On Sunday, 3/3/2024, the Legal Unit in the Control and Systems Engineering Department organized a seminar entitled (The Effect of Disciplinary Violation in Causing Corruption). This seminar was given by (Lect. Dr. Aliaa Abdel Ridha Abbas) from the Control and Systems Engineering Department and (Asst. Lect. Qutayba Taher Hussein) from the Applied Sciences Department. Corruption has been defined as a phenomenon that has become widespread in recent times as a result of accumulations experienced by society, a departure from religious motives, and a lack of values. It is misuse in various fields, lack of cooperation in applying laws and regulations, and maximizing personal interest over public interest. Finally, the lecturers pointed out the causes and types of corruption, and they stressed the necessity of cooperation in order to eliminate this phenomenon to build a successful society.

  The master’s thesis of the student (Heba Ali Ahmed), majoring in (Computer Engineering), was discussed on Sunday, 3/3/2024, in the discussion room (Hall No. 9) at the Control and Systems Engineering Department. The thesis title is “Intelligent Emergency Auto-car-stop System”

The discussion committee consisted of:

- Prof. Dr. Muhammad Ali Tawfiq / Chairman

- Asst. Prof. Dr. Ikhlas Kadhum Hamza / Member

- Lect. Dr. Qusay Fadhil Hassan / Member

- Prof. Dr. Muayad Sadiq Crook / Member and Supervisor

- Lect. Dr. Muhammad Abdullah Noman / Member and Supervisor

This thesis presents an innovative and integrated approach to enhancing road safety by designing an intelligent system using deep learning that contains two convolutional neural networks, the first network is to recognize the driver’s face before starting to drive to allow only authorized drivers to continue driving, and the second network is to monitor the state of awareness of the authorized driver while driving to alert him/her in drowsiness cases, in addition to declaring an emergency state and automatically stopping the car if the driver does not respond to the audio alert. This research presents a powerful and effective system that combines facial recognition and drowsiness detection in real time to ensure the safety and attention of drivers. The integration of advanced deep learning models and the cost-effective implementation on Raspberry Pi and the comprehensive sensors contribute to enhance road safety and prevent accidents caused by driver fatigue. On this occasion, we congratulate the student (Heba Ali Ahmed) and wish her continued success.

  The Master’s thesis of the student Ghada Adnan Miteb, majoring in Control Engineering, was discussed on Thursday, 3/7/2024, at Hall No. 9 in the Department of Control and Systems Engineering. Her research was entitled: Intelligent Control of a Fluid Catalytic Cracking Unit in petroleum Refineries

The discussion committee consisted of:

1- Prof. Dr. Amjad Jalil Hamidi / Chairman.

2- Prof. Dr. Ammar Ibrahim Majeed / Member

3- Asst. Prof. Dr. Laila Hatem Abboud / Member

4- Prof. Dr. Muhammad Youssef Hassan / Member and Supervisor

The development of the oil industry, which is considered one of the most important economies in the world, is achieved through high product quality. Therefore, there is a need to control the most important units in petroleum refineries by controlling the temperature of the product, which is the fluid catalytic cracking unit (FCCU). In this work, the Firefly algorithm is used to find the best parameter values for the two types of controllers. The second controller design consists of two NPID controllers and two PDLT2 controllers. This design aims to compensate for disturbances and uncertainties in the FCCU. The reactor temperature overshoot in the simulation results is reduced by 90% and the regenerator temperature overrun is reduced by 85% compared to the results shown in Reference 20. The inherent coupling, uncertainty, chattering, and nonlinearity in the FCCU are handled by this type of controller. All results were obtained by MATLAB2020b. The discussion was attended by the Assistant Head of the Department for Scientific Affairs and Postgraduate Studies Prof. Dr. Abbas Hussein Issa. On this occasion, we congratulate the student Ghada Adnan Miteb and wish her continued success.

  The postgraduate doctoral student (Shahed Sebeh Ghintab) from the Control and Systems Engineering Department received a doctorate degree in (Control Engineering) with a grade of (very good) for her thesis entitled (Deep Learning Based Autonomous Driving in Urban Areas). The discussion was held on Monday, March 4, 2024, in the discussion Hall (Hall No. 9) in the department,

and the discussion committee consisted of:

1- Prof. Dr. Hazem Ibrahim Ali / Chairman

2- Prof. Dr. Hadeel Nusrat Abdullah / Member

3- Asst. Prof. Dr. Firas Abdul Razzaq Rahim / Member

4- Asst. Prof. Dr. Osama Ali Awad / Member

5- Asst. Prof. Dr. Bushra Kadhum Oleiwi / Member

6- Prof. Dr. Muhammad Youssef Hassan / Member and Supervisor

Autonomous vehicles have emerged as an interesting field in engineering, offering a potential solution to reduce accidents caused by human error and maximize the use of parking in cities. This work investigates the significant impact of autonomous vehicle technology on transportation infrastructure, emphasizing the necessity of a comprehensive framework that integrates perception, localization, planning, and control to ensure reliable autonomous navigation. The thesis proposes an adaptive integrated derivative Type-2 fuzzy logic controller. Advanced technology has been employed in localization, where convolutional neural networks were used and trained on images of red, blue, and green types after modifying one of the networks known as AlexNet. The A* algorithm was used for path planning. For environmental perception, a network called You Only Look Once (YOLO) version 3 was utilized, providing accurate localization results for autonomous driving within the city despite various weather conditions. Only color images were relied upon, without resorting to expensive sensors, such as radar and LiDAR technology. The algorithm for merging regular images with depth images was adopted to obtain good results when examining the network trained using the MATLAB program. Subsequently, the trained network was rebuilt using the Python language so that it could be linked with the simulation program for autonomous driving cars called CARLA. The simulation results with the trained network and simulation showed remarkable and excellent performance in perceiving the surrounding environment of the vehicles. The achieved results from localization, perception, and path planning were used alongside the proposed controller, whose variables were tuned using the fly optimization algorithm, which demonstrated the accuracy and robustness of the proposed algorithm through the improvement ratio of the mean squared error for the steering angle compared to the reference results. In particular, the proposed algorithm achieved an improvement in the mean squared error for the steering angle by 89.68% compared to the results of reference [77]. As for speed, there was an improvement in the square error rate compared to the same reference with an improvement rate of 98.52%. The proposed solution effectively adjusts the parameters of the control unit accurately, while staying flexible and adaptable to changing conditions. It can be implemented in real-world scenarios to achieve safe and successful navigation. The defense was attended by the Assistant Head of the Department for Scientific Affairs and Postgraduate Studies (Prof. Dr. Abbas Hussein Issa) along with a group of professors in the department. On this occasion, we congratulate the student (Shahed Sebeh Ghintab) and wish her success and prosperity.