Pub Date : 2024-10-04DOI: 10.23919/SAIEE.2024.10705986
{"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}
Pub Date : 2024-10-04DOI: 10.23919/SAIEE.2024.10705984
Divan van der Walt;Philip Baron
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.
{"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}
Pub Date : 2024-10-04DOI: 10.23919/SAIEE.2024.10705981
Olanrewaju A. Lasabi;Andrew G. Swanson;Alan L. Jarvis
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.
直流电流通过 HVDC 系统中的电力变压器会导致严重的半周期饱和问题，给电力系统带来风险。HVDC 系统可在单极回地和不平衡双极（无回地导体）模式下运行。在这两种 HVDC 运行模式下，大量直流电流流经 HVDC 接地端子，在中性接地变压器之间产生接地直流电位差。因此，直流通过中性点流入变压器绕组。这项研究提出了一种变压器中性点直流补偿装置，该装置采用了新颖的控制方式来解决这一问题。利用适当的控制策略，向接地网注入反向直流电可以补偿变压器绕组中的直流电流，从而减轻电力变压器的偏置运行磁通。本文深入分析了变压器对直流偏置的响应。然后，提出了一种区间 II 型模糊逻辑控制（IT2FLC），作为管理中性点直流补偿系统的有效控制策略。通过与基于 I 型模糊逻辑（T1FLC）和基于 PI 的补偿系统进行比较，对其稳健性进行了评估和分析。使用 MATLAB/Simulink 模型检验了控制性能，并通过使用 Speedgoat™ 实时目标机进行的快速控制原型测试进行了验证，评估了直流偏置电压变化下补偿装置的瞬态响应、振荡和稳定时间。结果表明，IT2FLC 比其他控制器能更有效地控制补偿装置，以缓解半周期饱和。这种方法引入了一种防止变压器半周期饱和的新策略。
{"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}
Pub Date : 2024-10-04DOI: 10.23919/SAIEE.2024.10705975
Pascal O. Aloo;Evan W. Murimi;James M. Mutua;John M. Kagira;Mathew N. Kyalo
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.
在全球人口不断增长的情况下，肯尼亚的畜牧场面临着提高生产力的压力。养牛业占主导地位，但中小型农场在牛人工授精方面举步维艰。目前，发情检测采用目视观察法，由农民保存农场日志。利用传感器改进发情预测的现代方法耗时长、成本高，而且需要不断连接互联网。本研究提出了一种新方法--使用控制器上的机器学习算法来预测牛的发情。研究人员从肯尼亚基安布县的两头零放牧多胎荷斯坦弗里斯兰奶牛身上收集了11个月的运动和温度数据。数据经过清理和存储。通过对方向加速度计值进行均方根求和，并对其进行时间平均，得出运动强度曲线。验证是通过观察奶牛的行为，如不安、上座和外阴肿胀等指标，并将牧场主的预测记录在案。然后，收集到的数据被用于训练机器学习算法，并对多个模型进行了测试，最后发现神经网络最为合适。TensorFlow 库有助于在微控制器上实现该算法，从而开发出具有 ML 算法的动物标签。结果表明，在检测发情方面，灵敏度为 83.9%，特异度为 89.0%，准确率为 89.5%，而农夫目测的灵敏度仅为 37%。这些发现强调了将机器学习集成到精准畜牧业中进行发情预测的潜力，预测可直接在离线动物标签上进行。这种整合为农民带来了希望，尤其是在无需大量资金投入的情况下提高了授精成功率。
{"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}
Pub Date : 2024-10-04DOI: 10.23919/SAIEE.2024.10705976
{"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}
Pub Date : 2024-06-06DOI: 10.23919/SAIEE.2024.10551304
Lindizgani K. Ndovie;Emmanuel Masabo
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.
{"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}
Pub Date : 2024-06-06DOI: 10.23919/SAIEE.2024.10551303
Ronewa Mabodi;Jahvaid Hammujuddy
This research presents the implementation and evaluation of machine learning models to predict solar irradiance (W/m 2 ). 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 2 ) 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.
{"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\u0000<sup>2</sup>\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\u0000<sup>2</sup>\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}
Pub Date : 2024-06-06DOI: 10.23919/SAIEE.2024.10551310
{"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}
Pub Date : 2024-06-06DOI: 10.23919/SAIEE.2024.10551314
Bhakti Y. Suprapto;Suci Dwijayanti;Dimsyiar M.A. Hafiz;Farhan A. Ardandy;Javen Jonathan
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 * 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.
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Pub Date : 2024-06-06DOI: 10.23919/SAIEE.2024.10551320
{"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}
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