• 한국시뮬레이션학회논문지
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• 제30권3호
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• pp.19-29
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• 2021

코로나바이러스감염증-19의 지역 간 감염확산을 설명하기 위해 단일 집단의 구획모델(compartmental model)인 SEIQRD 모델을 응용하여 다 집단(multi-group) 구획모델을 설계하였다. 이 모델은 구획을 세분화하여 잠복기 및 무증상자와 같은 숨은 감염자에 대한 설명이 가능하며 각 지역 간 감염지수와 검사율을 비교할 수 있다. 이를 통해 2020년 8월 2차 대유행과 11월 3차 대유행 시기에 어느 지역을 중심으로 전파가 이뤄졌는지 추정해보았다. 대한민국 국민 전체를 모집단으로 두었을 때 하위집단(subgroup)을 서울, 경기+인천, 비수도권으로 설정하였다. 데이터는 보건복지부의 '코로나 19국내발생 현황'을 참고하여 격리중인 인원, 누적 사망인원, 완치(격리해제)인원을 적합시켜 지역 간 감염지수와 지역별 감염자들의 평균 검사율, 지역별 평균 완치기간, 지역별 예상되는 숨은 감염자 수를 추정하였다.

• 연구논문집
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• 통권29호
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• pp.57-67
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• 1999

This paper presents the status quo of the development of a piecewise bendable switch system for the urban transit MagLev. MagLev system as well as railroad requires switch systems to reach its destination. Requirements of the switch system for commercial lines are high speed operation satisfying about 2-3 minute headway and system reliability, etc. Parallel moving type switch system was installed on the test track of urban transit MagLev in KIMM. In this system, switch operation from one position to another can be done in about 90 seconds. Therefore, we concluded that this system can not satisfy the headway for the commercial lines. We decided to develop a high speed piecewise bendable switch system in which switching can be done in 20 seconds. Designed switch system is very complicated in view of operating mechanism. It consists of 11 segmented girder beams driven by hydraulic cylinders. To gain the idea of a piecewise bendable switch system, we manufactured and tested a 1/5 scale switch model. We are going to construct a full scale piecewise bendable switch system next year.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권12호
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• pp.3274-3292
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• 2013

Unsupervised methods for image segmentation are recently drawing attention because most images do not have labels or tags. A topic model is such an unsupervised probabilistic method that captures latent aspects of data, where each latent aspect, or a topic, is associated with one homogeneous region. The results of topic models, however, usually have noises, which decreases the overall segmentation performance. In this paper, to improve the performance of image segmentation using topic models, we propose two topic masks applicable to topic assignments of homogeneous regions obtained from topic models. The topic masks capture the noises among the assigned topic assignments or topic labels, and remove the noises by replacements, just like image masks for pixels. However, as the nature of topic assignments is different from image pixels, the topic masks have properties that are different from the existing image masks for pixels. There are two contributions of this paper. First, the topic masks can be used to reduce the noises of topic assignments obtained from topic models for image segmentation tasks. Second, we test the effectiveness of the topic masks by applying them to segmented images obtained from the Latent Dirichlet Allocation model and the Spatial Latent Dirichlet Allocation model upon the MSRC image dataset. The empirical results show that one of the masks successfully reduces the topic noises.

• 한국해양공학회지
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• 제17권4호
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• pp.1-7
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• 2003

A virtual reality technology for multipurpose numerical simulation is developed to reproduce and investigate a variety of ocean environmental problems in a 3D Numerical Wave Tank(NWT). The governing equations for solving incompressible fluid motion are Navier-Stokes equation and continuity equation. The Marker-Density function technique is adopted to implement the fully nonlinear freesurface kinematic condition. The marine environmental situations, i.e., waves, currents, etc., are reproduced by use of multi-segmented wavemakers on the basis of the so-called ″snake-principle″. In this paper, some numerical reproduction techniques for regular, and irregular waves, multi-directional waves, Bull's-eye wave. wave-current, and solitary wave are presented, and a model test in motion with large amplitude of roll angle is conducted in the developed 3D-NWT, using a overlaid grid system.

