Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

31,456 Article Results

State of charge prediction for new and second-life lithium-ion batteries based on the random forest machine learning technique

10.11591/ijpeds.v17.i1.pp487-501
Masoud A. Sahhouk , Mohd Junaidi Abdul Aziz , Mohd Ibthisham Ardani , Nik Rumzi Nik Idris , Tole Sutikno , Bashar Mohammad Othman
Accurate state of charge (SOC) estimation is a critical requirement for the safe and efficient operation of lithium-ion batteries (LIBs), particularly in second-life battery (SLB) applications where battery ageing, nonlinear degradation, and measurement noise introduce uncertainty. Although numerous SOC estimation techniques have been proposed, reliable prediction for new and second-life batteries under varied operating conditions remains challenging. In this study, a comparative investigation of the conventional coulomb counting (CC) method and a data-driven random forest (RF) model is conducted for SOC prediction in new and second-life LIBs. Experimental data are obtained from Murata US18650VTC5D cells under pulse discharge tests (PDT), constant discharge tests (CDT), and dynamic stress tests (DST) across a wide range of C-rates. PDT is conducted at 0.24 C, CDT at 0.2 C, 0.5 C, 1 C, and 2 C, while DST is performed at C-rates ranging from 0.5 C to 4 C at a controlled ambient temperature of 25 °C. The RF model is trained using voltage, current, and time features and evaluated against CC using MAE, MSE, RMSE, and R² metrics. Results show that RF consistently outperforms CC under all conditions, particularly for SLBs, achieving significantly lower errors and R² values approaching 0.998. These findings confirm the effectiveness of RF-based SOC estimation for intelligent battery management systems (BMS).
Volume: 17
Issue: 1
Page: 487-501
Publish at: 2026-03-01

Enhancing SAPF performance with VOC and SVM for electrical networks depollution

10.11591/ijpeds.v17.i1.pp593-601
Kamal Bayoude , Mohamed Moutchou , Yassine Zahraoui
This paper presents a significant enhancement in the filtering performance of shunt active power filters (SAPF) by leveraging the voltage oriented control(VOC) in combination with a three-level NPC inverter using space vector modulation (SVM). The VOC technique enables precise control of the SAPF by utilizing the orientation of the voltages, thereby optimizing harmonic compensation and reference tracking. Incorporating a three-level inverter allows for more refined voltage modulation, resulting in a substantial reduction in injected harmonic content. Simulation results from MATLAB/Simulink demonstrate the effectiveness of this approach. Before compensation, the measured total harmonic distortion (THD) reaches 27.98%, exceeding the IEEE 519-1992 standard threshold of 5%. However, after applying the SAPF, the THD drops to 0.85%, aligning with international standards for power quality. The figures included in the study illustrate the stability of the phase-locked loop(PLL)voltages and the noticeable improvement in the source current waveforms, which exhibit a near-sinusoidal profile after filtering. These findings validate the superiority of the VOC strategy coupled with an NPC inverter and SVM in effectively mitigating harmonic distortions and enhancing power quality in modern electrical networks.
Volume: 17
Issue: 1
Page: 593-601
Publish at: 2026-03-01

Linearity analysis of a brushed DC machine thermal system in response to speed input using transfer function

10.11591/ijpeds.v17.i1.pp95-106
M. S. Mat Jahak , M. A. H. Rasid
This study represents a preliminary step toward developing a real-time condition monitoring system for brushed DC machines by analyzing the linearity of their thermal behavior. The temperature response of an MY1016 DC motor was collected under no-load conditions at five different speed levels, ranging from 20% to 100% of the rated speed, until the motor reached steady-state conditions to emphasize the temperature increase due to speed variability. A transfer function model was identified using MATLAB’s System Identification Toolbox, and the system’s linearity was evaluated by analyzing the spread of pole values across different speeds. Results showed significant variability in the coefficient of variation (CV) for key components, with values ranging from 0.18 for the casing to 0.84 for the brush. These findings reveal significant deviations from linear thermal behavior, indicating that a single linear transfer function may be insufficient to model the system. This research highlights the need to validate linearity assumptions in thermal modeling and introduces a framework for assessing thermal variability under varying speed conditions.
Volume: 17
Issue: 1
Page: 95-106
Publish at: 2026-03-01

