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촛불집회와 태극기집회 사이에서 - 기독교인의 반정부적 정치참여에 대한 고찰
정원호(Jung, Wonho) 한국기독교사회윤리학회 2020 기독교사회윤리 Vol.47 No.-
로마서 13장 1-7절의 본문과 정교분리의 원칙은 정치에 대한 기독교인의 무관심이나 수동적 태도를 규범화하거나 정당화할 근거로 사용될 수 없다. 그것은 또한 특정한 종교적 교리를 떠나 모든 사람들에게 적용될 수 있는 도덕과 정의의 규범에 관련된 정치적 문제에 교회가 종교적 민감성과 권위를 가지고 개입하는 것을 금지하지 않는다. 이러한 주장을 뒷받침하기 위해 본 글은 로마서 13장 1-7절의 본문에 관련된 해석들을 살펴보고 그 구절을 오늘날의 정치 현실에 어떻게 적용하는 것이 가장 본문과 상황에 충실한 해석인지를 논할 것이다. 나아가 정교분리 원칙에 관한 미국 연방대법원의 몇 가지 중요한 판례들을 살펴봄으로써 기독교적 정치참여가 정교분리의 원칙과 조화를 이루고 정당성을 갖기 위해서는 엄밀한 의미의 정치적인 문제나 순수하게 종교적인 문제가 아닌 보편적 도덕과 정의의 문제에 근거한 것이어야 한다는 사실을 보여 줄 것이다. The biblical passage from Romans 13:1-7 and the principle of the Separation of Church and State cannot be legitimately invoked to justify Christian political indifference and passivity or to make them normative. Nor do the passage and the principle prohibit Christian political engagement with religious sensibilities and authority in the matters of moral and justice issues that are not specifically religious in nature but applicable to everyone. To support this argument the Romans passage is discussed for its interpretation that does justice to the biblical text and fits best for our political context. And then several important U.S. Supreme Court cases involving the Separation of Church and State are discussed to show that Christian political involvement should not be prompted by matters that are strictly political or purely religious but it should be made for the matters of universal moral values like justice.
Wonho Jung(정원호),Sungjin Cheong(정성진),Jae Woong Bae(배재웅),Yong-Hwa Park(박용화) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11
It is important to recognize the drowning person as soon as possible in maritime accidents. In real maritime accidents, it is difficult to identify the drowning person because of their small size compared to the marine environment. To solve this problem, this paper presents a methodology to detect small target using commercial games with 3D graphical engines. Proposed methodology combines as following four steps: (1) divide high-resolution original image into several small patches, (2) image processing using CLAHE and Canny edge detection, (3) detecting small targets using convolutional neural networks (4) restore patches into original image. To detect small target in the high-resolution original image, small patches and image processing techniques are considered to raise the signal-to-noise ratio of the small target. The small patches are uses as test data of convolutional neural networks (CNN), the softmax values of each patch are displayed on the reconstructed image. To enhance the accuracy of CNN, virtual image data acquired from the commercial game using the 3D graphical engine are used as training data. In order to verify the performance of the proposed methodology, a case study of real maritime accident situation was conducted. The performance of the proposed methodology outperforms original deep convolutional neural networks.
구조물 건전성 진단에서 데이터 부족 문제 극복을 위한 심층 생성 모델의 활용
정원호(Wonho Jung),정대현(Daehyeon Jeong),김영호(Youngho Kim),김창현(Changhyeon Kim),이후상(Hoosang Lee),유홍제(Hongje Yu),류제하(Jeha Ryu),오현석(Hyunseok Oh) 대한기계학회 2019 大韓機械學會論文集A Vol.43 No.3
딥러닝 알고리즘 훈련을 위해서 충분한 양의 데이터 확보가 필수적이다. 그러나, 공학시스템에서 데이터 취득은 매우 어렵거나, 상황에 따라 불가능한 경우가 존재한다. 이러한 데이터 부족 문제는 딥러닝 알고리즘 개발에 큰 걸림돌이 되고 있다. 본 논문은 구조물 건전성 진단을 위한 딥러닝 알고리즘 개발에서 발생하는 데이터 부족 문제 해결을 시도하였다. 깊은 생성 모델을 구축하고 딥러닝 학습을 위한 훈련 데이터를 생성하는 방법을 제안한다. 제안된 방법의 성능을 검증하기 위해 수상 양식장 어망 데이터를 바탕으로 사례 연구를 진행하였다. 본 연구는 제안된 심층 생성 모델을 통해 데이터를 직접 만들어 냄으로써 구조물 건전성 진단에서 발생되는 데이터 부족 문제 해결에 기여할 것으로 기대된다. A sufficient amount of data are required for training deep learning algorithms. However, in engineered systems, data acquisition is difficult or sometimes not feasible. A dearth of data is one of the major challenges for the development of deep learning algorithms. This paper proposes a deep generative model to generate pseudo data that emulate real data. To verify the performance of the proposed model, a case study is conducted using aquaculture fishnet image data. We demonstrate that the insufficient data problem in structural health monitoring can be relieved by generating data through the proposed deep generative model. The reliability of engineered systems can be improved by incorporating the deep learning algorithms developed with real data as well as generated data.
MiroCam® 캡슐내시경 검사의 완전 소장 검사 및 양성 진단에 영향을 미치는 요인
정원호 ( Wonho Jung ),고진성 ( Jin Sung Koh ),김성호 ( Sung Ho Kim ),임상아 ( Sang Ah Lim ),임은혜 ( Eun Hye Lim ),이준영 ( Joon Young Lee ),주문경 ( Moon Kyung Joo ),이범재 ( Beom Jae Lee ),김지훈 ( Ji Hoon Kim ),연종은 ( Jong Eu 대한장연구학회 2011 Intestinal Research Vol.9 No.1
Background/Aims: Mirocam® capsule endoscopy has been widely used in Korea; however, data with respect to Mirocam® capsule endoscopy is lacking. We have assessed the factors affecting complete small bowel studies and diagnostic yield in Mirocam® capsule endoscopic studies. Methods: We retrospectively analyzed 103 cases that were assessed with Mirocam® capsule endoscopy between June 2007 and February 2010 at Guro Korea University Hospital. Results: The mean age of the 103 cases was 55.47 years (range, 16-99 years) and 67 cases (65%) were male. The indications for capsule endoscopy were hematochezia/melena (77 cases, 74.8%), anemia (8 cases, 7.8%), abdominal pain (12 cases, 11.7%), and miscellaneous (weight loss and chronic diarrhea; 6 cases, 5.8%). The mean stomach transit time was 59.9±88.3 minutes (range, 1-630 minutes) and the mean small bowel transit time was 396.0±131.7 minutes (range, 117-708 minutes). The rate of successfully performing a complete small bowel study was 82.5% (85 cases), and the stomach transit time was a significant factor for a complete small bowel study (OR=0.991, 95% CI= 0.984-0.998, P=0.012). The diagnostic yield was 51.5% (53 cases); visual quality was a significant factor in determining the diagnostic yield (OR=6.776, 95% CI=1.32-34.70, P=0.022). Conclusions: In a Mirocam® capsule endoscopic study, short stomach transit time was a significant factor affecting completion of the small bowel study. Achieving excellent visual quality by good bowel preparation was a significant factor for improving the diagnostic yield. (Intest Res 2011;9:0-34)