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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems Automotive Experiences
Ghazaly, Nouby M.
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Mechanical Engineering

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Journal : Journal of Robotics and Control (JRC)

 1 
Using Grey Wolf Optimization Algorithm and Whale Optimization Algorithm for Optimal Sizing of Grid-Connected Bifacial PV Systems Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.
Journal of Robotics and Control (JRC) Vol 5, No 3 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i3.21777

Abstract

The shift towards renewable energies is driven by the shortage of fossil fuels for electricity generation and the associated harmful impacts. Grid-connected PV systems are a reliable and effective choice for power production across different uses, making them a key player in the global renewable energy landscape. Consequently, the careful selection of components for these systems is a crucial and widely studied aspect in this area of research. This paper introduced using gray wolf optimization algorithm GWO whale optimization algorithm WOA for determining the optimal number of grid - connected bifacial photovoltaic PV systems in Babylon Hilla. The considered factors included available space, desired energy production, radiation, dihedral factor, budget constraints, and grid connectivity requirements. The mathematical formulation of the problem and implementation details of the algorithms are presented. In addition, two cases studied are performed one for a residential area, and the other for a single house. The results demonstrated the efficiency and effectiveness of both algorithms in identifying optimal solutions for determining the size of systems in the area under study. However, the WOA surpassed the GWO in meeting the optimization criteria. The proper selection of these systems resulted in higher power generation, lower costs, improved energy management, and the advancement of sustainable solar energy solutions.
Predicting SI Engine Performance Using Deep Learning with CNNs on Time-Series Data Hofny, Mohamed S.; Ghazaly, Nouby M.; Shmroukh, Ahmed N.; Abouelsoud, Mostafa
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22558

Abstract

In this study, deep learning (DL) model is used to predict brake power (BP) of GX35-OHC 4-stroke, air-cooled, single-cylinder gasoline engine. The engine uses E15 (85% gasoline + 15% ethanol) as a fuel due to its high performance and low emissions. A convolutional neural networks (CNN) model is used on time-series data due to their ability to capture temporal patterns and relationships in sequential data, such as engine BP. While studying the performance of the network, it is found that the root mean squared error (RMSE) is 0.0007, explained variance score (EVS) is 0.9999, and mean absolute percentage error (MAPE) is 0.22%. Compared to traditional machine leaning methods, these metrics demonstrate the high accuracy and reliability of the model, confirming its effectiveness in predicting BP. Various performance curves are plotted such as comparing target and predicted values, regression plots (to indicate the generalization capability),  learning curve (to demonstrate the model's effective training progress and convergence), Bland-Altman plot (to show the convergence between the actual and predicted values), histogram and density plot (to show a close fit between predicted and actual values), density plot of actual and predicted outputs, and residual plot (to show randomly distributed errors). This high accuracy and reliability of this DL model help in effective real-time engine performance monitoring, and reducing emission levels, especially for the adoption and use of renewable fuels like E15.
Using Imperialist Competitive Algorithm Powered Optimization of Bifacial Solar Systems for Enhanced Energy Production and Storage Efficiency Hadi, Husam Ali; Kassem, Abdallah; Amoud, Hassan; Nadweh, Safwan; Ghazaly, Nouby M.; Abdulhasan, Mahmood Jamal
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22100

Abstract

Interest in renewable energy has grown due to increased environmental awareness and concern about climate change. Among the various renewable energy technologies, grid-connected bifacial PV systems are particularly important due to their higher efficiency compared to conventional systems. However, maximizing energy harvesting and storage efficiency remains a challenge for these systems, requiring the use of an efficient charge controller and an appropriate battery. The process of setting charge controller parameters and selecting the best storage technology is complex and requires a thorough study of various operating conditions. The main research contribution of this paper is the development of an efficient optimization methodology to increase the energy production and storage efficiency of the studied systems using optimization algorithms. The imperialist competitive algorithm (ICA) is used in the system design to improve performance through optimal adjustment of charge controller parameters and selection of appropriate storage technology. This decision was based on factors such as energy production from PV panels, energy consumption from loads, and energy storage in batteries. Performance is also evaluated using both the flower pollination algorithm (FPA) and Gray Wolf optimization (GWO) algorithms. The study evaluated system performance by analyzing energy production, storage efficiency, and cost effectiveness. The results showed that the ICA algorithm is effective in improving energy production and storage, resulting in higher energy output, better battery efficiency, and lower system costs. In addition, lithium-ion batteries were identified as the best storage technology. This research demonstrates the potential of the ICA approach to increase efficiency and reduce costs in the PV systems.
ESPNow Protocol-Based IIoT System for Remotely Monitoring and Controlling Industrial Systems Hailan, Maryam Abdulhakeem; Ghazaly, Nouby M.; Albaker, Baraa Munqith
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.21925

