• 한국재난정보학회 논문집
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• 제18권4호
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• pp.891-898
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• 2022

연구목적: 건설산업의 전 분야 및 전 단계에 대한 BIM 도입 의무화가 머지않아 시행될 만큼 BIM 업무의 실질적인 생산성 향상을 위한 기술 확보 방안이 필요하기 때문에 연구에서는 엔지니어가 BIM기반 설계 과정에서 건설기준을 검증할 수 있도록 BIM 모델링 절차별로 제작되는 주요 객체에 대해 자동으로 관련 건설기준을 검증할 수 있는 방안을 제시하였다. 연구방법: BIM기반 도로 설계 업무를 대상으로 모델링 업무 절차를 정의하고, 각 단계에서 도출되는 BIM 모델별로 관련 설계기준을 데이터베이스로 구축하는 방안을 마련하였다. 그리고 BIM기반 설계기준 검토 자동화 시스템 개발을 위한 프로세스도 제시하였다. 연구결과: Civil3D 및 Dynamo를 활용하여 BIM기반 설계기준 검토 자동화 모듈을 개발하고, 시범적용을 통해 설계과정에서 제작되는 BIM 객체의 건설설계기준 충족여부를 자동으로 신속하게 제공할 수 있음을 확인하였다. 결론: BIM기반 설계기준 검토 자동화 기술은 BIM 모델 제작 업무의 생산성 향상과 BIM 모델의 품질확보가 가능하다.

• 한국의류학회지
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• 제47권1호
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• pp.17-34
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• 2023

This study aimed to develop a fitted torso pattern with an improved fit for inverted-triangular males in their twenties. For this study, six torso patterns were collected, compared and evaluated, and a fitted torso pattern was developed using virtual fittings. The research results are as follows. First, the fitted torso pattern received a good rating as a result of the virtual fitting evaluation: waist dart set 2 of the front; the amount of comfort is set at 5 cm around the chest, 4 cm around the waist and 10 cm around the hips. Second, the evaluation of virtual fitting of the development pattern showed that fit evaluation was 4.11/5 points, ease evaluation was 6.53/7 points, and that the stress map and airgap were suitable for the human body. Third, the actual fit evaluation of the development pattern was 4.25/5 points, 6.35/7 points for ease evaluation, and 4.81/5 points for motion evaluation. Fourth, there was no significant difference between the results of the virtual and actual fitting evaluation with the objectivity test. It is therefore possible to apply a pattern developed through a virtual fitting to an actual human body and to confirm the objectivity of the pattern.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.732-737
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• 2009

Due to various project delivery methods and the complexity of construction projects in the construction industry, developing the framework of construction management for critical, highly complex projects in the construction industry has become problematic. Currently, a set of construction manuals play a pivotal role in planning and managing construction projects as subcontractors try to complete their scope of work according to the instructions of a general contractor. It is challenging for general contractors to write a construction management procedure manual to cover various types of project delivery methods and construction projects. In construction, the construction procedure manuals describe specific actions to be taken through the project. In reality a few contactors own such manuals and their construction schedules include more construction operation activities. Thus, it is hard to estimate the workload and productivity of construction managers because the manual and the schedule do not present the amount of management efforts required to complete a project. This paper proposes a framework to present construction management tasks according to project delivery methods which can be applied to various construction projects. Actions for management tasks were mapped and were integrated with construction activities throughout the project life cycle. The framework can then be used to give specific instructions to project participants, collect management actions, and replicate management actions throughout the project life cycle. The framework can also be can used to visualize complete construction project to analyze and manage construction management activities in each phase of a project in order to enhance productivity and efficiency. The studies of existing construction manuals were carried out to identify construction managers' responsibilities. An artificial intelligence program, CLIPS (C-Language Integrated Production System) was used to search for appropriate actions for impending tasks from a set of predefined actions to be performed for a given situation. The framework would significantly help construction managers to understand interrelations among management tasks or actions within a project. Furthermore, the framework can be embedded into Building Information Modeling (BIM) or Facility Management Systems (FMS) so that designers and constructors would execute constructability review before construction begins.

• 대한토목학회논문집
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• 제43권5호
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• pp.651-657
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• 2023

국내외 철도사업에서 철도의 신규 건설과 더불어 기존선의 개량을 통한 증속, 고속화 사업도 큰 비중을 차지고 있다. 특히 해외의 개발도상국은 경제수준을 고려할 때 고속철도의 건설은 막대한 규모의 예산소요와 수요를 고려할 때 한계가 있어, 기존 철도의 선형개량을 통한 증속사업이 실현가능성이 높은 것으로 판단하고 있다. 본 연구에서는 선형개량을 통향 기존선의 증속사업의 효율적 기획과 분석을 위하여, 기존에 신규 철도 건설사업 분석에 활용되고 있는 지능형 철도선형계획 프로그램(ei-Rail)에 선형개량사업 분석 기능을 추가할 수 있는 방법론을 개발하였다. 수치지도 또는 기존의 위성사진 자료를 기반의 베이스 맵에 기존의 철도선형을 입력하고, 목표속도등 선형개량사업의 목표를 입력하면, 사업의 비용과 도면, 증속효과 분석 결과 등을 자동적으로 제시함으로써, 철도선형개량 사업의 기획과 분석을 효율적으로 수행할 수 있는 도구로 활용될 수 있도록 하였다.

