• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1157-1164
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• 2003

제품개발과정에서 생성된 제품정보모델은 시간에 따라 계속 변하고 미확정적인 정보가 포함된 UDM(Under Defined Model)이다. 정보모델에서 관점(viewpoint)은 UDM을 표현하고 관리하는 중요한 요소이다. 토픽맵(Topic Map) 이용한 정보모델은 관점의 표현이 용이하며, 관점에 따라 인간이 정보를 이해하고 조작하는 것을 돕는다. 그러나 토픽맵은 제품개발과정의 정보모델과 같은 UDM의 표현은 가능하나, 적합하지는 않다. 따라서 본 논문에서는 토픽맵이 UDM에 적합하도록 토픽맵의 문법을 확장하였다. 그리고 UDM으로부터 전자상거래에 적용 가능만 FDM(Fully Defined Model)으로 변화하는 과정에 대하여 논하였다. 관점이 적용된 UDM으로는 제품을 개발하는 과정 중에 생성되는 제품 모델을 적용하였으며, 대량생산이 된 이후의 제품 모델이나 제품개발단계에서 결정이 이루어진 후의 제품모델을 FDM 또는 UDM보다 모델의 의미가 보다 확정적인 확정적UDM을 사용하였다. 그리고 세탁기의 제품정보모델을 구현 예로 사용하여, UDM이 FDM 또는 확정적UDM으로 변화하는 과정을 설명하였다.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.388-389
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• 2011

최근 보급이 확대되고 있는 HVDC는 설치 당시의 상황 등에 따라 제어기가 달라지게 되므로, HVDC 프로젝트마다 제어기가 달라지게 된다. 따라서 PSS/E 프로그램에는 HVDC 설비를 위한 전용의 HVDC 모델을 개발할 수 있는 Model Writing 기법을 제공하고 있다. 하지만 국내에서는 UDM 개발 기술이 널리 전파되지 않아 UDM 기술기반이 취약한 상태이다. 이에 본 논문에 CIGRE HVDC 벤치 마크 모델에 대한 UDM을 개발하여 전용의 HVDC 모델을 개발하기 위한 기반기술을 확보하였다.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.386-387
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• 2011

HVDC 프로젝트마다 제어기가 달라지게 된다. 따라서 HVDC 설비를 위한 전용의 해석 모델이 존재하지 않는다. 설비의 동특성을 모의하기 위해 모델 라이브러리가 없는 상황이 도래하면, 범용 모델에 적합하도록 데이터를 수정하는 방법과 UDM(User Define Model)을 개발하는 방법이 있다. UDM을 개발하는 것이 바람직하지만, 개발기간이 길고 UDM에 대한 검증의 필요성 때문에 범용 모델에 맞도록 데이터를 수정해야 할 필요가 있다. 이에 본 논문에 PSS/E 결과 데이터 또는 EMTDC 결과 데이터를 이용하여 CDC4 모델의 파라미터를 산정하는 방안을 제시하였다.

• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1079-1089
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• 2018

The majority of existing studies for quantifying uncertainties in climate change impact assessments suggest only the uncertainties of each stage, and not the total uncertainty and its propagation in the whole procedure. Therefore, this study has proposed a new method, the Uncertainty Delta Method (UDM), which can quantify uncertainties using the variances of projections (as the UDM is derived from the first-order Taylor series expansion), to allow for a comprehensive quantification of uncertainty at each stage and also to provide the levels of uncertainty propagation, as follows: total uncertainty, the level of uncertainty increase at each stage, and the percentage of uncertainty at each stage. For quantifying uncertainties at each stage as well as the total uncertainty, all the stages - two emission scenarios (ES), three Global Climate Models (GCMs), two downscaling techniques, and two hydrological models - of the climate change assessment for water resources are conducted. The total uncertainty took 5.45, and the ESs had the largest uncertainty (4.45). Additionally, uncertainties are propagated stage by stage because of their gradual increase: 5.45 in total uncertainty consisted of 4.45 in emission scenarios, 0.45 in climate models, 0.27 in downscaling techniques, and 0.28 in hydrological models. These results indicate the projection of future water resources can be very different depending on which emission scenarios are selected. Moreover, using Fractional Uncertainty Method (FUM) by Hawkins and Sutton (2009), the major uncertainty contributor (emission scenario: FUM uncertainty 0.52) matched with the results of UDM. Therefore, the UDM proposed by this study can support comprehension and appropriate analysis of the uncertainty surrounding the climate change impact assessment, and make possible a better understanding of the water resources projection for future climate change.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Journal of the Korean Recycled Construction Resources Institute
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• v.4 no.1
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• pp.101-109
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• 2009

