TELKOMNIKA Telecommunication, Computing, Electronics and Control
Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison
Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison
Subject
Edge detection, Embedded system, Moon date declaration, Moon phases, Morphology, Raspberry Pi, Sunfa Ata Zuyan
Description
The history recorded moon as the most inspiring object in the sky, but it
combined with visibility issues to study the phases. This research paper
proposes a novel algorithm named Sunfa Ata Zuyan (SAZ), which is meant to extend the shape detection algorithms to aim for lunar phase deceleration and overcome the difficulties encountered by the previous methods to find the moon and determine its phase. The paper sets to investigate two aims. First, propose the add-on algorithm SAZ to determine the lunar phase's data faster. Secondly, evaluate the Raspberry Pi as the main CPU due to its compact size and power as the primary processor based on the idea of a portable designed system. Then to examine the ability of the SAZ algorithm, it's combined with famous algorithms like hue, saturation and value (HSV), Canny, erosion, shape detection, and binarization has been tested on both personal computers (PC) and Raspberry Pi with the same images being compared. The results show that SAZ will help the shape detection algorithm to find the object and disclose the moon phases. Furthermore, the Raspberry Pi, functioning as a CPU, can perform as a hand-to-hand system to determine the lunar phase as a compact portable remote sensing structure.
combined with visibility issues to study the phases. This research paper
proposes a novel algorithm named Sunfa Ata Zuyan (SAZ), which is meant to extend the shape detection algorithms to aim for lunar phase deceleration and overcome the difficulties encountered by the previous methods to find the moon and determine its phase. The paper sets to investigate two aims. First, propose the add-on algorithm SAZ to determine the lunar phase's data faster. Secondly, evaluate the Raspberry Pi as the main CPU due to its compact size and power as the primary processor based on the idea of a portable designed system. Then to examine the ability of the SAZ algorithm, it's combined with famous algorithms like hue, saturation and value (HSV), Canny, erosion, shape detection, and binarization has been tested on both personal computers (PC) and Raspberry Pi with the same images being compared. The results show that SAZ will help the shape detection algorithm to find the object and disclose the moon phases. Furthermore, the Raspberry Pi, functioning as a CPU, can perform as a hand-to-hand system to determine the lunar phase as a compact portable remote sensing structure.
Creator
Ata Jahangir Moshayedi, Zu-yan Chen, Liefa Liao , Shuai Li
Source
DOI: 10.12928/TELKOMNIKA.v20i1.22338
Publisher
Universitas Ahmad Dahlan
Date
February 2022
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Ata Jahangir Moshayedi, Zu-yan Chen, Liefa Liao , Shuai Li, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/4274.
Sunfa Ata Zuyan machine learning models for moon phase detection: algorithm, prototype and performance comparison,” Repository Horizon University Indonesia, accessed September 20, 2024, https://repository.horizon.ac.id/items/show/4274.