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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

Automated smart handbag with enhanced women's safety using cutting edge technology

10.11591/ijra.v15i3.pp669-677
Vijayaraja Loganathan , Dhanasekar Ravikumar , Ashish Ragavendra Nattamai Uthayakumar , Arulmurugan Nagarajan Renukadevi , Rishikeshwaran Balamurugan Rani , Rupa Kesavan
Women’s safety has been an area of concern, especially in public places where timely assistance cannot be provided. To mitigate this problem, this paper proposes an intelligent handbag-based women’s safety system that utilizes the concept of biometric identification, location tracking, and edge computing-based AI threat verification. The proposed system, unlike other traditional women’s safety devices that rely on GPS-GSM for emergency alerts and are more likely to send false alarms, utilizes fingerprint identification for secure and authorized use, along with YOLO v3 vision model on an ESP32-CAM for threat verification. Upon failure in the authentication process or threat detection, the system sends an SOS message with the current location via GSM with the help of GPS coordinates. The system achieves an emergency response time of 33 seconds, primarily limited by GPS acquisition delay. The results confirm the effectiveness and applicability of the proposed system in providing an intelligent emergency response system for women’s safety.
Volume: 15
Issue: 3
Page: 669-677
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

Seamless UAV integration: a framework for network slicing as a service in 5G and beyond

10.11591/ijeecs.v43.i3.pp749-761
Bouzid Tarek , Noureddine Chaib , Mohamed Lahcen Bensaad
Unmanned aerial vehicles (UAVs) have demonstrated remarkable versatility across monitoring, delivery, and data collection applications. However, the growing complexity of network architectures with 5G and Beyond 5G (B5G) necessitates innovative solutions like network slicing, which faces challenges in radio access network (RAN) efficiency and optimization under higher frequencies. This paper introduces a novel framework for seamless UAV-assisted net work slicing as a service. By integrating UAVs directly into the network infrastructure, the framework enables on-demand delivery of tailored network slices to end users, addressing the limitations of traditional terrestrial approaches while enhancing efficiency and scalability. The proposed framework allows service providers and network administrators to deliver slices more effectively, while specifically tackling high-frequency RAN challenges by deploying UAVs as close-proximity relays to users. A key contribution is complete compatibility and interoperability with existing cellular infrastructure, ensuring seamless integration. The paper presents critical foundational concepts and a literature review, followed by the proposed framework’s architecture, layers, and methodology. Comprehensive tests and comparisons evaluate the framework’s performance, concluding with a discussion of applications and potential enhancements.
Volume: 43
Issue: 3
Page: 749-761
Publish at: 2026-09-01

Textile industry innovation: systematic review of key trends and particularities

10.11591/ijra.v15i3.pp720-736
Sebastián Cardona-Acevedo , Alejandro Arango-Correa , Diana Carolina Rios Echeverri , Alejandro Valencia-Arias , Jhon Edward Aguirre Cuervo
Innovation in the textile industry is a key strategic factor, influenced by geographic disparities, structural challenges, and rapid technological change. However, fragmented knowledge makes it difficult to fully understand the phenomenon. This study aimed to analyse how various types of innovation appear and interact in the global textile sector. A systematic literature review was carried out following PRISMA 2020 guidelines, using Scopus and Web of Science databases. From an initial pool of 94 articles, 19 met the inclusion criteria. Findings reveal that beyond specific advancements like automation or smart textiles, structural tensions hinder the integrated adoption of technological, organisational, and sustainability innovations. The diversity of analytical approaches shows there is no single, unified path to innovation in this sector. Instead, multiple innovation trajectories coexist, influenced by local conditions and unequal institutional capacities. In addition, knowledge gaps between developed and emerging regions, as well as the lack of focus on early stages of the supply chain, highlight the need to rethink research priorities. Ultimately, innovation in the textile industry must be understood as a comprehensive process that brings together technology, organisational change, and sustainability, requiring a holistic approach to improve competitiveness and ensure long-term transformation across the sector.
Volume: 15
Issue: 3
Page: 720-736
Publish at: 2026-09-01

Graph-guided contrastive transformer architecture for robust and explainable network intrusion detection

