Bulletin of Electrical Engineering and Informatics
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Optimized electric vehicle charging allocation with overload management and vehicle to grid support
Get PDFThe rapid proliferation of electric vehicles (EVs) in residential distribution networks poses significant challenges, particularly in managing peak demand and maintaining grid stability during the peak demand periods. This study employs a day-ahead EV charging framework in compliance with valley-filling technique to align charging during off-peak periods for a centralized residential charging station that balances grid stability with customer satisfaction. To mitigate network overloading, vehicle to grid support is integrated through optimization based on genetic algorithm (GA), enabling optimal scheduling of both charging and discharging activities under operational constraints. Simulation outcomes substantiate the efficacy of the proposed charging scheme in preventing overloads and demonstrate a notable enhancement in the load factor from 70.68% to 82.24%, reflecting enhanced utilization of energy resources. The approach offers technical and economic benefits for both utilities and EV users, highlighting its potential for scalable and efficient grid management
Real-time browser-integrated phishing uniform resource locator detection via deep learning and fuzzy matching
Get PDFPhishing attacks through deceptive URLs remain a critical cybersecurity threat, particularly in financial transactions and online payment systems. This study evaluates multiple deep learning (DL) models on the PhiUSIIL dataset of 235,795 URLs, with bidirectional gated recurrent unit (BiGRU) achieving the best performance—99.82% accuracy at a 60:40 split, along with high precision, F1-score, and the lowest test loss. To further improve detection of obfuscated URLs, an enhanced BiGRU variant is proposed using an expanded 366-character vocabulary. For real-time deployment, a Chrome extension is developed, integrating exact and fuzzy matching via the Ratcliff–Obershelp algorithm with cloud-based whitelist and blacklist checks. When fuzzy matching is inconclusive, the BiGRU model performs the final classification. By combining an adaptive browser-side tool with a robust DL backend, the proposed system ensures high accuracy, scalability, and efficiency for phishing detection in practical web environments
Harmony haven: usability evaluation of matrimonial websites for cultural sensitivity in Bangladesh
Get PDFIn the digital age, matrimonial websites have become essential platforms for matchmaking, particularly in culturally diverse societies like Bangladesh. This study explores the usability and cultural sensitivity of five popular matrimonial websites—Biyeta, OrdhekDeen, SensibleMatch, Shaadi.com, and Bangladeshi Matrimony—tailored for Bangladeshi users. Using Nielsen's Heuristics, the research identifies key usability issues and cultural misalignments. Data were collected via a questionnaire with 105 respondents, focusing on usability and cultural relevance. The results revealed that OrdhekDeen was the most used platform, with 40% of users preferring it, followed by Bangladeshi Matrimony at 23.8%. Usability issues were categorized, with 46.7% of users reporting challenges in navigating websites and 44.8% finding design consistency lacking. However, error prevention was highly rated, with 54.3% stating they never mistakenly sent messages due to unclear prompts. The study concludes with recommendations for improving cultural alignment and usability to enhance user satisfaction
Optimizing tilt angle of the roof for the best performance ratio of rooftop photovoltaic
Get PDFRooftop photovoltaic (RPV) systems are becoming increasingly popular as a source of renewable energy. One key factor that significantly affects the performance of RPV systems is the tilt angle of the solar panels. This study aims to determine the optimal tilt angle to maximize electricity production for the RPV system installed on academic building, Department of Electrical Engineering, Faculty of Engineering, University of Riau. The research method used is the PVSyst simulation. The simulation data input was used as daily weather for one year. In this study, variations in roof tilt angles of 5°, 10°, 15°, 20°, and 25° were examined. The results show that the optimal tilt angles for this location are 5° and 10°. At a 5° tilt angle, the RPV can generate 247,128 kWh per year with a performance ratio (PR) of 83%. And then at a 10° tilt angle, the system can generate 248,012 kWh per year with a PR of 82%. Based on the simulation results, other tilt angles also produced higher energy outputs but yielded lower PR values. This study provides practical recommendations for designing RPV systems in regions with similar weather conditions
Application of traction force observer and sliding mode controller for speed in enhancing the stability of electric vehicles
Get PDFWith the rapid advancement of electric vehicle (EV) technology, optimizing control and stability has become a key research focus. One major challenge is efficiently distributing traction force while minimizing disturbances under real-world conditions. This paper proposes a traction force observation method combined with a sliding mode speed controller to enhance EV performance. The observation method estimates the traction force from the motor to the wheels and detects disturbances affecting force transmission. This enables optimal traction force distribution among the wheels, reducing slip, improving road grip, and enhancing stability in complex driving conditions. Meanwhile, the sliding mode controller flexibly adjusts traction force as the vehicle navigates various terrains, ensuring stability and safety in hazardous situations. Simulations conducted using MATLAB Simulink and CarSim demonstrate that the proposed system significantly improves EV stability and control performance. Although these results are promising, further studies are necessary to address real-world implementation challenges and optimize the method for practical applications, including parameter tuning, sensor integration, and experimental validation. Overall, this research provides a practical framework for enhancing traction control and vehicle dynamics in future intelligent electric mobility systems
