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24,371 Article Results

Cascaded AC-DC parallel boost-flyback converter for power factor correction

10.11591/ijece.v15i1.pp224-234
Nur Vidia Laksmi B. , Muhammad Syahril Mubarok , Moh. Zaenal Effendi , Widi Aribowo , Tian-Hua Liu
A two-stage power factor correction (PFC) topology achieves a higher power factor quality and lower harmonic distortion than a single-stage converter. This paper introduces a two-stage PFC topology using a parallel boost and flyback converter which is employed as a voltage regulator The main boost converter is used for PFC and the other is the active filter circuit. The filter is implemented to improve the quality of phase-current and eliminate the switching loss. Furthermore, a reaction curve of Ziegler-Nichol’s method determines the controller parameter for cascaded PFC converter circuit. Simulated and experimental results are presented to validate the proposed method. The total harmonic distortion (THD) value decreases significantly from 83.35% become 0.98% in the simulation. In addition, experimental results show that the current response has good performances, including less harmonics, higher power factor, and lower THD value compared to without a PFC circuit. The PF increased from 0.43 become 0.96, the THD value decreased from 49.4% become 16.2%, and contains a small number of harmonics. The proposed controller method has better responses than the conventional one, including small steady-state error, fast rise time and settling time. A microcontroller (MCU), type STM32F407VG, produced by STMicroelectronics is used to execute the proposed control in both converters.
Volume: 15
Issue: 1
Page: 224-234
Publish at: 2025-02-01

Enhancing the reliance of emergency power supply systems for nuclear facilities using hybrid system

10.11591/ijece.v15i1.pp36-45
Mohammed Saade , Hussein El-Eissawi , Adel S. Nada
The performance of an atomic facility depends on the efficient supply of electricity, particularly emergency loads like monitoring and control equipment, radiation safety systems, and emergency lights. Most nuclear facilities rely on diesel generators to supply emergency loads during grid outages. Due to the diesel generator's imperfections, such as its starting time, it may fail to deliver power because it is unavailable due to maintenance, failure to start, or failure to run and supply the load. It cannot immediately supply the critical loads, resulting in a blackout and the release of radioactive substances into the environment. To address the previous issues, this paper proposes an improved method to enhance the reliability of nuclear facilities for providing electricity to safety and critical consumers during normal and emergency operating modes. The approach incorporates a photovoltaic (PV) system/battery, and its robustness and performance are tested using load flow and transient stability analysis. The simulation results demonstrated the effectiveness and speed of the proposed method when compared to the traditional method, as the emergency consumers were successfully powered within a very short time without fluctuations, and the voltage reduction and frequency were within the nominal values. The electrical transient analyzer program (ETAP) is used to validate these results.
Volume: 15
Issue: 1
Page: 36-45
Publish at: 2025-02-01

Handling class imbalance in education using data-level and deep learning methods

10.11591/ijece.v15i1.pp741-754
Rithesh Kannan , Hu Ng , Timothy Tzen Vun Yap , Lai Kuan Wong , Fang Fang Chua , Vik Tor Goh , Yee Lien Lee , Hwee Ling Wong
In the current field of education, universities must be highly competitive to thrive and grow. Education data mining has helped universities in bringing in new students and retaining old ones. However, there is a major issue in this task, which is the class imbalance between the successful students and at-risk students that causes inaccurate predictions. To address this issue, 12 methods from data-level sampling techniques and 2 methods from deep learning synthesizers were compared against each other and an ideal class balancing method for the dataset was identified. The evaluation was done using the light gradient boosting machine ensemble model, and the metrics included receiver operating characteristic curve, precision, recall and F1 score. The two best methods were Tomek links and neighbourhood cleaning rule from undersampling technique with a F1 score of 0.72 and 0.71 respectively. The results of this paper identified the best class balancing method between the two approaches and identified the limitations of the deep learning approach.
Volume: 15
Issue: 1
Page: 741-754
Publish at: 2025-02-01

Backstepping controller for speed loop of permanent magnet synchronous motors integrated with a time-varying disturbance load observer for Metro Nhon-Hanoi Station

10.11591/ijece.v15i1.pp235-242
An Thi Hoai Thu Anh , Lam Quang Thai , Pham Duc Minh
Urban rail systems offer the substantial potential for reducing environmental pollution, alleviating traffic congestion, ensuring safety, and maintaining punctuality. Nevertheless, the operation of urban rail demands substantial electrical energy, and saving energy solutions are crucial to exploiting the full advantages of electric trains. This paper proposes the replacement of traditional traction motors with permanent magnet synchronous motors (PMSMs) due to their superior efficiency, reduced power losses, and compact size compared to direct current (DC) motors or other asynchronous three-phase motors with equivalent power, developing a backstepping controller for the speed loop coupled with a load observer-time-varying disturbance (TVD). The simulation results were conducted in MATLAB/Simulink with parameters collected from the Nhon-Hanoi urban railway line, Vietnam, verifying the proposed algorithms' correctness and effectiveness.
Volume: 15
Issue: 1
Page: 235-242
Publish at: 2025-02-01

