Anwar Ja'afar Mohamad Jawad
@ruc.edu.iq
Computer Engineering Techniques / Quality Assurance Unit
Al Rafidain University college
Prof. Dr. Anwar Ja'afar Mohamed Jawad was born in Baghdad, Iraq (1961). He is one of the academic staff in Al-Rafidain University College, Baghdad-Iraq. His designation is professor in Applied Mathematics (2014). The academic Qualifications are PhD. in Applied Mathematics from University of Technology, Baghdad, (2000), M.Sc. in Operation Research from University of Technology, Baghdad, (1989), and B.Sc. in Mechanical Engineering from Baghdad University, (1983). He is interested in Differential equations, and Numerical analysis. He published in international journals more than 95 papers in solving nonlinear partial differential equations. He was teaching Mathematics, numerical analysis for graduate and postgraduate students in Iraqi and Syrian universities. He was a supervised for many MSc. and PhD. Thesis
EDUCATION
The academic Qualifications are PhD. in Applied Mathematics from University of Technology, Baghdad, (2000), M.Sc. in Operation Research from University of Technology, Baghdad, (1989), and B.Sc. in Mechanical Engineering from Baghdad University, (1983)
RESEARCH INTERESTS
Applied Mathematics
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Mohammed Mohammed, Jawad K. Oleiwi, Anwar Ja'afar Mohamad Jawad, Aeshah M. Mohammed, Azlin F. Osman, Rozyanty Rahman, Tijjani Adam, Bashir O. Betar, Subash C.B. Gopinath, and Omar S. Dahham
Elsevier BV
Mohammed Mohammed, Jawad K. Oleiwi, Aeshah M. Mohammed, Anwar Ja'afar Mohamad Jawad, Azlin F. Osman, Tijjani Adam, Bashir O. Betar, Subash C.B. Gopinath, Omar S. Dahham, and Mustafa Jaafar
Elsevier BV
Mohammed Mohammed, Anwar Ja'afar Mohamad Jawad, Aeshah M. Mohammed, Jawad K. Oleiwi, Tijjani Adam, Azlin F. Osman, Omar S. Dahham, Bashir O. Betar, Subash C.B. Gopinath, and Mustafa Jaafar
Elsevier BV
Yueqin Yang, Mohsin O. AL-Khafaji, Mohammad Ali Fazilati, Saeed Hassan Saeed, Nawras Ali Salman, Adnan Hashim Abdulkadhim, Murtadha Lafta Shaghnab, M. Abdulfadhil Gatea, Anwar Ja'afar Mohammad Jawad, and Davood Toghraie
Elsevier BV
Ahmad Taher Azar, Azher M. Abed, Farah Ayad Abdul-Majeed, Ibrahim A. Hameed, Anwar Ja’afar Mohamad Jawad, Wameedh Riyadh Abdul-Adheem, Ibraheem Kasim Ibraheem, and Nashwa Ahmad Kamal
MDPI AG
This paper presents a novel extended state observer (ESO) approach for a class of plants with nonlinear dynamics. The proposed observer estimates both the state variables and the total disturbance, which includes both exogenous and endogenous disturbance. The study’s changes can be summarized by developing a sliding mode higher-order extended state observer with a higher-order augmented state and a nonlinear function for the estimation error correction terms (SMHOESO). By including multiple enhanced states, the proposed observer can monitor total disturbances asymptotically, with the second derivative of the total disturbance serving as an upper constraint on the estimation error. This feature improves the observer’s ability to estimate higher-order disturbances and uncertainty. To extend the concept of the linear extended state observer (LESO), a nonlinear function can modify the estimation error in such a way that the proposed observer can provide faster and more accurate estimations of the state and total disturbance. The proposed nonlinearity also reduces the chattering issue with LESOs. This research thoroughly examines and analyzes the proposed SMHOESO’s convergence using the Lyapunov technique. According to this analysis, the SMHOESO is asymptotically stable, and the estimation error can be significantly reduced under real-world conditions. In addition to the SMHOESO, a modified Active Disturbance Rejection Control (ADRC) scheme is built, which includes a nonlinear state error feedback (NLSEF) controller and a nonlinear tracking differentiator (TD). Several nonlinear models, including the Differential Drive Mobile Robot (DDMR), are numerically simulated, and the proposed SMHOESO is compared to several alternative types, demonstrating a significant reduction in controller energy, increased control signal smoothness, and accurate tracking of the reference signal.
