SAIEE Africa Research Journal最新文献

{"title":"Notes for authors","authors":"","doi":"10.23919/SAIEE.2024.10705986","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10705986","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705986","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Locating positions for measuring a golf swing with inertial measurement units: A pilot study","authors":"Divan van der Walt;Philip Baron","doi":"10.23919/SAIEE.2024.10705984","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10705984","url":null,"abstract":"Golfers often face challenges in refining their swings, seeking cost-effective ways to enhance their techniques. Traditional coaching methods are costly and since they rely on the human eye, these techniques often miss important golf swing movements owing to the rapid pace of a golf swing. To address this shortcoming, an investigation into the potential of IMU sensors for the mapping of golf swings to aid both instructors and golfers was undertaken. Focusing on the leading shoulder's horizontal position relative to the club head, the study addresses two questions: determining whether IMUs can map a golf swing as well as determining the minimum IMU sensors required to track a golf swing. Thus, the goal of this pilot study was to identify if there are optimal placements for IMUs on the body. The premise is that by performing a consistent golf swing, golfers could improve their handicap. Thus, by tracking and visually displaying the phases of the golf swing, such data could aid in increased golf swing consistency by analysing not only the phases of the golf swing, but also the bodily movements. This pilot study relied on six participants who each repeatedly performed golf swings. IMUs were positioned in eight positions around the body from ankle to shoulder and several trials were conducted for each position. The results showed that IMUs were useful in tracking a golf swing; however, certain bodily positions, such as the hip, leading knee, and leading foot, did not yield meaningful data as compared to the other positions. The IMU data from the back and front of the wrist and the leading shoulder provided useful mappings of the golf swing, including the timing and intensity. Analysis of body posture angles, especially wrist flexion, hip, and shoulder rotation angles, offered valuable data that may be useful to both coaches and players. By discerning patterns in successful and unsuccessful swings, coaches could provide informed feedback to golfers, aiding golfers in refining their techniques. These findings demonstrate the potential of IMU sensors in golf instruction, offering a data-driven approach to enhance golfers' performance and consistency on the golf course.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705984","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interval type-II fuzzy logic control of neutral DC compensation method to moderate DC bias in power transformer","authors":"Olanrewaju A. Lasabi;Andrew G. Swanson;Alan L. Jarvis","doi":"10.23919/SAIEE.2024.10705981","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10705981","url":null,"abstract":"Direct current flow through power transformers in HVDC systems can lead to significant half-cycle saturation issues, putting the power system at risk. The HVDC system can function in monopolar ground return and unbalanced bipolar without earth return conductors. During these two HVDC modes of operation, a substantial direct current flows through the HVDC ground terminals, creating a ground DC potential difference between the neutrally grounded transformers. As a result, DC flows through the neutrals into the transformer windings. The study presents a transformer-neutral DC compensating device incorporating a novel control to solve the issue. Using a proper control strategy, injecting reverse DC into the grounding grid can compensate for direct current flow in transformer windings to mitigate the biased operating flux of power transformers. In this article, an in-depth analysis of transformer response to DC bias was investigated. Then, an Interval type-II fuzzy logic control (IT2FLC) was proposed as an effective control strategy for managing the neutral DC compensating system. Its robustness was assessed and analysed by comparing it with type-I fuzzy logic-based (T1FLC) and a PI-based compensation system. The control performance is examined using MATLAB/Simulink models and validated with rapid control prototype tests conducted with a Speedgoat™ real-time target machine, assessing the transient response, oscillations, and settling time of the compensation device under DC bias voltage variations. The outcomes indicate that the IT2FLC controls the compensation device more effectively than other controllers to mitigate half-cycle saturation. This approach introduces a novel strategy to prevent transformer half-cycle saturation.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705981","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of oestrus cycle in cattle using machine learning in Kenya","authors":"Pascal O. Aloo;Evan W. Murimi;James M. Mutua;John M. Kagira;Mathew N. Kyalo","doi":"10.23919/SAIEE.2024.10705975","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10705975","url":null,"abstract":"Livestock farms in Kenya face pressure to increase productivity amid rising global population. Cattle farming dominates, but small to medium-sized farms struggle with cattle insemination. Currently, visual observation is used for heat detection, with farmers maintaining farm journals. Modern methods utilizing sensors to improve estrus prediction are time-consuming, costly and need constant internet connection. This research proposes a novel approach—the use of an on-controller machine learning algorithm—for estrus prediction in cattle. Motion and temperature data was collected from two zero-grazed multiparous Holstein Friesian cows in Kiambu County, Kenya for 11 months. The data was cleaned and stored. Movement intensity profiles were derived by root-mean-squaring directional accelerometer values and averaging this over time. Validation was performed by observing cow behavior for indicators such as restlessness, mounting, and vulva swelling, with farmer predictions documented in their records. The collected data was then used to train a machine learning algorithm, with several models tested, and a neural network emerged as the best fit. The TensorFlow library facilitated the implementation of the algorithm on a microcontroller, allowing for the development of an animal tag featuring the ML algorithm. Results demonstrated 83.9% sensitivity, 89.0% specificity and 89.5% accuracy in detecting estrus, compared to farmer's visual observation, which had only 37% sensitivity. These findings underscore the potential to integrate machine learning into Precision Livestock Farming for estrus prediction, with prediction occurring directly on the animal tag offline. This integration holds promise for farmers, notably heightened insemination success rates, without necessitating significant financial investment.