Predicting personality traits from Arabic text: an investigation of textual and demographic features with feature selection analysis

10.11591/ijece.v15i1.pp970-979
Khaoula Chraibi , Ilham Chaker , Azeddine Zahi
Automatic personality recognition (APR) utilizes machine learning to predict personality traits from various data sources. This study aims to predict the big five personality traits from modern standard Arabic (MSA) texts, using both textual and demographic features. The “MSAPersonality” dataset is employed to conduct a comprehensive analysis of features and feature selection methods to evaluate their impact on APR model performance. We compared feature selection algorithms from the filter, wrapper, and embedded-based categories through a systematic experimental design that consisted of feature engineering, feature selection, and regression. This study showed that each trait was more accurately predicted using a distinct set of features. However, age and study level were the most common features among the five traits. Moreover, although there were no statistically significant differences in performance between the feature selection techniques, embedded-based methods offered the best compromise between performance, time, and interpretability. These findings contribute to the understanding of APR in general and among Arabic speakers.
Volume: 15
Issue: 1
Page: 970-979
Publish at: 2025-02-01

Evaluating the effectiveness of machine learning methods for keyword coverage using semantic data analysis

10.11591/ijece.v15i1.pp559-568
Anargul Shaushenova , Aigulim Bayegizova , Gulnaz Baidrakhmanova , Zhanargul Abuova , Akmaral Kassymova , Dana Bakirova , Yekaterina Golenko
This article presents a comprehensive comparative analysis of two advanced hybrid machine learning approaches for keyword extraction: bidirectional encoder representations from transformers (BERT) combined with autoencoder (AE) and term frequency-inverse document frequency (TF-IDF) combined with autoencoder. The research targets the task of semantic analysis in text data to evaluate the effectiveness of these methods in ensuring adequate keyword coverage across diverse text corpora. The study delves into the architecture and operational principles of each method, with a particular focus on the integration with autoencoders to enhance the semantic integrity and relevance of the extracted keywords. The experimental section provides a detailed performance analysis of both methods on various text datasets, highlighting how the structure and semantic richness of the source data influence the outcomes. The evaluation methodology includes precision, recall, and F1-score metrics. The paper discusses the advantages and disadvantages of each approach and their suitability for specific keyword extraction tasks. The findings offer valuable insights for the scientific community, aiding in the selection of the most appropriate text processing method for applications requiring deep semantic understanding and high accuracy in information extraction.
Volume: 15
Issue: 1
Page: 559-568
Publish at: 2025-02-01

Airport infrastructure and runway precision aids for forecasting flight arrival delays

10.11591/ijece.v15i1.pp1038-1050
Hajar Alla , Youssef Balouki
Recent research has concentrated on using machine learning approaches to forecast flight delays. The majority of prior prediction algorithms were based on simple and standard attributes collected from the database from which the data were pulled. This article is the first attempt to propose novel features linked to airport capacity and infrastructure. The total runways, the total runway intersections, the longest runway length, the shortest runway length, the runway precision rate, the total terminals, and the total gates were all examined. In this paper, we suggest an optimized multilayer perceptron to predict flight arrival retards implementing data for domestic flights operated in United States airports. We employed data normalization, sampling techniques, and hyper-parameter tuning to strengthen the reliability of the suggested model. The experimental findings demonstrated that data normalization, sampling approaches, and Bayesian optimization produced the most accurate model with 92.49% accuracy. The achievements of the study were compared to other benchmark research from literature. The time complexity for the proposed model was computed and presented at the end of the investigation.
Volume: 15
Issue: 1
Page: 1038-1050
Publish at: 2025-02-01

An integrated smart water management system for efficient water conservation

10.11591/ijece.v15i1.pp635-644
Jeya Rajanbabu , Giri Rajanbabu Venkatakrishnan , Ramasubbu Rengaraj , Mohandoss Rajalakshmi , Neythra Jayaprakash
Water is a fundamental resource that sustains life, supports ecosystems, and plays a crucial role in various natural processes on earth. Water wastage is a major problem in the world, with a variety of causes including leaks in infrastructure and inefficient usage methods. A typical cause of water wastage is overflow from reservoirs or tanks as a result of poor maintenance or monitoring. This paper proposes a novel water resource management using internet of things (WARM-IoT) system to monitor and regulate the water level remotely by integrating IoT technology with demand side management (DSM) strategies, real-time monitoring of water levels has been achieved. The approach utilizes an ultrasonic sensor and Raspberry Pi for data acquisition and processing, fuzzy logic for decision-making, and an Android app for remote monitoring and control. The WARM-IoT assesses the system's performance, showcasing its efficacy in managing water levels and lowering electricity expenses. By analyzing consumption costs under different activation timings, significant potential for cost savings is observed, with a notable reduction of up to 6% in electricity expenses. Overall, the proposed WARM-IoT offers a comprehensive solution to water wastage and inefficient electricity usage in water management systems.
Volume: 15
Issue: 1
Page: 635-644
Publish at: 2025-02-01

