Electrical & Electronics Engineering
Permanent URI for this collectionhttp://197.211.34.35:4000/handle/123456789/130
Electrical & Electronics Engineering
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Item Classification and Severity Estimation of Eccentricity Faults in Salient Pole Synchronous Machine using Deep Learning(IEEE, 2025) Yusuf, Latifa; Moa, Belaid; Ilamparithi, Thirumarai ChelvanThe presented research work is focused on the classification and severity estimation of eccentricity faults in Salient Pole Synchronous Machines. Building on our comparative study of Artificial Neural Network and Convolutional Neural Network for eccentricity fault classification, we propose an end-to-end deep learning model, namely Hierarchical Convolutional Neural Network, for eccentricity classification and severity estimation. The deep learning model inherently consists of an eccentricity detection component for fault classification and a severity estimation component for fault quantification. The deep learning model is built using the experimental data of a 3-phase, 2-kW, salient pole synchronous machine. The machine is subjected to 20%, 40%, and 60% severities of static and dynamic eccentricity faults under different loading conditions. Stator line currents and line-to-line voltages obtained from different operating conditions are used to train, validate and test the proposed model. To enhance the model's performance, time delay construction was incorporated to augment the datasets and carefully evaluate the impact of selected raw input features, specifically stator currents and voltages, as well as the load. Among the evaluated scenarios, the use of voltage with time delay (V, TD) as input features produced the best results, achieving 100% classification accuracy and a root mean square error of 0.0046 for static eccentricity and 0.0188 for dynamic eccentricity estimation. Results indicate that the model performs excellently in fault classification and severity estimation. Compared to traditional machine learning models, the presented model is an end-to-end deep learning architecture devoid of manual feature extraction and is robust to load variations.Item Development of stability charts for double salience reluctance machine modeled using hill’s equation(Bulletin of Electrical Engineering and Informatics, 2024-06-10) Enesi Asizehi Yahaya; Emenike Chinedozi EjioguThe paper presents a novel algorithm for the development of stability charts. The second-order differential homogeneous equation describing a double salient reluctance machine with a capacitance connected to its stator winding is transformed into hill’s equation. The circuit components are the stator coil time-varying inductance of a double salient reluctance machine, capacitance and resistance. All these are modeled by hill’s equation. The double salient reluctance machine acts as an energy conversion system. The maximum and minimum inductance of the energy conversion system is measured in laboratory by inductance, capacitance, and resistance (LCR) meter. These values help to determine the inductance modulation index. The inductance modulation indetx, the characteristic constant and the characteristic parameter obtained from modeling equations are used in the MATLAB/Simulink model. The MATLAB/Simulink simulations generate stable and unstable oscillations to form stability charts. The proposed stability charts are in good agreement with the Ince-Stritt stability chart, which is widely applied in physics, mechanics and in electrical engineering, especially where the state of stability of a system or an electric oscillatory circuit is to be determinedItem ANALYSIS OF SPECTRUM OCCUPANCY PREDICTION RESULTS FOR MAITAMA ABUJA(International Conference on Communication and Information Science (ICCIS), 2024) Ajiboye, Johnson Adegbenga; Mary Adebola Ajiboye; Babatunde Araoye Adegboye; Daniel Jesupamilerin Ajiboye; Jonathan Gana Kolo; Abiodun Musa AibinuThis research investigates the efficacy of Artificial Neural Networks (ANN) in predicting spectrum occupancy in Maitama, Abuja, Nigeria, focusing on frequency bands ranging from 30 MHz to 300 MHz. The primary objective was to evaluate the accuracy of ANN-based predictions of spectrum usage and compare these predictions with actual measurements. The study employed ANN to forecast spectrum occupancy across various frequency bands, and the predicted data were then compared with empirical measurements to assess the performance of the model. The analysis revealed that prediction errors were generally low across all frequency bands, with most errors falling below 1.5%. Specifically, the 30-47 MHz sub-band demonstrated an average percentage difference between the actual and predicted value of 0.087%, with a maximum error of 1.12% occurring at frequency of 44.65 MHz. For the 47.05-68 MHz band, the average percentage difference