SAIEE Africa Research Journal最新文献

{"title":"Advancements in electrical marine propulsion technologies: A comprehensive overview","authors":"N. Arish;M. J. Kamper;R. J. Wang","doi":"10.23919/SAIEE.2025.10755059","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10755059","url":null,"abstract":"The shipping industry is shifting towards efficient and environmentally friendly propulsion systems to reduce costs and ecological impact. Traditional diesel engines, predominant in maritime operations, face challenges of high operating costs and environmental pollution, prompting the exploration of alternatives. Electric propulsion emerges as a promising solution, offering cost reduction and environmental preservation through reduced noise, This paper reviews electric ship propulsion systems and compares various marine propulsion systems, including in-hull, azimuth, and POD propulsion, with an emphasis on the latest POD systems. Specifically, it analyzes AZIPOD (ABB) and Mermaid (Rolls-Royce) propulsion systems in terms of motor type, cooling system, and power range. Additionally, the paper provides insights into the electrical components of POD propulsion, and the latest technology in ship propulsion, such as transformers, frequency converters, and propulsion motors, and explores redundancy in ship propulsion systems. It offers a detailed comparison of different electric motors, including DC motors, induction motors, superconducting motors, synchronous motors, and permanent magnet motors, discussing the advantages and disadvantages of each. This comprehensive review underscores the potential of electric propulsion systems to transform the maritime industry toward sustainability and efficiency.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 1","pages":"14-29"},"PeriodicalIF":1.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645443","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.2025.10755054","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10755054","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 1","pages":"2-2"},"PeriodicalIF":1.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645448","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.2025.10755055","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10755055","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 1","pages":"41-41"},"PeriodicalIF":1.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645512","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 techniques for power plant failure and availability: A concise systematic review","authors":"Bathandekile M. Boshoma;Peter O. Olukanmi","doi":"10.23919/SAIEE.2025.10755051","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10755051","url":null,"abstract":"Electricity demand continues to exceed supply in many sub-Saharan countries like South Africa, and frequent plant failures further reduce energy availability. To address this issue, it is essential to proactively predict plant failures and inform decisions on when to plan for outages. Given a myriad of prediction techniques, this study systematically analyzed various literature to provide a collective view of prediction approaches, their use cases, and context. Following the PRISMA guideline, relevant literature was searched using the Scopus database, and retrieved from the corresponding publisher sites. The selected studies focused on predicting the unplanned capability loss factor or the availability of power plants within the electricity industry domain. A thematic analysis was performed to identify emerging patterns related to current knowledge. Results revealed that prediction studies focus more on predicting availability than failure in coal-fired plants. The prediction horizon is mainly short-term, mostly in renewable plant. Artificial neural network, Bayesian analysis, and fuzzy rules are the prevalent technique found in most studies. Scholars and researchers can benefit from this study as it provided a simplified summary of power plant prediction techniques in a consolidated view.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 1","pages":"30-39"},"PeriodicalIF":1.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645514","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":"Real-time recognition and translation of Kinyarwanda sign language into Kinyarwanda text","authors":"Erick Semindu;Christine Niyizamwiyitira","doi":"10.23919/SAIEE.2025.10755056","DOIUrl":"https://doi.org/10.23919/SAIEE.2025.10755056","url":null,"abstract":"Despite significant technological advancements, there continues to be a considerable communication gap between individuals with hearing disabilities and the rest of society. This gap is exacerbated by the fact that the development and research of technologies, such as caption glasses, aimed at bridging this divide, primarily focus on sign languages used in countries with prominent tech industries, including European countries and USA. Consequently, there is a lack of resources and attention devoted to sign language recognition and translation systems for languages spoken in Africa. This research addresses this issue by concentrating on twenty-two common gestures in Kinyarwanda sign language. Through extensive exploration and evaluation of various machine learning algorithms, the study identifies the most effective approach for recognizing and translating these gestures. To validate the effectiveness of the developed system, real-world Kinyarwanda sign language video data is utilized for thorough training and testing. The research successfully culminates in the creation of a functional web application capable of accurately recognizing the 22 Kinyarwanda sign language gestures, both in live video feeds and recorded videos. This achievement represents a significant outcome of the research, as it addresses the specific needs of the Kinyarwanda signing community. By providing a reliable and accessible tool for gesture recognition and translation, the research contributes to narrowing the communication gap between individuals with hearing disabilities who use Kinyarwanda sign language and the wider society.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"116 1","pages":"4-13"},"PeriodicalIF":1.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645513","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.2024.10705986","DOIUrl":"https://doi.org/10.23919/SAIEE.2024.10705986","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"115 4","pages":"157-157"},"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":"115 4","pages":"114-127"},"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":"115 4","pages":"142-155"},"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":"115 4","pages":"128-141"},"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":"115 4","pages":"112-112"},"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}