• ETRI Journal
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• v.42 no.2
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• pp.258-271
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• 2020

A correlation exists between luminance samples and chrominance samples of a color image. It is beneficial to exploit such interchannel redundancy for color image compression. We propose an algorithm that predicts chrominance components Cb and Cr from the luminance component Y. The prediction model is trained by supervised learning with Laplacian-regularized least squares to minimize the total prediction error. Kernel principal component analysis mapping, which reduces computational complexity, is implemented on the same point set at both the encoder and decoder to ensure that predictions are identical at both the ends without signaling extra location information. In addition, chrominance subsampling and entropy coding for model parameters are adopted to further reduce the bit rate. Finally, luminance information and model parameters are stored for image reconstruction. Experimental results show the performance superiority of the proposed algorithm over its predecessor and JPEG, and even over JPEG-XR. The compensation version with the chrominance difference of the proposed algorithm performs close to and even better than JPEG2000 in some cases.

• Applied Microscopy
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• v.45 no.3
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• pp.170-176
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• 2015

In this work, various electron microscopy and analysis techniques were used to investigate the microstructural evolution of a 9% Cr tempered martensite ferritic (TMF) steel T91 upon ultrasonic nanocrystalline surface modification (UNSM) treatment. The micro-dimpled surface was analyzed by scanning electron microscopy. The characteristics of plastic deformation and gradient microstructure of the UNSM treated specimens were clearly revealed by crystal orientation mapping of electron backscatter diffraction (EBSD), with flexible use of the inverse pole figure, image quality, and grain boundary misorientation images. Transmission electron microscope (TEM) observation of the specimens at different depths showed the formation of dislocations, dense dislocation walls, subgrains, and grains in the lower, middle, upper, and top layers of the treated specimens. Refinement of the $M_{23}C_6$ precipitates was also observed, the size and the number density of which were found to decrease as depth from the top surface decreased. The complex microstructure and microstructural evolution of the TMF steel samples upon the UNSM treatment were well-characterized by combined use of EBSD and TEM techniques.

• Korean Journal of Metals and Materials
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• v.48 no.7
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• pp.651-659
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• 2010

When high-speed interrupted cutting is carried out for die and mold steels with high hardness, CBN tools manifested a significantly longer wear life than carbide, ceramic, or cermet tools in an experiment of face milling characteristics. In addition, it was also found that they secured a stable surface roughness within a range of 1.6 S~6.3 S, an acceptable range for precision machining for polished machining parts. And it makes them acceptable in the precision machining field, except in industries where very high machining accuracy is required. In the high hardness interrupted cutting, it was advantageous to perform a negaland treatment and a honning treatment on the tools' cutting edge to extend tool life and surface roughness. Also, severe crater development was found on the sloped face in CBN tools following high-speed machining. This caused the cutting edge to be weakened and damaged, and ultimately resulted in a shorter tool life. Finally, as a result of EDX mapping inspection, Cr component was detected evenly on the entire crater wear area, which can be included only in STD 11.

• Spatial Information Research
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• v.14 no.1 s.36
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• pp.129-150
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• 2006

Mineral potential mapping is an important procedure in mineral resource assessment. The purpose of this study is to analyze mineral potential using weight of evidence model and a Geographic Information System (GIS) environment to identify areas that have not been subjected to the same degree of exploration. For this, a variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential mineral in the Gangreung area, Korea. for this, a spatial database considering mineral deposit, topographic, geologic, geophysical and geochemical data was constructed for the study area using a GIS. The used mineral deposits were non-metallic(Kaolin, Porcelainstone, Silicastone, Mica, Nephrite, Limestone and Pyrophyllite) deposits of sedimentary type. The factors relating to mineral deposits were the geological data such as lithology and fault structure, geochemical data, including the abundance of Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Si, Sr, V, Zn, $Cl^-,\;F^-,\;{PO_4}^{3-},\;{NO_2}^-,\;{NO_3}^-,\;SO_{42-}$, Eh, PH and conductivity and geophysical data, including the Bouguer and magnetic anomalies. These factors were used with weight of evidence model to analyze mineral potential. Probability models using the weight of evidence were applied to extract the relationship between mineral deposits and related factors, and the ratio were calculated. Then the potential indices were calculated by summation of the likelihood ratio and mineral potential maps were constructed from Geographic Information System (GIS). The mineral potential maps were then verified by comparison with the known mineral deposit areas. The result showed the 85.66% in prediction accuracy.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.522-528
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• 2009

This study focused on establishing prediction models using visible-near infrared spectrum to simultaneously detect multiple components of soils and enhancing the performance quality by suitably transformed input spectra and classification of soil spectral types for prediction model input. The continuum-removed spectra showed significant result for all cases in terms of soil properties and classified or bulk predictions. The prediction model using classified soil spectra at an absorption peak area around 500nm and 950nm efficiently indicating soil color showed slightly better performance. Especially, Ca and CEC were well estimated by the classified prediction model at $R^{2}$ > 0.8. For organic carbon, both classified and bulk prediction model had a good performance with $R^{2}$ > 0.8 and RPD> 2. This prediction model may be applied in global soil mapping, soil classification, and remote sensing data analysis.