Improvement of load frequency control performance for shipboard microgrid system
Get PDFThis research studies the shipboard microgrid (MG) scheme's frequency fluctuations problem contrary to the impulsiveness of renewable resources, load instabilities, and the uncertainty of the parameters in the ship MG plant. A shipboard MG system consists of some of the renewable energy resource s (RESs) such as photovoltaic (PV), wind turbine generator (WTG), battery energy storage system (BESS), ship diesel generator (DG), fuel cell (FC), aqua electrolyzer (AE), and loads. A new fuzzy proportional integral derivative (FPID) controller is established to attain the desired frequency stability for the shipboard MG system. Additionally, various scenarios are executed in this research to validate the robustness of the anticipated controller to various load disturbances, parameter changes of plant, and fluctuations of solar irradiance and wind speed. The numerical simulation results obtained in three scenarios compared with those of the conventional PID controller and the existing time-varying derivative fractional order PID (TVD-FOPID) controller in literatures to validate the high usefulness and applicability of the planned control strategy. In brief, the established load frequency controller (LFC) based on FPID technique can improve frequency deviation in shipboard MG plant effectively
Autism detection using facial and motor analysis using machine learning
Get PDFThis paper proposes a method for detecting autism spectrum disorders (ASD) through the analysis of facial and motor features using machine learning. The aim is to develop an algorithm for automatic ASD diagnosis based on spatiotemporal behavioral patterns. Traditional diagnostic methods rely on subjective expert observations, often delaying intervention. To address this, a hybrid convolutional neural network and long short-term memory (CNN+LSTM) model was employed. Convolutional layers extracted spatial features from video frames, while recurrent layers tracked temporal dynamics. Using MediaPipe face mesh, pose, and hands models, 1,639 parameters were obtained, including facial and pose coordinates, hand landmarks, mouth aspect ratio (MAR), and motion energy. The dataset comprised 100 children, aged 5–9 years (50 with ASD, 50 typically developing (TD)). Stratified cross-validation was applied to ensure subject-independent evaluation. Results showed 90% accuracy on the training set, 85–90% on validation, and an area under the curve (AUC) greater than 0.90, confirming model stability. Data visualization highlighted significant differences in motor activity and emotional expression between groups. The proposed approach demonstrates the potential for robust and objective ASD detection. It can be applied in clinical and educational contexts to improve early diagnosis and timely intervention
Memory faults using open and short defect models for nano technology applications
Get PDFAs technology progresses from sub-micron to nanometer scales, memory-based systems are increasingly prone to faults. Consequently, developing robust methodologies to achieve defect-free embedded static random-access memory (SRAM) has become a critical challenge in modern very large scale integration (VLSI) design. Also, the increased integration of layout layers leads to form unknown defects. From the existing literature, observed that huge parametric variation is present whenever technology is changed. This is the key issue addressed in this paper, by representing an analysis on the impact of open and short defect models that uses parasitic extraction method while drawing various fault models. Possible open/short defects between the existing nodes are considered for the development of fault models using 45 nm, 32 nm, and 7 nm technologies. The total number of fault models of both kinds observed are 147. Also observed that besides to the existing faults, few undetectable faults are found named as undefined short faults (USF), undefined write after read fault (UWARF), and few faults with multiple faulty behavior
MVC in machine learning: a decade of algorithmic advances, challenges, and applications–a systematic review
Get PDFThis systematic review evaluates the developments in multi-view clustering (MVC), its challenges, and applications from 2009 to 2024 and synthesizes 157 studies selected according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines. MVC overcomes the shortcomings of the traditional single-view approaches by using complementary information provided by heterogeneous data sources. We used a strict search strategy in the ACM Digital Library, IEEE Xplore, and Scopus, and then carefully examined the quality of the found articles. The significant results suggest that the MVC research has grown explosively, with China as the major contributor and IEEE/Elsevier as the leading publishers. Developments in algorithms include deep learning, graph-based models, and factorization. Ongoing issues include managing incomplete views, scalability, successful fusion strategies, and interpretability. The review points out the wide range of applications of MVC in various areas, including bioinformatics, social network analysis, and multimedia. Future research must create adaptive frameworks, improve the interpretability of models, and develop strong evaluation measures, thus unlocking the full potential of MVC in real-life data applications
Smart virtual rotor for frequency stability enhancement considering inverter-based renewable energy sources
Get PDFThis paper proposes a novel smart virtual rotor controller (VRC) that combines the Bat Algorithm (BA) with extreme learning machine (ELM) to enhance frequency stability in power systems. To reflect the impact of renewable integration, inverter-based power plants are incorporated to simulate high levels of penetration from power-electronics-based generation. The proposed method first tunes the virtual rotor parameters (virtual inertia and damping control) using BA under varying operating conditions. These parameters are then trained with ELM to enable adaptive control across different scenarios. Time-domain simulations demonstrate that the proposed approach outperforms existing methods in terms of frequency nadir and settling time, while also achieving a significant reduction in execution time, requiring only 0.0033 seconds