was slightly higher at 0.106%, and the maximum error was 2.18% occurring at frequency of 50.2 MHz. In the 68.05-74.8 MHz band, the average percentage error was 0.040%, but with highest error of 0.232% at frequency of 73.95 MHz. The 74.85-87.45 MHz band showed the most accurate predictions with an average error of just 0.010%, and a maximum error of 0.174% at 75.1 MHz. Overall, the highest prediction error was 0.106% in the 47.05-68 MHz band, whereas the lowest was 0.010% in the 74.85-87.45 MHz band. These results highlight the high accuracy of ANN in predicting spectrum usage, demonstrating its potential for effective spectrum management and planning in Maitama, Abuja.Item STATE OF THE ART ON PATH LOSS MODEL DEVELOPMENT(Humminbird Publications and Research International, 2024-01-29) Ibukun Aderele Adeyemi; Jonathan Gana Kolo; Ajiboye, Johnson AdegbengaThis is a study of path loss prediction modelling. Path loss modelling is widely applied in determining mobile wireless signal propagation in a given environment. This helps radio network planners to have an accurate view of requirements to obtain good quality of service when deploying radio networks. The empirical models are exhaustively analysed and compared with the emerging machine learning models. Also, mention is made of RIS models which are beginning to gather some attention due to their focus on the programmable electromagnetic properties. The study was able to establish empirical models as the most simple and efficient method of path loss prediction models. Attention is paid to the application of these models in both 900MHz and 1800MHz in urban, suburban and rural areas. This is due to the wide application of these frequencies in mobile wireless communication. The machine learning models present better results and give a high level of accuracy for diverse environments. However, they require large volume of data and environmental features extraction at both 2D and 3D to get reliable model. This makes it imperative to carry out field measurement tasks that are basically synonymous with methodologies employed in empirical approach to modelling. The variation in vegetation determines the best fit model for each particular case as well as the derivation of path loss exponent. The RIS modelling approach gives positive views especially at higher frequencies. The tuneable properties of the surfaces give a wide berth in application across different frequency spectrum. Complex and large volume of computation required in use of RIS implies that machine learning models, especially deep learning models will be better off incorporated into the process. It is thus beneficial to the researcher to ensure that a good grasp of the different approaches highlighted is obtained such that the benefits available are merged to produce finer results.Item Design and Development of An IoT – Based Multi – Health Vital Signs Monitoring System(I3C 2024, 2024-04-22) Adegboye N.J; Dauda U.S; Ajiboye, Johnson Adegbenga; Ohize H.O; Adegboye B.AThis study presents the design and development of an IoT-based multi-health vital signs monitoring system that can monitor a patient’s basic health physiological parameters in real-time. In this system, four (4) sensors were used to capture the data from the patient. These are body temperature sensor, electrocardiogram (ECG) sensor, accelerometer sensor and the eye blink sensor. The hardware modules were interfaced with the liquid crystal display (LCD) to display the required data. Memory modules stored the designated phone numbers. The GSM module retains connectivity with the cellular networks acting as SMS receiver, which sends data on the patient’s vital signs. The LCD displays the data, which can be seen through the IoT. The microcontroller was programmed using C++ programming language and connects all sensors. This enabled conveyance of data on the patient’s health condition via IoT to the doctor for further processing and analysis.Item Robust Multi-Target Tracking with a Kalman-Gain CPHD Filter: Simulation and Experimental Validation(2025-04-19) Abdullahi DaniyanWe introduce a novel cardinalized implementation of the Kalman-gain-aided particle probability hypothesis density (KG-SMC-PHD) filter, extending it to form the Kalman-Gain Particle Cardinalized Probability Hypothesis Density (KG- SMC-CPHD) filter. This new approach significantly enhances multi-target tracking by combining the particle-based state correction mechanism with the propagation of both the PHD and target cardinality distribution. Unlike conventional particle filters that require a large number of particles for acceptable performance, our method intelligently corrects selected particles during the weight update stage, resulting in a more accurate posterior with substantially fewer particles. Through comprehensive evaluations on both simulated and experimental datasets, the