico >12° y una flexión de cadera y rodilla insuficiente durante acciones rotatorias, los que sirven como indicadores claves para evitar riesgos y lesiones. La guía para diseñar los ejercicios se enfoca en la optimización y automatización de los patrones corregidos y es importante para trasmitir los movimientos táctico-técnicos óptimos bajo fatiga en el juego real, reduciendo drásticamente el riesgo de lesión y mejorando la eficiencia del movimiento. Las correcciones biomecánicas a partir de las tres fases críticas del movimiento, con la prevención de lesiones en la cadena cinemática de manera integrada para el fortalecimiento del glúteo medio en el gesto técnico a partir de ejercicios pliométricos y el fortalecimiento del core, previenen el "colapso" de la rodilla en valgo que es una de las principales causas de lesión.

Danni Liu, Shengda Wang, Weijia Su et al.

The multiple energy power plant-based microgrids (MEPPBM) gradually incorporates multiple energy sources such as solar, wind, and battery energy storage, ensuring reliable security &#x0026; protection has become a paramount challenge. Conventional fault detection methods often fail to address the unique dynamics of these MEPPBM, leading to delays in fault detection and classification. The need for cutting-edge protection schemes that can operate efficiently in such environments is paramount to maintaining system stability and avoiding potential damage. The main objective of this research is to design such protection and security schemes which detect, classify, and locate faults with high accuracy and rapidly with very low computational burden. Therefore, the paper presents a robust security &#x0026; protection method for modern MEPPBM, employing a hybrid methodology using the Unscented Kalman Filter (UKF) and Particle Filter (PF) algorithms. The UKF is employed for accurate state estimation of the current &#x0026; voltage signal from faulty bus. While the PF is employed to calculate fault detection &#x0026; classification indices named PF based residuals (PFBR) and PF-based Harmonic distortion (PFBHD) from UKF-estimated current signal. Then, the Fault section identification index named PF-computed reactive power (PFCRP) is generated from UKF estimated current &#x0026; voltage signals. Extensive simulations are performed IEC 61850 microgrid test bed using MATLAB/Simulink 2023b software. The presented scheme effectively detects both high-impedance faults (HIF) and solid faults with a remarkable 99.9% accuracy in under 4 milliseconds. Furthermore, the scheme offers low computational burden, making it highly appropriate &#x0026; efficient for real-time applications in modern MEPPBM.

Kun Peng

The transition towards renewable energy sources has led to an increasing reliance on lithium-ion batteries (LIBs) for applications in electric vehicles and portable electronics. Among the various components of LIBs, silicon-based anode materials are gaining attention due to their high theoretical capacity and the abundant availability of silicon. Despite their potential, silicon anodes encounter significant obstacles during charge-discharge cycles, including substantial volume expansion and poor electrical conductivity. To address these challenges, a range of structural optimization strategies has been investigated. This review highlights recent developments in the structural engineering of silicon-based anodes, particularly focusing on innovations in nanostructure design and composite materials. Nanostructuring silicon helps reduce particle size and optimize microstructures, which mitigates volume expansion, improves cycling stability, and promotes enhanced lithium-ion diffusion. Composite approaches, integrating silicon with carbon and metal oxide materials, further enhance conductivity and mechanical integrity. However, despite substantial advancements, issues such as production cost, long-term durability, and scalability remain significant barriers to the widespread adoption of silicon anodes. Future research should focus on integrating material design with interface engineering and exploring novel synthesis techniques to enable the large-scale implementation of silicon-based anodes in high-energy-density storage systems.

Seraz Ahmad

MATLAB has evolved from a numerical computing environment into a comprehensive engineering platform with rich toolboxes, modeling languages (Simulink, Stateflow), and code-generation capabilities. This paper surveys core applications of MATLAB across major engineering disciplines—electrical, electronics & communication, mechanical, civil, and computer engineering—emphasizing signal processing, control systems, power systems, image processing, communication systems, optimization, and machine learning. We present compact case studies, reproducible code snippets, and a discussion on performance, validation, and deployment (including embedded code generation). Limitations and best practices are articulated to guide students and practitioners in selecting MATLAB for research and industrial workflows.

A. Alkabaa, N. Alamri

Artificial Intelligence (AI) is increasingly adopted by educational institutions, particularly as a generative AI (GenAI) tool for e‐learning. This study explores the effectiveness of using GenAI with engineering students at a leading university in Saudi Arabia and the Middle East. It aims to assess GenAI's impact in the College of Engineering and examine gender‐based differences in how students utilize AI as a learning tool. The study also investigates how students from different engineering majors utilize AI in their learning. To achieve this objective, an online survey with 15 questions was distributed to 403 engineering students to analyze their perceptions of AI adoption in education. The study employs two non‐parametric rank‐based statistical tests: the Mann–Whitney test to analyze gender differences, and the Kruskal–Wallis test to examine how various engineering disciplines such as industrial, electrical, mechanical, civil, chemical, nuclear, and mining engineering influence GenAI adoption. The findings reveal significant differences between male and female students in their experiences with GenAI, particularly regarding inaccurate or misleading responses, accurate and reliable responses, and their opinions regarding the users from applied academic field toward GenAI adoption. The results also indicate notable differences among engineering majors in their proficiency with GenAI features, their experiences with hallucinated responses, their views on using GenAI in theoretical disciplines, and their trust in the accuracy of information provided by ChatGPT. These findings support educational decision‐makers in integrating AI as a learning technology for engineering students and in understanding student engagement with AI tools in education.

