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"K-means"

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"K-means"

Development of Image Process for Crack Identification on Porcelain Insulators
In-hyuk Choi, Koo-yong Shin, Ho-song An, Ja-bin Koo, Ju-am Son, Dae-yeon Lim, Tae-keun Oh, Young-geun Yoon
J Electr Electron Mater 2020;33(4):303-309.   Published online July 1, 2020
DOI: https://doi.org/10.4313/JKEM.2021.33.4.10
This study proposes a crack identification algorithm to analyze the surface condition of porcelain insulators and to efficiently visualize cracks. The proposed image processing algorithm for crack identification consists of two primary steps. In the first step, the brightness is eliminated by converting the image to the lab color space. Then, the background is removed by the K-means clustering method. After that, the optimum image treatment is applied using morphological image processing and median filtering to remove unnecessary noise, such as blobs. In the second step, the preprocessed image is converted to grayscale, and any cracks present in the image are identified. Next, the region properties, such as the number of pixels and the ratio of the major to the minor axis, are used to separate the cracks from the noise. Using this image processing algorithm, the precision of crack identification for all the sample images was approximately 80%, and the F1 score was approximately 70. Thus, this method can be helpful for efficient crack monitoring.
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Regular Paper : A Study on Pattern Making of Degradation Type Using K-means
Deok Jin Lee
J Electr Electron Mater 2014;27(12):877-882.   Published online December 1, 2014
It has been confirmed that the inner defect of transformer and the perfect diagnosis for aging are closely related to safe electric power transmission system and that the detection of accident and diagnosis technique turn out to be very important issues. Since electric power machinery consists of various kinds of components, however, it is very difficult to make a diagnosis for aging by one parameter. Thus, diagnosis for aging is feasible only through the combination of various parameters. Recently, various expert systems have been developed and applied to diagnosis for aging, but they are not yet reliable enough to apply to the real system. In this paper, XLPE which is ultra high voltage cableinsulator material were chosen to investigate the influence of void on insulator material using partial discharge. Obtained data have been processed by PRPD (phased resolved partial discharge) distribution function and K-means. And statistical and cluster distribution of partial discharge have been analysed and investigated.
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Partial Discharge Diagnosis of Interface Defect by the Distribution Statistical Analysis
Kyung Soon Cho, Kang Won Lee, Won Jong Kim, Jin Woong Hong, Jong Yeol Shin
J Electr Electron Mater 2008;21(4):348-353.   Published online April 1, 2008
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Partial Discharge Distribution Analysis on Interface Defects of Cable Joint using K-means Clustering
J Electr Electron Mater 2007;20(11):959-964.   Published online November 1, 2007
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Analysis of Partial Discharge Pattern of Closed Switchgear using K-means Clustering
J Electr Electron Mater 2007;20(10):901-906.   Published online October 1, 2007
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Analysis of the Partial Discharge Pattern in XLPE Insulators using Distribution Statistical Models
J Electr Electron Mater 2006;19(10):947-952.   Published online October 1, 2006
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