School of Electrical Engineering and Technology (SEET)
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School of Electrical Engineering and Technology (SEET)
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Item Comparative Analysis of Machine Learning Algorithms for Eccentricity Fault Classification in Salient Pole Synchronous Machine(IEEE, 2024-03-22) Shejwalkar, Ashwin; Yusuf, Latifa; Ilamparithi, Thirumarai ChelvanThe paper performs a comparative study of Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) for the classification of Static Eccentricity (SE) and Dynamic Eccentricity (DE) faults in a Salient Pole Synchronous Machine (SPSM). The SPSM was subjected to varying SE and DE severities, unbalanced source voltages, and load conditions. Stator and field current data were measured, and various time-domain and frequency-domain features were extracted from the above-mentioned data. Both networks were fed these features and compared based on classification accuracy, robustness, and computational complexity.Item Deployment of an Electronic-based Approach for Fruits Juice Ingredient Analysis(International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) Landmark University, 2024-04-04) Kufre Esenowo Jack; Lanre Joseph Olatomiwa; Yahaya Asizehi Enesi; Grace Idowu Olaleru; Nnaemeka Emmanuel Ugwuogor; Babawuya AlkaliThis paper considers the deployment of an electronic-based fruits juice ingredient analysis. Most of the fruits juice products available in the market now contain water in large quantities than the active ingredients. This design attempts to respond to the end user's complaints. By putting on this design, it is expected to serve as a quality check and control for our teeming enterprising fruit substance producers. The system was designed and simulated using proteus and implemented using hardware electronic components. The system uses an infrared transmitter, fruit sample handler, and infrared receiver to realize its design. The instrument was calibrated with natural pineapple juice with 60% of water content. The outputs of this device were displayed using a cathode ray oscilloscope and voltmeter respectively. Five different samples of fruit juice were analyzed namely: A, B, C, D, and E. Results showed that all fruit juice contains a reasonable quantity of water which is not regarded as an adulteration since it is the natural content of the fruit. However, water content above 60% may be considered as much. It is recommended that fruit juice producers employ this system for their quality and control checks. Moreover, further research should take into consideration, the colour and viscosity of different fruit juices with a view to seeing how the system can analyze them, while the output should incorporate a microcontroller for an intelligent analysis and digital display.Item Pulse Width Modulation Analysis of Five-Level Inverter- Fed Permanent Magnet Synchronous Motors for Electric Vehicle Applications(International Journal of Robotics and Control Systems, 2021-11-21) Omokhafe J. Tola; Edwin A. Umoh; Edwin A. Umohn recent times, intense research has been focused on the performance enhancement of permanent magnet synchronous motors (PMSM) for electric vehicle (EV) applications to reduce their torque and current ripples. Permanent magnet synchronous motors are widely used in electric vehicle systems due to their high efficiency and high torque density. To have a good dynamic and transient response, an appropriate inverter topology is required. In this paper, a five-level inverter fed PMSM for electric vehicle applications, realized via co-simulation in an electromagnetic suite environment with a reduced stator winding current of PMSM via the use of in-phase disposition (PD) pulse width modulation (PWM) techniques as the control strategy is presented. The proposed topology minimizes the total harmonic distortion (THD) in the inverter circuit and the motor fed and also improves the torque ripples and the steady-state flux when compared to conventional PWM techniques. A good dynamic response was achieved with less than 10A stator winding current, zero percent overshoot, and 0.02 second settling time synchronization. Thus, the stator currents are relatively low when compared to the conventional PWM. This topology contribution to the open problem of evolving strategies that can enhance the performance of electric drive systems used in unmanned aerial vehicles (UAV), mechatronics, and robotic systemsItem Deploying A Standalone Facial and Emotion Recognition Classroom Management System on Resource-Constrained.(Izmir Turkiye, 2024-12-20) Abdullahi, I.M., Maliki, D., Abdulqudus, A., Abraham, S.A, & Ibrahim, M.In recent times, it has been proven in most industries that deep learning can play a huge part in the development and automation of processes otherwise performed manually by humans alone. The trend however has encountered more of a shift and tend towards transfer learning where standalone systems can be built on weights that have been extensively trained to be use-case agnostic. This project seeks to address the problem of student truancy. The methodology applied is a combination of a deep learning use-case agnostic weight embedding obtained from a popular network called Face net. Recognition is performed by computing facial distances using the weight embedding. Also addressed is the common reliance on internet for functionality present in most modern-day systems by deploying all the resources necessary on a resource-constrained development board. Emotions during class are also analyzed to improve classroom experience which will be displayed on a web application dashboard powered by artificial intelligence back-end. The results obtained show an above average recognition rate of 0.63 with emotional recognition accuracy of 0.72. The implications of these results are that accurate attendance can be taken in an organization with minor increments to the system such as increased computational capabilities.Item Towards The Development of An Intelligent Evaporative Cooling System for Post-Harvest Storage of Selected Fruits(Federal University of Technology, Minna, 2024-12-03) Isah, O.R., Adebayo S.E., Nuhu, B.K., Umar, B.U., Maliki, DPoor management of post-harvest storage of fruits and vegetables has led to enormous food wastage and economic loss globally. Refrigerating systems have been adopted over the years to avert these losses; however, installing them is expensive and can cause chilling injury and moisture loss to the fruits and vegetables when they go below 20℃ temperature. An evaporative cooling