Nuha Al-harbi

Dan Du, Zetao Li, Boutaib Dahhou

This study develops a progressive optimal fault-tolerant control method based on insufficient fault information. By combining passive and active fault-tolerant control manners during the process of fault diagnosis, insufficient fault information is fully used, and optimal fault-tolerant control effect is achieved. In addition, the fault-tolerant control method based on guaranteed robust cost control is introduced. The proposed progressive optimal fault-tolerant control method considers two aspects. First, as the amount of fault information continually increases, the performance index of the progressive optimal fault-tolerant controller improves. Second, at each moment, based on the corresponding insufficient fault information and prior knowledge, optimal fault-tolerant control is achieved according to current fault information. The process of progressive optimal fault-tolerant control converges to active fault-tolerant control when the fault is completely identified, and the optimal fault-tolerant controller is no longer reconfigured until no more useful fault information can be provided. Furthermore, a progressive optimal fault-tolerant control algorithm based on the grid segmentation in the parameter uncertainty domain and the selection of different auxiliary center points is introduced. Simulation results verified the feasibility of the proposed algorithm and the validity of the proposed theory.

Gasper Petelin, Gjorgjina Cenikj

Characterizing optimization problems and their properties addresses a key challenge in optimization and is crucial for tasks such as creating benchmarks, selecting algorithms, and configuring them. Although several techniques have been proposed for extracting features from single-objective optimization problems, the proposed approach offers an alternative look at these problems and their properties. We propose an approach for creating problem representations by utilizing domain-specific filters. These filters have randomly initialized weights and are applied to samples of the optimization problem to extract relevant properties. Proposed features are subsequently used to classify problem instances from the Comparing Continuous Optimizers benchmark demonstrating that problem instances of the same problem tend to be situated near each other in a high-dimensional feature space. Additionally, we demonstrate that the proposed feature extraction method can be used to recognize complex characteristics of optimization functions, including multimodality and the presence of global and funnel structures. We also explore the extent to which these identified features can assist in the selection of algorithms. Our findings reveal that these features are suitable for constructing meta-models for algorithm selection, provided that the problems encountered do not substantially differ from those seen in the training phase. The proposed approach offers a versatile tool for feature extraction, highlighting its applicability across multiple tasks within the domain of optimization.

Yong Zhu, S. Howell

This paper illustrates the teaching approaches to assessment that foster independent learning in an undergraduate course in the discipline of Electrical and Electronic Engineering. A web‐based circuit simulator of CircuitLab was used to implement some of the laboratory tasks. Practical solutions have been applied to bridge the learning achievement gap between the formative laboratory assessment and the summative examination. To promote creative and independent learning, unique individual self‐discovery projects were given to each student. The projects are generated according to each student's student number in an innovative approach to prevent plagiarism. The scaffolding and self‐discovery lab activities can be modified to be pure online versions, which had been attested in the online course delivery during the COVID‐19 lockdown period in 2020.

Zeyad Khashroum, Hossein Rahimighazvini, Maryam Bahrami

Deep learning. Recently, there has been a lot of interest in integrating machine learning methods, specifically Convolutional Neural Networks (CNNs), with power electronics. An overview of the many developments and applications at the nexus of machine learning and power electronics is given in this review paper. We investigate how CNNs might help power electronics systems overcome obstacles and become more reliable and efficient. The study reviews the state of the field now and identifies areas for potential future research. According to the study, machine learning—especially Convolutional Neural Networks (CNNs)—is a promising field in electrical engineering with substantial promise for defect detection, classification, and pattern recognition when integrated into power electronics. Future directions emphasize integrating data sources, expanding real-time implementation, and optimizing the integration of renewable energy sources for higher efficiency and sustainability in power electronics. Challenges include the requirement for high-quality data and real-time implementation.

Alejandro Álvarez-Marín, Maximiliano Paredes-Velasco, J. Velázquez-Iturbide et al.

Students often find difficult to understand the concepts and working details of electricity because its mechanisms of operation are invisible. The visualization of electricity through an augmented reality (AR) app could assist students in understanding these concepts more intuitively and in improving their academic achievement. Due to the lack of studies on AR apps for electricity education, this study aimed to investigate the effects of an interactive AR app designed for teaching electrical circuits on students. The study investigates its impact on students' academic performance, explores its influence on their emotions, and examines the relationship between the perceived usability of the app and the student's learning outcomes and emotional experiences. The study was conducted in an electromagnetism laboratory with the participation of 28 engineering students. The findings revealed that the students who used the augmented reality application presented a better academic performance than those who participated in the traditional laboratory. Except for the students in the experimental group feeling less shame, there were no discernible variations between the students' feelings in the two groups. Anxiety increases in both groups. The AR application proved to have usability rated as good, but it was not evident that it correlated with academic performance, or the emotions students experienced. Only one relationship was determined between the perceived consistency of the system and hopelessness.

