http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
웹 서비스 지원을 위한 예외처리 기반 동적 서비스 조정 프레임워크
정종하(Jongha Jung),장우혁(woohyuk Jang),이성독,한동수(Dongsu Han) 한국정보과학회 2005 한국정보과학회 학술발표논문집 Vol.32 No.2
웹 서비스는 서비스 가용성(availability)과 성능(performance)면에서 신뢰성을 항상 보장해 주지 못한다. 동적 서비스 조정(Dynamic Service Coordination)은 웹 서비스를 호출하는 시스템이나 응용 프로그램 내에서의 비 신뢰적인 문제상황을 처리하는 기술이다. 이 환경 내에서의 웹 서비스는 제한된 시간내에 응답 하지 못하는 등의 문제가 발생 할 경우, 신뢰적인 웹 서비스의 호출을 위해 다른 웹 서비스로의 대체작업이 런타임(run-time)에 이뤄진다. 본 논문에서는 웹 서비스를 위한 동적 서비스 조정 프레임워크를 제안한다. 프레임워크 내에서 동적 서비스 조정의 지원 및 웹 서비스 호출을 담당하는 클래스와 워크플로우가 생성되고, 생성된 클래스 메소드를 호출함으로써 신뢰적인 웹 서비스 호출이 가능하다. 호출작업이 간접적으로 이뤄짐으로 인해 어느 정도의 성능적 손실이 발생하나, 이 방식을 통해 얻는 시스템 유연성과 신뢰성의 측면을 고려한다면, 충분히 감 수 될 수 있다.
( Min Sung Kim ),( Jongha Park ),( Jae Hyun Park ),( Hyung Jun Kim ),( Hyun Jeong Jang ),( Hee Rin Joo ),( Ji Yeon Kim ),( Joon Hyuk Choi ),( Nae Yun Heo ),( Seung Ha Park ),( Tae Oh Kim ),( Sung Yeon 대한소화기학회 2016 Gut and Liver Vol.10 No.2
Background/Aims: The aims of this study were to compare the bowel-cleansing efficacy, patient affinity for the preparation solution, and mucosal injury between a split dose of polyethylene glycol (SD-PEG) and low-volume PEG plus ascorbic acid (LV-PEG+Asc) in outpatient scheduled colonoscopies. Methods: Of the 319 patients, 160 were enrolled for SDPEG, and 159 for LV-PEG+Asc. The bowel-cleansing efficacy was rated according to the Ottawa bowel preparation scale. Patient affinity for the preparation solution was assessed using a questionnaire. All mucosal injuries observed during colonoscopy were biopsied and histopathologically reviewed. Results: There was no significant difference in bowel cleansing between the groups. The LV-PEG+Asc group reported better patient acceptance and preference. There were no significant differences in the incidence or characteristics of the mucosal injuries between the two groups. Conclusions: Compared with SD-PEG, LV-PEG+Asc exhibited equivalent bowel-cleansing efficacy and resulted in improved patient acceptance and preference. There was no significant difference in mucosal injury between SD-PEG and LV-PEG+Asc. Thus, the LV-PEG+Asc preparation could be used more effectively and easily for routine colonoscopies without risking significant mucosal injury. (Gut Liver 2016;10:237-243)
Highly Enhanced Raman Scattering on Carbonized Polymer Films
Yoon, Jong-Chul,Hwang, Jongha,Thiyagarajan, Pradheep,Ruoff, Rodney S.,Jang, Ji-Hyun American Chemical Society 2017 ACS APPLIED MATERIALS & INTERFACES Vol.9 No.25
<P>We have discovered a carbonized polymer film to be a reliable and durable carbon-based substrate for carbon enhanced Raman scattering (CERS). Commercially available SU8 was spin coated and carbonized (c-SU8) to yield a film optimized to have a favorable Fermi level position for efficient charge transfer, which results in a significant Raman scattering enhancement under mild measurement conditions. A highly sensitive CERS (detection limit of 10(-8) M) that was uniform over a large area was achieved on a patterned c-SU8 film and the Raman signal intensity has remained constant for 2 years. This approach works not only for the CMOS-compatible c-SU8 film but for any carbonized film with the correct composition and Fermi level, as demonstrated with carbonized-PVA (poly(vinyl alcohol)) and carbonized-PVP (polyvinylpyrollidone) films. Our study certainly expands the rather narrow range of Raman-active material platforms to include robust carbon-based films readily obtained from polymer precursors. As it uses broadly applicable and cheap polymers, it could offer great advantages in the development of practical devices for chemical/bio analysis and sensors.</P>
( Hyo-joon Yang ),( Chang Woo Cho ),( Jongha Jang ),( Sang Soo Kim ),( Kwang-sung Ahn ),( Soo-kyung Park ),( Dong Il Park ) 대한내과학회 2021 The Korean Journal of Internal Medicine Vol.36 No.4
Background/Aims: We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal neoplasia (ACRN) in asymptomatic adults, based on which colorectal cancer screening could be customized. Methods: We collected data on 26 clinical and laboratory parameters, including age, sex, smoking status, body mass index, complete blood count, blood chemistry, and tumor marker, from 70,336 first-time colonoscopy screening recipients. For reference, we used a logistic regression (LR) model with nine variables manually selected from the 26 variables. A deep neural network (DNN) model was developed using all 26 variables. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the models were compared in a randomly split validation group. Results: In comparison with the LR model (AUC, 0.724; 95% confidence interval [CI], 0.684 to 0.765), the DNN model (AUC, 0.760; 95% CI, 0.724 to 0.795) demonstrated significantly improved performance with respect to the prediction of ACRN (p < 0.001). At a sensitivity of 90%, the specificity significantly increased with the application of the DNN model (41.0%) in comparison with the LR model (26.5%) (p < 0.001), indicating that the colonoscopy workload required to detect the same number of ACRNs could be reduced by 20%. Conclusions: The application of DNN to big clinical data could significantly improve the prediction of ACRNs in comparison with the LR model, potentially realizing further customization by utilizing large quantities and various types of biomedical information.
Kim, Kyungsang,Lee, Taewon,Seong, Younghun,Lee, Jongha,Jang, Kwang Eun,Choi, Jaegu,Choi, Young Wook,Kim, Hak Hee,Shin, Hee Jung,Cha, Joo Hee,Cho, Seungryong,Ye, Jong Chul Published for the American Association of Physicis 2015 Medical physics Vol.42 No.9
<P>In digital breast tomosynthesis (DBT), scatter correction is highly desirable, as it improves image quality at low doses. Because the DBT detector panel is typically stationary during the source rotation, antiscatter grids are not generally compatible with DBT; thus, a software-based scatter correction is required. This work proposes a fully iterative scatter correction method that uses a novel fast Monte Carlo simulation (MCS) with a tissue-composition ratio estimation technique for DBT imaging.</P>