• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.2
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• pp.101-107
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• 2022

This study proposes the deconvolution method for the nanoindentation test results of geopolymer employing the Gaussian mixture model. Geopolymer has been studied extensively as an alternative construction material because it emits relatively lower CO₂ compared to ordinary Portland cement. Geopolymer is made of aluminosilicate and alkaline solution, and the Si/Al molar ratio affects its mechanical properties. Previous studies revealed that the Si/Al molar ratio of 1.8~2.0 results in the highest compressive strength, and the Si/Al molar ratio over 1.8 degrades the compressive strength of geopolymer severely; however the reason for the compressive strength degradation is still unclear. To understand the effect of the Si/Al molar ratio on the geopolymer structure, this study exploits the nanoindentation. The phase deconvolution of the indent modulus data is successful using the Gaussian mixture model, and it is observed that the Si/Al molar ratio alters the homogeneity of the geopolymer. Geopolymer becomes more homogeneous up to an Si/Al molar ratio of 1.8 at which geopolymer exhibits the highest compressive strength. The examination of this study is assumed to be adopted as evidence of strength degradation by the Si/Al ratio higher than the optimum value.

• The KIPS Transactions:PartB
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• v.9B no.1
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• pp.113-118
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• 2002

This paper presents a cluster-based image retrieval method. It retrieves images from a related cluster after classifying images into clusters using RAGMD, a clustering technique. When images are retrieved, first they are retrieved not from the whole image database one by one but from the similar cluster, a similar small image group with a query image. So it gives us retrieval-time reduction, keeping almost the same precision with the exhaustive retrieval. In the experiment using an image database consisting of about 2,400 real images, it shows that the proposed method is about 18 times faster than 7he exhaustive method with almost same precision and it can retrieve more similar images which belong to the same class with a query image.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.159-164
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• 2019

Detecting wildfire using only color in image information is a very difficult issue. This paper proposes an algorithm to detect forest fire area by analyzing color and motion of the area in the video including forest fire. The proposed algorithm removes the background region using the Gaussian Mixture based background segmentation algorithm, which does not depend on the lighting conditions. In addition, the RGB channel is changed to an HSV channel to extract flame candidates based on color. The extracted flame candidates judge that it is not a flame if the area moves while labeling and tracking. If the flame candidate areas extracted in this way are in the same position for more than 2 minutes, it is regarded as flame. Experimental results using the implemented algorithm confirmed the validity.