The effects of surface albedo and photovoltaic system tilt angle on improving light energy utilization efficiency

10.11591/ijpeds.v17.i1.pp740-751
Ahmed Daud Mosheer , Ahmed Hussein Duhis , Hussain Abdulkarim Hammas
The ground-surface reflection (albedo) significantly influences the amount of solar radiation absorbed by photovoltaic panels and, thus, the optimum tilt angle for maximizing annual energy generation. Nevertheless, the majority of design models presume a constant albedo value, therefore could not accurately represent actual field conditions. This study aims to identify the optimal tilt angle for each albedo value that maximizes the annual energy output of a stationary on-grid photovoltaic system of 20.48 kWp installed in Baghdad, Iraq. Seven albedo values, varying from 0.09 to 0.87, were simulated using PVsyst software, with the reference case established at an albedo of 0.2 and a tilt angle of 31°. The results indicate that the optimum tilt angle is directly proportional to the surface reflection. For albedo levels below the reference of 0.2 (0.18 and 0.09), increased energy generation occurred at reduced tilt angles of 30.5° and 29°, respectively. Conversely, for increased albedo values (i.e., exceeding the reference of 0.2, spanning from 0.25 to 0.87), greater tilt angles were necessitated, reaching 45° at an albedo of 0.87, where the annual energy rose from 35.212 to 36.999 MWh/yr, signifying a 5.07% increase relative to the reference condition. The results validate that the optimal tilt angle fluctuates with ground-surface albedo, as surface reflectivity affects solar irradiation and energy output. Integrating actual albedo values in photovoltaic models is crucial for precise tilt adjustment and enhanced system efficiency.
Volume: 17
Issue: 1
Page: 740-751
Publish at: 2026-03-01

Classification and regression tree model for diabetes prediction

10.11591/ijict.v15i1.pp207-216
Farah Najidah Noorizan , Nur Anida Jumadi , Li Mun Ng
Diabetes mellitus is characterized by excessive blood glucose that occurs when the pancreas malfunctions while producing insulin. High blood glucose levels can cause chronic damage to organs, particularly the eyes and kidneys. Diabetes prediction models traditionally use a variety of machine learning (ML) algorithms by combining data from the glucose levels, patient health parameters, and other biomarkers. Prior research on diabetes prediction using various algorithms, such as support vector machine (SVM) and decision tree (DT) models, demonstrates an accuracy rate of approximately 70%, which is relatively modest. Therefore, in this study, a classification and regression tree (CART) multiclassifier model has been proposed to improve the accuracy of diabetes prediction, which is based on three classes: non-diabetic, pre-diabetic, and diabetic. The study involved data preprocessing steps, hyperparameter tuning, and evaluation of performance metrics. The model achieved 97% accuracy while utilizing the value of 5 for the number of leaves per node, the value of 10 for the maximum number of splits, and deviance as the split criterion, which also resulted in a precision of 98%, recall of 97%, and F1-score of 98%, showing that the proposed multiclassifier model can accurately predict diabetes. In conclusion, the proposed CART model with the best hyperparameter setting can enable the highest accuracy in predicting diabetes classes.
Volume: 15
Issue: 1
Page: 207-216
Publish at: 2026-03-01

Development of numerical model-based photovoltaic emulator for half-cut cell PV panel with multiple peaks output characteristics curve emulation capability

10.11591/ijpeds.v17.i1.pp343-358
Jordan S. Z. Lee , Jia Shun Koh , Rodney H. G. Tan , Nadia M. L. Tan , Thanikanti Sudhakar Babu
This study introduces a photovoltaic (PV) emulator focusing on a developed numerical model specifically for half-cut cell PV panels under partial shading conditions (PSCs), addressing a gap in research focused on full-cell models. The emulator uses a DC-DC buck converter and PI control to accurately replicate half-cut cell PV panel characteristics. A cost-effective hardware prototype validated the model's effectiveness in emulating multi-peak PV behavior under dynamic PSCs with up to three peaks and user-defined shading. This flexible and affordable platform enables efficient testing of MPPT algorithms and grid integration for PV systems using increasingly prevalent half-cut cell technology. Simulation results show high accuracy, with MAPE in power as low as 0.175% under uniform irradiance conditions and less than 0.302% under multi-peaks PSCs. Hardware validation confirms reliability with low MAPE in the power of 0.499% under uniform conditions and below 0.614% multi-peak PSCs, demonstrating the developed half-cut cell PV panel numerical model's accuracy in reproducing dynamic shading effects for renewable energy research.
Volume: 17
Issue: 1
Page: 343-358
Publish at: 2026-03-01