Quadratic multivariate linear regressive distributed proximity feature engineering for cybercrime detection in digital fund transactions with big data

10.11591/ijece.v15i1.pp689-699
Arul Jeyanthi Paulraj , Balaji Thalaimalai
Digital fund transactions involve the electronic transfer of funds between parties through digital channels such as online banking platforms, mobile applications, and electronic payment systems. However, the rapid advancement of digital transactions has also directed cybercriminals to exploit vulnerabilities, engaging in money laundering and other illegal activities, resulting in substantial financial losses. The improve accuracy of cybercriminal detection by lesser time consumption, a novel technique called quadratic multivariate linear regressive distributed proximity feature engineering (QMLRDPFE) is developed. The proposed QMLRDPFE technique comprises two primary steps namely data preprocessing and feature engineering. Analyzed results prove that the QMLRDPFE technique outperforms existing methods in attaining superior accuracy and precision. Furthermore, QMLRDPFE method shows effective in reducing time utilization and space complexity for fraudulent transaction detection compared to existing approaches. Results to provide effective in reducing time utilization and space complexity for fraudulent transaction detection than the conventional methods.
Volume: 15
Issue: 1
Page: 689-699
Publish at: 2025-02-01

Multi-objective optimized task scheduling in cognitive internet of vehicles: towards energy-efficiency

10.11591/ijece.v15i1.pp1229-1241
M. Divyashree , H. G. Rangaraju , C. R. Revanna
The rise of intelligent and connected vehicles has led to new vehicular applications, but vehicle computing capabilities remain limited. Mobile edge computing (MEC) can mitigate this by offloading computation tasks to the network's edge. However, limited computational capacities in vehicles lead to increased latency and energy consumption. To address this, roadside units (RSUs) with cloud servers, known as edge computing devices (ECDs), can be expanded to provide energy-efficient scheduling for task computation. A new energy-efficient scheduling method called multi-objective optimization energy computation (MOEC) is proposed, based on multi-objective particle swarm optimization (MOPSO) to reduce ECDs' energy usage and execution time. Simulation results using MATLAB show that MOEC can balance the trade-off between energy usage and execution time, leading to more efficient offloading.
Volume: 15
Issue: 1
Page: 1229-1241
Publish at: 2025-02-01

Optimal task partitioning to minimize failure in heterogeneous computational platform

10.11591/ijece.v15i1.pp1079-1088
Divyaprabha Kabbal Narayana , Sudarshan Tekal Subramanyam Babu
The increased energy consumption by heterogeneous cloud platforms surges the carbon emissions and reduces system reliability, thus, making workload scheduling an extremely challenging process. The dynamic voltage- frequency scaling (DVFS) technique provides an efficient mechanism in improving the energy efficiency of cloud platform; however, employing DVFS reduces reliability and increases the failure rate of resource scheduling. Most of the current workload scheduling methods have failed to optimize the energy and reliability together under a central processing unit - graphical processing unit (CPU-GPU) heterogeneous computing platform; As a result, reducing energy consumption and task failure are prime issues this work aims to address. This work introduces task failure minimization (TFM) through optimal task partitioning (OTP) for workload scheduling in the CPU-GPU cloud computational platform. The TFM-OTP introduces a task partitioning model for the CPU-GPU pair; then, it provides a DVFS- based energy consumption model. Finally, the energy-load optimization problem is defined, and the optimal resource allocation design is presented. The experiment is conducted on two standard workloads namely SIPHT and CyberShake workload. The result shows that the proposed TFA-OTP model reduces energy consumption by 30.35%, reduces makespan by 70.78% and reduces task failure energy overhead by 83.7% in comparison with energy minimized scheduling (EMS) approach.
Volume: 15
Issue: 1
Page: 1079-1088
Publish at: 2025-02-01

From concept to application: building and testing a low-cost light detection and ranging system for small mobile robots using time-of-flight sensors

10.11591/ijece.v15i1.pp292-302
Andrés García , Mauricio Díaz , Fredy Martínez
Advancements in light detection and ranging (LiDAR) technology have significantly improved robotics and automated navigation. However, the high cost of traditional LiDAR sensors restricts their use in small-scale robotic projects. This paper details the development of a low-cost LiDAR prototype for small mobile robots, using time-of-flight (ToF) sensors as a cost-effective alternative. Integrated with an ESP32 microcontroller for real-time data processing and Wi-Fi connectivity, the prototype facilitates accurate distance measurement and environmental mapping, crucial for autonomous navigation. Our approach included hardware design and assembly, followed by programming the ToF sensors and ESP32 for data collection and actuation. Experiments validated the accuracy of the ToF sensors under static, dynamic, and varied lighting conditions. Results show that our low-cost system achieves accuracy and reliability comparable to more expensive options, with an average mapping error within acceptable limits for practical use. This work offers a blueprint for affordable LiDAR systems, expanding access to technology for research and education, and demonstrating the viability of ToF sensors in economical robotic navigation and mapping solutions.
Volume: 15
Issue: 1
Page: 292-302
Publish at: 2025-02-01

Evaluating geometrically-approximated principal component analysis vs. classical eigenfaces: a quantitative study using image quality metrics

10.11591/ijece.v15i1.pp311-318
Faouzia Ennaama , Sara Ennaama , Sana Chakri
Principal component analysis (PCA) is essential for diminishing the number of dimensions across various fields, preserving data integrity while simplifying complexity. Eigenfaces, a notable application of PCA, illustrates the method's effectiveness in facial recognition. This paper introduces a novel PCA approximation technique based on maximizing distance and compares it with the traditional eigenfaces approach. We employ several image quality metrics including Euclidean distance, mean absolute error (MAE), peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and structural similarity index measure (SSIM) for a quantitative assessment. Experiments conducted on the Brazilian FEI database reveal significant differences between the approximated and classical eigenfaces. Despite these differences, our approximation method demonstrates superior performance in retrieval and search tasks, offering faster and parallelizable implementation. The results underscore the practical advantages of our approach, particularly in scenarios requiring rapid processing and expansion capabilities.
Volume: 15
Issue: 1
Page: 311-318
Publish at: 2025-02-01

Design of a road marking violation detection system at railway level crossings

10.11591/ijece.v15i1.pp883-893
Helfy Susilawati , Sifa Nurpadillah , Wahju Sediono , Agung Ihwan Nurdin
When a train passed through a railway-level crossing, a common phenomenon was that many vehicles attempted to overtake others by crossing into lanes designated for oncoming traffic, resulting in both roads becoming congested with motorized vehicles. At that time, no system was in place to enforce penalties for violating road markings at level crossings. Therefore, a system capable of detecting such violations when trains pass through was needed. The designed system utilized a Raspberry Pi 4, a webcam, and an ultrasonic sensor. The single shot detector (SSD) method was employed for vehicle classification. The optical character recognition (OCR) method was used for character recognition on license plates. The research involved object detection at level crossings using varied objects (cars and motorcycles) with license plates categorized into two types: white background plates with black numbers and black background plates with white numbers. Based on the research results, turning on the webcam when the bar opened and closed using an ultrasonic sensor got an average error of 0.573% and 0.582%. The system could distinguish objects with an average recognition delay of 0.554 seconds and 0.702 seconds for car and motorbike objects. Regarding number plate detection, the success rate of character recognition stood at 64.45%.
Volume: 15
Issue: 1
Page: 883-893
Publish at: 2025-02-01

Implementing cloud computing in drug discovery and telemedicine for quantitative structure-activity relationship analysis

10.11591/ijece.v15i1.pp1132-1141
Palayanoor Seethapathy Ramapraba , Bellam Ravindra Babu , Nallathampi Rajamani Rejin Paul , Varadan Sharmila , Venkatachalam Ramesh Babu , Raman Ramya , Subbiah Murugan
This work aims to use cutting-edge machine learning methods to improve quantitative structure-activity relationship (QSAR) analysis, which is used in drug development and telemedicine. The major goal is to examine the performance of several predictive modeling approaches, including random forest, deep learning-based QSAR models, and support vector machines (SVM). It investigates the potential of feature selection techniques developed in chemoinformatics for enhancing model accuracy. The innovative aspect is using cloud computing resources to strengthen computational skills, allowing for managing massive amounts of chemical information. This strategy produces accurate and generalizable QSAR models. By using the cloud's scalability and constant availability, remote healthcare apps have a workable answer. The goal is to show how these methods may improve telemedicine and the drug development process. Utilizing cloud computing equips researchers with a flexible set of tools for precise and timely QSAR analysis, speeding up the discovery of bioactive chemicals for therapeutic use. This new method fits well with the dynamic nature of pharmaceutical study and has the potential to transform the way drugs are developed and delivered to patients via telemedicine.
Volume: 15
Issue: 1
Page: 1132-1141
Publish at: 2025-02-01
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