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31,456 Article Results

Optimization of solar panel orientation and tracking systems for standalone PV applications

10.11591/ijpeds.v16.i4.pp2721-2730
Singgih Dwi Prasetyo , Yuki Trisnoaji , Misbahul Munir , Meirna Puspita Permatasari , Abram Anggit Mahadi , Marsya Aulia Rizkita , Zainal Arifin
The performance of photovoltaic (PV) systems is greatly influenced by the angle of arrival of sunlight and the geometric orientation of solar panels, especially in tropical regions with the potential for solar energy throughout the year. This study aims to evaluate the effect of tilt angle variation and tracking systems on energy output and performance indicators of standalone PV systems using PVsyst software. The simulation was conducted at the State University of Malang, Indonesia, by comparing four fixed-angle configurations (20°, 40°, 60°, and 80°) as well as a two-axis tracking system. The simulation results showed that the two-axis tracking system produced the highest normalized daily energy production of 6.8 kWh/kWp/day, with a performance ratio (PR) of 77.2% and a solar fraction (SF) of 97.1%, while a fixed configuration with an angle of 80° showed the lowest performance. These findings confirm the importance of selecting optimal panel orientation to maximize the efficiency of PV systems, as well as being the basis for the development of advanced research, such as field-based experiments, integration of adaptive MPPT algorithms, and economic feasibility studies in the application of PV systems in tropical and off-grid regions.
Volume: 16
Issue: 4
Page: 2721-2730
Publish at: 2025-12-01

Impact of VR technology in physics teaching on students’ knowledge: a study on body acceleration

10.11591/ijere.v14i6.34942
Bakitzhan Kurbanbekov , Sherzod Ramankulov , Makpal Nurizinova , Beksultan Asanbek
The integration of virtual reality (VR) technology into education represents a promising innovation, particularly in enhancing the effectiveness of physics teaching. Traditional physics instruction often lacks interactive and immersive elements, which can limit students’ understanding of complex physical phenomena. This study addresses the challenge of improving comprehension of body acceleration by incorporating VR-based laboratory simulations. A quasi-experimental design was implemented, involving 222 university students randomly assigned to control and experimental groups. The experimental group conducted virtual experiments using VR simulations developed with Blender and Unity software, while the control group engaged in traditional lab activities. Data were collected through pre- and post-tests and analyzed using independent t-tests and G*Power software to assess statistical significance. The results revealed a notable improvement in learning outcomes for students exposed to VR-enhanced instruction, demonstrating increased engagement, deeper conceptual understanding, and improved ability to connect theoretical knowledge with practical application. This study confirms that VR technology is a powerful tool for modernizing science, technology, engineering, and mathematics (STEM) education and holds significant potential for improving cognitive outcomes and student motivation in scientific learning environments.
Volume: 14
Issue: 6
Page: 5038-5053
Publish at: 2025-12-01

Speed control of 3-phase induction motor with modified DTC using HTAF-ANN

10.11591/ijpeds.v16.i4.pp2197-2211
Arpita Banik , Raja Gandhi , Chandan Kumar , Achyuta Nand Mishra , Rakesh Roy
In this research paper, an artificial neural network (ANN) algorithm is implemented with modifications to enhance the performance of a direct torque controlled (DTC) induction motor drive. Since the main challenge in the conventional DTC technique is to tune the PI controller appropriately therefore in this work, an ANN technique is incorporated in place of the conventional PI controller. Sudden changes in speed and loading in induction motor drives lead to sharp fluctuations and disturb the motor performance. In order to overcome these issues, a trained ANN controller is initially used here to enhance motor drive performance. Subsequently, the performance is further improved by modifying the activation function in the ANN controller. Here, motor parameters at rated and variable speed with various loading conditions have been analyzed and compared for the DTC with a conventional PI controller with ANN, and a proposed ANN controller. Simulation of the complete model with the conventional and proposed controllers is done using MATLAB/Simulink platform to observe the various speed responses for different conditions, and the experimental setup is used to demonstrate the effectiveness and performance of the proposed system.
Volume: 16
Issue: 4
Page: 2197-2211
Publish at: 2025-12-01

Prospective teachers’ views on faculty members as content creators

10.11591/ijere.v14i6.33250
Seyithan Demirdağ , Anang Widhi Nirwansyah , Aji Heru Muslim , Bimo Bramantio
This study examines prospective teachers’ perceptions of faculty members as content creators in higher education, with a focus on how content development impacts effective teaching and learning. Using a qualitative descriptive phenomenological approach, the research explores the experiences of 26 senior students from diverse academic backgrounds at a university in Northwestern Turkey. The participants were selected through maximum variation sampling, representing fields such as special education, elementary education, science, social studies, preschool, and mathematics teaching. The findings reveal that prospective teachers value faculty members’ use of technology to create engaging, personalized learning experiences. Faculty expertise in digital content creation and contemporary teaching tools plays a key role in shaping students’ pedagogical development and boosting their confidence. However, challenges such as varying technological proficiency levels hinder some prospective teachers from fully integrating content creation into their future teaching practices. The study underscores the importance of faculty expertise and technological competence in teacher preparation. It advocates for comprehensive professional development programs to enhance educators’ content creation skills and encourages collaborations between faculty and technology experts to develop effective educational content. The study calls for addressing technological adoption barriers and equipping prospective teachers with necessary skills for effective 21st-century teaching. Future research should build on these findings with quantitative methods to further explore students’ perspectives on content creation.
Volume: 14
Issue: 6
Page: 4796-4811
Publish at: 2025-12-01

Enhanced voltage stability in power distribution networks through optimal reconfiguration using hybrid metaheuristic algorithms

10.11591/ijpeds.v16.i4.pp2582-2591
Mohammed Zuhair Azeez , Abbas Swayeh Atiyah , Yaqdhan Mahmood Hussein , Hatem Oday Hanoosh
An optimal network reconfiguration (ONR) is used in distribution power systems to improve voltage decreases within the permitted period and minimize real power losses. Consequently, attaining optimal reconfiguration in distribution systems is regarded as the primary objective of numerous researchers. Conventional heuristic techniques such as genetic algorithms (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) can reduce active power losses and enhance network stability. These algorithms indicate a greater number of difficulties, including inadequate convergence characteristics, a reduction in power loss, and an increase in bus voltage. This research proposes effective optimization strategies utilizing the salp swarm algorithm (SSA) and whale optimization algorithm (WOA) to augment bus voltage, reduce distribution losses, and improve network dependability. The proposed algorithms are executed and evaluated on the IEEE 33-bus and 69-bus networks to determine the ideal network architecture. The efficacy of the examined methodologies is illustrated through MATLAB under steady-state conditions, showcasing benefits in the reduction of active power loss relative to current algorithms. The comparison indicates that the SSA algorithm exhibits superior performance in terms of power losses and bus voltage enhancement relative to the WOA method. due to its enhanced exploration and exploitation capabilities, which help avoid local optima and ensure a more effective search for optimal solutions. SSA's adaptive mechanism and cooperative behavior improve convergence speed and solution accuracy, making it more efficient for optimization in network reconfiguration.
Volume: 16
Issue: 4
Page: 2582-2591
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

Implementation of a network intrusion detection system for man-in-the-middle attacks

10.11591/ijece.v15i6.pp3913-3927
Kennedy Okokpujie , William A. Abdulateef-Adoga , Oghenetega C. Owivri , Adaora P. Ijeh , Imhade P. Okokpujie , Morayo E. Awomoy
Intrusion detection systems (IDS) are critical tools designed to detect and prevent unauthorized access and potential network threats. While IDS is well-established in traditional wired networks, deploying them in wireless environments presents distinct challenges, including limited computational resources and complex infrastructure configurations. Packet sniffing and man-in-the-middle (MitM) attacks also pose significant threats, potentially compromising sensitive data and disrupting communication. Traditional security measures like firewalls may not be sufficient to detect these sophisticated attacks. This paper implements a network intrusion detection system that monitors a computer network to detect Address Resolution Protocol spoofing attacks in real-time. The system comprises three host machines forming the network. Using Kali Linux, a bash script is deployed to monitor the network for signs of address resolution protocol (ARP) poisoning. An email alert system is integrated into the bash script, running in the background as a service for the network administrator. Various ARP spoofing attack scenarios are performed on the network to evaluate the efficiency of the network IDS. Results indicate that deploying IDS as a background service ensures continuous protection against ARP spoofing and poisoning. This is crucial in dynamic network environments where threats may arise unexpectedly.
Volume: 15
Issue: 6
Page: 6027-6042
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

Bidirectional AC/DC converter connecting AC and DC microgrids for smart grids

10.11591/ijpeds.v16.i4.pp2549-2561
Nguyen Van Dung , Nguyen The Vinh
This paper proposes a converter connecting two independent AC and DC microgrids in a flexible microgrid and smart grid system. With this converter, basic DC/DC converter types such as Flyback are used to develop the power circuit and controller for the converter that is capable of integrating the operating functions for the operation between microgrids. The converter uses bidirectional switching locking technology to simplify the control algorithm. The energy is converted in two directions, AC/DC and DC/AC, with different working principles of increasing and decreasing voltage according to the standards of the distribution grid and DC microgrid. The TDH value is significantly limited when using the recovery circuit solution. The converter is designed, simulated based on OrCAD software, and tested with a capacity in the range of 2-10 kW. The DC microgrid output voltage is 400 VDC, voltage is 220 VAC.
Volume: 16
Issue: 4
Page: 2549-2561
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

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

Optimal battery sizing using modified spider monkey optimization in grid connected microgrids

10.11591/ijra.v14i4.pp356-365
Meraj Fatima , Manne Rama Subbamma
Microgrids (MGs) must have optimally sized storage and renewable energy sources to operate efficiently, economically, and reliably. MG may benefit from optimization techniques in their scheduling and sizing since they have a variety of energy sources with varying availability conditions and necessary costs. In this research, a novel modified spider monkey-based energy management system (MSM-EMS) has been proposed by increasing the photovoltaic (PV) or battery energy storage system (BESS) module capacity while minimizing grid connectivity dependency. The fundamental idea behind the proposed approach is greater dependability at the lowest feasible cost. By taking into account the BESS utilization factor and PV forced outage rates in a MG, the method becomes more realistic. Despite the absence of renewable energy sources and the grid, the proposed strategy provided critical loads according to schedule while maintaining reserve margins. Experimental findings demonstrate that the modified spider monkey optimization (MSMO)-based algorithm can determine the best BESS size and PV depending on cost. In comparison to particle swarm optimization (PSO) of $2756.1 and ABC of $2912.65, the ideal cost for EMS-MSMO is $2215.77 which is relatively low compared to the existing technique. As a result, the suggested MSMO algorithm and innovative energy management system has been optimized along with PV and battery dimensions.
Volume: 14
Issue: 4
Page: 356-365
Publish at: 2025-12-01

Enhanced integration of renewable energy and smart grid efficiency with data-driven solar forecasting employing PCA and machine learning

10.11591/ijpeds.v16.i4.pp2645-2654
Jayashree Kathirvel , Pushpa Sreenivasan , M. Vanitha , Soni Mohammed , T. Sathish Kumar , I. Arul Doss Adaikalam
A significant obstacle to preserving grid stability and incorporating renewable energy into smart grids is variations in solar irradiation. To improve solar power management's dependability, this research proposes a short-term solar forecasting framework powered by AI. Multiple machine learning models, such as long short-term memory (LSTM), random forest (RF), gradient boosting (GB), AdaBoost, neural networks (NN), K-Nearest neighbor (KNN), and linear regression (LR), are integrated into the suggested system, which also uses principal component analysis (PCA) for dimensionality reduction. The Abiod Sid Cheikh station in Algeria (2019-2021) provided real-world data for the model's validation. With a two-hour-ahead RMSE of 0.557 kW/m², AdaBoost had the most accuracy, whereas LR had the lowest, at 0.510 kW/m². In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. These findings highlight the efficiency of hybrid AI models based on PCA for accurate forecasting, which is crucial for smart grid stability.
Volume: 16
Issue: 4
Page: 2645-2654
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

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
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