system has recently been widely used to preserve fruits and vegetables because it’s cheap to implement, especially for small-scale farmers. This system reduces the temperature and increases the air humidity in their chamber by removing latent heat from the evaporated water when exposed to sunlight. The existing evaporative system has been efficient in preserving the quality of fruits and vegetables as well as extending their shelf-life; however, they lacked automated operation and control mechanisms, intelligent mechanisms capable of identifying the physical state of the fruits, adaptive control techniques for the storage and remote monitoring, feedback scheme of the system for use by the farmers. The abovementioned limitations have prevented the system from achieving optimal performance in preserving fruits. Hence, this research aims to develop a multi-chamber evaporative cooling preservative system for post-harvest storage of fruits. In the first step, Tomato images were collected and trained with the MobileNetV2 model, achieving accuracy, precision and recall of 88%, 89% and 88% respectively. Overall, the model performs well, however, fine- tuning the model or using more training data could help improve its performance furtherItem A Joint Optimization Scheme for Enhanced Breast Cancer Diagnosis using Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO)(International Conference of the Faculty of Science, 2025-01-14) Ahmed, Y.E., Abdullahi, I.M., Maliki, D., & Akogbe, M. AOne of the leading diseases globally is cancer and breast cancer is not exempted. The objective of the WHO Global Breast Cancer Initiative (GBCI) is to reduce global breast cancer mortality by 2.5% per year, thereby averting 2.5 million breast cancer deaths globally between 2020 and 2040. The three pillars toward achieving these objectives are: health promotion for early detection; timely diagnosis; and comprehensive breast cancer management. In this study we propose an early and comprehensive detection technique in combating breast cancer diagnosis by combining the strength of both PSO (Particle Swarm Optimization) and BPSO (Binary Particle Swarm Optimization) to achieve optimal solution. The results obtained indicated the superiority of the Hybrid PSO-BPSO model in detection over an existing solution by achieving an accuracy of 98.82% on both the WBCD and WDBC datasets.Item Dipole antenna design leveraging optimization techniques(ABU-Zaria, 2023) Akamike, Ogechi; Caroline Alenoghena; Oyewobi S. StephenDipole antenna are commonest type of antenna in terms of design. This paper presents a technical review on the subject matter of dipole antenna design leveraging optimization techniques to design a better dipole antennaItem Investigating the Thresholding Effect and Fingerprint Transformation Using Cross-Correlation Similarity Matching(Faculty of Science Lafiya, 2025-01-12) Garuba O.R., Abdullahi, I.M., Dogo, E.M., & Maliki, DOne of the leading diseases globally is cancer and breast cancer is not exempted. The objective of the WHO Global Breast Cancer Initiative (GBCI) is to reduce global breast cancer mortality by 2.5% per year, thereby averting 2.5 million breast cancer deaths globally between 2020 and 2040. The three pillars toward achieving these objectives are: health promotion for early detection; timely diagnosis; and comprehensive breast cancer management. In this study we propose an early and comprehensive detection technique in combating breast cancer diagnosis by combining the strength of both PSO (Particle Swarm Optimization) and BPSO (Binary Particle Swarm Optimization) to achieve optimal solution. The results obtained indicated the superiority of the Hybrid PSO-BPSO model in detection over an existing solution by achieving an accuracy of 98.82% on both the WBCD and WDBC datasets.Item Software Failures: A Review of Causes and Solutions.(JOURNAL OF SCIECNCE TECHNOLOGY AND EDUCATION, 2021-04-17) Dauda, I. A., Nuhu, B. K., Abubakar, J., Abdullahi, I. M., & Maliki, D.Software failure occurs when the developed software swerves from the expected behaviours or could not execute the task it was developed to perform. Software failures could lead to different degrees of harm to organizations or individual businesses, which include but not limited to financial losses, embarrassments and damage to organizations’ reputations. This study reviewed and analyzed several related works in this domain and put more lights on the factors that make software either fail or become inoperative. From the various analyses, it is discovered that failures occur due to schedule pressure, deficient requirements, lack of technical skill set, unrealistic requirements and lack of discrete allocation of tasks. It is therefore imperative to the new and existing organizations to understand these causes and devise a realistic measure to ensure their software perform adequately.Item Systematic Literature Review of Deep Learning models for Computer Vision Applications: Deployment Challenges in Nigeria(JOURNAL OF SCIENCE TECHNOLOGY AND EDUCATION, 2023-09-05) Abdullahi, I. M., Siyaka, H.O., Alhaji, G.S., Maliki, D., DaudaDeep learning has gained attention recently. Since its adoption, deep learning has provided state-of-the-art solutions to lots of standing computational problems. One of the areas it has gained unequaled success is computer vision. The success of deep learning is not limited to computer vision only, it has also recorded unmatchable success in areas like natural language processing and speech recognition. With the advent of big data, the use and importance of deep learning can only continue to grow. One downside of this algorithm is its computational requirements: large datasets and high-end computing devices. In this paper, we provide an overview of recent deep learning models for computer vision, and we also highlighted the challenges faced by developing countries in adopting these technologies. No review has covered the challenges faced by Nigeria in deploying this technology. Some of the challenges highlighted include manual data collection and lack of adequate cloud storage services. Inadequate infrastructures such as power and network facilities, and finally, lack of adequate funding of the sector. It was recommended that local cloud services be established to encourage local data storage and reduce storage cost. Also, adequate investment for power and network availability should be made. Finally, there should be enough budget allocation to IT sector that will encourage technocrat and experts to develop and fully harness the benefit of the technology.