High voltage asymmetric converter for electrostatic particle accelerators

10.11591/ijpeds.v17.i1.pp553-571
Diego Alberto Fanego , Orlando Silvio Sandini , Hernan Emilio Tacca , Andres Juan Kreiner
This work presents several topologies of asymmetric high voltage converters for electrostatic particle accelerators. The options are compared on the basis of their transfer functions and the magnetic components required, and the most suitable for the intended purpose is selected. Simulations and measurement results of the prototype, which has symmetrical voltage output and soft switching in the main transistor, are presented. The prototype built features output voltages of 10 kVand-10 kV, the converter uses a single common command ground for the transistors simplifying its drivers, and also by means of the presented snubber circuit it recovers energy during soft switching.
Volume: 17
Issue: 1
Page: 553-571
Publish at: 2026-03-01

Application of capacitor banks to enhance energy efficiency in aeration systems for fisheries cultivation

10.11591/ijpeds.v17.i1.pp335-342
I Made Aditya Nugraha , I Gusti Made Ngurah Desnanjaya
Electrical energy consumption in aeration systems represents a major component of operational costs, primarily due to the low power factor of inductive equipment such as blowers. This study evaluates the effectiveness of capacitor banks in improving energy efficiency and their economic feasibility in small- to medium-scale aquaculture aeration systems. Over 90 days, measurements were conducted on energy consumption, current, voltage, and water quality parameters, including dissolved oxygen (DO) and pH in two systems: without and with capacitor banks. The results showed that the use of capacitor banks reduced daily energy consumption from 15.01 ± 0.45 kWh to 13.13 ± 0.45 kWh (savings of 12.51%), equivalent to approximately 56.4 kWh per month or 686.2 kWh per year. The average current decreased from 2.44 A to 1.88 A, while voltage, DO (6.50-6.64 mg/L), and pH (7.20-7.25) remained stable within the optimal range. Economic analysis revealed that an initial investment of IDR 1,500,000 has a payback period of 18 months, a net present value (NPV) of IDR 2.15-2.33 million (at 8% discount rate), and an internal rate of return (IRR) exceeding 50% per year. These findings demonstrate that the application of capacitor banks not only enhances energy efficiency and reduces power losses but is also highly feasible and profitable for practical adoption in aquaculture operations.
Volume: 17
Issue: 1
Page: 335-342
Publish at: 2026-03-01

Miniaturized circular fractal patch antenna with defected ground structure for high-selectivity dual-band X-band applications

10.11591/ijaas.v15.i1.pp372-383
Raju Thommandru , Rengaraj Saravanakumar
Microstrip patch antennas are easily fabricated and have a low profile, making them widely used in radar, satellite, and defence applications. Achieving high selectivity and miniaturization in X-band dual-band operation remains a challenge. Conventional designs using simple patch geometries and defected ground structures (DGS) often exhibit limited bandwidth, poor impedance matching, and reduced gain. To address these limitations, this work presents a miniaturized circular fractal patch antenna with an optimized DGS to enhance frequency selectivity, improve impedance matching, and maintain compact size. Circular fractal slots are introduced in the radiating patch to extend the effective current path while preserving the footprint. A centrally placed diamond-shaped slot provides capacitive loading that aids impedance tuning. Electromagnetic simulations were conducted in Ansys HFSS 2023 R2, and a prototype was fabricated on an FR-4 substrate with εr = 4.4, loss tangent = 0.02, and thickness 1.6mm. Measurements verify two passbands: 8.637–9.173GHz (center 8.8025GHz, return loss −22.0267dB, voltage standing wave ratio (VSWR) 1.1720, gain 4.82dB, efficiency 63.51%) and 10.121–10.956GHz (center 10.3700GHz, return loss −25.2864dB, VSWR 1.1199, gain 3.42dB, efficiency 72.58%). The antenna shows steady radiation and improved matching across both bands, supporting use in compact X-band front ends.
Volume: 15
Issue: 1
Page: 372-383
Publish at: 2026-03-01