• 드라이브 ㆍ 컨트롤
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• 제20권2호
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• pp.15-23
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• 2023

In this study, two-dimensional location of crops for auto weeding was detected using deep learning. To construct a dataset for soybean detection, an image-capturing system was developed using a mono camera and single-board computer and the system was mounted on a weeding robot to collect soybean images. A dataset was constructed by extracting RoI (region of interest) from the raw image and each sample was labeled with soybean and the background for classification learning. The deep learning model consisted of four convolutional layers and was trained with a weakly supervised learning method that can provide object localization only using image-level labeling. Localization of the soybean area can be visualized via CAM and the two-dimensional position of the soybean was estimated by clustering the pixels associated with the soybean area and transforming the pixel coordinates to world coordinates. The actual position, which is determined manually as pixel coordinates in the image was evaluated and performances were 6.6(X-axis), 5.1(Y-axis) and 1.2(X-axis), 2.2(Y-axis) for MSE and RMSE about world coordinates, respectively. From the results, we confirmed that the center position of the soybean area derived through deep learning was sufficient for use in automatic weeding systems.

• Plant Breeding and Biotechnology
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• 제7권3호
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• pp.272-286
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• 2019

Developing elite hybrid rice varieties is one important objective of rice breeding programs. Several genes related to male sterilities, restores, and pollinators have been identified through map-based gene cloning within natural variations of rice. These identified genes are good targets for introducing genetic traits in molecular breeding. This study was conducted to breed elite hybrid lines with major genes related to hybrid traits and disease/insect resistance in 240 genetic resources and F₁ hybrid combinations of rice. Molecular markers were reset for three major hybrid genes (S5, Rf3, Rf4) and thirteen disease/insect resistant genes (rice bacterial blight resistance genes Xa3, Xa4, xa5, Xa7, xa13, Xa21; blast resistance genes Pita, Pib, Pi5, Pii; brown planthopper resistant genes Bph18(t) and tungro virus resistance gene tsv1). Genotypes were then analyzed using molecular marker-assisted selection (MAS). Biological assay was then performed at the Red River Delta region in Vietnam using eleven F₁ hybrid combinations and two control vatieties. Results showed that nine F₁ hybrid combinations were highly resistant to rice bacterial blight and blast. Finally, eight F₁ hybrid rice varieties with resistance to disease/insect were selected from eleven F₁ hybrid combinations. Their characteristics such as agricultural traits and yields were then investigated. These F₁ hybrid rice varieties developed with major genes related to hybrid traits and disease/insect resistant genes could be useful for hybrid breeding programs to achieve high yield with biotic and abiotic resistance.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.201-201
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• 2022

Rice bakanae disease is a serious global threat in major rice-cultivating regions worldwide causing high yield loss. It is caused by the fungal pathogen Fusarium fujikuroi. Varying degree of resistance or susceptibility to bakanae disease had been reported among Korean japonica rice varieties. We developed a modified in vitro bakanae disease bioassay method and tested 31 Korean japonica rice varieties. Nampyeong and Samgwang varieties showed highest resistance while 14 varieties including Junam and Hopum were highly susceptible with 100% mortality rate. We carried out mapping QTLs for bakanae disease resistance with four F2:F3 populations derived from the crosses between Korean japonica rice varieties. The Kompetitive Allele-Specific PCR (KASP) markers developed in our laboratory based on the SNPs detected in Korean japonica rice varieties were used in genotyping F2 plants in the populations. We found four major QTLs on chromosome 1, 4, 6, and 9 with LOD scores of 21.4, 6.9, 6.0, and 60.3, respectively. In addition, we are doing map-based cloning of the QTLs on chromosome 1 and 9 which were found with Junam/Nampyeong F2:F3 population and Junam/Samgwang F2:F3 population, respectively. These QTLs will be very useful in developing bakanae disease resistant high quality rice varieties.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제4권4호
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• 한국환경보건학회지
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• 제50권2호
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• pp.113-124
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• 2024

Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.

• 한국지리정보학회지
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• 제14권3호
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• pp.78-95
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• 2011

본 연구에서는 1:5,000 수치지형도를 이용하여 서로 다른 보간 방법으로 다양한 공간해상도를 갖는 여러 가지 DEM을 제작하였다. 그리고 제작한 다양한 DEM의 수직정확도를 network RTK GPS survey를 통해 획득한 다수의 검사점 자료를 활용하여 평가하였다. 연구 결과 전반적인 RMSE 값, 토지 유형별 RMSE 값 및 단면 평가(profile evaluation) 결과 등을 고려할 때 TIN 기반의 Terrain 방법으로 제작한 DEM이 비행안전구역에서의 신축건물 고도제한 평가에 유용함을 알 수 있었다. 그리고 비행안전구역에서의 신축건물 고도제한 평가에 적합한 DEM의 공간해상도는 3m임을 알 수 있었다. 한편, 제작한 DEM으로 획득한 지형고도 값(elevation value)은 점 추정 값(point estimation value)이 아닌 구간 추정 값(interval estimation value)이다. 이는비행장 주변의 잠재적인 신축 예정 건물의 높이가 그 지역에 설정된 제한 고도 값(height limitation value)에 저촉되는지에 대한 여부를 평가하는 데 활용할 수 있다. 본 연구에서는 구간 추정 값인 신축건물의 높이 값이 제한 고도 값에 저촉될 가능성을 3단계로 나누어 평가하는 방안을 제시하였다 - 1) 저촉 가능성 매우 높음, 2) 저촉 가능성 매우 낮음, 3) 저촉 가능성 판단 어려움. 본 연구의 결과는 비행장 주변 건물 고도제한 평가 관련 지리정보시스템(GIS)을 개발하는 데 중요한 기초를 제공한다. 아울러, 연구지역에 한정된 값이기는 하지만, 2차원 수치지형도를 활용하여 제작한 DEM의 수직정확도 값은 DEM을 이용하고자 하는 연구자들에게 의미 있는 유용한 정보가 될 것이다.