• KSBB Journal
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• v.22 no.1
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• pp.16-21
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• 2007

Although Energy demands of modern society increase rapidly, current energy would be exhausted shortly. Therefore development of bio-ethanol production process from cellulose containing materials was extremly demanded. Therefore development of highly functional cellulase is requisite for this purpose. In this study cellobio-hydrolase (CBH1) gene from Trichorderma reesei was used to increase cellulase activity by directed evolution and highly functional cellobio-hydrolase was obtained and characterized.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.2
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• pp.73-86
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• 2009

This paper proposes a new data hiding scheme for binary image authentication with minimizing the distortion of host image. Based on the Hamming-Code-Based data embedding algorithm, the proposed scheme makes it possible to embed authentication information into host image with only flipping small number of pixels. To minimize visual distortion, the proposed scheme only modifies the values of the flippable pixels that are selected based on Yang et al's flippablity criteria. In addition to this, by randomly shuffling the bit-order of the authentication information to be embedded, only the designated receiver, who has the secret key that was used for data embedding, can extract the embedded data. To show the superiority of the proposed scheme, the two measurement metrics, the miss detection rate and the number of flipped pixels by data embedding, are used for the comparison analysis between the proposed scheme and the previous schemes. As a result of analysis, it has been shown that the proposed scheme flips smaller number of pixels than the previous schemes to embed the authentication information of the same bit-length. Moreover, it has been shown that the proposed scheme causes smaller visual distortion and more resilient against recent steg-analysis attacks than the previous schemes by the experimental results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.269-274
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• 2021

In secret communication, steganography is the sending and receiving of secret messages without being recognized by a third party. In the spatial domain method bitwise information is inserted into the virtual bit plane of the decomposed pixel values of the image. That is, the bitwise secret message is sequentially inserted into the least significant bit(LSB) of the image, which is a cover medium. In terms of application, the LSB is simple, but has a drawback that can be easily detected by a third party. If the upper bit plane is used to increase security, the image quality may deteriorate. In this paper, I present a method for concealing Hangul secret messages in image steganography based on the lo-th bit plane and the decomposition of modified pixel intensity values. After decomposing the Hangeul message to be hidden into choseong, jungseong and jongseong, then a shuffling process is applied to increase confidentiality and robustness. PSNR was used to confirm the efficiency of the proposed method. It was confirmed that the proposed technique has a smaller effect in terms of image quality than the method applying BCD and Fibonacci when inserting a secret message in the upper bit plane. When compared with the reference value, it was confirmed that the PSNR value of the proposed method was appropriate.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.913-924
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• 2022

In this paper, we presented a database construction of undersea images for the Habitats of Ecklonia cava and Sargassum and conducted an experiment for semantic segmentation using state-of-the-art (SOTA) models such as High Resolution Network-Object Contextual Representation (HRNet-OCR) and Shifted Windows-L (Swin-L). The result showed that our segmentation models were superior to the existing experiments in terms of the 29% increased mean intersection over union (mIOU). Swin-L model produced better performance for every class. In particular, the information of the Ecklonia cava class that had small data were also appropriately extracted by Swin-L model. Target objects and the backgrounds were well distinguished owing to the Transformer backbone better than the legacy models. A bigger database under construction will ensure more accuracy improvement and can be utilized as deep learning database for undersea images.

• KSBB Journal
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• v.23 no.3
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• pp.231-238
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• 2008

The second family member of tissue inhibitors of matrix metalloproteinases, TIMP-2, is a 21kDa protein which inhibits matrix metalloproteinases 2 (MMP-2). Expression of mammalian proteins in E. coli often forms inclusion bodies that are made up of mis-folded or insoluble protein aggregates. The requirement for the formation of 6 disulfide bonds in the process of the TIMP-2 folding is likely to be incompatible with the reducing environment of E. coli. However, this incompatibility can be often overcome by introducing a mutagenesis that could lead to enhancement of the protein solubility. In this reason, we have attempted to express the soluble TIMP-2 in E. coli by applying a modified staggered extension process (StEP), one of the in vitro PCR-based recombinant mutagenesis methods, and error-prone PCR. C-terminally located CAT fusion protein with respect to mutated TIMP-2 proteins enables us to differentiate the soluble TIMP-2 from the insoluble in E. coli by virtue of chloramphenicol resistance. According to this scheme, E. coli harboring properly-folded CAT fused to TIMP-2 protein was selected, and some of the resulting colonies exhibited an enhanced, soluble expression of TIMP-2 compared to the wild type, implying (i) the StEP technique is successfully employed to enhance the proper folding thereby increasing the solubility of TIMP-2, and (ii) the CAT dependent screening may be a simple and effective method to differentiate the soluble protein expression in E. coli.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.949-965
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• 2023

In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.