• International Journal of Human Ecology
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• 제10권1호
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• pp.1-11
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• 2009

The effect of learning orientation on company innovation and innovation capability are explored based on survey data collected from 154 small and medium-sized manufacturing firms. The theoretical links between learning orientation and company innovation as well as innovation capability are investigated in four research models that compare textile and non-textile manufacturing firms. Learning orientation has a significant effect on company innovation and innovation capability in the model test. However, some of the three segmented factors (commitment to learning, shared vision, and open-mindedness) of learning orientation had no significant effect on company innovation and innovation capability. Company innovation and innovation capability of textile manufacturing firms are predicted by the commitment to learning and shared vision, whereas those of non-textile firms were determined by shared vision and open-mindedness. Differences show that firms may need to put weight on some distinctive aspects of learning orientation according to the business categories in order to enhance company innovation.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2002년도 추계학술대회 논문집
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• pp.174-180
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• 2002

A virtual reality technology for multipurpose numerical simulation is developed to reproduce and investigate a variety of ocean environmental problems in a 3D-Numerical Wave Tank. The governing equations for solving incompressible fluid motion are Navier-Stokes equation and continuity equation, and the Marker-Density function technique is adopted to implement the fully-nonlinear free-surface kinematic condition. The marine environmental situations, i.e. waves, currents, wind, etc., are reproduced by use of multi-segmented wavemaker on the basis of the so-called "snake-principle". In this paper, some numerical reproduction techniques for regular and irregular waves, multi-directional waves, Bull's-eye wave, wave-current, and solitary wave are presented, and a model test in motion with large amplitude of roll angle is conducted in the developed 3D-NWT, using a overlaid grid system.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.153-155
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• 2003

Laser scanning has become a viable technique for the collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. The inherent 3D nature of the sub-randomly distributed point cloud provides abundant spatial information. To explore valuable spatial information from laser scanned data becomes an active research topic, for instance extracting digital elevation model, building models, and vegetation volumes. The sub-randomly distributed point cloud should be segmented and classified before the extraction of spatial information. This paper investigates some exist segmentation methods, and then proposes an octree-based split-and-merge segmentation method to divide lidar data into clusters belonging to 3D planes. Therefore, the classification of lidar data can be performed based on the derived attributes of extracted 3D planes. The test results of both ground and airborne lidar data show the potential of applying this method to extract spatial features from lidar data.

• Smart Structures and Systems
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• 제32권2호
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• pp.111-121
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• 2023

This study proposes an automated assembly performance evaluation method for prefabricated steel structures (PSSs) using machine learning methods. Assembly component images were segmented using a modified version of the receptive field pyramid. By factorizing channel modulation and the receptive field exploration layers of the convolution pyramid, highly accurate segmentation results were obtained. After completing segmentation, the positions of the bolt holes were calculated using various image processing techniques, such as fuzzy-based edge detection, Hough's line detection, and image perspective transformation. By calculating the distance ratio between bolt holes, the assembly performance of the PSS was estimated using the k-nearest neighbors (kNN) algorithm. The effectiveness of the proposed framework was validated using a 3D PSS printing model and a field test. The results indicated that this approach could recognize assembly components with an intersection over union (IoU) of 95% and evaluate assembly performance with an error of less than 5%.

• Korean Journal of Radiology
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• 제21권6호
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• pp.660-669
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• 2020

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

• 한국측량학회지
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• 제25권6_1호
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• pp.575-583
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• 2007

Surface segmentation aims to represent the terrain as a set of bounded and analytically defined surface patches. Many previous segmentation methods have been developed to extract planar patches from LIDAR data for building extraction. However, most of them were not fully satisfactory for more general applications in terms of the degree of automation and the quality of the segmentation results. This is mainly caused from the limited information derived from LIDAR data. The purpose of this study is thus to develop an automatic method to perform surface segmentation by combining not only LIDAR data but also images. A region-based method is proposed to generate a set of planar patches by grouping LIDAR points. The grouping criteria are based on both the coordinates of the points and the corresponding intensity values computed from the images. This method has been applied to urban data and the segmentation results are compared with the reference data acquired by manual segmentation. 76% of the test area is correctly segmented. Under-segmentation is rarely founded but over-segmentation still exists. If the over-segmentation is mitigated by merging adjacent patches with similar properties as a post-process, the proposed segmentation method can be effectively utilized for a reliable intermediate process toward automatic extraction of 3D model of the real world.