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BIM 기반 설계 협업 성과 평가를 위한 정량적 지표 선정
이진강(Lee, Jingang),이현수(Lee, Hyeon-soo),박문서(Park, Moonseo),김소연(Kim, Soyeon) 대한건축학회 2016 대한건축학회논문집 Vol.32 No.10
Building Information Modelling (BIM) has been developed to enhance the collaboration performances of the construction industry by facilitating a more integrated design. However BIM has limited benefits if not used appropriately. Due to the limits, further research is necessary to improve the project teams’ collaboration performance. Previous studies focused on the benefits of measuring performance for a better management. Hence, this research aims to identify and investigate appropriate indicators to measure collaboration performance in a BIM environment. A survey supported by the Delphi method was conducted to evaluate indicators regarding two quality factors, which are appropriateness and suitability. To identify the ten most important performance indicators, a series of experts’ interviews were performed to set up an interpretation of key indicators. The results show that the indicators present a non-linear relation with collaboration performance, contrary to what previous BIM performance evaluation methods used. This research contributes to the efficiency of BIM practitioners and researchers in analyzing the collaboration performance. Ultimately, it will contribute to the quantification of BIM collaboration performance and the comparison of more efficient BIM collaborative practices.
BIM 활용 건축-엔지니어링간 협업지원시스템 필수기능도출에 관한 연구
김우상(Kim, Woosang),박문서(Park, Moonseo),이현수(Lee, Hyun-Soo),이진강(Lee, Jingang),이광표(Lee, Kwang-Pyo) 대한건축학회 2015 대한건축학회논문집 Vol.31 No.2
In a construction project, there are numerous information across various professional specialists. In particular, as design process requires constant communication among a variety engineering participants, effective collaboration for decision making has a high influence on productivity. BIM (Building Information Modeling) is adopted globally to improve the productivity of the collaboration. However, BIM causes a fundamental change in the way participants interact with each other. Currently, it is not easy for individuals to utilize the current BIM collaboration systems due to skeptical opinion and difficulties of the systems. Therefore, the collaboration system should be developed by considering optimized features, and this paper defines essential features of architecture-engineering design collaboration system to improve BIM collaboration environment. In order to conduct this research, (1) the current BIM collaboration systems are investigated and analyzed in depth and (2) the essential functions for the design collaboration between the architects and engineers within the BIM environment are defined by conducting delphi survey and focus group interview. The result of this research can contribute to provide the basis for improving BIM based collaboration environment.
K-city 자율주행 경진대회 참가를 위한 자율주행 플랫폼 개발
김태호(Teaho Kim),김동진(Dongjin Kim),민동규(Dongkyu Min),서현지(Hyeonji Seo),윤호진(Hojin yun),정은빈(Eunbin Jung),이진강(Jingang Lee),권혁재(Hyeokjae Kwon),김진석(Jinseok Kim),김대국(Daekuk Kim),문일주(Iljoo Moon),유정흠(Jeongheum You 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11
In this paper, a ROS-based autonomous driving framework designed by team ACCA from the School of Mechanical Engineering, Soongsil University, for the 2021 international college student creative car challenge held in K-city. The autonomous vehicle’s chassis used in this challenge is equipped with Velodyne 3D lidar, a Sick down-looking 2D lidar, Xsens MTi-30 AHRS, CCD camera, webcam, and PC-based controller. First, before the challenge in K-city, we evaluated the ROS-package-based SLAM such as LIO-SAM in a ring-shaped road environment on the campus of Soongsil University. After the successful SLAN and mapping process, the hdl_localization, which is a 3D lidar-based real-time 3D localization package, is used to estimate the global pose with respect to the global frame using NDT scan matching. For lane detection, traffic sign, and traffic signal recognition, the two well-known DNN models are utilized. Based on experimental results from both simulation and an actual autonomous vehicle platform, the Point Instance Network (PINet) for lane detection shows 88% of test accuracy, and the YOLO V4 for the traffic light and sign recognition offers 95% test accuracy.