Ibrahim A. Hameed, Luay Hashem Abbud, Jaafar Ahmed Abdulsaheb, Ahmad Taher Azar, Mohanad Mezher, Anwar Ja’afar Mohamad Jawad, Wameedh Riyadh Abdul-Adheem, Ibraheem Kasim Ibraheem, and Nashwa Ahmad Kamal
MDPI AG
A disturbance/uncertainty estimation and disturbance rejection technique are proposed in this work and verified on a ground two-wheel differential drive mobile robot (DDMR) in the presence of a mismatched disturbance. The offered scheme is the an improved active disturbance rejection control (IADRC) approach-based enhanced dynamic speed controller. To efficiently eliminate the effect produced by the system uncertainties and external torque disturbance on both wheels, the IADRC is adopted, whereby all the torque disturbances and DDMR parameter uncertainties are conglomerated altogether and considered a generalized disturbance. This generalized disturbance is observed and cancelled by a novel nonlinear sliding mode extended state observer (NSMESO) in real-time. Through numerical simulations, various performance indices are measured, with a reduction of 86% and 97% in the ITAE index for the right and left wheels, respectively. Finally, these indices validate the efficacy of the proposed dynamic speed controller by almost damping the chattering phenomena and supplying a high insusceptibility in the closed-loop system against torque disturbance.
Zahraa Sabah Hashim, Halah I. Khani, Ahmad Taher Azar, Zafar Iqbal Khan, Drai Ahmed Smait, Abdulkareem Abdulwahab, Ali Mahdi Zalzala, Anwar Ja’afar Mohamad Jawad, Saim Ahmed, Ibraheem Kasim Ibraheem,et al.
MDPI AG
In this paper, two new versions of modified active disturbance rejection control (MADRC) are proposed to stabilize a nonlinear quadruple tank system and control the water levels of the lower two tanks in the presence of exogenous disturbances, parameter uncertainties, and parallel varying input set-points. The first proposed scheme is configured from the combination of a modified tracking differentiator (TD), modified super twisting sliding mode (STC-SM), and modified nonlinear extended state observer (NLESO), while the second proposed scheme is obtained by aggregating another modified TD, a modified nonlinear state error feedback (MNLSEF), and a fal-function-based ESO. The MADRC schemes with a nonlinear quadruple tank system are investigated by running simulations in the MATLAB/SIMULINK environment and several comparison experiments are conducted to validate the effectiveness of the proposed control schemes. Furthermore, the genetic algorithm (GA) is used as a tuning algorithm to parametrize the proposed MADRC schemes with the integral time absolute error (ITAE), integral square of the control signal (ISU), and integral absolute of the control signal (IAU) as an output performance index (OPI). Finally, the simulation results show the robustness of the proposed schemes with a noticeable reduction in the OPI.
Ahmad Taher Azar, Drai Ahmed Smait, Sami Muhsen, Moayad Abdullah Jassim, Asaad Abdul Malik Madhloom AL-Salih, Ibrahim A. Hameed, Anwar Ja’afar Mohamad Jawad, Wameedh Riyadh Abdul-Adheem, Vincent Cocquempot, Mouayad A. Sahib,et al.
MDPI AG
In this work, a Nonlinear Higher Order Extended State Observer (NHOESO) is presented to replace the Linear Extended State Observer (LESO) used in Conventional Active Disturbance Rejection Control (C-ADRC) solutions. In the NHOESO, the standard LESO is completed with a two-term smooth nonlinear function with saturation-like characteristics. The proposed novel NHOESO enables precise observation of the generalized disturbances with higher-order derivatives. The stability of the NHOESO is examined with the aid of the Lyapunov method. A simulation of an uncertain nonlinear Single-Input–Single-Output (SISO) system with time-varying external disturbances confirms that the proposed NHOESO copes well with the generalized disturbance, which is not true for other ESOs.