KG-SMC-CPHD filter demonstrates superior robustness and accuracy, particularly in high-clutter environments and nonlinear target dynamics. Notably, it offers improved cardinality estimation and maintains the computational efficiency and performance advantages of its predecessor, the KG-SMC-PHD filter, making it a powerful tool for advanced multi-target tracking applications.Item Bayesian data driven modelling of kinetochore dynamics: space-time organisation of the human metaphase plate(PLOS Biology, 2025-01-23) Constandina Koki; Alessio V Inchingolo; Abdullahi Daniyan; Enyu Li; Andrew D. McAinsh; Nigel J BurroughsMitosis is a complex self-organising process that achieves high fidelity separation of duplicated chromosomes into two daughter cells through capture and alignment of chromosomes to the spindle mid-plane. Chromosome movements are driven by kinetochores, multi-protein machines that attach chromosomes to microtubules (MTs), both controlling and generating directional forces. Using lattice light sheet microscopy imaging and automated near-complete tracking of kinetochores at fine spatio-temporal resolution, we produce a detailed atlas of kinetochore metaphase-anaphase dynamics in untransformed human cells (RPE1). We fitted 18 biophysical models of kinetochore metaphase-anaphase dynamics to experimental data using Bayesian inference, and determined support for the models with model selection methods, demonstrating substantial sister force asymmetry and time dependence of the mechanical parameters. Our analysis shows that K-fiber pulling and pushing strengths are inversely correlated and that there is substantial spatial organisation of KT dynamic properties both within, and transverse to the metaphase plate. Further, K-fiber forces are tuned over the last 5 mins of metaphase towards a set point, which we refer to as the anaphase ready state.Item Probability hypothesis density filter for parameter estimation of multiple hazardous sources(Elsevier, 2024-08-30) Abdullahi Daniyan; Cunjia Liu; Wen-Hua ChenThis study introduces an advanced methodology for estimating the source term of multiple, variable-number biochemical hazard releases, where the exact count of sources is not predetermined. Focusing on environments monitored via a network of sensors, we tackle this challenge through a multi-source Bayesian filtering paradigm, employing the theory of random finite sets (RFS). Our novel approach leverages a modified particle filter-based probability hypothesis density (PHD) filter within the RFS framework, enabling simultaneous estimation of critical source characteristics (such as location, emission rate, and effective release height) and the quantification of source numbers. This method not only accurately estimates pertinent source parameters but is also adept at identifying the emergence of new sources and the cessation of existing ones within the monitored area. The efficacy of our approach is validated through extensive simulations, which mimic a range of scenarios with varying and unknown source counts, highlighting the proposed method’s robustness and precision.Item Adaptive Resource Allocation and Mode Switching for D2D Networks With Imperfect CSI in AGV-Based Factory Automation(IEEE, 2024-12-16) GBADAMOSI, Safiu Abiodun; HANCKE, Gerhard Petrus; ABU-MAHFOUZ, Adnan M.In industrial factory automation and control system, reliable communication for automated guided vehicles (AGVs) in dynamic, interference laden factory settings are essential particularly for real-time operations. Device-to-device (D2D) technology can enhance industrial network performance by offloading traffic and improving resource utilization. However, deploying D2D-enabled networks presents challenges such as interference control and imperfect channel state information (ICSI). In this paper, we investigate an adaptive resource allocation and mode switching strategy (ARAMS) in D2D-enabled industrial small cell (SC) networks with ICSI to maximize the system throughput and address reuse interference for AGVs. The ARAMS scheme integrates mode switching (MS), channel-quality factor (CQF), and power control (PC) within a bi-phasic resource-sharing (RS) algorithm to lower the computational complexity. In the initial phase, the operational mode for each D2D user (DU) per cell is adaptively selected based on the channel gain ratio (CGR). Subsequently, it computes the CQF for each cell with a reuse DU to identify an optimal reuse partner. The final phase employs the Lagrangian dual decomposition method to decide the DU’s and industrial cellular users (CUs) optimum distributed power to maximize the system throughput under the interference constraints. The numerical results show that as channel estimation error variance (CEEV) increases, the ARAMS scheme consistently outperforms other approaches in maximizing system throughput, except for the AIMS scheme.