10.11591/ijra.v15i3.pp698-708
Archana Jayapal , Kamalakkannan Somasundaram , Arun Kumar Ramamoorthy
Intrusion detection systems (IDS) are very instrumental in protecting contemporary network infrastructures against the ever-advancing cyberattacks. Conventional signature-based and machine learning-enabled IDS solutions frequently have difficulty when it comes to high false-positive rates, inability to flexibly adapt to novel attacks, and the lack of support for complex traffic dynamics. New deep learning architectures have better detection properties, yet are limited by feature overlap, temporality, and lack of extensiveness to generalization in changing network conditions. To overcome these issues, this paper presents a new graph-guided contrastive transformer-based intrusion detection system (GCT-IDS) which aims at improving detection accuracy and robustness and preserving real-time feasibility. The framework combines feature interaction by graph modeling, contrastive representation learning, and a sparse self-attention transformer to effectively learn global traffic relationships and behavioral variations. The CSE-CIC-IDS2018 data is used to test the proposed method in real network conditions.
Volume: 15
Issue: 3
Page: 698-708
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

Cost-effective hardware solutions for experimental validation in renewable energy emulation and storage systems

10.11591/ijape.v15.i3.pp1287-1298
Yassine El Asri , Abdellah Lassioui , Hassan El Fadil , Anwar Hasni , Marouane El Ancary , Hafsa Abbade , Mohammed Chiheb , Mohamed Koundi
Experimental validation is essential in renewable energy emulators and energy storage systems to ensure reliability, accuracy, and practical implementation. However, the high cost of AC/DC converters, measurement circuits, and microcontroller-based control platforms limits access to experimental validation, particularly in developing regions. This paper proposes cost-effective hardware and software solutions for validating renewable energy systems and storage management. The proposed approach is based on affordable power converters, low-cost voltage and current measurement circuits, data acquisition tools, and open-source control platforms. The methodology includes the design, implementation, and experimental testing of these low-cost solutions under different operating conditions. The obtained results demonstrate satisfactory measurement accuracy, stable behavior, and acceptable error margins, confirming the feasibility of the proposed setup for experimental validation. These solutions provide a scalable and accessible alternative to expensive laboratory platforms, enabling researchers and institutions with limited resources to perform reliable experimental studies and contribute to the advancement of sustainable energy technologies.
Volume: 15
Issue: 3
Page: 1287-1298
Publish at: 2026-09-01

A novel MVVR controlled solar-PV fed MF-DVR for compensation of islanding and PQ issues in utility-grid integrated distribution system

10.11591/ijape.v15.i3.pp1023-1035
Tharinaematam Bhavani , Durgam Rajababu , Md Mujahid Irfan
The depletion of fossil fuels, planning of new industries, and increased population are considered significant motivations for the expansion of new power generation in line with the requisite load demand. In recent days, the solar-PV-based distribution generation is the most suitable power generation in a utility-grid-integrated distribution system. The main intention of this work is to present the effective DG scheme; it delivers the required active power during sudden interruptions, grid-islanding, and sudden block-outs. And also enhancing the voltage stability during voltage-harmonics, voltage sags/swells, and unsymmetrical fault conditions occurred in the utility-grid integrated distribution system through solar-PV fed multi-functional dynamic-voltage restorer (MF-DVR) device. The effective compensation performance of MF-DVR relies on viable reference voltage signals, which are produced by well-known control schemes reported in literature studies. But these regular schemes have reported that the major problems are highlighted and have been eliminated by proposing the novel modified voltage vector reference (MVVR) control scheme. The proposed MVVR controller perfectly produces the unique reference voltage signals for delivering a feasible switching pattern to the MF-DVR device. In this work, the design and performance of the proposed MVVR-controlled solar-PV-fed MF-DVR have been verified to enhance power quality and grid-islanding issues through MATLAB/Simulation software tool. The extracted simulation findings are presented with appealing interpretations complying with IEEE-519/2022 standards.
Volume: 15
Issue: 3
Page: 1023-1035
Publish at: 2026-09-01

Performance evaluation of a GA-tuned PID controller for a buck-boost converter based on integral error metrics

10.11591/ijape.v15.i3.pp1168-1179
Mahabaleshwara Bhat P. , Subramanya Bhat
DC-DC buck-boost converters are widely used in photovoltaic (PV) systems to maintain a regulated output voltage under varying source and load conditions. Although genetic algorithms (GA) based proportional-integral-derivative (PID) controllers are commonly used, the influence of different integral error performance indices on controller behavior has not been systematically examined. This paper presents a detailed study of GA-based PID tuning for a buck-boost converter operating in both buck and boost modes. Four integral error metrics-integral absolute error (IAE), integral time absolute error (ITAE), integral square error (ISE), and integral time square error (ITSE)-are employed as fitness functions to obtain optimal controller gains. A detailed MATLAB/Simulink model of the converter working in continuous conduction mode (CCM) is developed to evaluate controller performance under step input, source transients, and load transients. The results demonstrate that the selected error metric significantly affects transient characteristics, including rise time, settling time, overshoot, and steady-state error. Controllers tuned using ITAE and ITSE provide better transient performance compared to IAE and ISE based tuning. The results highlight the critical role of objective function selection in GA based PID optimization and provide practical guidelines for buck-boost converters in PV applications.
Volume: 15
Issue: 3
Page: 1168-1179
Publish at: 2026-09-01

Sustainable e-mobility with controlled charging scheme based on grid energy using machine learning

10.11591/ijape.v15.i3.pp1036-1050
Archana Kadam , Ramesh Mali , Reena Gunjan , Virendra Shete , Pradeep Mane
The electric vehicle (EV) popularity has taken off among consumers, which has in turn led to efforts to create an efficient EV charging infrastructure. This paper addresses this challenge by proposing a scheduled charging scheme that uses real-time data from a grid-connected charging station at Baner, Pune, operated by Pune Mahanagar Parivahan Mahamandal Ltd (PMPML). The proposed system makes use of advanced machine learning techniques such as the Stochastic dual coordinate ascent (SDCA) and Fast Forest (FF) algorithm, both of which allow for precise and efficient computations to predict charging finish times and make optimal scheduling decisions. The use of these algorithms in conjunction with ToU tariffs is cost effective when compared to flat rate tariffs. Grid load analysis shows that scheduling according to time lowers peak demand, equalizes load distribution, and lowers operating costs. A quantitative comparison has demonstrated both grid stability and economic efficiency gains over uncontrolled charging. The result is an extremely flexible framework for different charging events or stations which will be a viable way of managing energy in the fast-growing EV charging networks.
Volume: 15
Issue: 3
Page: 1036-1050
Publish at: 2026-09-01

Near-zero NDZ islanding detection for multi-source DGs via hybrid ANFIS and adaptive fuzzy classification

10.11591/ijape.v15.i3.pp1117-1131
Madamaneri Ramya , Thangellamudi Devaraju
Increased penetration of distributed generation (DG) increases the likelihood of inadvertent islanding, in which traditional active/passive approaches are plagued with large non-detection zones (NDZ) and power-quality trade-offs. This article introduces a hybrid islanding detector that combines an adaptive neuro-fuzzy inference system (ANFIS) and an adaptive fuzzy classifier, concurrently benefiting from active frequency drift and rate-of-change-of-frequency (RoCoF) while consuming multi-signal features-RMS/THD of voltage and current, frequency, and active/reactive power sensed at the PCC. This architecture eliminates fixed-threshold brittleness and reduces the NDZ to zero without compromising power quality. Innovative aspects are i) a stacked, real-time sampling approach (Ts = 5 ms) that supplies per-signal ANFIS modules and a main decision ANFIS, ii) subtractive clustering for generating fuzzy rules data-driven, and iii) low iq perturbation to maintain unity power factor when querying doubtful NDZ examples. MATLAB/Simulink experimentation on a seven-case, seven-stage multi-source PV-interfaced microgrid (including power-matched NDZ) demonstrates fast, robust trips at disconnection with retention of IEEE-1547 voltage/frequency envelopes; the structure achieves minimum/ideal detection times of 0.04 s and reliably indicates islanding at ~0.4 s in matched and mismatched conditions, achieving normal breaker trip expectations (
Volume: 15
Issue: 3
Page: 1117-1131
Publish at: 2026-09-01

Hybrid AC/DC and conventional AC house efficiency for net zero energy homes

10.11591/ijape.v15.i3.pp1458-1474
Taufik Taufik , Heru Nurwarsito , Tyler Bury , Rahman Azis Prasojo
The transition toward net-zero energy homes (NZEH) requires residential electrical systems that can efficiently integrate renewable generation, battery storage, and both AC and direct current (DC) loads. Although DC and hybrid AC/DC residential systems have been widely studied, limited work directly compares hybrid AC/DC and conventional AC house architectures under different grid standards, power levels, and AC/DC load ratios while considering DC bus losses. This study presents a MATLAB/Simulink-based steady-state efficiency comparison between hybrid AC/DC and conventional AC residential electrical systems. Twelve models were developed, consisting of six hybrid AC/DC and six conventional AC configurations under 120 V/60 Hz and 230 V/50 Hz standards. The models include PV generation, battery storage, inverter, AC/DC converter, multiple-input single-output (MISO) converter, and line-resistance effects. Results show that hybrid AC/DC houses achieve 4-11% higher efficiency than conventional AC houses when DC load demand remains below approximately 1.5-2.0 kW, mainly due to reduced conversion stages. At higher DC load levels, the efficiency advantage decreases because of copper losses in the 48 V DC bus. Increasing the DC bus voltage to 60 V reduces current-related losses and extends the efficient operating range. These findings indicate that hybrid AC/DC distribution is most suitable for residential applications with low-to-moderate DC demand, such as lighting, electronics, communication devices, and other DC-compatible appliances. The main contribution of this study is identifying the operating range, efficiency limit, and practical design implications of hybrid AC/DC residential distribution for future NZEH applications.
Volume: 15
Issue: 3
Page: 1458-1474
Publish at: 2026-09-01

Effect of power and voltage variations on transformer core losses and geometry for two distinct core materials

10.11591/ijape.v15.i3.pp995-1008
Kamran Dawood , Furkan Gezer , Güven Kömürgöz Kırış , Semih Tursun
This study presents a comprehensive comparison of transformer core size and no-load losses, focusing on transformers with power ratings ranging from 400 kVA to 3200 kVA. The research evaluates transformer performance at three distinct primary voltage levels: 6 kV, 15 kV, and 33 kV, while maintaining a constant secondary voltage of 0.4 kV. Additionally, the study investigates the impact of two commonly used transformer core materials, M4 and H0, on core design, with a particular focus on their effects on no-load losses. Another central aspect of the analysis is the examination of core geometry, including the cross-sectional area and height of the transformer core, across various power ratings and voltage levels. The results reveal a clear relationship between core size and energy efficiency. M4 cores exhibit higher no-load losses compared to H0 cores; additionally, H0 cores demonstrate better overall efficiency, making them ideal for high-efficiency applications. The findings underscore the importance of selecting the right core materials and geometries to balance performance, efficiency, and size. These insights are intended to guide researchers and transformer designers in making informed decisions when developing energy-efficient solutions for diverse applications across the electrical power industry.
Volume: 15
Issue: 3
Page: 995-1008
Publish at: 2026-09-01

Sliding-mode assisted direct torque control for reliable wind turbine operation

10.11591/ijape.v15.i3.pp1180-1189
Nehal Ouassila , Dib Djalel , Billel Meghni , Dib Nour Elhouda
This paper investigates and compares the performance of two advanced control strategies, direct torque control (DTC) and sliding mode control (SMC), applied to a permanent magnet synchronous generator (PMSG) used in wind energy conversion systems. The control schemes aim to ensure efficient energy conversion and stable operation under variable wind conditions. The comparison is carried out through detailed simulations considering electromagnetic torque response, stator flux behavior, speed regulation, and robustness to disturbances and parameter variations. The results show that DTC provides a fast dynamic response with a relatively simple control structure, but suffers from torque and flux ripples and sensitivity to parameter variations. In contrast, SMC demonstrates higher robustness against uncertainties and disturbances, with smoother torque characteristics and improved speed regulation. Overall, the study indicates that SMC is a promising approach for enhancing the stability and performance of PMSG-based wind energy systems. Future work may explore hybrid strategies combining the fast response of DTC with the robustness of SMC, as well as intelligent control techniques to further optimize energy extraction.
Volume: 15
Issue: 3
Page: 1180-1189
Publish at: 2026-09-01
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