• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.412-417
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• 2008

In a traditional topology optimization method, material properties are usually distributed by finite element density and visualized by a gray level image. The distribution method based on element density is adequate for a great mass of 2-D topology optimization problems. However, when it is used for 3-D topology optimization, it is always difficult to obtain a smooth model representation, and easily appears a virtualconnect phenomenon especially in a low-density domain. The 3-D structural topology optimization method has been developed using the node density instead of the element density that is based on SIMP (solid isotropic microstructure with penalization) algorithm. A computer code based on Matlab was written to validate the proposed method. When it was compared to the element density as design variable, this method could get a more uniform density distribution. To show the usefulness of this method, several typical examples of structure topology optimization are presented.

• Journal of the Korean Statistical Society
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• 제31권1호
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• pp.93-107
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• 2002

Random effects generalised linear models are useful for analysing clustered count data in which responses are usually correlated. We propose a Bayesian approach to parameter estimation and variable selection in random effects generalised linear models for count data. A simple Gibbs sampling algorithm for parameter estimation is presented and a simple and efficient variable selection is done by using the Gibbs outputs. An illustrative example is provided.

• 한국실내디자인학회논문집
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• 제20권6호
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• pp.272-281
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• 2011

The purpose of this study is to analyze the correlation between spatial space structure and visitors' behavior and interpret visitors' behavior concretely from the aspect of exhibition environments. So, this study intends to qualify spatial space structure with integration, connectivity and control value by utilizing space syntax, limit to tracking score among the analysis index and reinterpret with exhibition density and extend of eyesight among the exhibition environments. The results of this study are as follows; First, in case of museums, tracking score shows plus correlation with connectivity and control value among the space syntax variables and very low correlation with integration. In case of art museums, tracking score shows plus correlation with integration and wide variable is judged to more useful to analyze visitor's behavior than minor variable. Second, museums doesn't make a great effect on visitors' behavior from the aspect of extent of eyesight, but from the aspect of exhibition density, visitors relatively watched evenly without short cut at the early stage of exhibition in spite of high exhibition density. And, they conducted short cut as they went to the middle stage of exhibition on the course of watching although the numerical value of exhibition density is low. Third, in case of art museums, visitors' behavior was relatively influenced by exhibition density, not extent of eyesight. But, as they went to the high level on the course of watching, watching speed became rapid and watching length became short in the place the value of extent of eyesight was high. Its reason is judged to be easy to grasp position or space structure of the next exhibition room visually. Therefore, when the concentration of watching is necessary from the aspect of exhibition, to control exhibition density properly before the space is useful to draw visiting to exhibition space afterward.

• 대한기계학회논문집B
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• 제24권12호
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• pp.1596-1607
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• 2000

The optimal design of auto-catalyst needs a good compromise between the pressure drop and flow uniformity in the substrate. One of the effective methods to achieve this goal is to use the concept of radially variable cell density. But this method has not been examined its usefulness in terms of chemical behavior and conversion performance. In this work, two-dimensional performance prediction of catalyst coupled with turbulent reacting flow simulation has been used to evaluated the benefits of this method n the flow uniformity and conversion efficiency. The results showed that two cell combination of 93cpsc and 62 cpsc was the most effective for improved pressure drop and conversion efficiency due to balanced space velocity and efficient usage of geometric surface area of channels. It was also found that large temperature difference between the bricks in case that the edge of the frontal face of brick has too much lower cell density(less than 67% of cell density of the center of the brick). This study has also demonstrated that the present computational results show the better prediction accuracy in terms of CO, HC and NO conversion efficiencies compared to those of conventional 1-D adiabatic model by comparison with experimental results.

• 한국측량학회지
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• 제32권3호
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• pp.225-231
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• 2014

When a spatial regression model that uses kernel density values as a dependent variable is applied to retail business data, a unique model cannot be selected because kernel density values change following kernel bandwidths. To overcome this problem, this paper suggests how to use the point pattern analysis, especially the L-index to select a unique spatial regression model. In this study, kernel density values of retail business are computed by the bandwidth, the distance of the maximum L-index and used as the dependent variable of spatial regression model. To test this procedure, we apply it to meeting room business data in Seoul, Korea. As a result, a spatial error model (SEM) is selected between two popular spatial regression models, a spatial lag model and a spatial error model. Also, a unique SEM based on the real distribution of retail business is selected. We confirm that there is a trade-off between the goodness of fit of the SEM and the real distribution of meeting room business over the bandwidth of maximum L-index.

• 생태와환경
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• 제43권1호
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• Journal of applied mathematics & informatics
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• 제4권2호
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• pp.469-476
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• 1997

In this paper we present a random number generation method which is one of the rejection methods, To accelerate ratio-of-uniform method we use an efficiency variable γ. After finding the optimal value of γwith respect to interesting distribution with pro-portional density random numbers can be generated in acceleration.

• 한국정밀공학회지
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• 제20권7호
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• pp.138-146
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• 2003

A critical issue in area-based stereo matching lies in selecting a fixed rectangular window size. Previous stereo methods doesn't deal effectively with occluding boundary due to inevitable window-based problems, and so give inaccurate and noisy matching results in areas with steep disparity variations. In this paper, a variable window approach is presented to estimate accurate, detailed and smooth disparities for three-dimensional structure reconstruction. It makes the smoothing of depth discontinuity reduced by evaluating corresponding correlation values and intensity gradient-based similarity in the three-dimensional disparity space. In addition, it investigates maximum connected match candidate points and then devise the novel arbitrarily shaped variable window representative of a same disparity to treat with disparity variations of various structure shapes. We demonstrate the performance of the proposed variable window method with synthetic images, and show how our results improve on those of closely related techniques for accuracy, robustness, matching density and computing speed.

• 대한수학회논문집
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• 제33권4호
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• pp.1367-1376
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• 2018

In practice, the density function of a random variable X is always unknown. Even its smoothness parameter is unknown to us. In this paper, we will consider a density smoothness parameter estimation problem via wavelet theory. The smoothness parameter is defined in the sense of equivalent Besov norms. It is well-known that it is almost impossible to estimate this kind of parameter in general case. But it becomes possible when we add some conditions (to our proof, we can not remove them) to the density function. Besides, the density function contains impurities. It is covered by some additive noises, which is the key point we want to show in this paper.

• Geomechanics and Engineering
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• 제22권3호
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• pp.207-217
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• 2020

Slope reliability analysis and risk assessment for spatially variable soils under rainfall infiltration are important subjects but they have not been well addressed. This lack of study may in part be due to the multiple and diverse evaluation indexes and the low computational efficiency of Monte-Carlo simulations. To remedy this, this paper proposes a highly efficient computational method for investigating random field problems for slopes. First, the probability density evolution method (PDEM) is introduced. This method has high computational efficiency and does not need the tens of thousands of numerical simulation samples required by other methods. Second, the influence of rainfall on slope reliability is investigated, where the reliability is calculated from based on the safety factor curves during the rainfall. Finally, the uncertainty of the sliding mass for the slope random field problem is analyzed. Slope failure consequences are considered to be directly correlated with the sliding mass. Calculations showed that the mass that slides is smaller than the potential sliding mass (shallow surface sliding in rainfall). Sliding mass-based risk assessment is both needed and feasible for engineered slope design. The efficient PDEM is recommended for problems requiring lengthy calculations such as random field problems coupled with rainfall infiltration.