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2024.10705976","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10705976","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging MobileNetV3 for In-Field Tomato Disease Detection in Malawi via CNN","authors":"Lindizgani K. Ndovie;Emmanuel Masabo","doi":"10.23919/SAIEE.2024.10551304","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10551304","url":null,"abstract":"Malawi’s economy heavily depends on agriculture, including both commercial and subsistence farming. Smallholder and small-medium enterprises leading the production of tomatoes in Malawi cannot satisfy local demand due to problems such as pests, diseases, unstable markets, and high costs. Many farmers lack the expertise to effectively manage these threats. To address the problem of tomato leaf disease identification, this research aimed to develop an automated system for tomato leaf disease detection by utilizing data augmentation techniques, MobileNetV3, and Convolutional Neural Network algorithms. We trained models on secondary data collected from the public PlantVillage dataset and tested the resultant classifiers on primary data of local farm images. The experimental results demonstrate that both models tested better on the PlantVillage dataset. Additionally, with an accuracy of 92.59% and a loss of 0.2805, the pre-trained MobileNetV3 model conventionally performs better than a CNN model. However, when tested on the primary field dataset, the models did not meet expectations for generalization, with the pre-trained MobileNetV3 achieving an accuracy of 9.2%, and loss of 12.91 and the CNN achieving an accuracy of 10.14%, and loss of 8.11. The experiments aided in showing that the models trained on the PlantVillage dataset are not as effective when applied in real-world scenarios. Further improvements are needed to enhance the models’ generalization in real-world scenarios.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10551304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solar Irradiance Forecasting for Informed Solar Systems Design and Financing Decisions","authors":"Ronewa Mabodi;Jahvaid Hammujuddy","doi":"10.23919/SAIEE.2024.10551303","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10551303","url":null,"abstract":"This research presents the implementation and evaluation of machine learning models to predict solar irradiance (W/m\u0000²\u0000). The objective is to provide valuable insights for making informed decisions regarding solar system design and financing. A thorough exploratory data analysis was conducted on the Southern African Universities Radiometric Network (SAURAN) data collected at the University of Pretoria’s station to gain insights into the patterns of solar irradiance over the past 10 years. Python’s functions and libraries are utilized extensively for conducting exploratory data analysis, model implementation, model testing, forecasting, and data visualization. Random Forest (RF), k-Nearest Neighbors (KNN), Feedforward Neural Network (FFNN), Support Vector Regression (SVR), and eXtreme Gradient Boosting models (XGBoost) are implemented and evaluated. The KNN model was found to be superior achieving a relative Root Mean Squared Error (RMSE), relative Mean Absolute Error (MAE), and R-Squared (R\u0000²\u0000) of 5.77%, 4.51% and 0.89 respectively on testing data. The variable importance analysis revealed that temperature (X!) exerted the greatest influence on predicting solar irradiance, accounting for 44% of the predictive power. The KNN model is suitable to inform solar systems design and financing decisions. Directions for future studies are identified and suggestions for areas of exploration are provided to contribute to the advancement of solar irradiance predictions.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10551303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2024.10551310","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10551310","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10551310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing an Autonomous Vehicle Using Sensor Fusion Based on Path Planning and Deep Learning Algorithms","authors":"Bhakti Y. Suprapto;Suci Dwijayanti;Dimsyiar M.A. Hafiz;Farhan A. Ardandy;Javen Jonathan","doi":"10.23919/SAIEE.2024.10551314","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10551314","url":null,"abstract":"Autonomous electric vehicles use camera sensors for vision-based steering control and detecting both roads and objects. In this study, road and object detection are combined, utilizing the YOLOv8x-seg model trained for 200 epochs, achieving the lowest segmentation loss at 0.53182. Simulation tests demonstrate accurate road and object detection, effective object distance measurement, and real-time road identification for steering control, successfully keeping the vehicle on track with an average object distance measurement error of2.245 m. Route planning for autonomous vehicles is crucial, and the A-Star algorithm is employed to find the optimal route. In real-time tests, when an obstacle is placed between nodes 6 and 7, the A-Star algorithm can reroute from the original path (5, 6, 7, 27, and 28) to a new path (5, 6, 9, 27, and 28). This study demonstrates the vital role of sensor fusion in autonomous vehicles by integrating various sensors. This study focuses on sensor fusion for object-road detection and path planning using the A\u0000^*\u0000 algorithm. Real-time tests in two different scenarios demonstrate the successful integration of sensor fusion, enabling the vehicle to follow planned routes. However, some route nodes remain unreachable, requiring occasional driver intervention. These results demonstrate the feasibility of sensor fusion with diverse tasks in third-level autonomous vehicles.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10551314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Notes for authors","authors":"","doi":"10.23919/SAIEE.2024.10551320","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10551320","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10551320","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141286623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}