Chang-Ju Park
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Chang-Ju Park has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Yangkyu Park
Department of Smart Plant Engineering, Chonnam National University, Yeosu, South Korea
2 shared publications - Daehyeon Ji
Department of Smart Plant Engineering, Chonnam National University, Yeosu, South Korea
1 shared publication - Ho-Jung Jeong
Inorganic Light-Emitting Display Research Center, Korea Photonics Technology Institute (KOPTI), Gwangju 61007, Republic of Korea
1 shared publication - Hyeon Woo Kim
Department of Urology, Pusan National University School of Medicine, Busan, South Korea
1 shared publication - Kihyun Kim
Department of Mechatronics Engineering, Chonnam National University, 50 Daehak-ro, Yeosu 59626, Chonnam, Republic of Korea
1 shared publication
Latest Publications
- 14 January 2026
- Published by Springer Nature in Journal of Inclusion Phenomena and Macrocyclic Chemistry
- Vol. 106 (5-6) , 363-375
Comparative study of the 1DCNN, WT–2DCNN, LSTM, and 1DCNN-LSTM models to predict machinery faults based on vibrations measured from a simulation facility for petrochemical processes
- 14 January 2026
- Published by Springer Nature in Journal of Inclusion Phenomena and Macrocyclic Chemistry
- Vol. 106 (5-6) , 363-375
Comparative study of the 1DCNN, WT–2DCNN, LSTM, and 1DCNN-LSTM models to predict machinery faults based on vibrations measured from a simulation facility for petrochemical processes
- Open Access
Discrimination of Explosive Residues by Standoff Sensing Using Anodic Aluminum Oxide Microcantilever Laser Absorption Spectroscopy with Kernel-Based Machine Learning
Standoff laser absorption spectroscopy (LAS) has attracted considerable interest across many applications for environmental safety. Herein, we propose an anodic aluminum oxide (AAO) microcantilever LAS combined with machine learning (ML) for sensitive and selective standoff discrimination of explosive residues. A nanoporous AAO microcantilever with a thickness of <1 μm was fabricated using a micromachining process; its spring constant (18.95 mN/m) was approximately one-third of that of a typical Si microcantilever (53.41 mN/m) with the same dimensions. The standoff infrared (IR) spectra of pentaerythritol tetranitrate, cyclotrimethylene trinitramine, and trinitrotoluene were measured using our AAO microcantilever LAS over a wide range of wavelengths, and they closely matched the spectra obtained using standard Fourier transform infrared spectroscopy. The standoff IR spectra were fed into ML models, such as kernel extreme learning machines (KELMs), support vector machines (SVMs), random forest (RF), and backpropagation neural networks (BPNNs). Among these four ML models, the kernel-based ML models (KELM and SVM) were found to be efficient learning models able to satisfy both a high prediction accuracy (KELM: 94.4%, SVM: 95.8%) and short hyperparameter optimization time (KELM: 5.9 s, SVM: 7.6 s). Thus, the AAO microcantilever LAS with kernel-based learners could emerge as an efficient sensing method for safety monitoring.
Open AccessDiscrimination of Explosive Residues by Standoff Sensing Using Anodic Aluminum Oxide Microcantilever Laser Absorption Spectroscopy with Kernel-Based Machine Learning
Standoff laser absorption spectroscopy (LAS) has attracted considerable interest across many applications for environmental safety. Herein, we propose an anodic aluminum oxide (AAO) microcantilever LAS combined with machine learning (ML) for sensitive and selective standoff discrimination of explosive residues. A nanoporous AAO microcantilever with a thickness of <1 μm was fabricated using a micromachining process; its spring constant (18.95 mN/m) was approximately one-third of that of a typical Si microcantilever (53.41 mN/m) with the same dimensions. The standoff infrared (IR) spectra of pentaerythritol tetranitrate, cyclotrimethylene trinitramine, and trinitrotoluene were measured using our AAO microcantilever LAS over a wide range of wavelengths, and they closely matched the spectra obtained using standard Fourier transform infrared spectroscopy. The standoff IR spectra were fed into ML models, such as kernel extreme learning machines (KELMs), support vector machines (SVMs), random forest (RF), and backpropagation neural networks (BPNNs). Among these four ML models, the kernel-based ML models (KELM and SVM) were found to be efficient learning models able to satisfy both a high prediction accuracy (KELM: 94.4%, SVM: 95.8%) and short hyperparameter optimization time (KELM: 5.9 s, SVM: 7.6 s). Thus, the AAO microcantilever LAS with kernel-based learners could emerge as an efficient sensing method for safety monitoring.