고유량비강캐뉼라를 적용하는 소아 환자에서 비침습적 지표를 이용한 호흡부전 경과 예측에 대한 연구

학위논문(석사) -- 서울대학교대학원 : 의과대학 의학과, 2023. 2. 서동인.연구배경: 고유량비강캐뉼라는 호흡부전 소아에서 유용한 호흡 보조 장치이지만 기관 삽관과 같은 침습적 기도 확보의 시점을 늦출 수 있다고 알려져 있다. 최근에는 고유량비강캐뉼라의 실패를 조기에 예측하기 위해 경피적 산소 포화도, 산소분압과 호흡수를 이용한 여러 지표가 제시되어 왔다. 본 연구는 고유량비강캐뉼라를 적용하는 환자에서 호흡부전을 예측하기 위한 다양한 비침습적 지표를 평가하고자 하였다. 연구방법: 고유량비강캐뉼라에서 추가 호흡 보조를 필요로 했던 환자들은 기관삽관의 원인에 따라 hypoxic respiratory failure (HRF)와 non-HRF (NHRF)로 분류되었다. 경피적 산소포화도를 산소분율로 나눈 비(S/F), S/F를 RR로 나눈 비 (ROX), S/F를 호흡수의 중간값 대비 환자의 호흡수로 나눈 비 (ROX-M), S/F를 환자 호흡수의 z score로 나눈 비를 계산하여 지표로서의 가치를 비교하였다. 고유량비강캐뉼라는 제거한 군, HRF, NHRF 군 사이에 나타난 각 지표의 차이를 비교하였다. 연구결과: 152명의 증례를 수집하였는데 이중 45명(29.6%0)는 고유량비강캐뉼라를 제거하지 못하였다. 이 중 21명(46.7%)는 HRF에 속하였고, 24명(53.3%)는 NHRF였다. 3시간과 6시간에 측정된 S/F와 ROX-M 값은 높은 AUC 값을 보여 각각 HRF를 예측하는 좋은 지표로 제시되었다. 반면 초기의 고탄산혈증과 저체중이 각각 NHRF의 위험 요소로 제시되었다. 결론: 고유량비강캐뉼라를 적용하는 소아에서 기관삽관을 조기에 결정하는데 있어 65mmHg 이상의 고탄산혈증 유무와 저체중 여부, 그리고 S/F, ROX-M 등을 모니터하는 것은 유용한 예측 지표로 사용될 수 있다.Background: High-flow nasal cannula (HFNC) is a useful respiratory support for children with respiratory distress; however, it elevates the risk of belated intubation. Recently, indices based on percutaneous oxygen saturation (SpO2),a fraction of inspired oxygen (FiO2), and respiratory rate (RR) have been suggested for predicting HFNC failure. We aimed to evaluate various indices predicting HFNC failure in children who started receiving HFNC at this tertiary center for 27months. Methods: Cases of HFNC failure were classified as hypoxic respiratory failure (HRF) or non-HRF (NHRF) according to the cause of intubation. Ratio of SpO2 by FiO2 (S/F), ratio of S/F by RR (ROX), ratio of S/F by RR/median RR (ROX-M), and ratio of S/F by z-score of RR (ROX-Z) were calculated and compared between groups. Results: Of the 152 cases, 45 (29.6%) failed to wean off the HFNC support, of which 21 (46.7%) were HRFs and 24 (53.3%) were NHRFs. S/F and ROX-M at 6 and 3 hours, respectively, showed good predictability for predicting HRF with high area under the curve. Whereas initial hypercapnia and low weight were good predictors for NHRF. Conclusions: For the management of children with HFNC, these risk factors and indicators should be monitored to make an early decision of intubation.Chapter 1. Introduction 1 Chapter 2. Methods 3 Chapter 3. Results 8 Chapter 4. Discussion 28 Chapter 5. Conclusions 33 Bibliography 34 Abstract in Korean 39석

