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All Journal Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems Control Systems and Optimization Letters Signal and Image Processing Letters IJMST Scientific Journal of Engineering Research Scientific Journal of Computer Science
Sharkawy, Abdel-Nasser
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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A New Adaptive Flux-Oriented Control Framework for Induction Motors with Online Neural Network Training Bekhiti, Belkacem; Al-Sabur, Raheem; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13727

Abstract

Unlike conventional field oriented control methods, this paper presents a mathematically novel control strategy for induction motor drives, formulated using a two-loop nonlinear dynamic inversion (NDI) framework inspired by aeronautical control architectures. Sensorless operation is realized with a conventional rotor flux observer, while several additional enhancements are introduced to raise overall performance. In particular, a real time radial basis function (RBF) neural network is systematically embedded in a model reference adaptive system (MRAS), replacing the traditional PI adaptation loop with an online training mechanism that improves speed estimation accuracy under parameter variations and load disturbances. The single layer RBF network is trained by gradient descent and incorporated into the nonlinear observer without compromising closed loop stability. The complete controller was implemented on a 1.1 kW, 1430 rpm induction motor using a dSPACE DS1104 real time platform. Experimental results show clear superiority over classical FOC as well as DTSFC and DTRFC schemes, achieving the lowest measured flux ripple (0.002 Wb), minimal torque ripple (0.043 N·m), and the fastest torque response time (0.65 ms). The steady state speed error was reduced by 91 % (from 0.65 to 0.08 rad/s), settling times remained below 60 ms, and both RMSE and ISE metrics decreased appreciably across all tested conditions. Although the proposed design incurs moderate computational overhead, it is fully compatible with real time execution. Future work will examine scalability to high power drives, improved resilience to temperature induced parameter drift, and adaptation of the NDI based framework to permanent magnet machines.
Temperature-Controlled Process for Recycled Waste Tire Polymer-Polymer Composites: An Innovative and Sustainable Solution for Marine Fender Applications Zaibel, Ali Habel; Almtori, Safaa A. S.; Al-Sabur, Raheem; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13813

Abstract

Marine fender prototypes play a critical role in protecting the ship and the berthing infrastructure from damage during docking. Recycling waste polymers, such as waste tires, into composite materials for marine fenders, can contribute to environmental sustainability and resource conservation. In marine Fender applications, compression testing often plays a crucial role; we should also test factors such as elasticity, stiffness, and hardness. In this study, pressure and hardness were selected, and Young's modulus was calculated for two types of composite materials: one manufactured from waste tires and high-density polyethylene (HDPE) and the other from waste tires and room-temperature vulcanized (RTV) silicone both in varying proportions. These types of materials were produced using a press machine equipped with a PID controller, which enables the adjustment of the temperature to a desired value, thereby achieving the best results. Prototypes containing 85% waste tire with 15% HDPE and 50% waste tire with 50% RTV silicone showed superior energy absorption and durability for marine fender applications. Despite achieving satisfactory hardness and hardness values, the waste tire and RTV silicone composite did not exceed those of the waste tire and HDPE composites, which had Young's modulus and Shore hardness values of 1.74 MPa and 56.6, respectively. The compression test showed that the waste tire and RTV silicone composites achieved higher values, surpassing 1990 kN. The findings provide a crucial foundation for utilizing waste composite materials in marine fender production.
Automated Water Cooling and Solar Tracking for Efficiency Improvement of PV Systems: A Systematic Review Hamed, Ahmed Hassan; Sharkawy, Abdel-Nasser; Hamdan, I.; Maghrabie, Hussein M.
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1642

Abstract

This article presented previous efforts for overcoming low photovoltaic (PV) solar panel electrical efficiencies resulted from excess heat problem reached in hot climates. Utilizing water cooling, temperature-controlled water cooling and solar tracking solar systems are discussed in this paper. Water is a perfect medium can be used for absorbing excess heat due to its high thermal capacity, availability and low cost. In addition to, utilizing control systems for water cooling systems based on Arduino unit and microcontroller chip which can be interfaced with Bluetooth, WIFI, and Internet of Things (IOT) enhances saving time and effort in large PV solar plants and PV performance. Solar tracking systems, depend on light-dependent resistors (LDRs) which are resistors operated by incident light, or ultraviolet (UV) sensors which are detectors based on incident ultraviolet radiation sensing enhances PV performance. Solar tracking systems enhances PV electrical efficiency compared to fixed PV panels. PV efficiencies of latest studies were presented and compared. Utilizing water cooling systems enhances PV electrical efficiency up to 30%, using an ON-OFF temperature-controlled water-cooling systems increased overall efficiency up to 51.4% and can reduce consumption of water up to 29.28%. In addition to, using two solar tracking systems enhances PV solar panel efficiency up to 65%. The increase in PV installation faces challenges includes millions of solar waste tons that harms environment and human health. However, it can be eliminated utilizing recycling technologies. Artificial intelligence (AI), machine learning techniques would enhance PV performance analyzing and data collection.
Trends and Impact of the Viola-Jones Algorithm: A Bibliometric Analysis of Face Detection Research (2001-2024) Wijaya, Setiawan Ardi; Famuji, Tri Stiyo; Mu'min, Muhammad Amirul; Safitri, Yana; Tristanti, Novi; Dahmani, Abdennasser; Driss, Zied; Sharkawy, Abdel-Nasser; Al-Sabur, Raheem
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): March
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.8

