• Korean Journal of Construction Engineering and Management
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• v.17 no.4
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• pp.40-48
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• 2016

The construction industry has been made diversified on the design process depending on qualitative growth of customers' demands. But this approach has lead to problems such as falling of building values due to lack of awareness of defects caused by long term utilization. So, the relationship on the characteristics of buildings and defects should be clearly analyzed to prevent falling of building values. This study, therefore, proposed a technique to quantify the relationship between building facade elements and defects. The technique was developed by applying pixel concept to the outside of the buildings. It has a feature to determine the clear relationship by presenting quantitative data that have been recognized qualitatively. The proposed technique is referred to "Pixelization Method". It separates building facade into unit compartment and makes database by assigning a code depending on the characteristics. Through the method, this study is expected to create a foundation for the quantitative analysis of relationship between building facade elements and defects as a basis on active responding to the defects.

• Journal of Astronomy and Space Sciences
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• v.31 no.2
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• pp.121-130
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• 2014

Essential ideas, successes, and difficulties of Areal Density Analysis (ADA) for color-magnitude diagrams (CMD's) of resolved stellar populations are examined, with explanation of various algorithms and strategies for optimal performance. A CMD-generation program computes theoretical datasets with simulated observational error and a solution program inverts the problem by the method of Differential Corrections (DC) so as to compute parameter values from observed magnitudes and colors, with standard error estimates and correlation coefficients. ADA promises not only impersonal results, but also significant saving of labor, especially where a given dataset is analyzed with several evolution models. Observational errors and multiple star systems, along with various single star characteristics and phenomena, are modeled directly via the Functional Statistics Algorithm (FSA). Unlike Monte Carlo, FSA is not dependent on a random number generator. Discussions include difficulties and overall requirements, such as need for fast evolutionary computation and realization of goals within machine memory limits. Degradation of results due to influence of pixelization on derivatives, Initial Mass Function (IMF) quantization, IMF steepness, low Areal Densities ($\mathcal{A}$), and large variation in $\mathcal{A}$ are reduced or eliminated through a variety of schemes that are explained sufficiently for general application. The Levenberg-Marquardt and MMS algorithms for improvement of solution convergence are contained within the DC program. An example of convergence, which typically is very good, is shown in tabular form. A number of theoretical and practical solution issues are discussed, as are prospects for further development.