Ahmed Alkhayyat, Ali Mahdi Zalzala, Asaad A. M. AL‐Salih, Anwar Ja'afar Mohamad Jawad, Wameedh Riyadh Abdul‐Adheem, Jamshed Iqbal, Ibraheem K. Ibraheem, Waleed K. Ibrahim, Mustafa Musa Jaber, and Asaad Shakir Hameed
Wiley
AbstractNumerous techniques have been proposed in the literature to improve the performance of high‐gain observers with noisy measurements. One such technique is the linear extended state observer, which is used to estimate the system's states and to account for the impact of internal uncertainties, undesirable nonlinearities, and external disturbances. This observer's primary purpose is to eliminate these disturbances from the input channel in real‐time. This enables the observer to precisely track the system states while compensating for the various sources of uncertainty that can influence the system's behaviour. So, in this paper, a novel nonlinear higher‐order extended state observer (NHOESO) is introduced to enhance the performance of high‐gain observers under noisy measurement conditions. The NHOESO is designed to observe the system states and total disturbance while eliminating the latter in real time from the input channel. It is capable of handling disturbances of higher‐order derivatives, including internal uncertainties, undesirable nonlinearities, and external disturbances. The paper also presents two innovative schemes for parametrizing the NHOESO parameters in the presence of measurement noise. These schemes are named time‐varying bandwidth NHOESO (TVB‐NHOESO) and online adaptive rule update NHOESO (OARU‐NHOESO). Numerical simulations are conducted to validate the effectiveness of the proposed schemes, using a nonlinear uncertain system as a test case. The results demonstrate that the OARU technique outperforms the TVB technique in terms of its ability to sense the presence of noise components in the output and respond accordingly. However, it is noted that the OARU technique is slower than the TVB technique and requires more complex parameter tuning to adaptively account for the measurement noise.
Mohammed Abdul J. Maktoof, , , , , , Anwar Ja’afar M. .., Hasan M. Abd, Ahmed Husain, and Ali Majdi
American Scientific Publishing Group
The free flow of people and products within metropolitan areas depends on well-managed transportation systems. However, public parking places in smart cities are often limited by traffic, causing cars and residents to waste time, money, and fuel. To counteract this issue, today's automobile systems combine information fusion with intelligent parking solutions. In this research, we present a Fuzzy Logic Integrated Machine Learning Algorithm (FL-MLA) for use in smart parking and traffic management in a metropolis. The FL-MLA use fuzzy induction to distinguish between parked and moving vehicles while calculating traffic flow. The suggested technique efficiently resolves the problem of locating suitable parking places by avoiding incorrect configurations that govern traffic management difficulties. Therefore, the FL-MLA is used in traffic management systems to boost performance metrics like efficiency ratio (98.1%) and accident detection (98.1%) based on simulation results like reduced energy consumption (95.3%), more accurate traffic estimation (97.9%), higher average daily park occupancy (97.2%), and higher efficiency ratio (98.1%).
admin admin, , , , , , Anwar Ja’afar M. .., Mohammed A. Jalil, Noor Sami, and Zaid Saad Madhi
American Scientific Publishing Group
The healthcare sector's use of cyber-physical systems to provide high-quality patient treatment highlights the need for sophisticated security solutions due to the wide range of attack surfaces from medical and mobile devices, as well as body sensor nodes. Cyber-physical systems have various processing technologies to choose from, but these technical methods are as varied. Existing technologies are not well-suited for managing complex information about problem identification and diagnosis, which is distinct from technology. To address this issue, intelligent techniques for fusion processing, such as multi-sensor fusion system architectures and fusion optimization, can be used to improve fusion score and decision-making. Additionally, the use of deep learning models and multimedia data fusion applications can help to combine multiple models for intelligent systems and enhance machine learning for data fusion in E-Systems and cloud environments. Fuzzy approaches and optimization algorithms for data fusion can also be applied to robotics and other applications.. In this paper, a computer vision technology-based fault detection (CVT-FD) framework has been suggested for securely sharing healthcare data. When utilizing a trusted device like a mobile phone, end-users can rest assured that their data is secure. Cyber-attack behavior can be predicted using an artificial neural network (ANN), and the analysis of this data can assist healthcare professionals in making decisions. The experimental findings show that the model outperforms with current detection accuracy (98.3%), energy consumption (97.2%), attack prediction (96.6%), efficiency (97.9%), and delay ratios (35.6%) over existing approaches.
Saif Saad Ahmed, , , , , , Anwar Ja’afar M. Jawad, Shorook K. Abd, Aymen Mohammed, and Amjed Hameed Majeed
American Scientific Publishing Group
Because of the proliferation of digital technologies, organizations now have access to previously unimaginable troves of data. In order to make educated choices and generate beneficial results, accurate data analysis and interpretation are essential. The use of data visualization in this context has proven its value. Recent studies found that data visualization increased business owners' drive to make a profit. To aid business owners in evaluating issues related to self-service data resources, a dynamic IoT-based enterprise management framework (IEMF-IDM) was presented. The suggested system uses fusion optimization techniques to maximize the fusion score and enhance decision-making through the use of various models and methods, such as machine learning and fuzzy approaches. Simulation studies in a number of domains, including robots, cloud settings, and multimedia data fusion, attest to the system's efficacy.
