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31,456 Article Results

Development of an advanced current mode charging control strategy system for electric vehicle batteries

10.11591/ijpeds.v15.i4.pp2639-2650
Liew Hui Fang , Muhammad Izuan Fahmi Romli , Rosemizi Abd Rahim , Muhammad Ezanuddin Abdul Aziz , Diyya Hidayah Abd Rahman , Habibah Haji Mokhtaruddin
Electric vehicles (EVs) face customer hesitancy due to challenges in locating fast charging stations, lengthy recharging times, and incompatible charging ports. This research addresses these issues by proposing a novel current mode control strategy for EV battery charging. Traditional charging methods often result in suboptimal rates, battery degradation, and safety risks. The primary objective is to enhance charging efficiency, safety, and battery lifespan by optimizing parameters such as voltage and current. Control mode charging offers significant advantages over plug-in charging by minimizing stress factors that contribute to degradation, such as high temperatures and excessive charging cycles. This approach aims to extend the lifespan of EV batteries while ensuring safe, efficient, and fast charging. The control system offers three charging modes: slow (0.49 A, 6.31 W, 264 mins), medium (2.74 A, 34.85 W, 50 mins), and fast (4.62 A, 50.80 W, 30 mins) using a 12 V single-phase supply. This advanced strategy significantly improves EV charging system efficiency, with fast charging achieving 80% higher efficiency than slow charging in both simulations and experimental testing. The key contribution of this research is the development of a tailored current mode charging strategy that optimizes charging efficiency while ensuring battery longevity and safety.
Volume: 15
Issue: 4
Page: 2639-2650
Publish at: 2024-12-01

Modelling soil deposition predictions on solar photovoltaic panels using ANN under Malaysia’s meteorological condition

10.11591/ijaas.v13.i4.pp796-805
Muhammad Aiman Amin Muhammad Suhaimi , Nofri Yenita Dahlan , Saidatul Habsah Asman , Natarajan Rajasekar , Hassan Mohamed
Solar photovoltaic (PV) panels performance is influenced by various external factors such as precipitation, wind angle, ambient temperature, wind speed, transient irradiation, and soil deposition. Soiling accumulation on panels poses a significant challenge to PV power generation. This paper presents the development of an artificial neural network (ANN)-based soil deposition prediction model for PV systems. Conducted at a Malaysian solar farm over three months, the research utilized power output data from the inverter as model output and meteorological data as input variables. The model employed the Levenberg-Marquardt backpropagation method with Tansig and Purline activation functions. Performance assessment via statistical comparison of experimental and simulated results revealed a coefficient of determination (R2) value of 0.68073 for the ANN architecture of 5 input layers, 30 hidden layers, and 1 output layer (5-30-1). Sensitivity analysis highlighted relative humidity and wind direction as the most influential parameters affecting PV soiling rate. The developed ANN model, combined with sensitivity analysis, serves as a robust foundation for enhancing the efficiency of smart sensors in PV module cleaning systems.
Volume: 13
Issue: 4
Page: 796-805
Publish at: 2024-12-01

Performance improvement of the RSM970S radar speed controller of ASECNA, Douala International Airport

10.11591/ijpeds.v15.i4.pp2080-2088
Moussa Bessike Angon , Offolé Florence , Nguiya Séverin , Moune Cédric Jordan , Ndoumbé Matéké Max
This article addresses the direct torque control associated with artificial intelligence for the control of the RSM970S radar of The Agency for the Safety of Air Navigation in Africa and Madagascar (ASECNA), Douala International Airport. The radar is driven by two identical induction motors powered by a single inverter. Direct torque control is associated with fuzzy logic and artificial neural networks. The test results performed in MATLAB/Simulink showed an improvement in the dynamic performance of the drive system compared to a conventional direct torque control. There is an overshoot in speed during the steady state of less than 0.955% of the neural technique over the fuzzy technique. But also, an accuracy of 99.58% of the two techniques proposed in this article. These results ensure good precision and stability of the radar in the detection of airplanes in the Cameroonian airspace.
Volume: 15
Issue: 4
Page: 2080-2088
Publish at: 2024-12-01

