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지석만,최주호,김준범,하덕식 한국 항공대학교 항공산업기술연구소 2001 航空宇宙産業技術硏究所 硏究誌 Vol.11 No.-
This paper addresses a method for numerical simulation in the pressing process of hot glass. Updated Lagrangian finite element formulations are employed for the flow and energy equations to accommodate moving meshes. The model is assumed axi-symmetric and creep flow is assumed due to the high viscosity. Commercial software ANSYS is used to solve the coupled flow and energy equations. Moving contact points as well as free surface during the pressing are effectively calculated and updated by utilizing API functions of CAD software Unigraphics. The mesh distortion problem near the wall is overcome by automatic remeshing, and the temperatures of the new mesh are conveniently interpolated by using a unique function of ANSYS. The developed model is applied to the pressing process of TV glasses. In conclusion, the presented method shows that the pressing process accompanying moving boundary can be simulated by effectively combining general purpose softwares without resorting to special dedicated codes.
BF 인증기준의 바닥 안전성에 관한 정량적 평가지표 검토
지석원 ( Ji Suk-won ),백권혁 ( Baik Kwon-hyuk ),최수경 ( Choi Soo-kyung ) 한국건축시공학회 2021 한국건축시공학회 학술발표대회 논문집 Vol.21 No.1
For Barrier-Free certification, the floor and ground surfaces must be finished with materials that are not slippery, flat, and have low-impact in fallis. However, the BF Certification Act does not provide specific methods to meet these regulations. In performance-based design, the responsibility of proving the performance rests with the building owner and architect, so quantitative evaluation indexes are needed to select suitable materials. Furthermore, changes in performance after completion should be checked periodically in 'As-Is' conditions. There are various methods for slips, trips and falls risk assessment, causing confusion for users. In this study, the results of previous studies on the evaluation methods of slips, trips and falls were considered closely, and each quantitative evaluation index that can be used in the new construction and maintenance phase was presented.
KOMPSAT-3A Cal/Val을 위한 Spatial Target 설치방법 연구
지석원,서두천,진청길 한국항공우주학회 2015 한국항공우주학회 학술발표회 논문집 Vol.2015 No.11
한국항공우주연구원은 2015 년 3 월 25 일 KOMPSAT-3A 위성을 성공적으로 발사하였다. KOMPSAT-3A 위성의 광학 및 IR 품질 확인 및 검보정을 수행하기 위해서 전라남도 고흥항공센터에 Spatial Edge 타겟을 설치하였다. Spatial 품질측정 인자인 MTF 측정과 IR 영상의 NETD 측정을 위해 위성진행방향과 5 도 차이가 영상에서 나타나도록 지상타겟을 설치하였으며, 설치된 타겟이 KOMPSAT-3A 의 영상 품질 측정 및 검보정 작업에 적합하도록 위성 탑재체의 특성치 및 건설재료기술 및 건설공법, 제작기술까지도 고려하였다. 본 논문에서는 KOMPSAT-3A Cal/Val 을 위해 적합한 지상 Spatial Edge 타겟설치 방법을 기술한다. KOMPSAT3A is the satellite Korea Aerospace Research Institute (KARI) launched on 25 March 2015. ground target was required to perform a quality check and calibration of KOMPSAT-3A image according to the launch of the satellite, KARI installed ground Spatial Edge target in Go-heung Aviation Center located in Jeollanam-do, Republic of Korea to perform KOMPSAT-3A Cal/Val. The spatial target was designed to appear bright/dark area for measuring NETD and including a line displaced diagonally to the satellite direction measurements for MTF measurement. Construction materials technology and construction method, manufacturing technologies as well as remote sensing technologies also should be considered for the installation of this kind of spatial target. This study is to evaluate the proper way to install Spatial Edge target on the ground for KOMPSAT-3A Cal / Val.
지석원 대한건축학회 2024 대한건축학회논문집 Vol.40 No.5
건물의 자산 가치와 경제성, 환경적 타당성을 정확하게 평가하기 위해서는 건물의 현실적인 수명 산정이 건설산업 전반의 주요 의사결정에 필수적이다. 그러나 건물의 수명에 영향을 미치는 다양한 요인을 종합적으로 고려하여 각 건물의 정확한 수명을 추정하는 것은 현실적으로 불가능하기 때문에 대부분의 연구에서는 건물의 주요 구조 유형에 따라 일정한 수명을 가정하였다. 이에 국내에서 건축·철거된 건축물 1,812,700건의 기록을 수집하여 각 건물의 수명을 정확하게 예측하고, 기존 연구에서는 딥러닝과 기존 머신러닝을 활용한 건물 수명 예측 모델을 개발하였다. 본 연구에서는 머신러닝 모델별 건물 수명 예측 모델이 예측 모델에 사용된 데이터 기간에 의해 과적합되었는지 확인하기 위한 검증 실험과 주요요인과 전체요인으로 만든 모델의 성능 평가 실험을 수행하였다. 실험 결과에 따르면 비선형 모델인 인공신경망 모델만이 건물 수명 예측 모델에 사용된 다양한 기간의 데이터에 대해 과대적합을 피하면서 높은 예측 성능을 유지하였고, 일부 주요요인들보다 전체요인에 의한 건물수명 예측모델의 성능이 우수하였다. 본 연구는 건물별 특성에 따라 건물 수명을 예측하는 유일한 방법인 빅데이터 기반의 AI 건물 수명 예측 방법의 타당성을 확인 가능하게 하고, 사회 전반에 걸쳐 건물수명 예측에 대한 수요를 충족시킬 수 있는 기반을 제시한다. Accurately estimating a building's lifespan is crucial for assessing its asset value and determining its economic and environmental feasibility,which is key for decision-making in the construction industry. However, because it's nearly impossible to precisely estimate the lifespan ofeach building due to the wide range of influencing factors, most studies have used uniform lifespans based on the building's primarystructural type. To address this limitation, 1,812,700 records were analyzed of buildings constructed and demolished in Korea to predict eachbuilding's lifespan with greater accuracy. Based on the previous study, a prediction model was developed using both deep learning andtraditional machine learning methods. This study evaluated whether the building lifespan prediction model experienced overfitting based on thedata period used to create the model. A performance evaluation was also conducted, comparing models using only key factors to those usinga broader set of factors. The results showed that among the machine learning models, the artificial neural network model, a nonlinearapproach, maintained high predictive accuracy without overfitting, regardless of the data period used. The model that used all available factorsperformed better than those based on just a few key factors. This research demonstrates the viability of using big data and AI for buildinglifespan prediction, providing a more reliable method for estimating building lifespan tailored to each building's unique characteristics. Thisapproach meets a growing societal demand for more accurate building lifespan predictions.
지석배 ( Ji Seog Bae ),황의원 ( Hwang Ui Won ),김진국 ( Kim Jin Gug ),황승덕 ( Hwang Seung Deog ) 대한내과학회 2003 대한내과학회지 증례 특집호 65-5 부록3 Vol.0 No.-
We report a case of rhabdomyolysis in a 29-year-old man, presenting pain on neck & shoulder and dark urine, which developed after taking Bear`s gall & swimming. Laboratory studies revealed serum creatinine 0.9 mg/dL, creatinine kinase 83,045 IU/L, serum m