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Design and performance optimization of a magnetic based fault current limiter using BBO

10.11591/ijeecs.v43.i3.pp695-708
Tirtha Sankar Daphadar , Amalendu Bikash Choudhury , Tapan Santra
Magnetic fault current limiters (MFCLs) are taking on greater importance in the modern power system because of their ability to suppress excessive fault currents, while having no effect on normal operation. This paper proposes a simulation-based design and performance optimization of a magnetic-based fault current limiter by using biogeography-based optimization (BBO). A coupled electromagnetic-electrical model is developed to describe the nonlinear interaction between current, inductance, magnetic flux, core saturation, and magnetic biasing. The optimization is concentrated on the core geometry, length of the air gap, number of turns of the winding, and size of the magnetic element to ensure effective reduction of the fault current without violating voltage and thermal limits. Finite element analysis is used to study magnetic field distribution, saturation phenomenon, and temperature rise in normal and faulty conditions. Time-domain simulations are also performed for symmetrical and asymmetrical fault cases for transient performance evaluation. The optimized MFCL shows a great reduction of peak fault current and energy dissipation compared with the initial design, with negligible impact during normal operation. Sensitivity and parametric analyses validate the optimization design robustness, which shows that the proposed BBO-based optimization design framework is effective in improving MFCL performance in power system protection applications.
Volume: 43
Issue: 3
Page: 695-708
Publish at: 2026-09-01

Visual and electrical approaches for automated verification of electronic components

10.11591/ijra.v15i3.pp658-668
Sowmya Santhanam , Aadhitya Swaminathan Velmurugan , Durkadevi Chandrahasan , Krithika Arcot Rathna Kumar , Jeevanthika Chepauk , Deepika SasiKumar , Chaithanya Sarangaraj
Reliable inspection and sorting of electronic components have become pivotal along the path of electronic manufacturing toward higher density and automation. Automated optical inspection systems at present depend on visual assessment methods because they do not include electrical testing capabilities, which results in a component verification reliability gap. In spite of recent advances, largely due to the fact that most automated optical inspection systems still abide by visual evaluation, verification of the actual electrical behavior of components became quite impossible. This paper is focused on bridging this gap through the introduction of a unified inspection framework whereby visual analysis is executed along with programmable electrical validation under a single automated process in conformity with Industry 4.0 practices. The system's synchronized workflow includes vision-based detection, optical character recognition, resistor color-band parsing, surface defect analysis, and electrical testing in real time. The component localization task uses YOLOv5, while EasyOCR with a convolutional neural network-long short-term memory (CNN-LSTM) structure and HSV-based segmentation delivers exact value extraction results. The testing system achieved 98.4% classification accuracy, 98.9% value recognition accuracy, and 94.9% overall sorting accuracy when tested on 3,000 photos and 400 physically inspected components at a throughput rate of seven components per minute.
Volume: 15
Issue: 3
Page: 658-668
Publish at: 2026-09-01

ROVAA: Offline attendance automation using a voice–OCR-based 3-DOF robotic arm with Raspberry Pi

10.11591/ijra.v15i3.pp561-576
Rajanikanth Kashi Nagaraj , Archana Harihara Ranganatha , Surendra Hanumanthaiah Honnamachanahalli , Venu Manighatta Gopalakrishnappa
Conventional classroom attendance systems suffer from limitations in accuracy, hygiene, data privacy, and reliability in low-connectivity environments, whether they are manual, cloud-dependent, or single-modality systems. To address these gaps, this paper presents ROVAA, a low-cost, fully offline, AI-driven robotic attendance system in the classroom environment that uniquely integrates three complementary modalities: offline voice recognition, optical character recognition (OCR), and a 3- degrees of freedom (DOF) robotic arm controlled via inverse kinematics, an integration not demonstrated in prior work. The system operates on a Raspberry Pi 4 model B and employs the Vosk speech recognition model and Tesseract OCR for accurate offline processing. Audio and visual inputs are matched in real time to enable the arm to mark attendance at pre-calibrated positions on a touchscreen. Experimental validation under varied lighting and acoustic conditions yielded 96.2% speech recognition accuracy, 95.8% OCR accuracy, and 97.6% robotic arm precision, producing an overall system success rate of 92.8%, demonstrating that high reliability is achievable without cloud infrastructure. The system is designed for cost-effectiveness, data privacy, and scalability, making it suitable for resource-constrained environments such as rural schools and institutions with limited network access. It additionally serves as an educational platform for human–robot collaboration.
Volume: 15
Issue: 3
Page: 561-576
Publish at: 2026-09-01

