Showing 4 results for Mohammed
M. K. Rashid, A. M. Mohammed,
Volume 19, Issue 2 (June 2023)
Abstract
Nowadays, magnetic gears (MGs) have become an alternative choice for mechanical gears because of their low maintenance, improved durability, indirect contact between inner and outer rotors, no lubrication, and high efficiency. Generally, although these advantages, MGs suffer from inherent issues, mainly the cogging torque. Therefore, cogging torque mitigation has become an active research area. This paper proposed a new cogging torque mitigation approach based on the radial slit of the ferromagnetic pole pieces of MGs. In this method, different numbers and positions of slits are applied. The best results are gained through an even number of slits which shows promising results of cogging torque mitigation on the inner rotor with a small mitigation in the mean torque on both rotors. This work is done by using Simcenter and MATLAB software packages. The inner rotor’s cogging torque has mitigated to 81.9 %, while the outer rotor’s cogging torque is increased only by 2.75 %.
Aws Alazawi, Huda Jameel, Mohammed Mohsen,
Volume 20, Issue 2 (June 2024)
Abstract
This study explores the use of distortion product otoacoustic emission (DPOAE) as a hearing screening modality for newborns and adults with hearing impairment. The goal is to improve cochlear response by developing digital filter characteristics to make it consistent for specialists to make accurate diagnoses. To accomplish this, the proposed system consists of a DPOAE ER-10C as stimulation and cochlear response probe, a digital signal processor, an oscilloscope, PC, and audio cables. Real-time distortion product frequency components were extracted using a signal processor of TMS320C6713. To validate the system, a senior medical physicist at Baghdad Medical City in Iraq conducted a study with five hearing-normal volunteers ages 38 and 55 at the center for hearing and communication. The results showed an ability to extract distortion product components in real-time implementation, with the superiority of shape parameters greater than 0.5. In addition, the quantization of filter coefficients was compared for both floating-point arithmetic and fixed-point arithmetic. Noisy environment-based noise reduction techniques have to be investigated by considering the implementation of robust digital signal processing techniques. Finally, the proposed system would contribute to advancements in hearing screening and treatment for those with hearing impairment.
Shamil Alnajjar, Prof. Dr. Khalid K. Mohammed,
Volume 20, Issue 3 (September 2024)
Abstract
This work presents an analysis and design of the two barrier-quantum well asymmetric spacer tunnel layer (QW-ASPAT) diodes for implantable rectenna circuits application. The RF and DC characteristic of a 10×10μm2 QW-ASPAT devices based on GaAs and In0.53Ga0.47As platform was simulated and extracted by using SILVACO atlas software. The highest extracted curvature coefficient, kv value of the both QW-ASPAT devices at zero bias was about 33V-1 compared with the standard structure GaAs/InGaAs was about 13V-1. The effects of changing in the thickness of the thin AlAs-barrier, the well width, and the spacer layer are fully investigated on the non-linear relationship between current and voltage of these diodes. A CV simulation was carried out, and it was found that the addition of the quantum-well layer between spacers and barrier reduced the junction capacitance of the QW-ASPAT device when compared with standard devices. The cut-off frequency of the proposed QW-GaAs and QW-InGaAs devices are 26GHz and 46GHz respectively. Finally, we conclude that the QW-ASPAT device is the best structure and can be used for microwave rectifiers in the miniaturized integrated rectenna systems.
Aboubakeur Hadjaissa, Mohammed Benmiloud, Khaled Ameur, Halima Bouchenak, Maria Dimeh,
Volume 20, Issue 4 (Special Issue on ADLEEE - December 2024)
Abstract
As solar photovoltaic power generation becomes increasingly widespread, the need for photovoltaic emulators (PVEs) for testing and comparing control strategies, such as Maximum Power Point Tracking (MPPT), is growing. PVEs allow for consistent testing by accurately simulating the behavior of PV panels, free from external influences like irradiance and temperature variations. This study focuses on developing a PVE model using deep learning techniques, specifically a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) with backpropagation as the learning algorithm. The ANN is integrated with a DC-DC push-pull converter controlled via a Linear Quadratic Regulator (LQR) strategy. The ANN emulates the nonlinear characteristics of PV panels, generating precise reference currents. Additionally, the use of a single voltage sensor paired with a current observer enhances control signal accuracy and reduces the PVE system's hardware requirements. Comparative analysis demonstrates that the proposed LQR-based controller significantly outperforms conventional PID controllers in both steady-state error and response time.