A multi-criteria trust-enhanced collaborative filtering algorithm for personalized tourism recommendations

10.11591/ijeecs.v36.i3.pp1919-1928
Qusai Y. Shambour , Mahran M. Al-Zyoud , Adeeb M. Alsaaidah , Mosleh M. Abualhaj , Ahmad A. Abu-Shareha
The exponential growth of online information has LED to significant challenges in navigating data overload, particularly in the tourism industry. Travelers are overwhelmed with choices regarding destinations, accommodations, dining, and attractions, making it difficult to select options that best meet their needs. Recommender systems have emerged as a promising solution to this problem, aiding users in decision-making by providing personalized suggestions based on their preferences. Traditional collaborative filtering (CF) methods, however, face limitations, such as data sparsity and reliance on single rating scores, which do not fully capture the complexity of user preferences. This study proposes a hybrid multi-criteria trust-enhanced CF (HMCTeCF) algorithm to improve the accuracy and robustness of tourism recommendations. HMCTeCF improves the quality of recommendations by integrating multi-criteria user preferences with trust relationships among users and between items. Experimental results using real-world datasets, including Restaurants-TripAdvisor and Hotels-TripAdvisor, demonstrate that HMCTeCF outperforms benchmark CF-based recommendation methods. It achieves higher prediction accuracy and coverage rate, effectively addressing the data sparsity problem. This innovative algorithm facilitates a more personalized and enriching travel experience, particularly in scenarios with limited user data. The findings highlight the importance of considering multiple criteria and trust relationships in developing robust recommendation systems for the tourism industry.
Volume: 36
Issue: 3
Page: 1919-1928
Publish at: 2024-12-01

Design of mean filter using field programmable gate arrays for digital images

10.11591/ijeecs.v36.i3.pp1430-1436
Duong Huu Ai , Van Loi Nguyen , Khanh Ty Luong , Viet Truong Le
In this paper, we design and analysis of mean filter using field programmable gate arrays (FPGAs) for digital images, FPGAs are integrated circuits consisting of interconnections that connect programmable internal hardware blocks allows users to customize operations for a specific application. FPGA is an ideal choice for real-time image processing, these FPGA devices are controlled in Verilog or VHDL languages, allowing to design at different levels and adapt to design changes or even support new applications throughout the life of the component. Digital image filtering is the most important task in image processing and with the help of computers, image recognition involves identifying and classifying objects in an image. This paper design of mean filter for digital image processing, implementation and analysis of image processing algorithms on FPGAs. The results obtained on the FPGA are compared and analyzed with the results by MATLAB software.
Volume: 36
Issue: 3
Page: 1430-1436
Publish at: 2024-12-01

Phishing detection using clustering and machine learning

10.11591/ijai.v13.i4.pp4526-4536
Luai Al-Shalabi , Yahia Hasan Jazyah
Phishing is a prevalent and evolving cyber threat that continues to exploit human vulnerability to deceive individuals and organizations into revealing sensitive information. Phishing attacks encompass a range of tactics, from deceptive emails and fraudulent websites to social engineering techniques. Traditional methods of detection, such as signature-based approaches and rule-based filtering, have proven to be limited in their effectiveness, as attackers frequently adapt and create new, previously unseen phishing campaigns. Consequently, there is a growing need for more sophisticated and adaptable detection methods. In recent years, machine learning (ML) and artificial intelligence (AI) have played a significant role in enhancing phishing detection. These technologies leverage large datasets to train models capable of recognizing subtle patterns and anomalies in both email content and website behavior. This research proposes a hybrid algorithm to detect phishing attacks based on clustering and classification machine learning methods (CMLM): deep learning (DL) and decision tree (DT). Simulation results show that the proposed technique achieves a high percentage of accuracy in detecting phishing. 
Volume: 13
Issue: 4
Page: 4526-4536
Publish at: 2024-12-01

