Jae-Pil Lee
Pohang University of Science and Technology · 工学
研究室紹介
Professor Jae-Pil Lee's research lab specializes in analytical chemistry and materials characterization, with a primary focus on laser-induced breakdown spectroscopy (LIBS) for industrial and environmental applications. The lab develops advanced signal processing and machine learning techniques—particularly transfer learning and feature selection—to enhance the accuracy and robustness of LIBS in real-world scenarios such as scrap metal recycling and contamination analysis. Research also extends to understanding the impact of surface conditions (e.g., paint, rust) on spectral data and improving diagnostic models for clinical applications, such as predicting contrast-induced nephropathy using biomarkers like cystatin C. The lab emphasizes practical, real-time analytical solutions that bridge laboratory innovation with industrial and medical needs.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Laser-induced breakdown spectroscopy (LIBS) is regarded as a promising technique for real-time sorting of scrap metals due to its capability of fast multi-elemental and in-air analysis. This work reports a method for signal processing which ensures high accuracy and high speed during similar metal sorting by LIBS. Similar metals such as aluminum alloys or stainless steel are characterized by nearly the same constituent elements with slight variations in elemental concentration depending on metal
Improvement in classification accuracy of stainless steel alloys by laser-induced breakdown spectroscopy based on elemental intensity ratio analysis, SHIN, Sungho, MOON, Youngmin, LEE, Jaepil, KWON, Eunsung, PARK, Kyihwan, JEONG, Sungho
BACKGROUND AND OBJECTIVES: The risk of contrast-induced nephropathy (CIN) is significantly influenced by baseline renal function and the amount of contrast media (CM). We evaluated the usefulness of the cystatin C (CyC) based estimated glomerular filtration rate (eGFRCyC) in the prediction of CIN and to determine the safe CM dosage. SUBJECTS AND METHODS: We prospectively enrolled a total of 723 patients who received percutaneous coronary intervention (PCI) and investigated the clinical factors a
In this study, we propose a transfer learning-based classification model for identifying scrap metal using an augmented training dataset consisting of laser-induced breakdown spectroscopy (LIBS) measurement of standard reference material (SRMs) samples, considering varying experimental setups and environmental conditions. LIBS provides unique spectra for identifying unknown samples without complicated sample preparation. Thus, LIBS systems combined with machine learning methods have been activel
Abstract Scrap metals are typically covered with surface contaminants, such as paint, dust, and rust, which can significantly affect the emission spectrum during laser-induced breakdown spectroscopy (LIBS) based sorting. In this study, the effects of paint layers on metal surfaces during LIBS classification were investigated. LIBS spectra were collected from metal surfaces painted with black and white paints by ablation with a nanosecond pulsed laser (wavelength = 1064 nm, pulse width = 7 ns). F