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Your search keyword '"Cheema, Khalid Mehmood"' showing total 34 results
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34 results on '"Cheema, Khalid Mehmood"'

6. Atomic physics-inspired atom search optimization heuristics integrated with chaotic maps for identification of electro-hydraulic actuator systems.

Author

Mehmood, Khizer, Chaudhary, Naveed Ishtiaq, Khan, Zeshan Aslam, Cheema, Khalid Mehmood, and Zahoor Raja, Muhammad Asif

Subjects
ELECTROHYDRAULIC effect, ACTUATORS, ATOMS, BIOLOGICALLY inspired computing, SCIENTIFIC community, HEURISTIC, METAHEURISTIC algorithms
Abstract

Electro-hydraulic actuator system (EHAS) has imposed a challenge in the research community for accurate mathematical modeling and identification due to non-linearities. In this paper, autoregressive exogenous (ARX) structure is used for EHAS modeling and identification is performed by exploiting the competency of atomic physics-based chaotic atom search optimization (CASO) that adapts ten chaotic maps (Chebyshev, Circle, Gauss, Iterative, Logistic, Piecewise, Sine, Singer, Sinusoidal and Tent) in position update of atom search optimization (ASO). The fitness/merit function of the EHAS model is developed in mean-square error (MSE) sense between desired and approximated values. Simulations and analysis show that ASO with a chaotic logistic map (CASO5) performs better than the ASO and its other chaotic variants, as well as other recently introduced metaheuristics for diverse variations in the system model. Statistics based on MSE, learning plots, results of autonomous trials and average fitness analyses verify the consistency and reliability of the CASO5 for the identification of the EHAS model. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Open switch fault diagnosis of cascaded H-bridge 5-level inverter using deep learning

Author

Arif, Muhammad Nouman, Din, Zaki Ud, ul Haq, Azhar, Cheema, Khalid Mehmood, Milyani, Ahmad H., Naeem-ul-Islam, Ashfaq, Iqra, Arif, Muhammad Nouman, Din, Zaki Ud, ul Haq, Azhar, Cheema, Khalid Mehmood, Milyani, Ahmad H., Naeem-ul-Islam, and Ashfaq, Iqra

Abstract

Cascaded H-bridge 5-level inverters (CHB-5LIs) have gained significant traction in high-power applications owing to their capacity to produce high-quality output voltage with minimal harmonic distortion. However, their intricate architecture presents notable challenges for fault diagnosis, particularly concerning open switch faults. In this study, we propose a deep learning-based approach for diagnosing open switch faults in CHB-5LIs. We present a simulation model of the CHB-5LI with open switch faults and generate a dataset comprising voltage waveforms for various fault scenarios. Leveraging this dataset, we train a Convolutional-1D Neural Network (CNN-1D) featuring a multi-layer architecture comprising convolutional and fully connected layers, culminating in the Softmax function for classification. Our method achieves an impressive classification accuracy exceeding 98 percent on previously unseen fault scenarios, underscoring the efficacy of our approach for CHB-5LI fault diagnosis. Additionally, we conducted a thorough analysis of CNN-1D performance and compared it with traditional and other deep learning models for fault diagnosis techniques. The accuracy of other deep learning models on the generated dataset is as follows: RNN is 88.9 percent, 1D-ResNet is 88.8 percent, and Time Inception model is 89.4 percent. Simulation results showcase that our proposed CNN-1D based approach surpasses other methods in terms of accuracy and robustness, elucidating the potential of deep learning for fault diagnosis in intricate power electronics systems. The fault diagnosis time for the proposed method as a fault diagnosis tool for the simulation case is 0.060 ms, compared to 0.062 ms for RNN and 0.065 ms for ResNet.

Published
2024

29. Multiple Cues-Based Robust Visual Object Tracking Method

Author

Khan, Baber, primary, Jalil, Abdul, additional, Ali, Ahmad, additional, Alkhaledi, Khaled, additional, Mehmood, Khizer, additional, Cheema, Khalid Mehmood, additional, Murad, Maria, additional, Tariq, Hanan, additional, and El-Sherbeeny, Ahmed M., additional

Published
2022
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34. Design and analysis of dual‐stator hybrid excited linear flux switching machine for long‐stroke applications.

Author

Ullah, Basharat, Khan, Faisal, Milyani, Ahmad H., Ahmad, Naseer, and Cheema, Khalid Mehmood

Subjects
ELECTRIC machines, ELECTROMAGNETIC forces, LINEAR machines (Electric machines), MAGNETIC flux, THERMAL analysis
Abstract

In this work, a novel dual‐stator hybrid excited linear flux switching machine (DSHELFSM) is proposed. The proposed machine is suitable for long‐stroke applications as all the excitation sources are mounted on a short mover and the stator is completely passive. Second, the proposed machine uses ferrite magnets rather than the expensive rare earth permanent magnets (PMs) with the addition of field excitation to achieve flux‐regulation capability at various field currents. An iron bridge that provides an additional path for flux linkage to complete its circuit is used. Various design parameters such as split ratio, stator pole width, width and height of PM, the width of the iron bridge, width of mover's tooth, and stator slot depth are optimised using multivariable geometric optimisation (MGO) while keeping the slot area, electric and magnetic loading constant. The optimised design has improved the performance as compared to the initial design. Furthermore, the results obtained by 2D analysis are verified by 3D analysis. Thermal analysis is carried out to set the operating limit of the proposed machine. [ABSTRACT FROM AUTHOR]

Published
2021
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