• Journal of the Microelectronics and Packaging Society
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• v.22 no.1
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• pp.15-19
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• 2015

We demonstrated an inspection system for detecting discoloration of PCB Cu ball pad with an OSP surface finish. Though the OSP surface finish has many advantages such as eco-friendly and low cost, however, it often shows a discoloration phenomenon due to a heating process. In this study, the discoloration was analyzed with device-independent CIELAB color space. First of all, the PCB samples were inspected with standard lamps and CCD camera. The measured data was processed with Labview program for detecting discoloration of Cu ball pad. From the original PCB sample image, the localized Cu ball pad image was selected to reduce the image size by the binarization and edge detection processes and it was also converted to device-independent CIELAB color space using $3{\times}3$ conversion matrix. Both acquisition time and false acceptance rate were significantly reduced with this proposed inspection system. In addition, $L^*$ and $b^*$ values of CIELAB color space were suitable for inspection of discoloration of Cu ball pad.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.229-239
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• 2019

All vegetation colonies have layered structure. This layer is called 'forest vertical structure.' Nowadays it is considered as an important indicator to estimate forest's vital condition, diversity and environmental effect of forest. So forest vertical structure should be surveyed. However, vertical structure is a kind of inner structure, so forest surveys are generally conducted through field surveys, a traditional forest inventory method which costs plenty of time and budget. Therefore, in this study, we propose a useful method to classify the vertical structure of forests using remote sensing aerial photographs and machine learning capable of mass data mining in order to reduce time and budget for forest vertical structure investigation. We classified it as SVM (Support Vector Machine) using RGB airborne photos and LiDAR (Light Detection and Ranging) DSM (Digital Surface Model) DTM (Digital Terrain Model). Accuracy based on pixel count is 66.22% when compared to field survey results. It is concluded that classification accuracy of layer classification is relatively high for single-layer and multi-layer classification, but it was concluded that it is difficult in multi-layer classification. The results of this study are expected to further develop the field of machine learning research on vegetation structure by collecting various vegetation data and image data in the future.

• Journal of Sasang Constitutional Medicine
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• v.10 no.1
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• pp.101-142
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• 1998

1. Objects The base of classification of Sasang Constitution was the different congenital formation of organs such as lungs, spleen, liver, kidneys, and it was expanded from the viscera region of lungs, spleen, liver, kidneys to the body shape of trunk. The researched about body shape of Sasang Constitution have had points of issues which derived by measurement with hands. But this study was measured the characteristics of body shape putting in importance of anatomical position with the computerized 3-Dimension scanner, which minimized the errors of measurement, and it was abled the cubic measurement such as volume, area of cross section as well as round length. 2. Materials & Method The examinee was healthy male 40 persons and female 20 persons from 20's to 40's, who was diagnosed by the specialist of Sasang Constitutional Medicine. The body shape was measured round length, area of cross section, and volume of 31 anatomical points and the 25 hypothesis with the Rapid 3D Color Scanner Model 3030 RGB/PS. And the characteristics of each constitution's body shape was derived. 3. Results & Concousion In female, Taeumin had the largest trunk and Soeumin had the smallest trunk compared to other constitution, but Soyangin had only the smallest neck. Soeumin has the smallest round length of nipple and the $CV_{12}$, and Soyangin has the smallest breadth of ASIS. Soyangin had the smallest volume from thyroid bone to the highest points of armpits and from sternum to nipple. Taeumin had the largest volume from sternum to the $CV_{12}$ and from sternum to xiphoid process. In male, Taeumin had the largest trunk and Soeumin had the smallest trunk from the level of thyroid bone to ASIS. Soyangin has the longest distance and Soeumin has the shortest distance from nipple to the lowest of breast. Taeumin had the largest volume of trunk and Soeumin had the smallest volume of trunk. In the ration of four-Cho, Taeumin had the longest distance from the highest points of armpits to nipple and Soyangin had the shortest distance of that. Soyangin had the smallest ratio of the height of upper middle cho. Soeumin had the smallest ratio from the $CV_{12}$ to navel among trunk. In the correlation among the four Cho, Taeumin had the negative correlation between the Upper-Cho and the Lower-Middle-Cho significantly.