Yadvendra Singh, Shakti Singh, P. Bhatnagar et al.

Power electronics is an emerging and rapidly advancing technology within the realm of electrical engineering, finding wide-ranging applications in areas such as renewable energy, electric vehicles, and industrial automation. This paper aims to provide an in-depth exploration of the tools employed for comprehending power electronics technology. A notable aspect of this paper is its systematic approach, guiding students through a sequential progression of design, hardware implementation, and procedural steps for a series of power electronic circuits. Moreover, the paper elucidates the various tools, software, and their respective applications in the power electronics domain, contributing to a comprehensive understanding of the field.

Ashraf Aboshosha, Sameh Moawad, Ahmed El-Tantawy et al.

Smart Microgrid is an integration of Artificial Intelligence AI, Information and Communication Technology ICT, and Electrical Engineering EE. The presence of harmonics of load currents in smart grids has many negative consequences and can occur Voltage waveform distortion at a point common coupling (PCC). This research article aims to reduce harmonics and distortion using Open Source Electronics (OSE), Arduino technology, Butterworth digital filters, and MOSFET power transistors, which led to the emergence of an effective solution to reduce the harmonics caused by various non-linear loads. A practical circuit is developed to reduce harmonics and its results are compared with a theoretical simulation circuit using MATLAB program. The harmonic distortion limit is reached in the simulation circuit from (6.99%) to 37.32% and in the practical circuit for the same non-linear load values from (8%) to (35%). The results showed the extent of convergence between the results of the practical circuit and the simulation circuit, which will be presented later in this article. Moreover, the OSE is used for detection and cancelation of the Broadband over powerline (BPL) in nuclear microgrids.

Mohammad Tayyab, Adil Sarwar, Shadab Murshid et al.

Abstract In recent times, multilevel inverters (MLIs) have become very popular for commercial and industrial applications. Here, an eleven‐level inverter and its power flow control are presented. The presented topology has a lesser component count than other existing topologies, thus reducing the devices and overall cost of the inverter. This inverter comprises six bidirectional switches, two DC sources, one four‐quadrant switch, and two capacitors for the voltage divider circuit. The conduction modes and corresponding switching states of the presented eleven‐level inverter are shown in detail. Further, the apparent power control of the presented inverter under grid‐connected operation is discussed, which provides simultaneous active and reactive power control over the power injected into the grid. Switching and conduction losses are calculated for 3 and 6 kVA grid injected power at 0.8 power factor lagging. The obtained results show that the total harmonic distortion (THD) of the inverter output voltage and grid current is 12.10% and 0.23%, respectively, under 6 kVA power transfer conditions. The real‐time analysis is also carried out for 3 and 6 kVA power transfer conditions for the presented eleven‐level inverter to validate the active and reactive power flow control.

Alassane Compaoré, Moustapha Sawadogo, Youssouf Sawadogo et al.

The paper describes lightweight-foamed concretes (LFC) that were formulated from cement, natural sand, and foam from a foaming agent with densities of 600 and 700 kg/m3. The identified mineral phases in the cement are alite, belite, Celite, ferrite, calcite, and gypsum. The obtained foamed concretes have a porosity varying from 29.17 to 37.14 vol%, a thermal conductivity below 0.2 W/m.K, and a mechanical strength greater than 2 MPa. At 28 days setting, the relative quantities of crystalline and amorphous phases were identified by XRD and DTA/TG. These techniques allowed to show the importance of the carbonation process and hydrated phases formation on the macroscopic strength increase. The microstructural characterization by image analyses evidences that when the density decreases, growth of both crystalline and amorphous phases in the bubble walls during setting is a mean of compensating the role of density in strength.

Aarushi Jain, Rani Lathwal

Power electronics plays a pivotal role in modern energy systems, contributing to improved efficiency, reduced emissions, and enhanced control in various applications such as renewable energy integration, electric vehicles, and smart grids. This vital domain within electrical engineering, has undergone a remarkable transformation through the integration of Machine Learning, as the power electronics industry has witnessed significant advancements in optimizing operations, improving reliability, and enabling sustainable energy solutions. This research paper presents various strategies of Machine learning used in power electronics. It focuses on using Machine Learning based models to examine and train various voltage and current record units obtained by simulated malfunctioning transmission line model created with Matlab /Simulink. This yielded a dataset consisting of 12,001 fault data samples. Machine Learning models SVM, KNN and Decision Tree were trained on the same data and were employed to detect the faults. Next, the model was chosen for its strong fault detection capabilities and potential, which were evaluated based on accuracy, precision, and the F1- score. Effective fault detection techniques play a crucial role in optimizing power system control, reducing energy wastage, and ensuring top-notch performance.