Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Jong-Chan Kim
Sunchon National University
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data Se-Hoon Jung; Jong-Chan Kim; Chun-Bo Sim
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 1: February 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (445.196 KB) | DOI: 10.11591/ijece.v6i1.pp330-336

Abstract

Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.
Cartoon single-image super-resolution approach based on generative adversarial network Guangxing Wang; Seong-Yoon Shin; Jong-Chan Kim
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1557-1566

Abstract

In recent years, the study of a single image super-resolution (SISR) is crucial to improving image resolution and using hardware technology to improve image resolution. SISR is widely used in satellite remote sensing, video surveillance, and medical image processing because it mainly relies on deep learning algorithms to realize the conversion from low-resolution (LR) images to high-resolution images. It has the advantages of low cost, simple operation, and high efficiency. This paper proposes an image super-resolution method based on a generative adversarial network named text localization generative adversarial nets (TLGAN) model. The method is improved based on super-resolution generative adversarial networks (SRGAN), and the batch normalization layer is removed, which significantly reduces the computational burden of the model. In TLGAN model, we used the transfer learning method to pre-trained the model on the large dataset ImageNet, and then apply the pre-trained model to the cartoon image data set animes to achieve image super-resolution. Experimental results report that the proposed method has the advantages of fast running speed and excellent visual perception of super-resolution images compared with bicubic interpolation and SRGAN method.
Co-Authors Chun-Bo Sim Guangxing Wang Se-Hoon Jung Seong-Yoon Shin