• Journal of the Korean BIBLIA Society for library and Information Science
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• v.29 no.1
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• pp.27-46
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• 2018

This study is to suggest the bibliographic description of RDA & KCR4 cataloging rules for FRBRizing the aggregates based on aggregates modeling. It is to suggest bibliographic description of RDA & KCR4 cataloging rules of aggregates through analyzing FRBR and LRM aggregates modeling and comparing RDA and KCR4 cataloging rules about aggregates. First, it is to describe the bibliographic records based on object oriented model, and to describe both aggregates works and separate works appropriately. Second, in case of aggregates by one person, family, or corporate body, collective title as aggregates work and separate works in aggregates must be regulated in RDA. In KCR4, collective titles rules should be regulated for aggregate works and separate works should be described. Third, aggregates of works by different persons, families, or corporate bodies should be accessible by aggregates work and separate works, and aggregates of works by different persons, families, or corporate bodies without collective title should be accessible by each of the works in both RDA and KCR4. Fourth, augmentation aggregates could be accessible by main work's expression, the expression of aggregates work, and separate expressions of the augmentation. This study will contribute to FRBRize the aggregates by suggesting bibliographic description of RDA & KCR4 cataloging rules.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.153-159
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• 2021

In this study, basic data that can be referenced for ventilation modeling was presented by analyzing the characteristics of wind pressure coefficients(Cp) according to wind direction angles under conditions of different building layouts and aspect ratios through CFD (Computational Fluid Dynamics) analysis for flat-type apartment complexes. In the case of a wind direction angle of 0°, Cp distribution in the form of an inverted S-shape was shown on the front of the building located on the windward side. And Cp corresponding to the lowest floor, the uppermost floor, and the two inflection points showed relatively close values regardless of the height of the building. The inflection point of the low-rise part was formed at a height of about 11m, and the height of the high-rise part could be calculated through a trend formula proportional to the height of the building. It was confirmed that the averaged Cp value can be applied in most conditions except for the wind direction angle of 45 degrees.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.111-121
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• 2006

Recently, many studies have been performed to apply airborne LIDAR data to extracting urban models. In order to model efficiently the man-made objects which are the main components of these urban models, it is important to extract automatically planar patches from the set of the measured three-dimensional points. Although some research has been carried out for their automatic extraction, no method published yet is sufficiently satisfied in terms of the accuracy and completeness of the segmentation results and their computational efficiency. This study thus aimed to developing an efficient approach to automatic segmentation of planar patches from the three-dimensional points acquired by an airborne LIDAR system. The proposed method consists of establishing adjacency between three-dimensional points, grouping small number of points into seed patches, and growing the seed patches into surface patches. The core features of this method are to improve the segmentation results by employing the variable threshold value repeatedly updated through a statistical analysis during the patch growing process, and to achieve high computational efficiency using priority heaps and sequential least squares adjustment. The proposed method was applied to real LIDAR data to evaluate the performance. Using the proposed method, LIDAR data composed of huge number of three dimensional points can be converted into a set of surface patches which are more explicit and robust descriptions. This intermediate converting process can be effectively used to solve object recognition problems such as building extraction.

• Journal of the Korea Concrete Institute
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• v.14 no.6
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• pp.1022-1031
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• 2002

This paper presents efficient models for bridge structures using CART-ANFIS (classification and regression tree-adaptive neuro fuzzy inference system). A fuzzy decision tree partitions the input space of a data set into mutually exclusive regions, each region is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it continuous and smooth everywhere. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.1
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• pp.81-89
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• 2015

This study takes shear wall of reinforced concrete structure as study object, and the purpose of this study is to suggest structure BIM based on automatic reinforcing bar placement system applying set-based design through the most optimum reinforcing bar placement group that was selected by applying AHP (analytical hierarchy process) method from design step. For this, the most optimum reinforcing bar placement group was selected by pairwise comparison analysis on complex standard of multiple alternatives. And shear wall automatic reinforcing bar placement system has been developed, which can automatically generate members and arrange reinforcing bar by structure design algorithm and using open API (application programming interface) provided by a BIM software vendor. As a result, the most optimum reinforcing bar placement group of the highest weight, ALT1, was selected and was generated using Tekla Structure program.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.147-163
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• 2023

