Hoonseok Park
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Hoonseok Park has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Jae-Yoon Jung
Kyung Hee University
3 shared publications - Chang S. Nam
Northern Illinois University
2 shared publications - Sangyeon Kim
Division of Artificial Intelligence Engineering, Sookmyung Women's University, Seoul, Republic of Korea
2 shared publications Donghyun Park
Department of Big Data Analytics, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, Republic of Korea
1 shared publication- Donghyun Park
Autonomous Manufacturing Research Center, Korea Electronics Technology Institute , Seongnam 13449
1 shared publication - Haejoong Kim
Department of Industrial and Management Engineering, Kyonggi University, Suwon, Republic of Korea
1 shared publication
Latest Publications
- 5 May 2023
- Published by Elsevier in Expert Systems with Applications
- Vol. 227
Effectiveness of multi-task deep learning framework for EEG-based emotion and context recognition
- 5 May 2023
- Published by Elsevier in Expert Systems with Applications
- Vol. 227
Effectiveness of multi-task deep learning framework for EEG-based emotion and context recognition
- Open Access
Traffic Flow Speed Prediction in Overhead Transport Systems for Semiconductor Fabrication Using Dense-UNet
To improve semiconductor productivity, efficient operation of the overhead hoist transport (OHT) system, which is an automatic wafer transfer device in a semiconductor fabrication plant (“fab”), is very important. A large amount of data is being generated in real time on the production line through the recent production plan of a smart factory. This data can be used to increase productivity, which in turn enables companies to increase their production efficiency. In this study, for the efficient operation of the OHT, the problem of OHT congestion prediction in the fab is addressed. In particular, the prediction of the OHT transport time was performed by training the deep convolutional neural network (CNN) using the layout image. The data obtained from the simulation of the fab and the actual logistics schedule data of a Korean semiconductor factory were used. The data obtained for each time unit included statistics on volume and speed. In the experiment, a layout image was created and used based on the statistics. The experiment was conducted using only the layout image without any other feature extraction, and it was shown that congestion prediction in the fab is effective.
Open AccessTraffic Flow Speed Prediction in Overhead Transport Systems for Semiconductor Fabrication Using Dense-UNet
To improve semiconductor productivity, efficient operation of the overhead hoist transport (OHT) system, which is an automatic wafer transfer device in a semiconductor fabrication plant (“fab”), is very important. A large amount of data is being generated in real time on the production line through the recent production plan of a smart factory. This data can be used to increase productivity, which in turn enables companies to increase their production efficiency. In this study, for the efficient operation of the OHT, the problem of OHT congestion prediction in the fab is addressed. In particular, the prediction of the OHT transport time was performed by training the deep convolutional neural network (CNN) using the layout image. The data obtained from the simulation of the fab and the actual logistics schedule data of a Korean semiconductor factory were used. The data obtained for each time unit included statistics on volume and speed. In the experiment, a layout image was created and used based on the statistics. The experiment was conducted using only the layout image without any other feature extraction, and it was shown that congestion prediction in the fab is effective.