Abstract

The shift from conventional manufacturing facilities to intelligent manufacturing facilities is a topic of significant interest due to its profound and enduring implications for the evolution of manufacturing practices on a global scale. The advent of Industry 4.0 is geared toward advancing the manufacturing sector by facilitating the production of goods with brief product life spans and tailored to individual customer preferences in a financially efficient manner. This paper introduces an Industrial Internet of Things system that powers the ESP32 microcontroller, the Blynk platform, and the ESP-Now protocol for remote monitoring and control of industrial processes. The system aims to improve operational efficiency and data management in industrial settings by addressing challenges associated with communication protocols and user interfaces. The implementation of the system comprises configuring the ESP32 to collect data from several sensors dispersed across factory sites. Integration with the Blynk platform enables real-time data visualization and device management, while the ESP-Now protocol facilitates efficient communication among IoT devices for seamless monitoring and control functionalities. The developed system shows significant advancements in industrial monitoring and control by offering enhanced scalability, interoperability, and adaptability to diverse industrial environments. Monitoring capabilities include weather conditions, motion detection, gas levels, and water quality assessment, with control functionalities extending to regulating water pumps and lamps. Metrics for assessing GUI performance include response time, data visualization accuracy, and user interaction efficiency. Robust encryption protocols and authentication mechanisms are implemented to ensure data security and privacy, enhancing the system's reliability and trustworthiness in industrial applications. The integrated system provides a comprehensive solution for industrial monitoring and control, offering efficient communication, scalability, and data security measures to optimize operational efficiency in diverse industrial environments. The system's advanced features and capabilities position it as a valuable tool for enhancing industrial processes and ensuring seamless data management and control.
Predicting SI Engine Performance Using Deep Learning with CNNs on Time-Series Data Hofny, Mohamed S.; Ghazaly, Nouby M.; Shmroukh, Ahmed N.; Abouelsoud, Mostafa
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22558

Abstract

In this study, deep learning (DL) model is used to predict brake power (BP) of GX35-OHC 4-stroke, air-cooled, single-cylinder gasoline engine. The engine uses E15 (85% gasoline + 15% ethanol) as a fuel due to its high performance and low emissions. A convolutional neural networks (CNN) model is used on time-series data due to their ability to capture temporal patterns and relationships in sequential data, such as engine BP. While studying the performance of the network, it is found that the root mean squared error (RMSE) is 0.0007, explained variance score (EVS) is 0.9999, and mean absolute percentage error (MAPE) is 0.22%. Compared to traditional machine leaning methods, these metrics demonstrate the high accuracy and reliability of the model, confirming its effectiveness in predicting BP. Various performance curves are plotted such as comparing target and predicted values, regression plots (to indicate the generalization capability),  learning curve (to demonstrate the model's effective training progress and convergence), Bland-Altman plot (to show the convergence between the actual and predicted values), histogram and density plot (to show a close fit between predicted and actual values), density plot of actual and predicted outputs, and residual plot (to show randomly distributed errors). This high accuracy and reliability of this DL model help in effective real-time engine performance monitoring, and reducing emission levels, especially for the adoption and use of renewable fuels like E15.
Co-Authors Abd - Elwahab, M. Rabie Abdulhameed, Ahmed N. Abdulhasan, Mahmood Jamal Abouelsoud, Mostafa Albaker, Baraa Munqith Amoud, Hassan Faris, Waleed F. Faris, Waleed Fekry Hadi, Husam Ali Hailan, Maryam Abdulhakeem Hofny, Mohamed S. Kassem, Abdallah Makrahy, Mostafa M. Moaaz, Ahmad O. Moubayed, Nazih Nadweh, Safwan Rabie, M. Shmroukh, Ahmed N.