Optimal robust control for self-balancing robot-based feedback linearization and Atom Search Optimization

10.11591/ijra.v15i3.pp519-528
Alaa Jumaah Al-Maiahy , Yahya Ghufran Khidhir , Adnan Jabbar Attiya , Hisham H. Jasim
In this paper, a robust control method is suggested for attitude control of the two-wheeled self-balancing robot by combining feedback linearization with sliding mode control techniques. The proposed method takes into account important challenges such as external disturbance and system uncertainty. Feedback linearization cancels the nonlinearities in the dynamics of the robotic system, while the sliding mode control handles the uncertainties and the external disturbance. The parameters of the proposed controller are selected by tuning the controller with the Atom Search Optimization algorithm. MATLAB is used to simulate the proposed controller. Simulation results indicate a good performance of the presented controller with high robustness compared with the proportional-integral-derivative (PID) controller. Moreover, the proposed method reduces the rise time by approximately 40% and 50% with respect to PID. These results illustrate the feasibility of the presented control method to be used for real-time implementation in autonomous robotic balancing systems.
Volume: 15
Issue: 3
Page: 519-528
Publish at: 2026-09-01

IoT-based approach to locate books in library system for integration with the automated library systems

10.11591/ijra.v15i3.pp647-657
Sagar S. Ajanalkar , Harshadeep S. Joshi
The main challenge in a large library is to retrieve a book effectively, where traditional barcode- or RFID-based systems require significant infrastructure and complex inventory management. An integrated IoT system consisting of LED-assisted shelf identification and an interactive web visualization application is proposed in this study for enhancing the reliability and effectiveness of automated library book retrieval. In this, a networked microcontroller architecture is used for real-time identification of the exact physical location of books by allowing users to visually locate the required books to be retrieved using LED indicators. Along with this, an interactive web visualization application is developed using HTML5, CSS, JavaScript, PHP, JSON, and MySQL for visualization of the shelf real-time location and location of the book within the shelf. The proposed system offers a cost-effective, scalable, and user-friendly solution compared to conventional RFID or barcode-based systems. Experimental testing of the proposed system passed all 9 tests conducted for its conformance in identifying the required book locations across four distinct test conditions. Beyond the library book retrieval application, the proposed system architecture has potential applications in smart warehouses, storage inventory management, and application as a supporting architecture for future systems such as autonomous service robots.
Volume: 15
Issue: 3
Page: 647-657
Publish at: 2026-09-01

AI driven automated library assistance using pick-to-light system

10.11591/ijra.v15i3.pp639-646
Archana S. Ubale , Vaishali Baste , Harshada Bhushan Magar , Nilakshee R. Rajule
This paper presents the design and implementation of a library assistance system that uses a pick-to-light mechanism and integrates AI-based book recommendations. Book retrieval done manually in libraries is frequently time-consuming, prone to errors, and inefficient. As a solution, we will suggest a hybrid library assistance system, which would be based on an ATmega328 microcontroller, user identification by RFID, pick-to-light, and an AI-based collaborative filtering recommender. The user is directed to the preferred books through the LEDs placed on the shelves, and the AI module gives the user personalized suggestions. The experimental outcomes of 100 users show significant advances over the old traditional manual processes. The time spent searching for a book dropped by 96.4 seconds to 34.8 seconds, the error rate dropped to 3.2%, user satisfaction went up to 4.6, and the success rate of the entire book search process went up to 98.1%. The 500 retrieval cycles of the stress testing showed stability, reliability, and uniformity of the system, and the performance of the LED-RFID. These findings suggest that the proposed system is highly efficient for the library, minimizes human error, and positively impacts the user experience, making it applicable to real-life library environments.
Volume: 15
Issue: 3
Page: 639-646
Publish at: 2026-09-01

SqueezeNet-driven AI framework for OCR of handwritten English characters and digits