Ibtisam A. Hasan and Anwar Ja’afar Mohamad Jawad
AIP Publishing
Ahmad Taher Azar, Azher M. Abed, Farah Ayad Abdulmajeed, Ibrahim A. Hameed, Nashwa Ahmad Kamal, Anwar Jaafar Mohamad Jawad, Ali Hashim Abbas, Zainab Abdulateef Rashed, Zahraa Sabah Hashim, Mouayad A. Sahib,et al.
MDPI AG
In the photovoltaic system, the performance, efficiency, and generated power of the PV system are affected by changes in the environment, disturbances, and parameter variations, and this leads to a deviation from the operating maximum power point (MPP) of the PV system. Therefore, the main aim of this paper is to ensure the PV system operates at the maximum power point under the influence of exogenous disturbances and uncertainties, i.e., no matter how the irradiation, temperature, and load of the PV system change, by proposing a maximum power point tracking for the photovoltaic system (PV) based on the active disturbance rejection control (ADRC) paradigm. The proposed method provides better performance with excellent tracking for the MPP by controlling the duty cycle of the DC–DC buck converter. Moreover, comparison simulations have been performed between the proposed method and the linear ADRC (LADRC), conventional ADRC, and the improved ADRC (IADRC) to investigate the effectiveness of the proposed method. Finally, the simulation results validated the accuracy of the proposed method in tracking the desired value and disturbance/uncertainty attenuation with excellent response and minimum output performance index (OPI).
Kathiresan Shankar, Sachin Kumar, Ashit Kumar Dutta, Ahmed Alkhayyat, Anwar Ja’afar Mohamad Jawad, Ali Hashim Abbas, and Yousif K. Yousif
MDPI AG
Automated fruit classification is a stimulating problem in the fruit growing and retail industrial chain as it assists fruit growers and supermarket owners to recognize variety of fruits and the status of the container or stock to increase business profit and production efficacy. As a result, intelligent systems using machine learning and computer vision approaches were explored for ripeness grading, fruit defect categorization, and identification over the last few years. Recently, deep learning (DL) methods for classifying fruits led to promising performance that effectively extracts the feature and carries out an end-to-end image classification. This paper introduces an Automated Fruit Classification using Hyperparameter Optimized Deep Transfer Learning (AFC-HPODTL) model. The presented AFC-HPODTL model employs contrast enhancement as a pre-processing step which helps to enhance the quality of images. For feature extraction, the Adam optimizer with deep transfer learning-based DenseNet169 model is used in which the Adam optimizer fine-tunes the initial values of the DenseNet169 model. Moreover, a recurrent neural network (RNN) model is utilized for the identification and classification of fruits. At last, the Aquila optimization algorithm (AOA) is exploited for optimal hyperparameter tuning of the RNN model in such a way that the classification performance gets improved. The design of Adam optimizer and AOA-based hyperparameter optimizers for DenseNet and RNN models show the novelty of the work. The performance validation of the presented AFC-HPODTL model is carried out utilizing a benchmark dataset and the outcomes report the promising performance over its recent state-of-the-art approaches.
Ahmad Azar, Farah Abdul-Majeed, Hasan Majdi, Ibrahim Hameed, Nashwa Kamal, Anwar Jawad, Ali Abbas, Wameedh Abdul-Adheem, and Ibraheem Ibraheem
MDPI AG
Dynamic observers are commonly used in feedback loops to estimate the system’s states from available control inputs and measured outputs. The presence of measurement noise degrades the performance of the observer and consequently degrades the performance of the controlled system. This paper presents a novel nonlinear higher-order extended state observer (NHOESO) for efficient state and disturbance estimation in presence of measurement noise for nonlinear single-input–single-output systems. The proposed nonlinear function allows a fast reconstruction of the system’s states and is robust against uncertainties and measurement noise. An analytical parameterization technique is proposed to parameterize the coefficients of the proposed nonlinear higher-order extended state observer in the case of measurement noise in the output signal. Several scenarios are simulated to demonstrate the effectiveness of the proposed observer.
Azher M. Abed, Zryan Najat Rashid, Firas Abedi, Subhi R. M. Zeebaree, Mouayad A. Sahib, Anwar Ja'afar Mohamad Jawad, Ghusn Abdul Redha Ibraheem, Rami A. Maher, Ahmed Ibraheem Abdulkareem, Ibraheem Kasim Ibraheem,et al.
