Automated negative lightning return strokes classification system
Over the years, many studies have been conducted to measure and classify the lightning-generated electric field waveform for a better understanding of the lightning physics phenomenon. Through measurement and classification, the features of the negative lightning return strokes can be accessed and a...
| Main Authors: | , , , , , |
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| Format: | Article |
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IOP Publishing
2021
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| _version_ | 1825937674706354176 |
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| author | Abdul Haris, Faranadia Ab. Kadir, Mohd. Zainal Abidin Sudin, Sukhairi Johari, Dalina Jasni, Jasronita Mohammad Noor, Siti Zaliha |
| author_facet | Abdul Haris, Faranadia Ab. Kadir, Mohd. Zainal Abidin Sudin, Sukhairi Johari, Dalina Jasni, Jasronita Mohammad Noor, Siti Zaliha |
| author_sort | Abdul Haris, Faranadia |
| collection | UPM |
| description | Over the years, many studies have been conducted to measure and classify the lightning-generated electric field waveform for a better understanding of the lightning physics phenomenon. Through measurement and classification, the features of the negative lightning return strokes can be accessed and analysed. In most studies, the classification of negative lightning return strokes was performed using a conventional approach based on manual visual inspection. Nevertheless, this traditional method could compromise the accuracy of data analysis due to human error, which also required a longer processing time. Hence, this study developed an automated negative lightning return strokes classification system using MATLAB software. In this study, a total of 115 return strokes was recorded and classified automatically by using the developed system. The data comparison with the Tenaga Nasional Berhad Research (TNBR) lightning report showed a good agreement between the lightning signal detected from this study with those signals recorded from the report. Apart from that, the developed automated system was successfully classified the negative lightning return strokes which this parameter was also illustrated on Graphic User Interface (GUI). Thus, the proposed automatic system could offer a practical and reliable approach by reducing human error and the processing time while classifying the negative lightning return strokes. |
| first_indexed | 2024-03-06T11:03:06Z |
| format | Article |
| id | upm.eprints-96168 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T11:03:06Z |
| publishDate | 2021 |
| publisher | IOP Publishing |
| record_format | dspace |
| spelling | upm.eprints-961682023-02-15T01:36:44Z http://psasir.upm.edu.my/id/eprint/96168/ Automated negative lightning return strokes classification system Abdul Haris, Faranadia Ab. Kadir, Mohd. Zainal Abidin Sudin, Sukhairi Johari, Dalina Jasni, Jasronita Mohammad Noor, Siti Zaliha Over the years, many studies have been conducted to measure and classify the lightning-generated electric field waveform for a better understanding of the lightning physics phenomenon. Through measurement and classification, the features of the negative lightning return strokes can be accessed and analysed. In most studies, the classification of negative lightning return strokes was performed using a conventional approach based on manual visual inspection. Nevertheless, this traditional method could compromise the accuracy of data analysis due to human error, which also required a longer processing time. Hence, this study developed an automated negative lightning return strokes classification system using MATLAB software. In this study, a total of 115 return strokes was recorded and classified automatically by using the developed system. The data comparison with the Tenaga Nasional Berhad Research (TNBR) lightning report showed a good agreement between the lightning signal detected from this study with those signals recorded from the report. Apart from that, the developed automated system was successfully classified the negative lightning return strokes which this parameter was also illustrated on Graphic User Interface (GUI). Thus, the proposed automatic system could offer a practical and reliable approach by reducing human error and the processing time while classifying the negative lightning return strokes. IOP Publishing 2021 Article PeerReviewed Abdul Haris, Faranadia and Ab. Kadir, Mohd. Zainal Abidin and Sudin, Sukhairi and Johari, Dalina and Jasni, Jasronita and Mohammad Noor, Siti Zaliha (2021) Automated negative lightning return strokes classification system. Journal of Physics: Conference Series, 2107. art. no. 012022. pp. 1-8. ISSN 1742-6588; ESSN: 1742-6596 https://iopscience.iop.org/article/10.1088/1742-6596/2107/1/012022 10.1088/1742-6596/2107/1/012022 |
| spellingShingle | Abdul Haris, Faranadia Ab. Kadir, Mohd. Zainal Abidin Sudin, Sukhairi Johari, Dalina Jasni, Jasronita Mohammad Noor, Siti Zaliha Automated negative lightning return strokes classification system |
| title | Automated negative lightning return strokes classification system |
| title_full | Automated negative lightning return strokes classification system |
| title_fullStr | Automated negative lightning return strokes classification system |
| title_full_unstemmed | Automated negative lightning return strokes classification system |
| title_short | Automated negative lightning return strokes classification system |
| title_sort | automated negative lightning return strokes classification system |
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