• 한국연소학회:학술대회논문집
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• 2015.12a
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• pp.251-255
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• 2015

Unsteady Shock-Induced Combustion has been studied for the past few decades since it is considered as one of the potential ways to reach supersonic flights. Experimental observations of Unsteady SIC were observed as early as 1960's. But Lehr was the first to report in detail the mechanisms of Shock-Induced Combustion experimentally. Numerical Studies on SIC were helpful in explaining the insight into the oscillatory behaviour in the mid 90's to early 2000's. Detailed reaction mechanisms is required to prediction the SIC flowfield more in detail. However at that time, very few reaction mechanisms on hydrogen-oxidation were reported. In the last decade, various number of hydrogen reaction mechanisms were reported. In this study, an attempt has been made to analyze the effect of various reaction mechanisms in an unsteady mode of Shock-Induced Combustion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.140-143
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• 2004

The mage enhancement of dental On image using wavelet coefficients is presented. The processing is that make gray image from On image by preprocessing, extract high frequency from detail coefficient after acquisition detail coefficient by wavelet transform and emphasize edge appling input image. Experimental results show that enhanced contrast of dental On image, improved mage quality.

• Proceedings of the KIPE Conference
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• 2014.07a
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• pp.150-151
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• 2014

높은 SOC(state-of-charge) 추정알고리즘의 성능을 위해서는 측정된 배터리 단자전압의 정확도가 요구된다. 그렇지만, 예기치 않은 에러로 인해 단자전압에 노이즈 성분이 추가될 경우 SOC 추정성능의 저하를 피할 수 없다. 그러므로, 본 논문에서는 이산 웨이블릿 변환(DWT;discrete wavelet transform)의 다해상도 분석(MRA;multi resolution analysis)의 디노이징(denoising)기법을 적용한 이차전지의 SOC 추정방법을 소개한다. MRA의 시간-주파수 분석을 통해 분해(decomposition)된 저주파 성분(approximation;$A_n$)과 고주파 성분(detail;$D_n$)중 노이즈에 관계된 $D_n$의 고주파 상세 계수(detail coefficient) $d_{j,k}$를 새로이 조정하고 이를 합성(synthesis)하여 디노이징을 마무리 한다. 확장 칼만필터(EKF;extended Kalman filter)의 비교 분석을 통해 제안된 방법의 타당성을 검증한다.

• Journal of IKEEE
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• v.27 no.3
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• pp.355-367
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• 2023

Local Interconnect Network (LIN) is a low-speed in-vehicle network bus, and it is widely used in body applications such as windows, doors, HVAC, and lighting. This review explains protocols and message frames of LIN bus in detail. LIN bus basically transmits ID and payloads in data frame. How to interpret ID and payloads is defined in LIN Description file (LDF). Each LIN bus has unique LDF and its corresponding unique configuration. This review also explains syntax and example of LDF in detail.

• Journal of the Korean Chemical Society
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• v.67 no.5
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• pp.319-332
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• 2023

Ever since the development of the Guide to the Expression of Uncertainty in Measurement (GUM) in 1993, the concept of measurement uncertainty has been considered a core concept in metrology and the importance of proper uncertainty evaluation has continuously been increasing. Unfortunately, few papers in Korean are available that introduce the concept of measurement uncertainty and the GUM correctly and in sufficient detail. This review describes in detail the mathematical, historical, and philosophical background behind the concept of measurement uncertainty and the GUM, and also discusses some special aspects of uncertainty evaluation in chemical analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.473-474
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• 2011

Image quantization 기술은 영역 평탄화 기술중 하나로서 NPR과 같은 컴퓨터 그래픽스 분야에서 널리 쓰이고 있는 기술 중 하나이다. 하지만 기존의 image quantization은 중요부분을 고려하지 않기 때문에 detail한 칼라 정보를 가지는 주요 영역이 있는 경우에는 그 결과가 좋지 못한 단점을 가지고 있다. 본 논문에서는 이러한 단점을 극복하기 위해, Sailency map을 이용해 영상의 주요 영역을 고려한 image Flattening 기법을 제안한다. 제안한 방법은 검출된 주요 영역을 좀 더 세분화해서 표현하므로 기존의 방법과 비교해 좀 더 좋은 결과를 보여준다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.176-179
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• 2021

