Halima S. Yakubu, S. Hussein, Gokhan Koyunlu, E. Ewang, S. Abubakar
Abstract: The Deep Space Antennas are essential in achieving communication over very large distances. However, the pointing accuracy of this antenna needs to be as precise as possible to enable effective communication with the satellite. Therefore, this work addressed the pointing accuracy for a Deep Space Antenna using Fuzzy-PID control technique by improving the performance objectives (settling time, percentage overshoot rise time and mainly steady-state error) of the system. In this work, the PID controller for the system was first of all designed and simulated after which, a fuzzy controller was also designed and simulated using MATLAB and Simulink respectively for the sake of comparison with the fuzzy-PID controller. Then, the fuzzy-PID controller for the system was also designed and simulated using MATLAB and Simulink and it gives a better performance objective (rise time of 1.0057s, settling time of 1.6019s, percentage overshoot of 1.8013, and steady-state error of 2.195e-6) over the PID and fuzzy controllers respectively. Therefore, the steady state error shows improved pointing accuracy of 2.195e-6. Keywords/
{"title":"Fuzzy-PID Controller for Azimuth Position Control of Deep Space Antenna","authors":"Halima S. Yakubu, S. Hussein, Gokhan Koyunlu, E. Ewang, S. Abubakar","doi":"10.47231/ewae8443","DOIUrl":"https://doi.org/10.47231/ewae8443","url":null,"abstract":"Abstract: The Deep Space Antennas are essential in achieving communication over very large distances. However, the pointing accuracy of this antenna needs to be as precise as possible to enable effective communication with the satellite. Therefore, this work addressed the pointing accuracy for a Deep Space Antenna using Fuzzy-PID control technique by improving the performance objectives (settling time, percentage overshoot rise time and mainly steady-state error) of the system. In this work, the PID controller for the system was first of all designed and simulated after which, a fuzzy controller was also designed and simulated using MATLAB and Simulink respectively for the sake of comparison with the fuzzy-PID controller. Then, the fuzzy-PID controller for the system was also designed and simulated using MATLAB and Simulink and it gives a better performance objective (rise time of 1.0057s, settling time of 1.6019s, percentage overshoot of 1.8013, and steady-state error of 2.195e-6) over the PID and fuzzy controllers respectively. Therefore, the steady state error shows improved pointing accuracy of 2.195e-6. Keywords/","PeriodicalId":118988,"journal":{"name":"Covenant Journal of Informatics & Communication Technology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124731315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. N. Odaudu, E. A. Adedokun, A. T. Salaudeen, Francis Franklin Marshall, Y. Ibrahim, D. E. Ikpe
Intelligent systems an aspect of artificial intelligence have been developed to improve satellite image interpretation with several foci on objectbased machine learning methods but lack an optimal feature selection technique. Existing techniques applied to satellite images for feature selection and object detection have been reported to be ineffective in detecting objects. In this paper, differential Evolution (DE) algorithm has been introduced as a technique for selecting and mapping features to Haarcascade machine learning classifier for optimal detection of satellite image was acquired, pre-processed and features engineering was carried out and mapped using adopted DE algorithm. The selected feature was trained using Haarcascade machine learning algorithm. The result shows that the proposed technique has performance Accuracy of 86.2%, sensitivity 89.7%, and Specificity 82.2% respectively. Keywords/
{"title":"Sequential Feature Selection Using Hybridized Differential Evolution Algorithm and Haar Cascade for Object Detection Framework","authors":"S. N. Odaudu, E. A. Adedokun, A. T. Salaudeen, Francis Franklin Marshall, Y. Ibrahim, D. E. Ikpe","doi":"10.47231/olrl4991","DOIUrl":"https://doi.org/10.47231/olrl4991","url":null,"abstract":"Intelligent systems an aspect of artificial intelligence have been developed to improve satellite image interpretation with several foci on objectbased machine learning methods but lack an optimal feature selection technique. Existing techniques applied to satellite images for feature selection and object detection have been reported to be ineffective in detecting objects. In this paper, differential Evolution (DE) algorithm has been introduced as a technique for selecting and mapping features to Haarcascade machine learning classifier for optimal detection of satellite image was acquired, pre-processed and features engineering was carried out and mapped using adopted DE algorithm. The selected feature was trained using Haarcascade machine learning algorithm. The result shows that the proposed technique has performance Accuracy of 86.2%, sensitivity 89.7%, and Specificity 82.2% respectively. Keywords/","PeriodicalId":118988,"journal":{"name":"Covenant Journal of Informatics & Communication Technology","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123475484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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