Abstract

The Viola-Jones algorithm remains a cornerstone in computer vision, particularly for object and face detection. This bibliometric study provides a comprehensive analysis of the algorithm’s academic impact and research trends, encompassing publication patterns, citation metrics, influential authors, and co-occurrence of keywords. The findings indicate a significant rise in research outputs and citations between 2016 and 2020, reflecting the algorithm's sustained relevance and application in various domains. Network visualization maps further reveal the algorithm's integration with diverse fields, including machine learning, image processing, and neural networks, emphasizing its versatility and adaptability to emerging technological challenges. Key research contributions include advancements in hybrid approaches, combining the Viola-Jones framework with techniques such as convolutional neural networks and HOG-SVM for improved detection accuracy. However, limitations such as computational inefficiency and sensitivity to environmental factors persist, presenting opportunities for innovation. This study concludes by highlighting future research directions, such as integrating deep learning and edge computing to enhance algorithmic performance in real-time and complex scenarios. This study provides a valuable reference for researchers and practitioners aiming to extend the Viola-Jones algorithm’s capabilities and applications by consolidating existing knowledge and identifying research gaps.
DC to AC Inverter Prototype for Small Scale Power Supply with SPWM Method Listyantoro, Fiki; Ma'arif, Alfian; Sharkawy, Abdel-Nasser; Marhoon, Hamzah M.
Control Systems and Optimization Letters Vol 1, No 2 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v1i2.24

Abstract

Inverter is a device to convert Dc current voltage into AC current voltage. In this article, the inverter is designed using the H-Bridge configuration. The advantage of this configuration is that there is no need for diodes and capacitors to balance the voltage. The main components in designing this inverter are the Arduino Nano, the IR2103 IC, and also the MOSFET. The switching method used is SPWM with H-Bridge configuration which is controlled by 2 IC IR2103. Based on the test results, the output voltage of this inverter is 7.45 Volts. With a DC voltage of 12 Volts. With the use of an oscilloscope that is used to measure the output waveform from the switching results of the MOSFET, the signal is still in the form of a square wave. To make a sine wave, an inductor filter is needed which functions to produce a signal to become a sine wave, and to keep the frequency at 50Hz. The voltage generated from the step-up transformer can be adjusted by rotating the feedback trimpot. The resulting voltage is 88 Vac to 260 Vac. and can accommodate a maximum load of 250 Watts. This inverter is also equipped with a PZEM-004T sensor which functions to read voltage, current, frequency and power data.
Improved DeepFake Image Generation Using StyleGAN2-ADA with Real-Time Personal Image Projection Abed, Ali A.; Talib, Doaa Alaa; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14659

Abstract

This paper presents an improved approach for DeepFake image generation using StyleGAN2-ADA framework. The system is designed to generate high-quality synthetic facial images from a limited dataset of personal photos in real time. By leveraging the Adaptive Discriminator Augmentation (ADA) mechanism, the training process is stabilized without modifying the network architecture, enabling robust image generation even with small-scale datasets. Real-time image capturing and projection techniques are integrated to enhance personalization and identity consistency. The experimental results demonstrate that the proposed method achieve a very high generation performance, significantly outperforming the baseline StyleGAN2 model. The proposed system using StyleGAN2-ADA achieves 99.1% identity similarity, a low Fréchet Inception Distance (FID) of 8.4, and less than 40 ms latency per generated frame. This approach provides a practical solution for dataset augmentation and supports ethical applications in animation, digital avatars, and AI-driven simulations.
A Systematic Review of Machine Learning and Deep Learning Approaches in MRI-Based Brain Tumour Analysis, Detection and Classification Omran, Hanan M.; Ibrahim, Khalil; Abdel-Jaber, Gamal T.; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.14673

