Ju Am Son
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ju Am Son has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Do Yeon Bae
KEPCO Research Institute, Daejeon 34056, Korea
1 shared publication - In-Hyuk Choi
KEPCO Research Institute, Daejeon 34056, Korea
1 shared publication - Ja-Bin Koo
KEPCO Research Institute, Daejeon 34056, Korea
1 shared publication - Jung-Wook Woo
KEPCO Research Institute, Daejeon 34056, Korea
1 shared publication - Taekeun Oh
Department of Safety Engineering, Incheon National University, Incheon 22012, Republic of Korea
1 shared publication - Young-Geun Yoon
Department of Occupational Safety & Housing Management, Cyber Hankuk University of Foreign Studies, Seoul 02450, Republic of Korea
1 shared publication
Latest Publications
- Open Access
- 20 December 2019
- Published by MDPI AG in Applied Sciences
- Vol. 10 (1) , 84
Damage Evaluation of Porcelain Insulators with 154 kV Transmission Lines by Various Support Vector Machine (SVM) and Ensemble Methods Using Frequency Response Data
Frequency response signals have been used for the non-destructive evaluation of many different structures and for the integrity evaluation of porcelain insulators. However, it is difficult to accurately estimate the integrity of porcelain insulators under various environmental conditions only by using general frequency response signals. Therefore, this study used a method that extracted several features that can be derived from the frequency response signal and reduced their dimensions to select features suitable for the evaluation of the soundness of porcelain insulators. The latest machine learning techniques were used to identify correlations and not for basic feature analyses. Two machine learning models were developed using the support vector machine and ensemble methods in MATLAB. Both models showed high reliability in distinguishing between normal and defective porcelain insulators, and they could visualize the distribution area of the data by extracting quantitative values and applying machine learning, rather than simply verifying the frequency response signal.
- 20 December 2019
- Published by MDPI AG in Applied Sciences
- Vol. 10 (1) , 84
Open AccessDamage Evaluation of Porcelain Insulators with 154 kV Transmission Lines by Various Support Vector Machine (SVM) and Ensemble Methods Using Frequency Response Data
Frequency response signals have been used for the non-destructive evaluation of many different structures and for the integrity evaluation of porcelain insulators. However, it is difficult to accurately estimate the integrity of porcelain insulators under various environmental conditions only by using general frequency response signals. Therefore, this study used a method that extracted several features that can be derived from the frequency response signal and reduced their dimensions to select features suitable for the evaluation of the soundness of porcelain insulators. The latest machine learning techniques were used to identify correlations and not for basic feature analyses. Two machine learning models were developed using the support vector machine and ensemble methods in MATLAB. Both models showed high reliability in distinguishing between normal and defective porcelain insulators, and they could visualize the distribution area of the data by extracting quantitative values and applying machine learning, rather than simply verifying the frequency response signal.