이재필 교수
Jae-Pil Lee
포항공과대학교 환경공학부 · 공학
연구실 소개
이재필 교수의 연구실은 레이저 유도 분광법(LIBS)을 기반으로 한 금속 폐기물의 실시간 정밀 분류 기술을 핵심으로 연구를 진행하고 있습니다. 특히, 표면 오염물질의 영향을 최소화하고 다양한 환경 조건에서도 높은 정확도를 확보하기 위한 신호 처리 기법과 전이 학습 기반의 머신러닝 모델 개발에 초점을 맞추고 있습니다. 또한, 실생활에서의 응용 가능성을 높이기 위해 실제 금속 합금의 미세한 조성 차이를 정밀하게 구분할 수 있는 분광 분석 기법을 지속적으로 발전시키고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
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
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