• Proceedings of the KWS Conference
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• 1996.10a
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• pp.105-108
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• 1996

• Journal of Welding and Joining
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• v.16 no.1
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• pp.98-105
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• 1998

The accuracy of the finite element method depends upon the mesh that is used in the analysis. The temperature around the arc is higher than the melting point of the materials, and it drops sharply in the regions just away from the arc. This requires an extremely fine mesh in the confined high temperature region to predict the temperature accurately in that region. But the computational time increases with the fineness of mesh. Since fine mesh is required only around the arc source, adaptivity of the input mesh according to the position of the arc source is efficient. The remeshing technique gives a fine mesh in the high temperature region around the arc and a coarse mesh in other region at any time step. With this it is possible to achieve desired accuracy with less computation time. In this study a transient adaptive mesh, remeshing technique, is developed and calculated temperature for a sample problem.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.04a
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• pp.39-44
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• 1992

This paper proposes a method of h-type adaptive mesh generation for the finite element analysis of two dimensional elasticity problem. The error energy norm of a posteriori error estimation is difined based on the complementary energy of each element. Computer codes are developed and some examples are investigated. It is shown that the approach to the optimized mesh in this paper is effective and useful.

• Journal of Korean Port Research
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• v.10 no.2
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• pp.61-68
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• 1996

The main problems in structural analysis by Finite Eelement Method are difficulty in making data file and error estimation. For decreasing these problems' pays. have been suggesting the adaptive mesh refinement and error estimation method. Posteriory error estimation methods suggested by Jang[1], Babuska[2,3], Ohtsubo[8,9], and this paper. Comparing these methods and examine their properties. According this paper, In the problem supposed having singularity, the method suggested by this paper is good, But the problem supposed having no singularity, the method suggested by Jang[1] is good. For decreasing the effect of initial mesh in p-refinement, make application h-refinement at first and apply p-refinement, and confine polynomial's degree to two, for making program simply by plural mesh models are not needed.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• The korean journal of orthodontics
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• v.41 no.1
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• pp.6-15
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• 2011

Objective: The aim of this study was to evaluate the biomechanical aspects of peri-implant bone upon root contact of orthodontic microimplant. Methods: Axisymmetric finite element modeling scheme was used to analyze the compressive strength of the orthodontic microimplant (Absoanchor SH1312-7, Dentos Inc., Daegu, Korea) placed into inter-radicular bone covered by 1 mm thick cortical bone, with its apical tip contacting adjacent root surface. A stepwise analysis technique was adopted to simulate the response of peri-implant bone. Areas of the bone that were subject to higher stresses than the maximum compressive strength (in case of cancellous bone) or threshold stress of 54.8MPa, which was assumed to impair the physiological remodeling of cortical bone, were removed from the FE mesh in a stepwise manner. For comparison, a control model was analyzed which simulated normal orthodontic force of 5 N at the head of the microimplant. Results: Stresses in cancellous bone were high enough to cause mechanical failure across its entire thickness. Stresses in cortical bone were more likely to cause resorptive bone remodeling than mechanical failure. The overloaded zone, initially located at the lower part of cortical plate, proliferated upward in a positive feedback mode, unaffected by stress redistribution, until the whole thickness was engaged. Conclusions: Stresses induced around a microimplant by root contact may lead to a irreversible loss of microimplant stability.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.29-32
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• 2000

본 논문에서는 음성인식 시스템의 음향모델 개선을 위한 기초적 연구로서, 문맥적인 요소를 필요로 하는 SSS(Successive State Splitting)와 필요로 하지 않는 SSS-free 알고리즘을 이용한 HMnet(Hidden Markov Network) 음향모델 작성방법에 대해 검토하고 작성한 음향모델을 한국어에 적용하여 그 유효성을 확인하였다. HMnet을 이용한 음소모델의 작성방법은 전체 학습 데이터에 대해서 각각 2개의 상태를 가지는 초기 모델을 작성한 후, 이를 시간과 문맥방향으로의 최대 분포를 가지는 상태를 재분할한 후 임의의 상태수가 될 때까지 상태분할을 계속적으로 수행케 하여 각 음소모델을 작성하게 된다. 작성한 HMnet 음향모델의 유효성을 확인하기 위해 ETRI 445 단어의 3인에 대한 화자종속 음소인식 실험을 수행하였다. 인식실험 결과, SSS 알고리즘을 이용한 화자종속실험의 경우 상태수 520에서 평균 $62.8\%$의 인식률을, SSS-free 알고리즘의 경우 상태수 420에서 평균 $64.2\%$의 인식률을 얻었다. 이 결과는 HMM을 이용한 경우(약$43.4\%$)보다 $20\%$이상의 인식률 향상을 보여 이 알고리즘의 유효성을 확인할 수 있었다. SSS와 SSS-free를 비교한 경우, SSS-free가 SSS보다 낮은 상태수에서 평균 $1.4\% 향상된 인식률을 보였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2163-2170
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• 2009

We present an automated, energy minimized-based method for Lung parenchyma segmenting Chest Computed Tomography(CT) datasets. Deformable model is used for energy minimized segmentation. Quantitative knowledge including expected volume, shape of Chest CT provides more feature constrain to diagnosis or surgery operation planning. Segmentation subdivides an lung image into its consistent regions or objects. Depends on energy-minimizing, the level detail image of subdivision is carried. Segmentation should stop when the objects or region of interest in an application have been detected. The deformable model that has attracted the most attention to date is popularly known as snakes. Snakes or deformable contour models represent a special case of the general multidimensional deformable model theory. This is used extensively in computer vision and image processing applications, particularly to locate object boundaries, in the mean time a new type of external force for deformable models, called gradient vector flow(GVF) was introduced by Xu. Our proposed algorithm of deformable model is new external energy of GVF for exact segmentation. In this paper, Clinical material for experiments shows better results of proposal algorithm in Lung parenchyma segmentation on Chest CT.