Recently, there has been an increase in the research and development of automated vehicles worldwide. Research focused on automated vehicles in Korea is steadily progressing as a national R&D project. Since automated driving technology comprises diverse technology fields, it is necessary to identify the current position of the research. In this study, we propose a methodology for analyzing research trends using the NTIS data. In addition, we review the effectiveness of the currently developed research trend methodology by deriving primary keywords and major topics using the proposed method. We expect that the methodology developed in this study can be applied to identify and analyze future automated vehicle research trends.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.444-444
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• 2021

하천의 오염부하량 관리 계획은 지속적인 모니터링을 통한 자료 구축과 모형을 이용한 예측결과를 기반으로 수립된다. 하천의 모니터링과 예측 분석은 많은 예산과 인력 등이 필요하나, 정부의 담당 공무원 수는 극히 부족한 상황이 일반적이다. 이에 정부는 전문가에게 관련 용역을 의뢰하지만, 한국과 같이 지형이 복잡한 지역에서의 오염부하량 배출 특성은 각각 다르게 나타나기 때문에 많은 예산 소모가 발생 된다. 이를 개선하고자, 본 연구는 합성곱 신경망 (convolution neural network)과 수문학적 이미지 (hydrological image)를 이용하여 강우 발생시 BOD 및 총인의 부하량 예측 모형을 개발하였다. 합성곱 신경망의 입력자료는 일반적으로 RGB (red, green, bule) 사진을 이용하는데, 이를 그래도 오염부하량 예측에 활용하는 것은 경험적 모형의 전제(독립변수와 종속변수의 관계)를 무너뜨리는 결과를 초래할 수 있다. 이에, 본 연구에서는 오염부하량이 수문학적 조건과 토지이용 등의 변수에 의해 결정된다는 인과관계를 만족시키고자 수문학적 속성이 내재된 수문학적 이미지를 합성곱 신경망의 훈련자료로 사용하였다. 수문학적 이미지는 임의의 유역에 대해 2차원 공간에서 무차원의 수문학적 속성을 갖는 grid의 집합으로 정의되는데, 여기서 각 grid의 수문학적 속성은 SCS 토양보존국(soil conservation service, SCS)에서 발표한 수문학적 토양피복형수 (curve number, CN)를 이용하여 산출한다. 합성곱 신경망의 구조는 2개의 Convolution Layer와 1개의 Pulling Layer가 5회 반복하는 구조로 설정하고, 1개의 Flatten Layer, 3개의 Dense Layer, 1개의 Batch Normalization Layer를 배열하고, 마지막으로 1개의 Dense Layer가 연결되는 구조로 설계하였다. 이와 함께, 각 층의 활성화 함수는 정규화 선형함수 (ReLu)로, 마지막 Dense Layer의 활성화 함수는 연속변수가 도출될 수 있도록 회귀모형에서 자주 사용되는 Linear 함수로 설정하였다. 연구의 대상지역은 경기도 가평군 조종천 유역으로 선정하였고, 연구기간은 2010년 1월 1일부터 2019년 12월 31일까지로, 2010년부터 2016년까지의 자료는 모형의 학습에, 2017년부터 2019년까지의 자료는 모형의 성능평가에 활용하였다. 모형의 예측 성능은 모형효율계수 (NSE), 평균제곱근오차(RMSE) 및 평균절대백분율오차(MAPE)를 이용하여 평가하였다. 그 결과, BOD 부하량에 대한 NSE는 0.9, RMSE는 1031.1 kg/day, MAPE는 11.5%로 나타났으며, 총인 부하량에 대한 NSE는 0.9, RMSE는 53.6 kg/day, MAPE는 17.9%로 나타나 본 연구의 모형은 우수(good)한 것으로 판단하였다. 이에, 본 연구의 모형은 일반 ANN 모형을 이용한 선행연구와는 달리 2차원 공간정보를 반영하여 오염부하량 모의가 가능했으며, 제한적인 입력자료를 이용하여 간편한 모델링이 가능하다는 장점을 나타냈다. 이를 통해 정부의 물관리 정책을 위한 의사결정 및 부족한 물관리 분야의 행정력에 도움이 될 것으로 생각된다.