10.11591/ijeecs.v43.i3.pp928-939
Areen Arabiat , Muneera Altayeb
Optical character recognition (OCR) plays a pivotal role in automated data entry, document digitization, and intelligent user interfaces, particularly as digital content continues to expand across diverse applications. Handwritten character recognition remains a challenging task due to variations in writing styles, noise, and structural complexity. This study investigates the use of artificial intelligence (AI) and machine learning (ML) strategies for classifying handwritten English characters and digits, employing the orange3 platform and a dataset of 2,728 images sourced from Kaggle, partitioned into 70% training and 30% testing sets. A SqueezeNet-based feature extraction approach was implemented to generate 1,000 discriminative features, which were subsequently used to train multiple ML classifiers, including support vector machine (SVM), decision tree (DT), and random forest (RF). Experimental evaluation revealed that the RF classifier achieved the highest accuracy of 99.3%, outperforming DT (90.3%) and SVM (89.9%). These findings highlight the effectiveness of lightweight deep learning architectures such as SqueezeNet in enhancing OCR performance, while demonstrating the robustness of ensemble learning methods in achieving near-perfect classification accuracy. The proposed framework underscores the potential of combining efficient feature extraction with ML classifiers to advance OCR systems for handwritten text recognition in real-world applications.
Volume: 43
Issue: 3
Page: 928-939
Publish at: 2026-09-01

Fall detection system based on accelerometer/gyroscope data fusion

10.11591/ijeecs.v43.i3.pp726-736
Amina Makhlouf , Isma Boudouane , Nadia Saadia , Amar Ramdane-Cherif
Falls are one of the major causes of death among the elderly worldwide. Faced with this public health challenge, many researchers have designed continuous monitoring systems to detect this type of incident at an earlier time, in order to facilitate immediate treatment and reduce potentially serious consequences. In this paper, we present a solution for the continuous monitoring of elderly or disabled people. We developed a fall detection system based on the fusion of data from a gyroscope and an accelerometer of a wearable inertial station. Two algorithms were developed: the first uses only the data from the gyroscope, while the second is based on the fusion of gyroscopic and accelerometric data. The evaluation of the system took into account several types of falls and activities, as well as different categories of people of different gender, age, height and weight, taken from the SISFALL database. The algorithm based on the gyroscope achieved an accuracy of 87.13% and a specificity of 84.27%, while its fusion with the accelerometer improved these performances to 90.76% and 89.92%, respectively, demonstrating the benefit of sensor fusion.
Volume: 43
Issue: 3
Page: 726-736
Publish at: 2026-09-01

Zeroing neurodynamics proportional-integral-derivative controller for order-2 system: design, analysis, and verification

10.11591/ijra.v15i3.pp690-697
Yunong Zhang , Junyan Liu , Zhonghua Li
This paper presents a zeroing neurodynamics proportional-integral-derivative (PID) controller for an order-2 single-input single-output (SISO) affine-in-control nonlinear system. The controller is derived from zeroing neurodynamics (ZN) and reformulates PID gains through ZN parameters, establishing an explicit correspondence to the closed-loop poles of the error dynamics. This enables systematic pole placement for stability and performance tuning, eliminating empirical trial-and-error. Theoretical analysis proves exponential convergence of the tracking error. The inverted pendulum, a benchmark system with strong geometric nonlinearity, is used to verify the controller. Simulations with large initial offsets and constant references show that the proposed controller maintains stable tracking in operating conditions where the conventional PID controller exhibits substantial performance degradation. The resulting framework provides a model-based and theoretically grounded alternative for nonlinear control.
Volume: 15
Issue: 3
Page: 690-697
Publish at: 2026-09-01

Cost-aware global frontier matching for ROS 2 multi-robot exploration

10.11591/ijra.v15i3.pp589-596
Chu Van Cuong , Tran Tuan Anh
Multi-robot frontier exploration supports warehouse mapping and inspection robotics, but geometric assignment can produce overlapping motion and inefficient target pairing. This paper presents a ROS 2 frontier-allocation layer that combines a weighted frontier cost with global one-to-one matching. The study isolates global matching from sequential assignment while keeping the cost formulation and ROS 2 execution stack fixed. Three policies are evaluated on two indoor maps, four team sizes, and three seeds. Across 72 completed main-policy runs, global matching gives the lowest mean completion time, travelled distance, path overlap, and assignment conflict. Relative to sequential cost-based assignment, it reduces completion time by 24.3%, travelled distance by 14.2%, and path overlap by 65.8%, with lower final coverage under the same stopping rule. The results support a coordination-efficiency benefit in the tested ROS 2 simulations; broader claims require larger, heterogeneous, dynamic, and physical deployments.
Volume: 15
Issue: 3
Page: 589-596
Publish at: 2026-09-01

