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항균성 Ag-30CaO·70SiO<sub>2</sub> Gel의 MC3T3 세포적합성에 관한 연구
윤금재,류재경,안응모,김윤종,김택남,노인섭,조성백,Yoon, Geum-Jae,Ryu, Jae-Kyung,An, Eung-Mo,Kim, Yun-Jong,Kim, Taik-Nam,Noh, In-Sup,Cho, Sung-Beck 한국재료학회 2014 한국재료학회지 Vol.24 No.12
It is known that bones get damaged by accidents and aging. Since the discovery of Bioglass, various kinds of ceramics have been also found to bond to living bone; some of these ceramics are already being clinically used as bone-repairing materials. In the present study, antibacterial calcium silicate gel ($Ag-30CaO{\cdot}70SiO_2$ gel) was prepared by sol-gel method in order to control the microstructure, which is related to the dissolution rate and induction period of apatite formation in body environment. In addition, biological $Ag-30CaO{\cdot}70SiO_2$ is tested. This was done to impart antimicrobial activity to the $30CaO{\cdot}70SiO_2$. Ag ion was added during sol-gel synthesis to replace the $H_2O$ added during the making of the $30CaO{\cdot}70SiO_2$ gel, which has silver solutions of various concentration. After the sol-gel process, 1N-$HNO_3$ solution was used to wash the gel when synthesizing the gel, in order to maintain the porous structure and remove PEG, water soluble polymers. Then, the apatite forming ability of the sol-gel derived CaO-$SiO_2$ gels was investigated using simulated body fluid (SBF), which had almost the same ion concentration as that of human blood plasma. The gels were analyzed by FT-IR spectroscopy, SEM observation, XRD, and fluorescent microscopy. The apatite was successfully created even after washing the gel; apatite is present in an amorphous state, and was found to affect the concentration of the Ag ion in cells in MC3T3 live & dead assay results. From these results, it is suggested that a good material that can be used to repair defects of nature bone is $Ag-30CaO{\cdot}70SiO_2$ gel.
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Voxceleb과 한국어를 결합한 새로운 데이터셋으로 학습된 ECAPA-TDNN을 활용한 화자 검증
윤금재,박소영,Keumjae Yoon,Soyoung Park 한국통계학회 2024 응용통계연구 Vol.37 No.2
Speaker verification is becoming popular as a method of non-face-to-face identity authentication. It involves determining whether two voice data belong to the same speaker. In cases where the criminal's voice remains at the crime scene, it is vital to establish a speaker verification system that can accurately compare the two voice evidence. In this study, to achieve this, a new speaker verification system was built using a deep learning model for Korean language. High-dimensional voice data with a high variability like background noise made it necessary to use deep learning-based methods for speaker matching. To construct the matching algorithm, the ECAPA-TDNN model, known as the most famous deep learning system for speaker verification, was selected. A large dataset of the voice data, Voxceleb, collected from people of various nationalities without Korean. To study the appropriate form of datasets necessary for learning the Korean language, experiments were carried out to find out how Korean voice data affects the matching performance. The results showed that when comparing models learned only with Voxceleb and models learned with datasets combining Voxceleb and Korean datasets to maximize language and speaker diversity, the performance of learning data, including Korean, is improved for all test sets.
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