Speed control analysis of voltage source inverter fed brushless DC motor

10.11591/ijape.v13.i4.pp928-933
Ambarapu Sudhakar , Bodapati Venkata Rajanna , Madhireddi Bhaskara Naidu , Munuswamy Siva Kumar , Nelaturi Nandaprakash , Mukku Bhavana , Yeluripati Lalitha Kameswari
The brushless DC (BLDC) motor requires to be controlled at the preferred speed in order to operate. A brushless DC motor's speed can be adjusted by adjusting the input voltage. In general, speed increases with voltage. The application of a Luo converter is made to satisfy the load demand, get rid of output voltage ripples, and reduce parasitic effects. The magnitude of stator input voltage to BLDC motor is controlled through the pulses applied by ATMEGA 328P micro controller to voltage source Inverter which in turn controls the magnitude of speed of BLDC motor. The position of the brushless DC (BLDC) motor is continually monitored by infrared sensors, which are then processed by a PIC16F872 microcontroller to produce the necessary pulses for BLDC motor speed regulation. The BLDC motor speed can be regulated by the pulses applied to voltage source inverter through the IR sensors placed at the motor. The outcomes of controlling the speed of a BLDC motor using voltage variation values have been shown.
Volume: 13
Issue: 4
Page: 928-933
Publish at: 2024-12-01

Deep learning approach for forensic facial reconstruction depends on unidentified skull

10.11591/ijai.v13.i4.pp3858-3868
Doaa M. Mohammed , Mostafa Elgendy , Mohamed Taha
Facial reconstruction, or facial approximation, is an essential problem in a criminal investigation involving reconstructing a victim's face from his skull to determine the victim's identification at a crime scene. Facial approximation plays a crucial part when there is a lack of clues with investigators. Investigators utilize facial approximation to guess the victims' identities. This research attempted to use computer-aided face reconstruction rather than traditional approaches. Traditional methods of face reconstruction include the use of clay or gypsum. Traditional procedures necessitate forensic professionals to rebuild the victim's face. This research uses the convolution neural network skull part with sift (CNNSPS) model is employed to reconstruct facial features from a skull image utilizing public datasets CelebAMask-HQ and MUG500+. The proposed algorithm was tested on unidentified skull databases, and celebrity faces were used. The genuine datasets are not available, which is the key issue in this research.
Volume: 13
Issue: 4
Page: 3858-3868
Publish at: 2024-12-01

Optimal DSSC’s deployment in power system using PSO

10.11591/ijape.v13.i4.pp835-843
Sandeep R. Gaigowal , Mohan M. Renge , Bhushan Y. Bagde , Manish Devendra Chawhan , Shweta L. Tiwari
Owing to the high cost of installation and operation, distributed flexible AC transmission system (FACTS) technology gives opportunity to provide cost-effective solution in power system operation and control. A distributed static series compensator (DSSC) is a series FACTS device and it is placed equidistantly placed on the existing line to vary the line current. Active power can be controlled in the line with the help of DSSC. This paper presents DSSC for active power flow control to enhance system loadability index and to minimize reactive power generation by generators. Since DSSC is of low power. a small value of reactance is emulated in the line. Large number of DSSC’s are distributed along the transmission line at regular intervals to realize considerable change in the current. A particle swarm optimization is implemented to determine DSSC’s emulated reactance optimally. In this condition, all the lines flowing power in their limits with increased loading condition. Maximum system loadability index is evaluated by employing optimal number of DSSC’s on the line. A multiobjective problem is formulated. One objective function is formed such that no line would become overloaded even when loading is increased. Other objective is formulated to minimize reactive power generation by generators. A compromise solution is investigated for optimally connected of DSSC devices to achieve both the objective functions. IEEE 14 bus system is taken for MATLAB simulation of DSSC compensated system.
Volume: 13
Issue: 4
Page: 835-843
Publish at: 2024-12-01