Measurement and Control (United Kingdom) SAGE Publications
This work proposes a new kind of trajectory tracking controller for the differential drive mobile robot (DDMR), namely, the nonlinear neural network fractional-order proportional integral derivative (NNFOPID) controller. The suggested controller’s coefficients comprise integral, proportional, and derivative gains as well as derivative and integral powers. The adjustment of these coefficients turns the design of the proposed NNFOPID control further problematic than the conventional proportional-integral-derivative control. To handle this issue, an Enhanced Fruit Fly Swarm Optimization algorithm has been developed and proposed in this work to tune the NNFOPID’s parameters. The enhancement achieved on the standard fruit fly optimization technique lies in the increased uncertainty in the values of the initialized coefficients to convey a broader search space. subsequently, the search range is varied throughout the updating stage by beginning with a big radius and declines gradually during the course of the searching stage. The proposed NNFOPID controller has been validated its ability to track specific three types of continuous trajectories (circle, line, and lemniscate) while minimizing the mean square error and the control energy. Demonstrations have been run under MATLAB environment and revealed the practicality of the designed NNFOPID motion controller, where its performance has been compared with that of a nonlinear Neural Network Proportional Integral Derivative controller on the tracking of one of the aforementioned trajectories of the DDMR.
Maha S.M. Shehata, Hadi Rezazadeh, Anwar J.M. Jawad, Emad H.M. Zahran, and Ahmet Bekir
Sociedad Mexicana de Fisica A C
In this article the perturbed Gerdjikov-Ivanov (GI)-equation which acts for the dynamics of propagation of solitons is employed. The balanced modified extended tanh-function and the non-balanced Riccati-Bernoulli Sub-ODE methods are used for the first time to obtain the new optical solitons of this equation. The obtained results give an accuracy interpretation of the propagation of solitons. We held a comparison between our results and those are in the previous work. The efficiency of these methods for constructing the exact solutions has been demonstrated. It is shown that these different technique's reduces the large volume of calculations.
Salwan Abdulmuaen Al-Shami, Anwar Hassan Jawad, Qassim Talib Jamil, and Roaa Raheem Hamza
IOP Publishing
AbstractThe study aims to demonstrate the effect of some traits on the virulence of E.coli bacteria, the important of which are the number and type of bacterial isolates, their sensitivity to some antibiotics and the production of the β-lactamase enzyme. We adopted in this research a multi-studies relative to some countries and different governorates inside Iraq to show the prevalence range of this gender and its great impact on urinary tract infection. It was found through various previous studies that E.coli is present in a large extent in the urinary tract compared to other isolates, as it is a suitable environment for causing infection after seizing the opportunity and reaching to the urinary tract after ascending from the digestive system, which are endemic organisms in which the normal flora. And it is found more often in women than in men for physiological reasons.It also contains multiple resistance to MDR antibiotics. This resistance may be depend on the presence of the plasmid that carries the genetic information to produce the enzyme β-lactamase, which works to break the β-lactamase cycle and create the bio resistance to the antibiotic. This resistance varies from gender to another and from one environment to another. Various studies around the world, especially penicillin’s, the difference in the degree of sensitivity to this antibiotic or the degree of resistance according to the place in which the patient is located and according to the manufacture of the antibiotic and its chemical composition. This type of infection depends on its rapid identification and taking the appropriate antibiotic as soon as possible and for the required time, lest it lead to other complications, the most important of which is the kidney failure caused by this genus, and it is preferable to make a sensitivity test for antibiotics in order to be able to determine the appropriate antibiotic.
Anwar Ja’afar Mohamad Jawad, Mahmoud Jawad Abu-AlShaeer, Elsayed M.E. Zayed, Mohamed E.M. Alngar, Anjan Biswas, Mehmet Ekici, Abdullah Kamis Alzahrani, and Milivoj R. Belic
Elsevier BV
Yakup Yıldırım, Anjan Biswas, Anwar Ja’afar Mohamad Jawad, Mehmet Ekici, Qin Zhou, Salam Khan, Abdullah Kamis Alzahrani, and Milivoj R. Belic
Elsevier BV
Yakup Yıldırım, Anjan Biswas, Anwar Ja’afar Mohamad Jawad, Mehmet Ekici, Qin Zhou, Abdullah Kamis Alzahrani, and Milivoj R. Belic
Elsevier BV