• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.7 s.262
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• pp.739-746
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• 2007

Recently, models in STL files are widely used in reverse engineering processes, CAD systems and analysis systems. However the models have poor geometric quality and include only triangles, so the models are not suitable for the finite element analysis. This paper presents a general method that generates finite element mesh from STL file models. Given triangular meshes, the method estimates triangles and makes clusters which consist of triangles. The clusters are merged by some geometric indices. After merging clusters, the method applies plane meshing algorithm, based on domain decomposition method, to each cluster and then the result plane mesh is projected into the original triangular set. Because the algorithm uses general methods to generate plane mesh, we can obtain both tri and quad meshes unlike previous researches. Some mechanical part models are used to show the validity of the proposed method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.724-727
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• 2000

Rapid Prototyping(RP)이란 3차원 솔리드 모델을 단면화한 뒤 하나씩 적층하는 가공방식을 총칭한다. 이때 단면화하는 방법에 따라서 uniform, adaptive slicing으로 나뉘며, 입력 모델에 따라서 direct slicing과 STL을 이용한 방식으로 나뉜다. 적층 방법에 따라서는 연속된 2D 윤곽을 기반으로 적층하는 vertical layer 방식과 인접한 두 개의 2D 윤곽들을 연결하며 만들어진 3D layer를 기반으로 가공하는 sloping layer방식으로 나뉠 수 있다. 현재 상용 RP 시스템들에서는 거의 모든 경우 vertical layer 방식이 채택되어 사용되고 있다. RP와 절삭 공정, 예를 들면 CNC 밀링의 장점을 효율적으로 결합하기 위해서는 임의의 복잡한 형상을 갖는 솔리드 모델을 정밀도에 제한이 없이 제조할 수 있어야 한다. 그러나 절삭 공정은 특별한 전문적 지식들을 필요로 한다 또한 상용 RP에서 사용하는 순차적인 적층 작업으로는 가공할 수 없는 형상들이 많다. 대표적인 것으로 지지대를 필요로 하는 형상들이 있다. 이러한 형상들을 지원하기 위해서는 복잡한 3D 형상을 절삭 가능한 형식으로 분할하는 것과 적층 가능한 순서대로 공정 계획하는 것이 필요하게 된다. 본 연구에서는 SDM에서 제시된 3D 분할 방법이 솔리드 모델을 기반으로 전개되어 STL file과 같은 삼각다면체 형식으로 근사화된 모델에 적용하기 어렵다는데 착안하여 STL file에서 읽어들인 삼각 다면체 모델을 가공 가능한 3D 형상으로 분할하는 알고리즘을 제시하고자 한다.

• Journal of KIISE
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• v.41 no.12
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• pp.1066-1074
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• 2014

In this paper, we present a visual analytics system that uses serial-correlation to detect an abnormal event in spatio-temporal data. Our approach extracts the topic-model from spatio-temporal tweets and then filters the abnormal event candidates using a seasonal-trend decomposition procedure based on Loess smoothing (STL). We re-extract the topic from the candidates, and then, we apply STL to the second candidate. Finally, we analyze the serial-correlation between the first candidates and the second candidate in order to detect abnormal events. We have used a visual analytic approach to detect the abnormal events, and therefore, the users can intuitively analyze abnormal event trends and cyclical patterns. For the case study, we have verified our visual analytics system by analyzing information related to two different events: the 'Gyeongju Mauna Resort collapse' and the 'Jindo-ferry sinking'.

• Journal of Korea Water Resources Association
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• v.57 no.10
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• pp.785-795
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• 2024

In this study, the Seasonal and Trend Composition Using Loess (STL) technique, which enables time series analysis, was applied to indirectly identify the contribution of water quality between nearby water quality measurement points and to interpret the influence between the main stream and tributary streams in the Miho River basin. This technique can identify and remove outliers in the time series data of the water system and analyze water quality change trends and seasonal characteristics. In addition, in order to analyze the correlation and similarity between water quality data at adjacent measurement points, a correlation analysis was conducted, and the points where common factors affect the water quality of the mainstream and inflow tributaries were classified. As a result of factor analysis between points judged to have high correlations in Miho River, it was found that the influence of the mainstream was absolute over tributaries, and it was analyzed that water quality management for the mainstream including the upstream of the Miho River should be given priority. As the degree of water quality influence between points can be analyzed by the method used in this study, it is judged that it can be effectively applied to the development of alternatives for water quality management of rivers in the future.

• Korean Journal of Computational Design and Engineering
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• v.12 no.4
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• pp.274-282
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• 2007

In this paper, a method of constructing a rectangular net from unorganized point cloud data is presented. In the method an implicit surface that fits the given point data is generated by using principal component analysis(PCA) and adaptive domain decomposition method(ADDM). Then a complete and quality rectangular net can be obtained by extracting voxel data from the implicit surface and projecting exterior faces of extracted voxels onto the implicit surface. The main advantage of the proposed method is that a quality rectangular net can be extracted from randomly scattered 3D points only without any further information. Furthermore the results of this works can be used to obtain many useful information including a slicing data, a solid STL model and a NURBS surface model in many areas involved in treatment of large amount of point data by proper processing of implicit surface and rectangular net generated previously.

• Atmosphere
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• v.34 no.2
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• pp.107-121
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• 2024

In this study, we empirically analyzed the impact of physical risks due to climate change on the soundness and operational performance of the financial industry by combining economics and climatology. Particularly, unlike previous studies, we employed the Seasonal-Trend decomposition using LOESS (STL) method to extract trends of climate-related risk variables and economic-financial variables, conducting a two-stage empirical analysis. In the first stage estimation, we found that the delinquency rate and the Bank for International Settlement (BIS) ratio of commercial banks have significant negative effects on the damage caused by natural disasters, frequency of heavy rainfall, average temperature, and number of typhoons. On the other hand, for insurance companies, the damage from natural disasters, frequency of heavy rainfall, frequency of heavy snowfall, and annual average temperature have significant negative effects on return on assets (ROA) and the risk-based capital ratio (RBC). In the second stage estimation, based on the first stage results, we predicted the soundness and operational performance indicators of commercial banks and insurance companies until 2035. According to the forecast results, the delinquency rate of commercial banks is expected to increase steadily until 2035 under assumption that recent years' trend continues until 2035. It indicates that banks' managerial risk can be seriously worsened from climate change. Also the BIS ratio is expected to decrease which also indicates weakening safety buffer against climate risks over time. Additionally, the ROA of insurance companies is expected to decrease, followed by an increase in the RBC, and then a subsequent decrease.