Fitness function determination of uav anomaly detection in large data set via pso

This project is based on fitness function determination of Unmanned Aerial Vehicle (UAV) anomaly detection in large data set. Fitness function is a solution to the issue as input and outputs how "fit" or "excellent" the answer is with regard to the problem under discussion. Based...

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Main Author: Fatimah, Daing Jamil
Format: Undergraduates Project Papers
Language:English
Published: 2022
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Online Access:http://umpir.ump.edu.my/id/eprint/39010/1/EA18061_FATIMAH%20DAING%20JAMIL_THESIS%20-%20Fatimah%20Daing.pdf
http://umpir.ump.edu.my/id/eprint/39010/
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spelling my.ump.umpir.390102023-10-25T01:36:31Z http://umpir.ump.edu.my/id/eprint/39010/ Fitness function determination of uav anomaly detection in large data set via pso Fatimah, Daing Jamil TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering This project is based on fitness function determination of Unmanned Aerial Vehicle (UAV) anomaly detection in large data set. Fitness function is a solution to the issue as input and outputs how "fit" or "excellent" the answer is with regard to the problem under discussion. Based on previous research there are limited used of Particle Swarm Optimization (PSO). In this project, by using the PSO method define the fault of motor or blade by detecting it with acceleration, it is measure of how quickly speed changes with time. The measure of acceleration is expressed in units of (metres per second) per second or metres per second squared (m/s2). PSO method along with the monitoring based, can identify where exactly the fault has happened. Vibration velocity will be increase about two times from the normal velocity if the fault detected. To reduce the costing part of the Unmanned Aerial Vehicle (UAV) testing and detection of fault, the data is collected by using software in the loop with three program such as mission planner, ardupilot and flight gear. Through the simulation, that has been done it is verified by using PSO the fault occur at the motor/blade of UAV can be detected with a true positive detection rate of 76%. 2022-02 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39010/1/EA18061_FATIMAH%20DAING%20JAMIL_THESIS%20-%20Fatimah%20Daing.pdf Fatimah, Daing Jamil (2022) Fitness function determination of uav anomaly detection in large data set via pso. College of Engineering, Universiti Malaysia Pahang.
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Fatimah, Daing Jamil
Fitness function determination of uav anomaly detection in large data set via pso
description This project is based on fitness function determination of Unmanned Aerial Vehicle (UAV) anomaly detection in large data set. Fitness function is a solution to the issue as input and outputs how "fit" or "excellent" the answer is with regard to the problem under discussion. Based on previous research there are limited used of Particle Swarm Optimization (PSO). In this project, by using the PSO method define the fault of motor or blade by detecting it with acceleration, it is measure of how quickly speed changes with time. The measure of acceleration is expressed in units of (metres per second) per second or metres per second squared (m/s2). PSO method along with the monitoring based, can identify where exactly the fault has happened. Vibration velocity will be increase about two times from the normal velocity if the fault detected. To reduce the costing part of the Unmanned Aerial Vehicle (UAV) testing and detection of fault, the data is collected by using software in the loop with three program such as mission planner, ardupilot and flight gear. Through the simulation, that has been done it is verified by using PSO the fault occur at the motor/blade of UAV can be detected with a true positive detection rate of 76%.
format Undergraduates Project Papers
author Fatimah, Daing Jamil
author_facet Fatimah, Daing Jamil
author_sort Fatimah, Daing Jamil
title Fitness function determination of uav anomaly detection in large data set via pso
title_short Fitness function determination of uav anomaly detection in large data set via pso
title_full Fitness function determination of uav anomaly detection in large data set via pso
title_fullStr Fitness function determination of uav anomaly detection in large data set via pso
title_full_unstemmed Fitness function determination of uav anomaly detection in large data set via pso
title_sort fitness function determination of uav anomaly detection in large data set via pso
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39010/1/EA18061_FATIMAH%20DAING%20JAMIL_THESIS%20-%20Fatimah%20Daing.pdf
http://umpir.ump.edu.my/id/eprint/39010/
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score 13.235362