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딥러닝 기반 이미지 자동 레이블링을 활용한 건축물 파사드 데이터세트 구축 기술 개발
구형모(Gu, Hyeong-Mo),서지효(Seo, Ji-Hyo),추승연(Choo, Seung-Yeon) 대한건축학회 2019 대한건축학회논문집 Vol.35 No.12
The construction industry has made great strides in the past decades by utilizing computer programs including CAD. However, compared to other manufacturing sectors, labor productivity is low due to the high proportion of workers" knowledge-based task in addition to simple repetitive task. Therefore, the knowledge-based task efficiency of workers should be improved by recognizing the visual information of computers. A computer needs a lot of training data, such as the ImageNet project, to recognize visual information. This study, aim at proposing building facade datasets that is efficiently constructed by quickly collecting building facade data through portal site road view and automatically labeling using deep learning as part of construction of image dataset for visual recognition construction by the computer. As a method proposed in this study, we constructed a dataset for a part of Dongseong-ro, Daegu Metropolitan City and analyzed the utility and reliability of the dataset. Through this, it was confirmed that the computer could extract the significant facade information of the portal site road view by recognizing the visual information of the building facade image. Additionally, In contribution to verifying the feasibility of building construction image datasets. this study suggests the possibility of securing quantitative and qualitative facade design knowledge by extracting the facade design knowledge from any facade all over the world.
건축법규 자동검토를 위한 데이터베이스 구축 방안 - 기획업무단계의 매스생성을 중심으로 -
김동욱(Kim, Dong-Wuk),구형모(Gu, Hyeong-Mo),추승연(Choo, Seung-Yeon) 대한건축학회 2021 대한건축학회 학술발표대회 논문집 Vol.41 No.2
The AEC industry has encountered complications due to the amount of unremunerated time and effort in the initial stage of design that requires examination of building codes and generating a corresponding building mass. A significant amount of time and effort is required to generate a building mass that is in accordance with building codes. Hence, the above process of examination and mass generation requires automation which will also increase the efficiency of architects in this initial stage of design. Similar researches and services have explored automation using databases that involve selected main sections without consideration to sub-sections of building codes and thereby, producing results that are insufficient and unsatisfactory. This study, proposes a methodology of construction of a database that extensively examines architecture-related codes and legislation.
건축 기획업무 효율성 향상을 위한 공간 인접성 그래프 추출 알고리즘 개발
김근재(Kim, Geun-Jae),구형모(Gu, Hyeong-mo),박혜진(Park, Hye-jin),추승연(Choo, Seung-yeon) 대한건축학회 2022 대한건축학회논문집 Vol.38 No.1
The architectural design process requires spatial planning that involves searching for building drawings with similar compositions to the ongoing project. However, the current search methods involve searching for architectural drawings based on a high level of shared similarities in specific details. Spatial elements such as spatial relationships and compositions are vital for design case studies but are complicated search terms for finding similarities in architectural drawings. To extract the spatial relationships in a graph format from a drawing, this research proposes a methodology that utilizes BIM and Dynamo algorithm to detect drawing elements and generate an intersection area that defines the type of drawing based on the adjacency of spatial elements in the drawing. Therefore, the methodology proposed in this paper is a method of building a spatial relationship database that utilizes an algorithm that finds similarities in spatial relationships in existing architectural drawings. It is expected that through this algorithm, architectural design data, which has been qualitatively accumulated so far, can be accumulated in a quantitative form for an automated space planning system that architects can refer to in the spatial planning process.
치올기 함유 저분자 화합물과의 반응성을 이용하는 간단한 in chemico 피부감작성 시험법의 개발 가능성 평가
김건호 ( Geon Ho Kim ),차동호 ( Dong Ho Cha ),구형모 ( Hyung Mo Gu ),박재성 ( Jae Sung Park ),정재훈 ( Jae Hoon Jeong ),황진선 ( Jin Sum Hwang ),네팔마헤시 ( Mahesh Raj Nepal ),정태천 ( Tae Cheon Jeong ) 한국동물실험대체법학회 2020 동물실험대체법학회지 Vol.14 No.1
For the initiation of skin sensitization reaction, a sensitizer should react with endogenous proteins. During the haptenization of a sensitizer with proteins, it is also possible for a sensitizer to react with small molecules that contain either amino or thiol group, indicating a possibility to develop a simple skin sensitization test using the reactivity of test compounds with thiol-containing low molecular weight compounds. In the present study, 10 compounds that contain a thiol group were pre-tested to determine whether the reactivity with a thiol-detecting monobromobimane would be in concentration-dependent manners. Subsequently, among thiol compounds, a simple spectrofluorometrical method to identify skin sensitizers in chemico was tested with glutathione, an endogenous substance that contains amino and thiol groups. To flurorometrically quantitate the remaining thiol-containing compounds following a reaction with skin sensitizers, monobromobimane was employed. The conditions optimized included: incubation conditions of thiol compounds including glutathione with monobromobimane, molar ratios of thiol compounds to monobromobimane, optimal concentration and incubation time of monobromobimane, and pH and incubation time with test compounds. With a tentatively optimized conditions, 4 skin sensitizers and 2 non-sensitizers were tested to know whether the method could correctly identify skin sensitizers and non-sensitizers. The results indicated a good possibility. Although further optimization is required, it would be a useful screening tool for determining skin sensitization potential of test compounds, because the present method employs a simple endogenous thiol compound as an acceptor for sensitizers with a spectrofluorometric detection system requiring neither experimental animals nor cell cultures.