최근 딥 러닝 기법의 하나인 합성곱 신경망(Convolutional Neural Network, CNN)은 영상 잡음(Noise) 제거 분야에서 전통적인 기법보다 좋은 성능을 나타내고 있지만 학습하는 과정에서 영상 내 디테일한 부분이 손실될 수 있다. 본 논문에서는 웨이블릿 변환(Wavelet Transform)을 기반으로 영상 내 디테일 정보도 같이 학습하여 영상 디테일을 향상하는 잡음 제거 합성곱 신경망 네트워크를 제안한다. 제안하는 네트워크는 디테일 향상 서브 네트워크(Detail Enhancement Subnetwork)와 영상 잡음 추출 서브 네트워크(Noise Extraction Subnetwork)를 이용하게 된다. 실험을 통해 제안하는 방법은 기존 알고리듬보다 디테일 손실 문제를 효과적으로 해결할 수 있었고 객관적 품질 평가인 PSNR(Peak Signal-to-Noise Ratio)와 주관적 품질 비교에서 모두 우수한 결과가 나온 것을 확인하였다.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.419-425
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• 2013

Recently AEC industry has required construction automation according to becoming large and complex. Thus BIM-based construction project is increased and used in whole fields of AEC industry. Quantity take-off and estimation fields are important factor for decision-making in conceptual and schematic design stages of construction projects. The purpose of this study improves reliability of the estimation through QTO based on Open BIM. Scope and method to apply QTO is to select conceptual design stage through LoD(Level of Detail) in AEC field and to extract information from BIM model through analysis of IFC structure. This study proceeds three step to make BIM model and check the model quality and calculate QTO. The methodology of QTO using IFC is to verify of result in this study and expects utilizing in design stage of construction projects. The result from this study is expected to decrease the risk factor and time of estimation in the project early phase through improving reliability of schematic estimation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.277-278
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• 2023

In 2022, construction had a five fold higher accident and fatality rate compared to the overall industry. The Construction Technology Promotion Act requires parties in construction contracts to calculate safety costs following Ministry of Land, Infrastructure and Transport guidelines. Understanding safety expense components is vital for evaluating accident prevention costs and planning anti-accident strategies. A study surveyed safety professionals to analyze the importance and influence of safety management expense components, providing basic data for future research. Survey results highlighted the significance of safety management plans, structural safety assurance, and preventing collateral damage.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.709-710
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• 2024

Super resolution에서 High-frequency Details를 개선하는 것이 최근 문제이다. 기존에는 Super resolution을 Regression task로 접근하므로써 L2 Loss를 사용하여 이미지가 흐릿하게 되었다. 이를 해결하기위해, Classification task로 바꾸므로써 Cross Entropy Loss을 적용하여 Cross-entropy Super-resolution (CS)를 설계한다. CS를 통해 선명도와 Details이 개선되지만, 저주파의 CE Loss 학습으로인한 Black Artifacts가 발생한다. 그래서, L2 Loss는 저주파와 같이 큰 신호에 더 초점을 맞추므로, 성능 개선을 위해 저주파를 L2 Loss에서, 고주파를 CE Loss에서 학습시킨 Frequency-specific Cross-entropy Super-resolution (FCS)을 제안한다. 우리는 왜곡에 강하며 Human의 인식과 유사한 측정지표인 Learned Perceptual Image Patch Similarity (LPIPS)로 평가한다. 실험한 모든 데이터 셋에서 우리의 FCS는 Baseline보다 LPIPS가 약 1.7배 정도 개선되었다.