Dr. Omer Saleh Mahmod Jomah
@academy.edu.ly
Department of Electrical and Computer Engineering/ School of Applied Sciences and Engineering
The Libyan Academy for Postgraduate Studies
I am a distinguished academic and professional with extensive experience in engineering and education. I currently serve as the Director of the E-learning and Distance Learning Center at the Libyan Academy for Postgraduate Studies, a position I have held since 2021.
Professional Experience:
Director of E-learning and Distance Learning Center, Libyan Academy for Postgraduate Studies (2021 - Present)
Director of EITRC, General Director of Engineering and IT Research Center, Libya (2017 - 2021)
International Cooperation Committee Member, Bani Waleed University (2016 - 2017)
Head of Faculty of Education & Computer Science Department, Bani Walid University (2012 - 2015)
Assistant Lecturer, 7 October University and Higher Centre for Comprehensive Professions, Libya (2005 - 2007)
Engineer, General Electrical Company, Tripoli, Libya (1998-2000 )
EDUCATION
I hold a Ph.D. in Automatics and Robotics from AGH University of Science and Technology, Poland. I also earned a Master of Science in Electrical and Computer Measurement, Cracow University of Technology and a Bachelor of Science from the Higher Institute of Electronics, Beni Walid, Libya.
RESEARCH, TEACHING, or OTHER INTERESTS
Control and Systems Engineering, Artificial Intelligence, Computer Engineering, Education
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Nasar Aldian Ambark Shashoa, Omar Abusaeeda, Omer S.M. Jomah, and Musa Faneer
IEEE
Fault detection and classification is critical to the reliability of modern control systems in different industries, where detecting and classifying faults in operational processes are very important things while failure to detect and classify them, may cause irreparable damage. In this paper, fault detection and classification approach is presented. The first step, multi stage recursive least squares parameter estimation approach for controlled autoregressive autoregressive moving average systems (CARARMA) is developed with a view to estimate the parameters of the system, additionally, improve the effectiveness of the computation. By means of multi stage approach, the (CARARMA) system is decomposed into three simple identification models, and the parameters of each simple model is identified one by one. These parameters estimated by this approach are referred to as features, and not all of them have the same useful data about the system. In order to select the valuable features and improve a classification accuracy, the Linear Discriminant Analysis (LDA) approach based on scattering matrices is applied for dimension reduction. The classification between these reduced classes is done based on the Naive Bayes classifier. Finally, the obtained results explain the performance of this proposed approach.
Omer.S. M. Jomah, Nachaat Mohamed, Abdussalam Ali Ahmed, Abdulgader Alsharif, Mohamed Mohamed Khaleel, and Yasser F. Nassar
IEEE
The article discusses exploring the Photovoltaic (PV) emulator for simulating photovoltaic systems for Renewable Energy Analysis (REA) focuses on creating and applying a PV emulator to simulate photovoltaic systems and concludes by emphasizing the value of precise simulation tools in comprehending and enhancing photovoltaic system performance. The first portion of the article emphasizes how renewable energy sources especially solar energy are becoming more and more important in tackling the world's energy problems. As a result, the pv output along with the voltage and current have been presented
Omer.S. M. Jomah, Abdussalam Ali Ahmed, Abdulgader Alsharif, Yasser F. Nassar, Nachaat Mohamed, and Ibrahim Imbayah
IEEE
This paper employs Monte Carlo Simulation (MCS) to assess how randomly power flow is distributed in Vehicle-to-Grid (V2G) operations. V2G technology facilitates a bidirectional flow of electricity by allowing Electric Vehicles (EVs) to feed excess power back into the grid in addition to drawing power from it. The researchers used Monte Carlo simulation as a computational technique that involves generating random samples to analyze the behaviour of a system, to assess the impact of random variations in power flow on V2G operations. They considered various factors such as EV charging and discharging patterns, grid conditions, and user behaviour to create a realistic simulation environment. The main objective function in this study wants to achieve is to reduce the dependency on the grid and develop a stochastic framework for the efficient management of microgrids with the presence of EVs. reduce cost. As a result, the analysis of the MCS shows the performance of the bidirectional operation, and the output power of the integrated sources is obtained.
Nasar Aldian A. Shashoa, Omer S.M. Jomah, Omar Abusaeeda, and Abdurrezag S. Elmezughi
IEEE
The application of feature selection to fault diagnosis is presented. First, Filtered data identification algorithm is derived for controlled autoregressive autoregressive (CARAR) system in order to estimate the system parameters. The Proposed technique offers a high computational efficiency. These estimated parameters are referred as features and these features are not all have the same informative value. Next, features selection is carried out using principal components analysis. Finally, the simulation results demonstrate the value of the suggested procedures.
Nachaat Mohamed, Saif Khameis Almazrouei, Adel Oubelaid, Abdussalam Ali Ahmed, Omer.S. M. Jomah, and Alghannai Aghnaiya
IEEE
The emergence of China as a global superpower has presented numerous challenges, among which is the growing threat posed by Chinese cyber warfare units. Operating under the People's Liberation Army (PLA), these units have been implicated in various high-profile cyber-attacks targeting both civilian and military entities worldwide. This paper endeavors to offer a comprehensive analysis of the risks presented by Chinese cyber warfare units, encompassing their organizational structure, tactics, and capabilities. By elucidating the nature and extent of this menace, we hope to enable governments and organizations to implement measures for better protection against Chinese cyber-attacks, as 80% of them target government bodies. In this study, we utilize the MITRE ATT&CK framework as a foundation to deliver an accessible, informative overview.