Prevalence and determinants associated with adolescent idiopathic scoliosis: results of screening in Surakarta, Indonesia

10.11591/ijphs.v13i4.23884
Alinda Nur Ramadhani , Dea Linia Romadhoni , Almas Awanis
Scoliosis is defined as a vertebral deformity characterized by a lateral deviation of at least 10° with vertebral rotation. Adolescent idiopathic scoliosis is generally found in children aged 10-18 years. Changes in the scoliosis curve are influenced by various etiologic factors including age, sex, growth factors, curve location and biomechanical factors. The study was conducted to determine the prevalence of adolescent iidophatic scoliosis in school students aged 12-15 years in Surakarta city. Physical examination using Adam's forward bending test, scoliometer and risk factor questionnaire. The research subjects were 325 people with an age range of 12-15 years. Research subjects are included in the adolescent idiopathic scoliosis category if the Adam's forward bending test is positive and the scoliometer value is >10°. The highest prevalence of adolescent idiopathic scoliosis was found in school children aged 14 years. The prevalence of adolescent idiopathic scoliosis is higher in female (3.4%) than male students (0.3%). Determining factors that might influence the onset of adolescent idiopathic scoliosis including age, gender, biomechanical factors, habits or lifestyles factors and physical activity.
Volume: 13
Issue: 4
Page: 1548-1553
Publish at: 2024-12-01

Low cost pulsed electric field generator using DC-DC boost converter and capacitor diode voltage multiplier

10.11591/ijape.v13.i4.pp874-885
Jeya Shree Thulasidas , Srinivasan Purushothaman , Srivatsen Ravishanker , Thejaswaroopan Mourougaiyan , Arruthra Anilkumar
Traditional high-voltage pulse generators, like Marx generators often face challenges related to efficiency and complexity. In this paper, a solid-state multi-module high-voltage pulse generator that integrates capacitor-diode voltage multipliers (CDVM) with DC-DC boost converters and closed-loop voltage control is proposed to overcome these challenges. The system achieves high output voltage by coupling the pulsed output voltages of individual low-voltage DC sources in series across each module. The proposed design was modeled using MATLAB, and experimental testing was conducted on a single stage. Comparative analyses between timedomain parameters, proportional-integral (PI), and fractional order proportional integral derivative (FOPID) controllers were performed. Both MATLAB simulations and experimental validations demonstrate the effectiveness of this approach. The rise time, peak time, settling time, and steady-state error are all improved using an FOPID controller, decreasing from 0.32 to 0.31 seconds, 0.42 to 0.35 seconds, and 3.15 to 2.20 seconds, respectively. These findings indicate that a closed-loop FOPID controller enhances time-domain performance parameters more effectively than a PI controller for a two-stage DC-DC voltage multiplier.
Volume: 13
Issue: 4
Page: 874-885
Publish at: 2024-12-01

Hybrid load forecasting considering energy efficiency and renewable energy using neural network

10.11591/ijaas.v13.i4.pp759-768
Adriana Haziqah Mohd Aizam , Nofri Yenita Dahlan , Saidatul Habsah Asman , Siti Hajar Yusoff
In recent years, the relationship between a country's gross domestic product (GDP) and its electricity consumption has changed significantly due to increased energy efficiency (EE) and renewable energy (RE) adoption. This decoupling disrupts conventional load forecasting models, affecting utility companies. This study has developed an innovative solution using an artificial neural network (ANN) Hybrid method for load forecasting, resulting in a remarkably accurate model with 99.68% precision. Applying this model to Malaysia's electricity consumption from 2020 to 2040 reveals a significant 13% reduction when accounting for EE and RE trends. This method aids risk management, contingency planning, and decision-making by accurately reflecting changing energy usage dynamics influenced by EE and RE sources.
Volume: 13
Issue: 4
Page: 759-768
Publish at: 2024-12-01

Optimized control strategy for a three-phase grid connected inverter using PI controller and DQ frame

