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서정헌,Junghun Suh,Emil Thomas Kaiser Korean Chemical Society 1978 대한화학회지 Vol.22 No.3
상온에서 카르복시펩티다제 A에 의한 에스테르 기질의 가수분해 반응을 여러가지 외부 시약의 존재하에 행하였다. 산무수물 형태의 아실-효소중간체가 외부시약에 의해 공격받는다면 아실 부분에 포획된 생성물이나 효소부분에 포획된 생성물이 형성될 것이다. 반응생성물의 분광도와 효소활성도의 변화를 조사한 결과 포획반응의 생성물은 검출되지 않았다. 또한, O-(o-hydroxyphenylacetyl)-L-${\beta}$-phenyllactate가수분해에 대한 효소 반응속도변수를 측정하였다. 이 기질의 o-히드록시기가 분자내 포획기로서 작용하여 산무수물형태의 아실-효소중간체를 공격하여 20coumaranone이 형성되었나를 조사하였으나 분자내 포획반응이 일어났다는 증거는 얻지 못하였다. 이러한 중간체 포획반응의 실패는 포획용 시약이 아실-효소 중간체의 무수산기에 접근할때 입체적 방해를 받거나 중간체의 가수분해 단계도 효소에 의해 촉매되기 때문이라고 생각된다. Carboxypeptidase A-catalyzed hydrolysis of ester substrates was carried out at room temperature in the presence of a number of external reagents. If the acyl-enzyme intermediate, an anhydride, is attacked by the external reagents, products formed by trapping at the acyl portion or at the enzyme portion of the anhydride group can be obtained. Examination of the uv/vis spectral properties of the reaction products and of changes in enzyme activity indicated that such trapping reactions did not occur. Also performed was evaluation of enzymatic rate parameters for the the hydrolysis of O-(o-hydroxyphenylacetyl)-L-${\beta}$-phenyllactate. Detection of 2-coumaranone possibly formed by attack of the o-hydroxy group as an intramolecular trapping group at the acyl-enzyme intermediate was tried, but no evidences for the intramolecular trapping reaction were obtained. Failure to trap the intermediate was discussed in terms of steric hindrance imposed on the approach of the trapping reagents to the anhydride group of the acyl-enzyme intermediate and of the fast enzymatic breakdown of the intermediate.
Immobile Artificial Metalloproteases
서정헌,Myoung-soon Kim 대한화학회 2005 Bulletin of the Korean Chemical Society Vol.26 No.12
Effective artificial metalloproteases have been designed by using cross-linked polystyrene as the backbone. Artificial active sites comprising Cu(II) complexes as the catalytic site and other metal centers or organic functionalities as binding sites were synthesized. The activity of Cu(II) centers for peptide hydrolysis was greatly enhanced on attachment to polystyrene. By placing binding sites in proximity to the catalytic centers, the ability to hydrolyze a variety of protein substrates at selected cleavage sites was improved. Thus far, the most advanced immobile artificial proteases have been obtained by attaching the aldehyde group in proximity to the Cu(II) complex of cyclen.
임정선(Jeongseon Lim),한미경(Mikyung Han),윤현진(Hyunjin Yoon) 한국정보과학회 2018 정보과학회논문지 Vol.45 No.12
일반 영화를 4D 영화로 변환하기 위해서 실감효과를 추가할 구간을 검출 할 필요가 있다. 이를 자동화하기 위해 본 논문에서는 시각적 · 청각적 특징을 이용하여 실감효과 구간을 검출하는 멀티모달 딥러닝 모델을 제안한다. 실감효과 여부를 분류하기 위해 오디오 기반 컨볼루션 순환 신경망과 비디오 기반 롱 쇼트-텀 메모리, 다층 신경망을 이용하였다. 오디오 기반 모델과 비디오 기반 실감효과 분류 모델을 특징값-단계에서 결합하였다. 또한, 대화 구간에서는 실감효과가 잘 나타나지 않는다는 점을 이용하여 오디오 기반 컨볼루션 신경망 모델을 이용하여 비대화 구간을 검출하고, 앞서 획득한 실감효과 분류 모델결과와 스코어-단계에서 결합하였다. 마지막으로, 입력 윈도우 구간의 예측 스코어를 이용하여 전체 영화의 연속된 실감효과 구간을 검출하였다. 실제 4D 영화를 이용한 실험을 통해 시각적 · 청각적 특징을 모두 사용한 멀티모달 딥러닝 모델이 유니모달 딥러닝 모델에 비해 높은 검출 성능을 보여주는 것을 확인하였다. A conventional movie can be converted into a 4D movie by identifying effect scenes. In order to automate this process, in this paper, we propose a multimodal deep learning model that detects effect scenes using both visual and audio features of a movie. We have classified effect/non-effect scenes using audio-based Convolutional Recurrent Neural Network (CRNN) model and video-based Long Short-term Memory (LSTM) and Multilayer Perceptron (MLP) model. Also, we have implemented feature-level fusion. In addition, based on our own observation that effects typically occur during non-dialog scenes, we further detected non-dialog scenes using audio-based Convolutional Neural Network (CNN) model. Subsequently, the prediction scores of audio-visual effect scene classification and audio-based non-dialog classification models were combined. Finally, we detected sequences of effect scenes of the entire movie using prediction score of the input window. Experiments using real-world 4D movies demonstrate that the proposed multimodal deep learning model outperforms unimodal models in terms of effect scene detection accuracy.
서정헌,박서영,곽중민,강인석 경상대학교 생산기술연구소 2002 工學硏究院論文集 Vol.2 No.-
Recently, our construction industry is using various work sections classification systems for civil engineering projects. However, because the public IDB(Integrated DataBase) for the work sections is insufficiently built, it is difficult to connect construction operation information from construction site to standard specification or standard method of measurment. Therefore, this study suggests a methodology to link the construction work sections information, such as cost, schedule, drawings and specification, with civil engineering standard method of measurement. And the methodology suggested in this study was applied to a web-based work sections information management system with IDB. An IDB modeling was designed by using DFD (Data Flow Diagram) and ERD (Entity Relationship Diagram).