• Korean Journal of Heritage: History & Science
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• v.52 no.3
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• pp.74-93
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• 2019

The Bird Track Site in the Haman Formation in Yongsanri (Natural Monument No. 222) was reported on the named Koreanaornis hamanensis and Jindongornipes kimi sauropod footprint Brontopodus and ichnospecies Ochlichnus formed by Nematoda. This site has outstanding academic value because it is where the second-highest number of bird tracks have been reported in the world. However, only 25% of the site remains after being designated a natural monument in 1969. This is due to artificial damage caused by worldwide fame and quarrying for flat stone used in Korean floor heating systems. The Haman Formation, including this fossil site, has lithofacies showing reddish-grey siltstone and black shale, alternately. The boundary of the two rocks is progressive, and sedimentary structures like ripple marks and sun cracks can clearly be found. This site was divided into seven formations according to sedimentary sequences and structures. The results of a nondestructive deterioration evaluation showed that chemical and biological damage rates were very low for all formations. Also, physical damage displayed low rates with 0.49% on exfoliation, 0.04% on blistering, 0.28% on break-out; however, the joint crack index was high, 6.20. Additionally, efflorescence was observed on outcrops at the backside and the northwestern side. Physical properties measured by an indirect ultrasonic analysis were found to be moderately weathered (MW). Above all, the southeastern side was much fresher, though some areas around the column of protection facility appeared more weathered. Furthermore, five kinds of discontinuity surface can be found at this site, with the bedding plane showing the higher share. There is the possibility of toppling failure occurring at this site but stable on plane and wedge failure by means of stereographic projection. We concluded that the overall level of deterioration and stability were relatively fine. However, continuous monitoring and conservation treatment and management should be performed as situations such as the physicochemical weathering of the fossil layer, and the efflorescence of the mortar adjoining the protection facility's column appear to be challenging to control.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.