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31,456 Article Results

Assessment of the efficiency and performance of different PV system configurations under various fault conditions

10.11591/ijpeds.v16.i4.pp2744-2756
Raghad Adeeb Othman , Omar Sharaf Al-Deen Yehya Al-Yozbaky
Partial shadowing, bypass-diode issues, photovoltaic (PV) module deterioration, and wiring issues are examples of PV failures that have a substantial effect on power production and cause distinct peaks in a PV system's P-V curves. Various PV fault types have been used in the solar cell system in this work. Four types were used: open circuit, line to ground, cross-line to line, and intra-line to line. The impact of various PV system failure types on the system's performance was emphasized in this study. MATLAB is used to display the simulation results for the four approaches (series parallel (SP), total cross tied (TCT), honeycomb (HC), and bridge link (BL)) under various fault scenarios. The current-voltage (I-V) and power-voltage (P-V) curves are used to compare the results for each fault scenario. The open circuit fault between PV (7.8) in the first string and PV (18.19) in the fourth string resulted in a 40% decrease in the short-circuit current of the photovoltaic system compared to its normal value in the SP topology, while in the HC and BL topologies, the current value exceeded the allowable limit. This, in turn, had an impact on the (I-V) characteristics of this topology. The fault's impact was minimal and within the typical bounds of its (I-V) characteristics in the TCT topology.
Volume: 16
Issue: 4
Page: 2744-2756
Publish at: 2025-12-01

Forecasting business exceptions in robotic process automation with machine learning

10.11591/ijra.v14i4.pp450-458
Igor Saez , Sara Segura , Mónica Gago
Business exceptions interrupt robotic process automation (RPA) workflows and oblige costly human intervention. This paper explores the application of machine learning (ML) time series forecasting techniques to predict business exceptions in RPA. Using RPA robot logs from a financial service company, we employ ARIMA, SARIMAX, and Prophet statistical models, comparing their performance with ML models such as XGBoost and LightGBM. Furthermore, we explore hybrid approaches that combine the strengths of statistical models with ML techniques, specifically integrating Prophet with XGBoost and LightGBM. Our findings reveal that a hybrid LightGBM model substantially outperforms traditional methods, achieving a 40% reduction in the weighted absolute percentage error (WAPE) when compared to the top-performing statistical model. These results suggest the potential of ML forecasting in optimizing RPA operations through the analysis of log-generated data.
Volume: 14
Issue: 4
Page: 450-458
Publish at: 2025-12-01

Comparative analysis of optimization techniques for optimal EV charging station placement

10.11591/ijpeds.v16.i4.pp2860-2867
Deepa Somasundaram , G. Prakash , N. Rajavinu , D. Lakshmi , P. Kavitha , V. Devaraj
The optimal placement of electric vehicle (EV) charging stations plays a crucial role in improving accessibility, reducing travel distances, and minimizing infrastructure costs in smart urban planning. This study presents a comparative analysis of traditional optimization techniques-such as linear programming (LP), particle swarm optimization (PSO), k-means clustering, and greedy heuristic methods-alongside a machine learning-based approach using genetic algorithms (GA). A machine learning framework is implemented to simulate EV charging demand, optimize station deployment, and incorporate real-world constraints like cost, grid capacity, and user travel penalties. The results demonstrate that GA achieves superior performance in balancing cost-efficiency and user convenience, outperforming traditional techniques in solution quality under dynamic demand conditions. PSO and LP provide faster convergence but are less adaptive to changing parameters. The study highlights the potential of integrating machine learning into infrastructure planning and provides actionable insights for urban planners and policymakers in developing resilient and intelligent EV charging networks.
Volume: 16
Issue: 4
Page: 2860-2867
Publish at: 2025-12-01

Integration and optimization of grid through ANN-based solar MPPT and battery

10.11591/ijape.v14.i4.pp988-998
Kolli Sujran , Ankala Sirisha , Ganapaneni Swapna , Malligunta Kiran Kumar , Kambhampati Venkata Govardhan Rao
Integration of solar energy into the grid is the most important aspect for achieving sustainable energy systems. This paper presents an artificial neural network-based maximum power point tracking (ANN-MPPT) system with battery storage to enhance grid efficiency. The proposed ANN-MPPT is dynamically adapted to the varying irradiance and temperature, hence ensuring optimal power extraction from the photovoltaic system. Excess energy is stored in batteries during high solar radiation and discharged when solar generation is low or grid demand is high, maintaining a stable power supply. This system enhances the grid performance in terms of supporting real-time energy exchange, load balancing, and grid stability. Efficient management of the energy fluctuations ensures reliability even at times of grid failures. Further, integration of ANN-based MPPT with battery storage reduces dependence on non-renewable sources and harmonizes solar energy utilization. It can be achieved through enabling smarter energy management and thus contributing to the resilience and efficiency of a grid for better integration of renewable energies. The proposed system can tolerate fluctuating grid demands apart from supporting the features of smart grid, hence viable for increasing stability and sustainability in the grid.
Volume: 14
Issue: 4
Page: 988-998
Publish at: 2025-12-01

Eco-friendly innovation: green energy empowered by IoT

10.11591/ijape.v14.i4.pp903-911
Nikita Amoli , Jitendra Singh , Rahul Mahala , Rajesh Singh , Anita Gehlot , Mahim Raj Gupta
Energy demand is high globally, impacting daily life and promoting sustainable modernization. Goal 9 aims to build an elastic framework for economies, while Goal 15 of the Sustainable Development Goals (SDGs) emphasizes the preservation of terrestrial environment, sustainable woodland management, and biodiversity conservation. The International Energy Agency predicts a significant increase in global renewable capacity, with solar PV being two-third of this growth. Green technology is crucial to combat global warming and Industry 4.0, a digital transformation that aims to create a strong framework for sustainable modernization. The growth of the smart grid is vital, involving energy sources, control techniques, computation, generation, transmission, distribution, and more. Supercapacitors store and deliver energy at high capacity, while green energy transforms fossil fuels into eco-friendly sources using natural resources like hydro, solar, wind, thermal, and biomass. This study explores the efficient use of microprocessors in solar and wind energy, as well as the application of actuators in the green energy sector. Green energy is a sustainable solution to increasing energy needs, reducing dependence on fossil fuels. IoT technologies, including sensors, actuators, microprocessors, and microcontrollers, are used in energy generation, transmission, distribution, and composition.
Volume: 14
Issue: 4
Page: 903-911
Publish at: 2025-12-01

A survey on convolutional neural network hardware acceleration through approximate computing multiple and accumulates unit

10.11591/ijra.v14i4.pp366-375
Suvitha Pathiyadan Sudhakaran , Aathmanesan Thangakalai
Convolutional neural networks (CNNs) are applied to a different range of real-world complex tasks to provide effective solutions with high accuracy. Based on the application's complexity, CNN demands a lot of processing units and memory spaces for its effective implementation. Bringing this computational task to hardware for processing the data to enhance the acceleration helps in achieving real-time performance improvement. Recent studies focused on approximation methodology to overcome this problem. This proposed survey analyzes various recent methods involved in implementing approximating computing-based processing elements and their usage in CNNs. Primarily, the survey focuses on multiple and accumulates (MAC) unit and their various approximation methods, which acts as a fundamental block as a processing element in the CNN layers. Secondly, it focuses on various CNN hardware acceleration architectures and their layers designed using different methods and their wide range of applications. Some of the recent design methods applied to various ranges of applications are also analyzed in the proposed survey. This detailed analysis gives an outlook on effective approximation blocks and the CNN architecture to be effectively used in various designs, with a scope of area in which future improvement can be made.
Volume: 14
Issue: 4
Page: 366-375
Publish at: 2025-12-01

Design of low-power, high-speed approximate 4:2 compressors for efficient partial product reduction in multipliers

10.11591/ijra.v14i4.pp459-467
Jabez Daniel Vincent David Michael , Anusha Gorantla , Ahilan Appathurai , Dinesh Ramachandran
Partial product reduction becomes the main task in the multiplication process. Therefore, the partial product stages of multipliers are reduced with the usage of compressors, by using compressors in the multiplier. Using compressors in the multiplier circuit significantly impacts multiplier performance. Approximate compressors are crucial for achieving better design metrics in parallel multipliers. This paper proposes to create various new approximate 4:2 compressor circuits. A trade-off is made between the performance and accuracy of this approximate circuit design approach. The proposed designs have been implemented using XOR-XNOR gates with a 2-to-1 multiplexer, and also XOR-XNOR gates with transmission gates. All these circuits have been simulated using Cadence in different technological nodes. Compared with the existing technique, the proposed 4:2 approximation compressor provides 51.4% power reduction and 26.45% delay reduction for 45 nm equipment.
Volume: 14
Issue: 4
Page: 459-467
Publish at: 2025-12-01

Antecedents and consequences of memorable experience in the airline industry: service robots versus human staff

10.11591/ijra.v14i4.pp409-417
Jinsoo Hwang , Ja Young (Jacey) Choe , Kyuhyeon Joo , Jinkyung Jenny Kim
The study aims to examine the type of service providers, such as service robots and human staff, as a potential moderator in the relationship between SERVQUAL and memorable experience in the airport industry. In order to verify 15 hypotheses, data were collected from 313 travelers who acquired information from service robots and 313 travelers who acquired information from human staff at the airport. The results of data analysis revealed that the five sub-dimensions of SERVQUAL, including tangibles, reliability, responsiveness, assurance, and empathy, enhance memorable experience. In addition, a memorable experience has a positive effect on customer satisfaction, which subsequently influences attitude and intention to use. In addition, the type of service providers moderated the links between i) responsiveness and memorable experience and ii) empathy and memorable experience.
Volume: 14
Issue: 4
Page: 409-417
Publish at: 2025-12-01

Enhancing health status prediction and data security using transformer-based deep learning architectures

10.11591/ijra.v14i4.pp418-428
Subramaniyan Senthamarai , Raja Manickam Mala , Vellaiyan Palanisamy
This paper proposes a privacy-preserving transformer-based federated learning (PPTFL) framework designed to enhance privacy, accuracy, and computational efficiency in healthcare data analysis. Federated learning (FL) has emerged as a promising solution for distributed machine learning while preserving data privacy, especially in sensitive sectors like healthcare. However, challenges such as maintaining high accuracy and managing communication overhead remain. The proposed PPTFL framework leverages the power of transformer models to improve the performance of federated learning while integrating privacy-preserving techniques. The model demonstrates superior performance with an accuracy of 92.87%, an F1 score of 92.37%, and a privacy budget (ϵ) of 1.6, outperforming existing approaches in terms of both privacy and accuracy. The model also exhibits computational efficiency, with lower communication cost and reasonable training time. Comparative evaluations with four relevant literature models further validate the effectiveness of the proposed PPTFL framework. This work highlights the potential of PPTFL to revolutionize healthcare informatics by providing secure, accurate, and efficient solutions for federated learning applications.
Volume: 14
Issue: 4
Page: 418-428
Publish at: 2025-12-01

ADC-LIO: A direct LiDAR-inertial odometry method based on adaptive distortion covariance

10.11591/ijra.v14i4.pp399-408
Lixiao Yang , Youbing Feng
Focusing on the localization challenges for robots in dynamic navigation environments, this study proposes a direct LiDAR-inertial odometry (LIO) system named ADC-LIO, which achieves robust pose estimation and accurate map reconstruction using adaptive distortion covariance. ADC-LIO is engineered to address uncertain motion patterns in autonomous mobile robots, effectively integrating LiDAR scan undistortion within the Kalman filtering update process by embedding an iterative smoothing process and a backpropagation strategy. The ADC-LIO architecture enhances point cloud accuracy, improving the system's overall performance and robustness. In addition, an adaptive covariance processing method is developed to resolve motion-induced sensing uncertainties, which calculates different covariances according to the error characteristics of the point cloud. This method enhances the constraints of high-quality point clouds, reduces the limitations on low-quality point clouds, and utilizes information more effectively. Experiments on the publicly available NTU-VIRAL dataset validate the effectiveness of ADC-LIO, which improves pose estimation accuracy and reduces absolute position errors compared to other state-of-the-art methods, including FAST-LIO, Faster-LIO, FR-LIO, and Point-LIO. The proposed ADC-LIO is an appealing odometry method that delivers accurate, real-time, and reliable tracking and map-building results, posing a practical solution for robotic applications in structured indoor and GPS-denied outdoor environments.
Volume: 14
Issue: 4
Page: 399-408
Publish at: 2025-12-01

Mobile robot replacement in multi-robot fault-tolerant formation

10.11591/ijra.v14i4.pp311-319
Ahmed M. Elsayed , Mohamed Elshalakani , Sherif Ali Hammad , Shady Ahmed Maged
Formation control in multi-robot systems (MRS) is essential for collaborative transport, environmental surveillance, material handling, and distributed monitoring. A major challenge in MRS is maintaining predefined formations or cooperative task execution when individual robots experience operational faults, potentially isolating them from the group. In mission-critical scenarios, preserving the number of operational robots is crucial for task success. To address this, we propose a Robot Replacement approach framework for differential wheeled mobile robots. This approach isolates faulty robots and dynamically replaces them with pre-deployed spares, ensuring uninterrupted formation tasks. A graph theory-based framework models inter-robot communication and formation topology, enabling decentralized coordination. The proposed techniques were implemented in a MATLAB/Simulink simulation environment. The simulated robots are equipped with LiDAR, an inertial measurement unit (IMU), and wheel encoders for navigation. Simulation results demonstrate that the framework successfully maintains the target formation and task continuity during robot failures by dynamically integrating replacements with minimal disruption.
Volume: 14
Issue: 4
Page: 311-319
Publish at: 2025-12-01

Effect on saturated and unsaturated fatty acids on various vegetable oils on droplet combustion characteristic

