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승용 경유 차량의 실제도로 주행 배출가스 시험에서 주행 경로와 운전 성향이 질소산화물에 미치는 영향
유영수,정준우,전문수,차준표,Yu, Young Soo,Jeong, Jun Woo,Chon, Mun Soo,Cha, Junepyo 한국분무공학회 2019 한국액체미립화학회지 Vol.24 No.2
It is expected that the introduction of real-driving emission will strengthen the exhaust emission. However, various researches have been reported that real-driving emission has been influenced by factors such as characteristics of the test routes and driving characteristics for drivers. In order to reflect this effect, European Commission applied the concept of driving dynamics to prevent deliberately driving of excessive and acceleration over RDE test. The purpose of this study is to analyze the characteristics of exhaust emissions according to real-driving test in three test routes and driving style. As a result of the test, it was confirmed that when the same driver tested real-driving test under three test routes, it depends on the driving characteristics of the route. Also, RDE-NOx for driving style was that severe driving has been about 16 times higher than normal driving in KNUT route.
수정 RVS와 IRVS시스템의 비교분석을 통한 국내 고층건물 테러위험도 평가
유영수,윤성원,주영규,Yu, Young-Su,Yoon, Sung-Won,Ju, Young-Kyu 한국공간구조학회 2012 한국공간구조학회지 Vol.12 No.4
As the occurrence rate of terror and hazard is increasing throughout the world, GSA, DoD, and FEMA are proceeding a study about mitigating the damage of terror. Korea is no more a safe place from the terrorist's threat, so we need to make measures against them. In this study we developed modified RVS System by revising some items to adjust the system to the domestic condition and conducted a risk assessment on several tall buildings in Korea. By using IRVS system which is developed by DHS, we also carried out the risk assessment. Comparing the results between RVS with IRVS, we performed terror risk evaluation of tall buildings. Through risk assessment of several tall buildings, we analyzed key factors of each scenarios and suggested the mean value of each items, so we would like to help the counter-terrorism in the design phase.
외기온도 및 냉간시동에 따른 PEMS와 SEMS 장비의 소형 경유차의 실제도로 주행 질소산화물 상관성 연구
유영수(Young Soo Yu),정준우(Jun Woo Jeong),김승리(Seung Lee Kim),심인한(Inhan Sim),전문수(Mun Soo Chon),차준표(Junepyo Cha) 한국자동차공학회 2020 한국 자동차공학회논문집 Vol.28 No.4
To determine the correlation between PEMS and SEMS in real driving, PEMS and SEMS equipment were installed on a diesel vehicle with LNT and SCR systems. There were some problems, such as the expensive charge for the test operation and the need for complex equipment to conduct the RDE test with the PEMS equipment. As such, the SEMS equipment is simpler than the PEMS equipment as it consists of an OBD signal from the test vehicle and NOx sensors. These equipment were sensitive to the ambient temperature and to cold or hot start in the measurement of the NOx emissions exhausted from the test vehicle. Therefore, the purpose of this study was to measure the NOx emissions using the PEMS and SEMS equipment simultaneously to examine the correlation between NOx emissions and ambient temperature as well as cold or hot start. It was apparent that the lower the ambient temperature was, the higher the NOx emissions. Moreover, the NOx emissions in the cold-start condition were higher than those in the hot-start condition due to the warm-up after the treatment, such as LNT and SCR. There was a small difference, however, between the correlation between the NOx emissions and the ambient temperature as well as cold or hot start measured using PEMS equipment and that measured using SEMS equipment.
소형 경유차량에서 OBD 데이터를 통한 실제주행 CO₂ 배출량 예측 연구
유영수(Young Soo Yu),정준우(Jun Woo Jeong),전문수(Mun Soo Chon),차준표(Junepyo Cha) 한국자동차공학회 2019 한국 자동차공학회논문집 Vol.27 No.12
In addition to a laboratory for the reduction of harmful gas emitted by diesel vehicles, various research studies on real driving conditions have been carried out by the governments of each country. Recently, there have been constant reports of vehicles regarding compliance with exhaust emission regulations for nitrogen oxides and particulate number, both in real driving emissions tests and in-lab certification tests. However, current emission regulations for carbon dioxide from vehicles are being conducted only through in-lab tests aside from a real driving emissions test. Therefore, it is necessary to carry out various research studies on the emission characteristics of carbon dioxide emitted from vehicles in real driving conditions. In this study, a small diesel vehicle was selected in order to carry out real driving tests using the portable emission measurement system(PEMS) on the routes developed by the National Institute of Environment Research(NIER). The data obtained via real driving test was classified by the 5 ㎞/h of vehicle speed section. After a vehicle speed classification, vehicle power and trip duration according to the speed section were calculated in order to obtain vehicle work. This study attempted to predict the real driving CO₂ emissions by using vehicle work. Based on the results, the correlation coefficient between the predicted CO₂ emissions and the real driving CO₂ emissions was observed to be over 0.96, which has proven to be highly reliable.
Neural Structured Learning 기반 그래프 합성을 활용한 BIM 부재 자동분류 모델 성능 향상 방안에 관한 연구
유영수(Yu, Youngsu),이고은(Lee, Koeun),구본상(Koo, Bonsang),이관훈(Lee, Kwanhoon) 대한토목학회 2021 대한토목학회논문집 Vol.41 No.3
IFC 정보의 시멘틱 무결성 확보를 위해 BIM 부재와 IFC 엔티티 간 매핑 검증이 필요하다. 이와 관련된 기존 연구들은 기하정보 기반으로 학습시킨 기계학습 알고리즘을 활용하여 BIM 부재 인식 및 분류를 통해 매핑 검증을 실시하였으나, 유사한 기하특성을 가진 부재를 구분하지 못한다는 한계점이 존재하였다. 이에 본 연구는 BIM 모델의 주요 부재를 인공신경망 기반으로 자동 분류하되, 부재 간 관계정보를 삽입하여 분류성능을 향상시키는 것을 목적으로 하였다. 이를 위해 기존 특성 외에 구조화된 신호를 함께 학습하는 NSL 프레임워크를 활용하여 8개의 BIM 부재를 분류하는 모델을 구축하였으며, 그 결과 기하정보 기반 인공신경망 모델과 대비하여 부재 간 관계정보를 삽입한 NSL 모델의 분류정확도가 현저히 상승한 것을 확인하였다. Building information modeling (BIM) element to industry foundation classes (IFC) entity mappings need to be checked to ensure the semantic integrity of BIM models. Existing studies have demonstrated that machine learning algorithms trained on geometric features are able to classify BIM elements, thereby enabling the checking of these mappings. However, reliance on geometry is limited, especially for elements with similar geometric features. This study investigated the employment of relational data between elements, with the assumption that such additions provide higher classification performance. Neural structured learning, a novel approach for combining structured graph data as features to machine learning input, was used to realize the experiment. Results demonstrated that a significant improvement was attained when trained and tested on eight BIM element types with their relational semantics explicitly represented.