Abdulgader Alsharif, Abdussalam Ali Ahmed, Mohamed Mohamed Khaleel, Ahmed Salem Daw Alarga, Omer. S. M. Jomah, and Ibrahim Imbayah
IEEE
A vehicle is a means of transportation, such as a car, truck, or train, that is capable of moving people or goods from one place to another. Vehicles can be classified based on various factors, such as the type of fuel they use (e.g. gasoline, diesel, electricity), the number of wheels they have (e.g. two, four, six), and their intended use (e.g. passenger transportation). Vehicles may have connectors, such as plug sockets or fuel ports, that allow them to be connected to other devices or systems to form Vehicle-to-Everything (V2X) technology. For example, an Electric Vehicle (EV) may have a charging port that allows it to be connected to an electric power source to recharge its batteries such Vehicle-to-Grid (V2G) as one of the V2X forms. One of the challenges in charging EVs is the availability of charging infrastructure. In many places, there are relatively few public charging stations, which can make it difficult for EV owners to find a place to charge their vehicles when they are away from home. Additionally, charging an electric vehicle can take significantly longer time than filling up a gasoline-powered vehicle, which can be inconvenient for some drivers. In this review, the various topologies of V2X, connectors, charging challenges, and EV impact types on the grid are conducted.
Abdulgader Alsharif, Abdussalam Ali Ahmed, Mohamed Mohamed Khaleel, Ahmed Salem Daw Alarga, Omer.S. M. Jomah, and Alarabi Bin Eisa Alrashed
IEEE
Electric Vehicles (EVs) as hotspot research are increasingly being used as alternative energy sources outside of transportation due to global variables like energy usage and environmental concerns. Gridable Electric Vehicle (GEV) is exploiting the opportunity for connecting EVs to the grid. EVs are now capable of contributing to addressing power limitations and acting as a reserve source of energy for the distribution grid in the Electric Vehicle Charging Facility (EVCF) to form Vehicle-to-Home (V2H). This paper is introduced to estimate impacts on the load for the uncertain behavior of EVs under domestic load after being sized using algorithms. Although, the integration of EVs provides an economical and environmental solution with a positive impact, however, it affects the power system when the uncertain number of EVs is provided as heavy duty. Sensitivity analysis has been considered in the study to investigate the expected changes in the system from the affected components.
Omer S.M. Jomah and Khuloud A.B. Zargoun
IEEE
This work intends to improve the educational process through the development of teachers skills and raise the level of learning by reducing the problems and obstacles facing the teacher and providing many different learning methods. This is done through expert systems, which is one of the important fields of knowledge-based artificial intelligence, where the expert system acquires all the information acquired by the expert in the field of mathematics by transferring all his scientific experience and how the mathematics expert deals with it, so that the knowledge acquired by experts in the field of mathematics is collected. In order to prepare a knowledge base and deal with it through the expert system, as well as the possibility of making the expert system that the teacher deals with, ready to answer any inquiries or the possibility of explaining any part of the curriculum on demand.
Aisha Douma, Abdussalam Ali Ahmed, Gokhan Sengul, Johnson Santhosh, Omer.S. M. Jomah, and Fathia G. Ibrahim Salem
IEEE
For Arabic letters recognition, we achieve three of pattern recognition approaches namely gray level co-occurrence matrix (GLCM), local binary pattern recognition (LBP) and artificial neural network (ANN) and compare between them to result best performance. Two of these methods level co-occurrence matrix and local binary pattern recognition are used for feature extraction whereas in artificial neural network (ANN) we use the intensity values of pixels for input of the neural network. Two classifiers are used, the K-Nearest Neighbor classifier (KNN) for the LBP, GLCM and neural network classifier for (ANN) artificial neural network. Also, we evaluate the results by using leave one person out approach, fold classification and leave one out.
Abdussalam Ali Ahmed and Omer.S. M. Jomah
IEEE
Vehicle safety and control is attracting attention increasingly in an attempt to improve the stability and manoeuvrability of vehicles. Three degrees of freedom vehicle dynamic model (called planar vehicle model) is established. Based on theories of fuzzy PID control and neural network based-Controller, controller of vehicle stability is designed by using the method of direct yaw rate control and the two different control strategies. The controllers were compared under one road condition which is a lane change of manoeuvre. By comparing and analyzing the control effect of fuzzy PID control and neural network based-Controller, the result shows as follows: the two controllers improved the yaw rate to follow the reference yaw rate but, using the fuzzy PID controller gave a better and closer path for the desired path of yaw rate compared to using of the neural network controller.
Abdussalam Ali Ahmed and Omer.S. M. Jomah
IEEE
This paper aims to demonstrate the application of two different control techniques, namely the Linear Quadratic Regulator (LQR) and a neural network-based controller to evaluate and control the vibrations that occurred in the car's suspension system. When the car suspension is designed, a quarter car model with 1-DOF is used. A complete control system is needed to provide the desired suspension performance and characteristics such as passenger comfort, road handling, and suspension deflection, this control system performed by using the Matlab software and includes three parts: input signals (actuator force and road profile), Controller, and the suspension system model. The simulation results show a comparison between the uncontrolled suspension system and the suspension system with a neural network-based controller and the active suspension system of the car based on the linear-quadratic regulator, and it is explained thoroughly.