Modeling of solar and wind energy using MATLAB/Simulink: a review

10.11591/ijaas.v15.i1.pp107-122
Nicholas Pranata , Fahmy Rinanda Saputri
This paper presents a concise review of solar (photovoltaic (PV)) and wind (horizontal axis) energy systems, focusing on their modeling and simulation using MATLAB)/Simulink. The advantages, disadvantages, strengths, and weaknesses of each system are discussed, providing a comprehensive overview of their characteristics. The review explores the mathematical modeling approaches for PV cells and modules specific for single diode model, as well as horizontal-axis wind turbine systems, highlighting the key equations and parameters involved. Furthermore, the paper discusses the emerging trend of hybrid solar-wind energy systems and their potential for optimizing power output, efficiency, and reliability. The review emphasizes the importance of accurate modeling based on fundamental knowledge, which serves as a practical implication for readers to understand the mechanism. Future research directions and challenges in the field of renewable energy modeling and simulation are also outlined. This review serves as a valuable resource for researchers, engineers, and decision-makers involved in the development and implementation of solar and wind energy systems.
Volume: 15
Issue: 1
Page: 107-122
Publish at: 2026-03-01

DCNNVA: a deep convolutional neural network for volcanic activity classification using satellite imagery

10.11591/ijaas.v15.i1.pp281-292
Yasir Hussein Shakir , Reem Ali Mutlag , Eshaq Aziz Awadh AL Mandhari , Mohamed Shabbir Abdulnabi
Monitoring and classifying volcanic activity are a critical task for disaster risk reduction and hazard management. Recent discoveries in machine learning and deep learning have proved excellent satellite image classification and volcanic anomaly identification capabilities, yet the majority of existing methods suffer from small datasets, particularly on solitary data modalities or particular cases, merely as examples. In this research work, we put forward develop deep convolutional neural network for volcanic activity (DCNNVA) classification specifically designed for satellite imagery on volcanic activity. We rigorously benchmarked DCNNVA model's strength against a total of eight state-of-the-art transfer learning models: ResNet50, NASNetLarge, DenseNet121, MobileNet, InceptionV3, Xception, VGG19, and VGG16. Comparative experimental results show that proposed DCNNVA framework's overall performance significantly surpasses its competitors with an accuracy of 99.33%, precision of 100%, recall of 98.67%, and F1-score of 99.33%, significantly beating existing state-of-the-art methods. Also, we create a deployable graphical user interface (GUI) system that is capable of real-time monitoring on volcanic activity and generates multi-modal alert processing that can make this research directly applicable for practical use on disaster management as well as in early warning systems. This research contributes a scalable, strong, as well as practical solution towards volcanic hazard identification as well as a baseline system toward developing future multi-modal as well as real-time geohazard tracking system frameworks.
Volume: 15
Issue: 1
Page: 281-292
Publish at: 2026-03-01

Crop prediction in Tamil Nadu according to environmental and soil factors using hybrid machine learning architecture

10.11591/ijaas.v15.i1.pp405-415
Sundaraj Kannan Susee , Shenbagaramasubramanian Shenbaga Vadivu , Murugesan Senthil Kumar
Mathuranthagam, Tamil Nadu, India is the site of this research initiative that employs state-of-the-art hybrid machine learning (ML) architectures to forecast crop suitability in relation to environmental and soil characteristics. The model takes advantage of the strengths of linear support vector machine (SVM) classifier, bidirectional long short-term memory (BiLSTM), and convolutional LSTM (ConvLSTM) networks, and the data to capture complicated temporal and spatial correlations. To prepare the dataset for model training, it is normalized using min-max scaling and then feature selected using a Jaya optimization technique. The dataset contains variables such as humidity, rainfall, temperature, and pH. Both the BiLSTM and the ConvLSTM improve the model's comprehension of context from both previous and subsequent time steps. The ConvLSTM also records spatial dependencies. A powerful decision-making tool for differentiating across crop varieties is the linear SVM classifier. Comparing the hybrid model's performance to that of traditional LSTM approaches using measures such as recall, accuracy, precision, and F1-score shows that it performs much better. Using this approach can see how deep learning (DL) can supplement more conventional ML methods and see how important local environmental data is for agricultural policy and planning.
Volume: 15
Issue: 1
Page: 405-415
Publish at: 2026-03-01