Abstract

A brain tumour develops when abnormal cell growth happens in or near the brain. These tumours can grow slowly and not be cancerous, or they can grow quickly and spread, which is known as malignancy. Brain tumours put pressure on the surrounding brain tissues, causing symptoms like memory loss, migraines, movement dysfunction, and vision impairment. Brain tumours are often divided into two groups: primary tumours, which start in the brain, and secondary tumours, which are caused by cancers that spread to other regions of the body. Although brain tumours provide a significant medical challenge, patient outcomes have improved thanks to recent advancements in diagnostic and treatment methods. Because of its better soft-tissue contrast and noninvasive nature, magnetic resonance imaging (MRI) is one of the most important medical imaging modalities for the early identification and precise localization of brain tumours. Clinical practice also makes use of other imaging methods such as PET-CT and functional MRI (fMRI). Artificial intelligence and deep learning techniques have demonstrated significant promise in automated brain cancer analysis in recent years. These methods enable precise cancer diagnosis, classification, and segmentation by identifying intricate patterns from MRI data that are challenging to recognize through manual examination. A thorough study of current deep learning and machine learning techniques for MRI-based brain tumour analysis is provided in this paper. The current thorough literature search includes papers released between 2019 and 2024. 67 pertinent articles are chosen for in-depth analysis after predetermined inclusion and exclusion criteria is used. Many of these studies make use of publicly accessible datasets like Figshare, TCIA, and BraTS. The results show that deep learning models frequently outperform traditional machine learning methods in terms of accuracy and robustness, especially convolutional neural network-based designs. However, there are still issues with clinical generalisation, model interpretability, and data heterogeneity.
Bandwidth Management Using the Hierarchical Token Bucket Method to Enhance Server Network Performance Jayadi, Ahmad; Kusnayadi, Dedi Satriawan; Lonang, Syahrani; Dahmani, Abdennasser; Driss, Zied; Sharkawy, Abdel-Nasser
Scientific Journal of Computer Science Vol. 1 No. 2 (2025): December
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v1i2.2025.40

Abstract

Villa Nomada, as an accommodation in Kuta, Central Lombok, is experiencing internet network instability due to uneven and uncontrolled bandwidth distribution, which disrupts user comfort, especially for foreign guests who require an optimal connection. The solution implemented is bandwidth management using the Hierarchical Token Bucket (HTB) method to allocate bandwidth fairly and efficiently. This research contributes to improving quality of service (QoS) by optimizing network performance through HTB. The method used is HTB configuration to allocate bandwidth based on user categories (VIP, Regular, and Office). Network performance was evaluated before and after implementation to measure improvements in speed and stability. The research results showed that HTB successfully distributed bandwidth evenly, with VIP users receiving priority, while regular and office users obtained stable connections without interruptions. Network efficiency improved, reducing congestion and increasing user satisfaction. We rated the HTB method as “Good” for optimizing network performance. In conclusion, the implementation of HTB successfully addressed the bandwidth management issues at Villa Nomada, ensuring fair distribution and optimal network performance for all users.
Co-Authors Abdel-Jaber, Gamal T. Abed, Ali A. Abidin, Minhajul Al-Sabur, Raheem Alfian Ma’arif Almtori, Safaa A. S. Ameen, Yahya Muhammed Ammi, Yamina Ardana, Regina Olivia Fitri Basil, Noorulden Bekhiti, Belkacem Bintang Grendis, Nuraqilla Waidha Dahmani, Abdennasser Driss, Zied Fragulis, George F. Furizal, Furizal Hadi San, Ahmad Syahrul Hamdan, I. Hamed, Ahmed Hassan Hanini, Salah Hariche, Kamel Ibrahim, Khalil Ikram, Kouidri Imantoyo, Adi Jayadi, Ahmad Khairullah, Shawkat Sabah Khalaf, Hassanein I. Kherrour, Sofiane Kusnayadi, Dedi Satriawan Laidi, Maamar Listyantoro, Fiki Maghrabie, Hussein M. Mahmoud, Khaled H. Mangkunegara, Iis Setiawan Marhoon, Hamzah M Marhoon, Hamzah M. Mishra, Akshansh Mostfa, Ahmed A. Muhammad Amirul Mu'min Munzir, Misbahul Nazzal, Jamal Mahmoud Omran, Hanan M. Purwono Purwono, Purwono Salah, Wael Syahrani Lonang Talib, Doaa Alaa Tri Stiyo Famuji Tristanti, Novi Wijaya, Setiawan Ardi Yana Safitri, Yana Zaibel, Ali Habel