10.11591/ijape.v13.i4.pp790-797
Nagasridhar Arise , Madde Saiteja , V. Siddu , Vadluri Kavya , Mada Vijay
This paper provides a proportional-integral (PI) controller and direct-quadrature (DQ) frame transformation-based optimum control method for a three-phase grid-connected inverter. In terms of grid synchronization, voltage regulation, and harmonic abatement, the proposed control technique attempts to improve the inverter's performance. By separating the control of active and reactive power, the control structure is made simpler and independent regulation of these parameters is possible. This improves the inverter's capacity to quickly react to grid disruptions and track reference values accurately. In order to lower carbon emissions and improve grid dependability, it has become vital to integrate renewable energy sources into the current power grid. Grid-connected inverters are essential in this situation because they transform DC electricity from renewable sources into grid-safe AC power. This abstract outline a proportional-integral (PI) controller and direct-quadrature (DQ) frame-based optimal control method for a three-phase grid-connected inverter using a MATLAB simulation.
Volume: 13
Issue: 4
Page: 790-797
Publish at: 2024-12-01

Ensemble stacking classifier model for prediction of diabetes

10.11591/ijict.v13i3.pp499-508
Mrunalini Bhandarkar , Varsha S. Bendre , Yash Venkatesh Bellary , Anuj Kiran Bhole , Abhishek Abasaheb Bhadange
Diabetes, being a chronic condition, possesses the capacity to instigate a global healthcare catastrophe. This condition can be managed and potentially cured with prompt diagnosis and treatment. Integrating machine learning technology with medical science enables precise prognosis of an individual’s susceptibility to diabetes. The proposed work presents the ensemble stacking classifier model. This efficient and effective diabetes prediction model predicts a patient’s diabetes risk by combining the output of multiple machine-learning techniques into a single model. The performance parameters of four distinct machine learning classification algorithms K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), and decision tree (DT) are compared in this study with those of the proposed stacked classifier model. The suggested model is developed using ensemble methods, where the previously discussed algorithms are integrated to create the base classifier layer of the stack classifier. The meta-classifier is implemented in the form of the logistic regression (LR) algorithm. Upon evaluating the performance of both the developed model and its algorithms, it is proved that the proposed model attains a testing accuracy of 88.5%, surpassing the accuracy of all baseline classification algorithms. As a result, this work determines that the ensemble stacking classifier model exhibits higher prediction accuracy than the base classifier algorithms. This finding underscores the model’s potential as a viable instrument for predicting diabetes in individuals.
Volume: 13
Issue: 3
Page: 499-508
Publish at: 2024-12-01

Enhancing flexural resistance in hot mix asphalt: a study of effects of wire mesh on load-bearing capacity

10.11591/ijaas.v13.i4.pp1039-1045
Romaynoor Ismy , Suhaimi Suhaimi , Raden Dedi Iman Kurnia , Richard Mareno
In transportation engineering, road pavement is commonly categorized as either flexible pavement or rigid pavement. The pavement demonstrates a distinct capacity to endure a variety of loads, including compressive and tensile loads. The capacity to endure compressive and tensile forces is extremely important, especially in the field of pavement construction, as it ensures both the longevity and the safety of the pavement. The objective of this study was to evaluate the capacity of hot mix asphalt to endure compressive and tensile pressures. The experimental methodology employed four different wire mesh deployment configurations on hot asphalt mixtures, utilizing three-point flexural test equipment. The data indicates the most effective method for mimicking hot mix asphalt involves adding a wire mesh layer at a depth of 30 mm below the surface of the experimental specimen. The particular modeling method showed a measurement of flexural resistance up to 291.85 kN. The study's findings indicate that the hot asphalt mixture exhibits a state of balance in its capacity to endure both compressive and tensile pressures. Incorporating a wire mesh layer within the middle section of the hot asphalt mixture has been perceived to enhance its ability to withstand tensile loads, hence improving its overall performance.
Volume: 13
Issue: 4
Page: 1039-1045
Publish at: 2024-12-01
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