10.11591/ijape.v14.i4.pp980-987
Dony Perdana , Muhamad Nur Rohman , Mochamad Choifin
Vegetable oils have composed of triglycerides, which one consist of 3 fatty acids combined with glycerol. Each saturated and unsaturated fatty acid has a different effect on burning characteristics. This study aimed to investigated effect of fatty acids at ceiba pentandra and jatropha oils on the flame behavior of the droplet combustion process. The combustion characteristic was observed by an ignited droplet at the junction using a thermocouple and a high-speed camera (120 fps). Results showed that a higher saturated fatty acid content resulted in long-life and steady flames. This is because more oleic and linoleic acid carbon atoms leave the droplet area and react with air. Jatropha oil produces a higher temperature of 780 °C than ceiba pentandra oil. Temperature of a vegetable oils flame is influenced by number of carbon chains, double bond, and heating value. Ceiba pentandra oil has a higher burning rate of 0.185 mm/s than jatropha oil at 0.155 mm/s. The chain content of polyunsaturated fatty acids has significant effect on rate of combustion, which is due to the weak van der Waals dispersion forces, such that heat absorption is more active and energetic. The highest flame height for ceiba pentandra oil is 55.03 mm compared to for jatropha oil it is 46.82 mm. Long-chain unsaturated double bonds and glycerol cause micro-explosions. This micro-explosion caused the shape of the flame to split and expand so that evaporation occurred faster, thus increasing the size of the flame.
Volume: 14
Issue: 4
Page: 980-987
Publish at: 2025-12-01

Edge-aware distilled segmentation with pseudo-label refinement for autonomous driving perception

10.11591/ijra.v14i4.pp376-386
Novelio Putra Indarto , Oskar Natan , Andi Dharmawan
Achieving precise semantic segmentation is essential for enabling real-time perception in autonomous systems, yet leading approaches typically require substantial annotated data and powerful hardware, restricting their use on devices with limited resources. This work introduces an efficient segmentation framework that integrates pseudo-label refinement, knowledge distillation, and entropy-based confidence filtering to train compact student networks suitable for edge deployment. High-quality pseudo-labels are first produced by a robust teacher network, then further improved using a dense conditional random field to boost spatial consistency. An entropy-based selection mechanism removes unreliable predictions, ensuring that only the most trustworthy labels guide the student model's training. The use of knowledge distillation effectively transfers detailed semantic understanding from the teacher to the student, enhancing accuracy without added computational overhead. Experimental results with multiple EfficientNet backbones reveal that this pipeline improves segmentation accuracy and output clarity, while also supporting real-time or near real-time inference on CPUs with limited processing power. Extensive ablation and qualitative studies further confirm the method's robustness and flexibility for real-world edge applications.
Volume: 14
Issue: 4
Page: 376-386
Publish at: 2025-12-01

Design and development of a modular magnetic wheeled robot for out-pipe inspection

10.11591/ijra.v14i4.pp331-344
Sugin Elankavi Rajendran , Kuppan Chetty Ramanathan , Harish Kumar Guasekaran , Arun Kumar Pinagapani , Dinakaran Devaraj , Ramya Mathanagopal
This paper presents the design of a modular mobile robot capable of climbing and inspecting vertical ferromagnetic pipes using magnetic wheels. Mobile robots used for climbing ferromagnetic surfaces employ magnetic tracks, wheels, and magnets attached to the robot’s body. When it comes to ferromagnetic pipes, magnetic wheels and magnets attached to the body can be used. Among them, magnetic wheels are commonly used for inspecting ferromagnetic pipes. While current robots are suitable for large pipes, they are not practical for smaller ones. To address this gap, a small-sized robot equipped with a magnetic wheel system that ensures both strong attachment and smooth movement along vertical ferromagnetic surfaces is developed. The robot’s magnetic adhesion performance was analyzed through simulations using finite element method magnetics and validated through laboratory experiments. The results show an average error of only 8.25% between simulation and real-world tests, confirming the system’s reliability for external pipe inspection.
Volume: 14
Issue: 4
Page: 331-344
Publish at: 2025-12-01

Dynamic modelling and small-signal analysis of an efficient bridge-type multi-input DC converter for hybrid low-power systems

10.11591/ijpeds.v16.i4.pp2441-2452
Baya Reddy Lomada , Vangala Naga Bhaskar Reddy
This paper presents the dynamic modelling and small-signal analysis of a bridge-type multi-input DC converter designed for hybrid low-power systems. The converter architecture supports the integration of multiple energy sources such as fuel cells and photovoltaic (PV) arrays, enabling enhanced flexibility and reliability. A CUK-based configuration is employed to achieve continuous input current and reduced voltage stress across the switches. The dynamic behavior of the converter is analyzed through average large-signal and small-signal state-space modelling. Stability is assessed using the Routh-Hurwitz criterion, and steady-state analysis is carried out to support performance evaluation. Experimental results obtained from a 250 W prototype confirm the validity of the developed models and demonstrate the efficiency and suitability of the proposed converter for hybrid renewable energy systems.
Volume: 16
Issue: 4
Page: 2441-2452
Publish at: 2025-12-01
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