Effectiveness of a structured ball game stimulation program on gross motor development among preschool children: a quasi-experimental study in Indonesia

10.11591/ijphs.v15i3.27020
Moh. Arip , Ely Mawaddah , Ade Wulandari , Mardiatun Mardiatun , Saskiyanti Ari Andini
Gross motor development during ages 3-6 years is critical for children's independence and social confidence. This study induced a structured ball play stimulation program in Indonesian children using quasi-experimental design, testing the specific impact on gross motor indicators compared to local PAUD routine activities. This quasi-experimental pretest-posttest control group study assessed ball game stimulation for gross motor development in 143 preschool children in Mataram, Indonesia (intervention n = 72; control n = 71). The intervention delivered structured ball activities three times weekly for six months (August 2025-January 2026): ball introduction, throwing/catching, kicking, and group games. Controls followed the regular curriculum. Outcomes used a standardized gross motor tool. Baseline demographics were homogeneous (age p = 0.998; gender p = 0.921). Results demonstrated dramatic improvements in the intervention group, with the "Normal" category increasing from 9.72% to 94.44%, while the "Doubtful" category decreased from 83.33% to 5.56%, and "Delayed" reached 0%. Conversely, the control group showed deterioration, with "Normal" decreasing to 0% and "Delayed" increasing to 88.73%. Mann-Whitney U test confirmed statistically significant differences between groups at posttest (p = 0.000), with intervention group mean score of 1.00 versus control group 2.79. These findings provide empirical evidence that structured ball game stimulation effectively improves gross motor development in preschool children.
Volume: 15
Issue: 3
Page: 696-705
Publish at: 2026-09-01

Spatial-channel reconstruction for efficient multiscale attention in robotic object detection

10.11591/ijra.v15i3.pp544-552
Mohammed Maiza , Chahira Cherif , Samira Chouraqui , Abdelmalik Taleb-Ahmed
Real-time object detection is a core capability for autonomous robots, unmanned aerial vehicles (UAVs), and self-driving systems operating in resource-constrained environments. This paper presents spatial-channel enhanced multiscale attention (SCEMA), a novel lightweight attention module designed to enhance robotic perception while minimizing computational overhead for embedded deployment. SCEMA employs a parallel dual-branch architecture that synergistically combines spatial-channel reconstruction with multiscale attention mechanisms. When integrated into the YOLOv8n framework (3.01M baseline parameters), the proposed YOLO-SCEMA model achieves significant performance gains across multiple challenging benchmarks relevant to robotics automation. Experiments on an NVIDIA RTX 4080 GPU demonstrate that on the ExDark dataset, YOLO-SCEMA improves mAP@50 by 7.37% over the baseline (69.07% to 76.44%) while reducing parameters by 36.88% (3.01M to 1.90M) and computational cost by 8.64% (8.1 to 7.4 GFLOPs). Consistent improvements are also observed on VisDrone2019 (+3.24% mAP@50) and FYP (+1.50% mAP@50) datasets. Comparative analysis demonstrates that YOLOSCEMA achieves superior accuracy-efficiency trade-offs, making it particularly suitable for deployment in low-light conditions, dense scenes, and complex structural environments for autonomous navigation, robotic surveillance, and industrial automation applications.
Volume: 15
Issue: 3
Page: 544-552
Publish at: 2026-09-01

Performance assessment of fractional order PID control for a 2DOF flexible joint robotic manipulator under various operating conditions