This CW(Construction wastes) are increasing as construction industry is growing, so many countries make efforts to recycle CW. Korea also made a stipulation for recycling CW. But the main content of this stipulation is for using recycled aggregates. Advanced countries try to increase reuse rate of not only recycled aggregate but also other kinds of wastes. So they are adopting SDM(separating dismantlement) method and we are also planning to make the system for SDM. This study is about SDM analysis through construction field investigation and difference analysis between SDM and UDM comparing predictive amount by UDM with real generated amount by SDM. First, the generated amount of construction wastes by SDM is more than estimated amount by UDM, and mixed waste was specially reduced more than UDM. The warehouse is easier than the office building to applicate SDM. But still there is no manual for SDM in the site, so establishment of SDM is demanded.

• The korean journal of orthodontics
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• v.54 no.1
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• pp.48-58
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• 2024

Objective: To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods: A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results: The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs. 1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANS-mid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions: The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.762-769
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• 2017

The extensional stiffness in quasi-isotropic laminates is uniform in the radial direction, but the bending stiffness varies radially due to the stacking sequence. This paper addresses the directional dependency of the bending stiffness and its radial variation in three types of quasi-isotropic laminate reflectors consisting of unidirectional fiber composite materials (UDM) and randomly distributed composite materials (short fiber, RDM). The extensional stiffness and bending stiffness in optical reflectors using RDM are uniform, while the bending stiffness in those using UDM varies radially from 11% to 26%. Also, the stiffness sensitivity, such as the bend-twist or bend-torsion effect, due to the differences in the stiffness value in the composite, is large. These factors are problematic in the optical field requiring precision surfaces. Utilizing RDM might be one way to eliminate the presence of bending stiffness in composite mirror substrates.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.144-155
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• 2003

In this paper, a conceptual data model, called the UDM(Unified Data Model), to efficiently represent database structures related with object technology and complex structured data, is proposed. This model integrates major features of modern data models, such as E-R model, Semantic Object Model, and UML, especially from the viewpoint of database design. This model is basically a simplified, but extended version of the Object-Relationship Model, which was proposed to model complex structures of temporal-spatial multimedia data. This model incorporates some of the important semantic and structural information of modern database applications and it is designed to support all of the major logical database models, including relational, object-relational, object-oriented, and (semi-)structured databases. A special diagrammatic technique, called the UDD(Unified Data Diagram), is introduced as a tool for database design. Also, possible ways to derive logical views of data from this unified data model are presented. The proposed model can be utilized as a convenient and practical tool for conceptual database designs.

• Journal of Korea Water Resources Association
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• v.51 no.5
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• pp.439-449
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• 2018

Many potential sources of bias are used in several steps of the radar-rainfall estimation process because the hydrological and meteorological radars measure the rainfall amount indirectly. Previous studies on radar-rainfall uncertainties were performed to reduce the uncertainty of each step by using bias correction methods in the quantitative radar-rainfall estimation process. However, these studies do not provide comprehensive uncertainty for the entire process and the relative ratios of uncertainty between each step. Consequently, in this study, a suitable approach is proposed that can quantify the uncertainties at each step of the quantitative radar-rainfall estimation process and show the uncertainty propagation through the entire process. First, it is proposed that, in the suitable approach, the new concept can present the initial and final uncertainties, variation of the uncertainty as well as the relative ratio of uncertainty at each step. Second, the Maximum Entropy Method (MEM) and Uncertainty Delta Method (UDM) were applied to quantify the uncertainty and analyze the uncertainty propagation for the entire process. Third, for the uncertainty quantification of radar-rainfall estimation at each step, two quality control algorithms, two radar-rainfall estimation relations, and two bias correction methods as post-processing through the radar-rainfall estimation process in 18 rainfall cases in 2012. For the proposed approach, in the MEM results, the final uncertainty (from post-processing bias correction method step: ME = 3.81) was smaller than the initial uncertainty (from quality control step: ME = 4.28) and, in the UDM results, the initial uncertainty (UDM = 5.33) was greater than the final uncertainty (UDM = 4.75). However uncertainty of the radar-rainfall estimation step was greater because of the use of an unsuitable relation. Furthermore, it was also determined in this study that selecting the appropriate method for each stage would gradually reduce the uncertainty at each step. Therefore, the results indicate that this new approach can significantly quantify uncertainty in the radar-rainfall estimation process and contribute to more accurate estimates of radar rainfall.