Traditional wooden buildings deform over time and are vulnerable to fire or earthquakes. Therefore, traditional wooden buildings require continuous management and repair, and securing architectural drawings is essential for repair and restoration. Unlike modernized CAD drawings, traditional wooden building drawings scan and store hand-drawn drawings, and in this process, many noise is included due to damage to the drawing itself. These drawings are digitized, but their utilization is poor due to noise. Difficulties in systematic management of traditional wooden buildings are increasing. Noise removal by existing algorithms has limited drawings that can be applied according to noise characteristics and the performance is not uniform. This study presents deep artificial neural network based noised reduction for architectural drawings. Front/side elevation drawings, floor plans, detail drawings of Korean wooden treasure buildings were considered. First, the noise properties of the architectural drawings were learned with both a cycle generative model and heuristic image fusion methods. Consequently, a noise reduction network was trained through supervised learning using training sets prepared using the noise models. The proposed method provided effective removal of noise without deteriorating fine lines in the architectural drawings and it showed good performance for various noise types.

The number of pixel defects including bright and black dots on a panel is one of the critical factors determining the quality of TFT-LCD. Since pixel defects on the TFT-LCD panels are sometimes unavoidable, manufacturers have to inspect the panels so that any panel with an unacceptable number of defects will not be delivered to the buyers. However, the buyers demand for the manufacturers to meet different pixel defects tolerances (acceptable number of pixel defects on a TFT-LCD panel) around central(tight) and peripheral(loose) inspection zones. The disagreement in quality standard among different buyers also cause confusions in screening non-confirmative products and unstable yield of production. Few research has focused on the effects of defect locations on a TFT-LCD panel on their detection probabilities and the rational division of defect inspection zones. In this research, experiments were conducted to find the detection probabilities of black dot defects with respect to their varying locations on a TFT-LCD. It is proposed a rational division of inspection zone on a TFT-LCD panel on the basis of detection probabilities of the defects. With these division of inspection zones and the mean defect detection probability within each zone, it is expected to establish a more reasonable pixel defects tolerances.

An experiment was conducted to identify salient colours for information coding at electronic video displays. CRT and TFT-LCD were used to evaluate the effect of different types of electronic displays on the salience of colours. Total of 100 Subjects, 50 for each display were asked to select more salient 10 colours among 24 given colours. There was no statistically significant difference in the salience of colours between the two display types. The result showed that the tested colours could be clustered into 5 categories according to their brightness, saturation, and the number of R, G, B elements occupied to reproduce the colours. Three achromatic colours (black, white, grey) and eight chromatic colors (red, yellow, green, blue, cyan, orange, magenta, and indigo) were identified as the salient colours at the electronic video displays. The result also showed that the eight chromatic colours could be clustered into two distinct categories, landmark colours(red, yellow, green, and blue) and the other basic colours (cyan, magenta, orange, and indigo). It is noticeable that cyan, magenta, and indigo substituted for pink, purple, and brown that were recommended as the salient colours for the environment not using electronic video displays by the previous researches.

배양산삼근을 5 및 (w/v)를 첨가하고, 설탕과 포도당으로 당도를 Brix로 조절한 후, 3종의 효모를 이용하여 발효주를 제조하였다. 발효기간 중 품질특성(알콜함량, 사포닌함량, 효모균수, 총산도, 당도, 휘발성성분)을 조사하고 관능검사를 실시하였다. 배양산삼근 , 포도당 및 S. bayanus (ATCC 10601) 효모 처리구에서 의 가장 높은 알콜함량을 보였으며 주된 알콜성분은 ethanol과 1-propanol이었다. 효모균수는 발효 5