10.11591/ijra.v15i3.pp597-606
Ahmed Alkamachi , Ali Hussien Mary
Flexible joint robot manipulators (FJM) exhibit high nonlinear dynamics and coupling effects, which make their control a challenging task. This paper proposes a fractional order PID (FOPID) cascade control strategy for a 2DOF FJM. The controller performance is compared with that of a sliding mode controller (SMC) under identical operating conditions. Both controllers’ parameters are optimally tuned using the particle swarm optimization (PSO) algorithm to ensure a fair performance evaluation. The proposed control consists of an inner loop that regulates motor dynamics and an outer loop that ensures accurate link position tracking. Additionally, a gravity compensator is integrated to improve control efficiency by reducing the nonlinear load on the controller. Both controllers are evaluated under nominal conditions, external disturbances, and variable payloads. Numerical results show a performance trade-off: under nominal conditions, FOPID achieved superior tracking performance (ITAE = 0.1165 for link 1) compared to SMC (ITAE = 0.2423), while SMC provided a smooth, zero-overshoot response and consumed much less actuator energy (ISCE = 23.35 vs. FOPID's 154.78). Furthermore, when subjected to a severe 1.0 N.m external disturbance, FOPID showed superb robustness by maintaining an ITAE of 0.178, whereas SMC suffered severe performance degradation. These findings illustrate the trade-offs between energy efficiency and robust precision in FJM control.
Volume: 15
Issue: 3
Page: 597-606
Publish at: 2026-09-01

Design of a portable IoT robot with azure machine learning for monitoring mine workers’ health

10.11591/ijra.v15i3.pp577-588
Shanthi Natarajan , Vijayaraja Loganathan , Dhanasekar Ravikumar , Diwakar Venkat Nalini , Harish Elangovan , Balaji Arikrishnan
The mining environment exposes workers to physical, environmental, and health risks. The lack of effective real-time health monitoring systems leads to delayed medical responses. Hence, this paper discusses developing a portable Internet of Things (IoT) robot with advanced machine learning and cloud computing to monitor mine workers’ health and send out alerts. The system is equipped with IoT sensors that monitor parameters continuously, such as heart rate, body temperature, blood pressure, and environmental factors (gas concentrations, air quality). Data collected in real-time is transmitted to a cloud-based platform for analysis using advanced machine learning algorithms. MQ-135 detects harmful gases, and DHT11 measures humidity and transmits data to the Arduino UNO. The HC-SR04 sensor measures object distances by emitting ultrasonic waves and detecting their echoes, aiding in obstacle detection. The NEO-6M GPS with GSM SIM900 modules transmit location data and emergency alerts via the GSM network, enabling responses to potential dangers. Simulation via Proteus validates the robot’s transceiver connectivity, mobility, and sensing functions. To enhance monitoring precision, the system adopts XGBoost, which classifies mine conditions, and the training model achieves 96.77% accuracy with high precision and recall. The system with Azure Machine Learning improves detection accuracy, raising temperature, CO, NH₄, and NO₂ precision by 7.25%, 15%, 17%, and 18%, respectively. Thus, the system features an intelligent alert mechanism to notify users of emergencies, enhancing worker safety and minimizing health-related risks in mining operations.
Volume: 15
Issue: 3
Page: 577-588
Publish at: 2026-09-01

Remote controlled agricultural robot for spraying liquid type pesticides

10.11591/ijra.v15i3.pp553-560
Kalagotla Chenchireddy , Vadthya Jagan , M. Aruna Bharathi , Malaji Sushama , Varghese Jegathesan , Shabbier Ahmed Sydu
In modern agriculture, the use of automation and robotics is becoming increasingly essential to enhance efficiency, reduce labor, and ensure the safe handling of hazardous materials. This paper presents the design and development of a remote-controlled agricultural robot specifically intended for spraying liquid-type pesticides using radio frequency communication. The system comprises a mobile robot platform equipped with a pesticide tank, a spraying mechanism powered by a DC pump, and a set of drive motors for navigation. The robot is remotely controlled through an RF transmitter and receiver pair, allowing the operator to manually guide the robot across agricultural fields without direct contact with pesticides. The RF module transmits commands such as movement directions and spray activation, which are interpreted by an Arduino microcontroller onboard the robot. The system aims to reduce human exposure to harmful chemicals, minimize labor efforts, and increase precision in pesticide application. This solution is cost-effective, user-friendly, and adaptable for small to medium-sized farms, offering a practical step toward smart farming.
Volume: 15
Issue: 3
Page: 553-560
Publish at: 2026-09-01
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