Advances in dermatological imaging: enhancing skin melanoma classification for improved patient outcomes

10.11591/csit.v7i1.p111-120
Debadutta Sahoo , Soumya Mishra
The study presents an enhanced AlexNet-based deep learning system for binary classification of melanoma skin cancer as either benign or malignant using two paired dermatoscopic and clinical image datasets. The study evaluates the resilience of the models across different image sets with common preprocessing and specific data augmentation, using a melanoma dataset containing 10,000 images and a benign versus malignant dataset with 3,600 images. The AlexNet refinement exceeded several standard machine learning (ML) classifiers and other deep architectures on the two datasets with practical training times, gaining 97.12% and 96.21% in balanced accuracy. The training proceeded with SGD as optimiser and cross-entropy as loss on 256×256 images. Benchmarking against support vector machine (SVM), k-nearest neighbour (KNN), and other convolutional neural networks (CNNs) designs shows that the selected architecture and hyperparameters achieved the highest performance on cost-effective computation for the routine melanoma triage. The report highlights the need for external validation, incorporation into dermatological workflows, and explainability to improve trust, diminish dataset bias, and support the safe clinical deployment in practice.
Volume: 7
Issue: 1
Page: 111-120
Publish at: 2026-03-01

Enhancing service reliability in heavy-duty commercial vehicles industry

10.11591/ijaas.v15.i1.pp99-106
Jonny Jonny , Januar Nasution
Reducing breakdown lead time is a critical factor in ensuring customer productivity and sustaining competitiveness in the heavy-duty commercial vehicle (HDCV) industry. This was tackled by applying a methodology called define, measure, analyze, improve, and control (DMAIC), which stands for DMAIC. By deploying it, the breakdown lead time of an Indonesian HDCV company can be minimized. Before the initiative, the lead time was 4 days with 81.54% or 815,400 defects per million opportunities (DPMO) or less than 1 sigma with only 303 parts within target. The reduction target was 2 days as required by its customers, with 40% or 400,000 DPMO or less than 2 sigmas, with 658 parts within target. After following the methodology, the lead time was less than 2 days, meeting customer requirements with 31.2% or 312,000 DPMO, or about 2 sigmas. It shows an improved lead time, which is less than 2 days from 4 days, and a sigma level which is less than 2 sigmas from less than 1 sigma, with 908 parts within target. The study demonstrates how integrating digital applications, remanufactured spare parts, and a centralized command center significantly shortens breakdown handling.
Volume: 15
Issue: 1
Page: 99-106
Publish at: 2026-03-01

An uneven cluster-based routing protocol for WSNs using a hybrid MCDM and max-min ant colony optimization

10.11591/csit.v7i1.p74-82
Man Gun Ri , Pyong Gwang Kim , JinSim Kim
In energy-constrained wireless sensor networks (WSNs) composed of sensor nodes (SNs) characterized by multi-criteria contradictory with each other, it is still one of the challenges to be solved to figure out how to combine multi-criteria with each other and how to use an intelligent optimization (IO) algorithm for developing an optimal cluster-based routing protocol. In this article, we overture a new routing protocol based on uneven cluster using the hybrid FCNP-VWA-TOPSIS (FVT) and an improved max-min ant colony optimization (ACO). This scheme uses the hybrid FVT to perform the clustering, and uses an improved max-min ACO to configure a routing tree for the relay transmission of sensed data. The extensive simulation experiments have been carried out to show that the proposed scheme greatly prolongs the network lifetime (NL) by achieving an energy consumption balance superior to the previous schemes.
Volume: 7
Issue: 1
Page: 74-82
Publish at: 2026-03-01
1 2 3
...
2097 2098
Show 127 of 2098

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration