Hyeonho Kwun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Hyeonho Kwun has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Dohee Kim
Safe & Clean Supply Chain Research Center, Pusan National University, Busan, Republic of Korea
1 shared publication - Eunju Lee
Industrial Data Science and Engineering, Department of Industrial Engineering, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, Korea
1 shared publication - Hyerim Bae
Pusan National University
1 shared publication - Yulim Choi
Department of Industrial Engineering, Pusan National University, Busan 609-735, Korea
1 shared publication
Latest Publications
- Open Access
Residual Life Prediction for Induction Furnace by Sequential Encoder with s-Convolutional LSTM
Induction furnaces are widely used for melting scrapped steel in small foundries and their use has recently become more frequent. The maintenance of induction furnaces is usually based on empirical decisions of the operator and an explosion can occur through operator error. To prevent an explosion, previous studies have utilized statistical models but have been unable to generalize the problem and have achieved a low accuracy. Herein, we propose a data-driven method for induction furnaces by proposing a novel 2D matrix called a sequential feature matrix(s-encoder) and multi-channel convolutional long short-term memory (s-ConLSTM). First, the sensor data and operation data are converted into sequential feature matrices. Then, N-sequential feature matrices are imported into the convolutional LSTM model to predict the residual life of the induction furnace wall. Based on our experimental results, our method outperforms general neural network models and enhances the safe use of induction furnaces.
Open AccessResidual Life Prediction for Induction Furnace by Sequential Encoder with s-Convolutional LSTM
Induction furnaces are widely used for melting scrapped steel in small foundries and their use has recently become more frequent. The maintenance of induction furnaces is usually based on empirical decisions of the operator and an explosion can occur through operator error. To prevent an explosion, previous studies have utilized statistical models but have been unable to generalize the problem and have achieved a low accuracy. Herein, we propose a data-driven method for induction furnaces by proposing a novel 2D matrix called a sequential feature matrix(s-encoder) and multi-channel convolutional long short-term memory (s-ConLSTM). First, the sensor data and operation data are converted into sequential feature matrices. Then, N-sequential feature matrices are imported into the convolutional LSTM model to predict the residual life of the induction furnace wall. Based on our experimental results, our method outperforms general neural network models and enhances the safe use of induction furnaces.