Automated negative lightning return strokes characterization using brute-force search algorithm
Over the years, many studies have been conducted to measure, analyze, and characterize the lightning electric field waveform for a better conception of the lightning phenomenon. Moreover, the characterization mainly on the negative return strokes also significantly contributed to the development of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
UPM Press
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/100496/1/Automated%20negative%20lightning%20return%20strokes%20characterization.pdf |
| _version_ | 1825938381523124224 |
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| author | Abdul Haris, Faranadia Ab. Kadir, Mohd Zainal Abidin Sudin, Sukhairi Jasni, Jasronita Johari, Dalina Zaini, Nur Hazirah |
| author_facet | Abdul Haris, Faranadia Ab. Kadir, Mohd Zainal Abidin Sudin, Sukhairi Jasni, Jasronita Johari, Dalina Zaini, Nur Hazirah |
| author_sort | Abdul Haris, Faranadia |
| collection | UPM |
| description | Over the years, many studies have been conducted to measure, analyze, and characterize the lightning electric field waveform for a better conception of the lightning phenomenon. Moreover, the characterization mainly on the negative return strokes also significantly contributed to the development of the lightning detection system. Those studies mostly performed the characterization using a conventional method based on manual observations. Nevertheless, this method could compromise the accuracy of data analysis due to human error. Moreover, a longer processing time would be required to analyze data, especially for larger sample sizes. Hence, this study proposed the development of an automated negative lightning return strokes characterization using a brute-force search algorithm. A total of 170 lightning electric field waveforms were characterized automatically using the proposed algorithm. The manual and automated data were compared by evaluating their percentage difference, arithmetic mean (AM), and standard deviation (SD). The statistical analysis showed a good agreement between the manual and automated data with a percentage difference of 1.19% to 4.82%. The results showed that the proposed algorithm could provide an efficient structure and procedure by reducing the processing time and minimizing human error. Non-uniformity among users during negative lightning return strokes characterization can also be eliminated. |
| first_indexed | 2024-03-06T11:13:00Z |
| format | Article |
| id | upm.eprints-100496 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T11:13:00Z |
| publishDate | 2022 |
| publisher | UPM Press |
| record_format | dspace |
| spelling | upm.eprints-1004962023-11-24T09:07:00Z http://psasir.upm.edu.my/id/eprint/100496/ Automated negative lightning return strokes characterization using brute-force search algorithm Abdul Haris, Faranadia Ab. Kadir, Mohd Zainal Abidin Sudin, Sukhairi Jasni, Jasronita Johari, Dalina Zaini, Nur Hazirah Over the years, many studies have been conducted to measure, analyze, and characterize the lightning electric field waveform for a better conception of the lightning phenomenon. Moreover, the characterization mainly on the negative return strokes also significantly contributed to the development of the lightning detection system. Those studies mostly performed the characterization using a conventional method based on manual observations. Nevertheless, this method could compromise the accuracy of data analysis due to human error. Moreover, a longer processing time would be required to analyze data, especially for larger sample sizes. Hence, this study proposed the development of an automated negative lightning return strokes characterization using a brute-force search algorithm. A total of 170 lightning electric field waveforms were characterized automatically using the proposed algorithm. The manual and automated data were compared by evaluating their percentage difference, arithmetic mean (AM), and standard deviation (SD). The statistical analysis showed a good agreement between the manual and automated data with a percentage difference of 1.19% to 4.82%. The results showed that the proposed algorithm could provide an efficient structure and procedure by reducing the processing time and minimizing human error. Non-uniformity among users during negative lightning return strokes characterization can also be eliminated. UPM Press 2022-04-01 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/100496/1/Automated%20negative%20lightning%20return%20strokes%20characterization.pdf Abdul Haris, Faranadia and Ab. Kadir, Mohd Zainal Abidin and Sudin, Sukhairi and Jasni, Jasronita and Johari, Dalina and Zaini, Nur Hazirah (2022) Automated negative lightning return strokes characterization using brute-force search algorithm. Pertanika Journal of Science & Technology, 30 (2). 983 - 1001. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-3087-2021 10.47836/pjst.30.2.07 |
| spellingShingle | Abdul Haris, Faranadia Ab. Kadir, Mohd Zainal Abidin Sudin, Sukhairi Jasni, Jasronita Johari, Dalina Zaini, Nur Hazirah Automated negative lightning return strokes characterization using brute-force search algorithm |
| title | Automated negative lightning return strokes characterization using brute-force search algorithm |
| title_full | Automated negative lightning return strokes characterization using brute-force search algorithm |
| title_fullStr | Automated negative lightning return strokes characterization using brute-force search algorithm |
| title_full_unstemmed | Automated negative lightning return strokes characterization using brute-force search algorithm |
| title_short | Automated negative lightning return strokes characterization using brute-force search algorithm |
| title_sort | automated negative lightning return strokes characterization using brute force search algorithm |
| url | http://psasir.upm.edu.my/id/eprint/100496/1/Automated%20negative%20lightning%20return%20strokes%20characterization.pdf |
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