Wooi Chin Leong
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Preferred name
Wooi Chin Leong
Official Name
Leong, Wooi Chin
Alternative Name
Wooi, Chin-Leong
Wooi, C. L.
Wooi, Chin-leong
Main Affiliation
Scopus Author ID
55912811600
Researcher ID
V-2268-2019

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  • Publication
    Lightning generated electric field over land and sea at northern region of peninsular malaysia: Measuring setup
    ( 2020-09-01)
    Ahmad Muhyiddin Yusof
    ;
    Abdul-Malek Z.
    ;
    Wooi Chin Leong
    Lightning electric fields can be characterized based on its origins which include the preliminary breakdown, stepped leader, return stroke, subsequent return stroke, dart leader, dart-stepped leader, narrow bipolar pulses, cloud activity between strokes, regular pulse trains, K and M changes, and chaotic pulse trains. The aim of this work is to describe a lightning measuring setup suitable for characterizing lightning electric fields and their propagation when the measuring sensors are close to the sea. A location close to the sea in Northern Malaysia was identified and prepared for the study. Parallel plate and vertical whip antennas are used as the main sensors, which are connected to a high-speed digital storage oscilloscope through customized analog filter buffer circuits. Noise-distortion effects in the recorded data are filtered using MATLAB computer simulation before further analyses are carried out. The 1-D wavelet technique is used in the signal denoising process. Preliminary results show that the installed setup and denoising method is adequate and potentially efficient for the pre-processing of lightning electric field signals, and hence can be used for the detailed characterization study including the effects of sea on lightning characteristics and propagation.
      3  30
  • Publication
    Artificial neural network application in an implemented lightning locating system
    (Elsevier, 2020)
    Kamyar Mehranzamir
    ;
    Zulkurnain Abdul-Malek
    ;
    Hadi Nabipour Afrouzi
    ;
    Saeed Vahabi Mashak
    ;
    Wooi Chin Leong
    ;
    Roozbeh Zarei
    Time difference of arrival (TDOA) technique is one of many bases to determine lightning strike location employed in a lightning locating system (LLS). In this technique, at least four measurement sensors are required to correctly locate a lightning strike. Usage of fewer number of sensors will result in non-unique solutions to the generated hyperbolas, and hence wrong lightning strike point. This research aims to correctly determine the strike point even if only three measuring sensors are utilized. An artificial neural network (ANN) based algorithm was developed for a 400 km2 coverage area in Southern Malaysia using time of arrival data collected at the three measuring stations over a certain period. The Levenberg–Marquardt algorithm is demonstrated to correctly identify the lightning strike coordinates with an average error of 350 m. The algorithm has helped the three-station TDOA-based LLS to successfully locate the lightning strike point with a remarkable accuracy comparable to that of commercial systems.
      3  18