간 조영술을 위한 혈관 모델 기반의 국부 적응 2D-3D 정합 알고리즘 기법 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 신영길.Two-dimensional–three-dimensional (2D–3D) registration between intra-operative 2D digital subtraction angiography (DSA) and pre-operative 3D computed tomography angiography (CTA) can be used for roadmapping purposes. However, through the projection of 3D vessels, incorrect intersections and overlaps between vessels are produced because of the complex vascular structure, which make it difficult to obtain the correct solution of 2D–3D registration. To overcome these problems, we propose a registration method that selects a suitable part of a 3D vascular structure for a given DSA image and finds the optimized solution to the partial 3D structure. The proposed algorithm can reduce the registration errors because it restricts the range of the 3D vascular structure for the registration by using only the relevant 3D vessels with the given DSA. To search for the appropriate 3D partial structure, we first construct a tree model of the 3D vascular structure and divide it into several subtrees in accordance with the connectivity. Then, the best matched subtree with the given DSA image is selected using the results from the coarse registration between each subtree and the vessels in the DSA image. Finally, a fine registration is conducted to minimize the difference between the selected subtree and the vessels of the DSA image. In experimental results obtained using 10 clinical datasets, the average distance errors in the case of the proposed method were 2.34 ± 1.94 mm. The proposed algorithm converges faster and produces more correct results than the conventional method in evaluations on patient datasets.Chapter 1 Introduction 1 1.1 Background 1 1.2 Problem statement 6 1.3 Main contributions 8 1.4 Contents organization 10 Chapter 2 Related Works 12 2.1 Overview 12 2.1.1 Definitions 14 2.1.2 Intensity-based and feature-based registration 17 2.2 Neurovascular applications 19 2.3 Liver applications 22 2.4 Cardiac applications 27 2.4.1 Rigid registration 27 2.4.2 Non-rigid registration 31 Chapter 3 3D Vascular Structure Model 33 3.1 Vessel segmentation 34 3.1.1 Overview 34 3.1.2 Vesselness filter 36 3.1.3 Vessel segmentation 39 3.2 Skeleton extraction 40 3.2.1 Overview 40 3.2.2 Skeleton extraction based on fast marching method 41 3.3 Graph construction 45 3.4 Generation of subtree structures from 3D tree model 46 Chapter 4 Locally Adaptive Registration 52 4.1 2D centerline extraction 53 4.1.1 Extraction from a single DSA image 54 4.1.2 Extraction from angiographic image sequence 55 4.2 Coarse registration for the detection of the best matched subtree 58 4.3 Fine registration with selected 3D subtree 61 Chapter 5 Experimental Results 63 5.1 Materials 63 5.2 Phantom study 65 5.3 Performance evaluation 69 5.3.1 Evaluation for a single DSA image 69 5.3.2 Evaluation for angiographic image sequence 75 5.4 Comparison with other methods 77 5.5 Parameter study 87 Chapter 6 Conclusion 90 Bibliography 92 초록 109Docto

A study on Machine Learning for Prediction of the Shipbuilding Lead Time

In recent years, big data technology, which is one of the biggest issues in IT field, has been applied in various fields as data has increased exponentially compared to the past, however, in the shipbuilding and offshore industries, the use of big data related technology is relatively rare compared to other manufacturing industries such as automobile and electronics industries. But, shipbuilding and offshore industry is one-piece manufacturing industry, and statistics-based analysis such as the Big Data methodology can be very effective because vast amounts of data are generated throughout the entire life cycle and are highly variable in the manufacturing environment. As a result, the big data-based machine learning research is progressing slowly in the shipbuilding industry. However, this is limited to the design field that manages the fixed variables and it is difficult to apply it in terms of production management such as lead time which is the basis of construction activity. In particular, the standard data such as production lead time is highly variable due to various process variables so, it is necessary to study changing from causation viewpoint to correlation to solve it. Therefore, in this paper, I has constructed a prediction model applying machine learning and deep learning algorithm to improve the standard data for the time factor of production lead time. In order to predict the variable lead time considering the various properties of the product in comparison with the standard lead time, I collect data from several shipyards and apply various machine learning and deep learning algorithms to predict the production lead time according to the process. Respectively. To analyze the data, open source such as R and Python language was used and a lead time prediction model based on the algorithm was created. Various evaluation indices were used to evaluate the prediction model generated by the analysis algorithm. In addition, I compared the results of machine learning and deep learning algorithms with those of previous studies, and the decision support for the establishment of standard information according to various process variables is made possible. | 최근 IT 분야에서 가장 큰 화두인 빅데이터 기술은 과거에 비해 데이터가 기하급수적으로 증가함에 따라 다양한 분야에서 적용되고 있지만 자동차, 전자 업종 등의 다른 제조업에 비해 조선 및 해양산업에서는 빅데이터 관련 기술의 활용사례가 상대적으로 드문 실정이다. 하지만 조선 및 해양산업은 일품 제조 산업으로 영업, 설계, 건조, 유지 보수 등 전체 수명 주기에서 방대한 데이터가 생성되고, 제조 환경에 따라 변동성이 크기 때문에 빅데이터 방법론과 같은 통계 기반 분석이 큰 효과를 발휘할 수 있다. 이로 인해 빅데이터 기반의 기계학습 연구는 조선업에서도 서서히 진행되고 있으나 이는 고정변수를 관리하는 설계 분야에 한정되어 있으며 건조 활동의 근간이 되는 기준 정보, 즉 원단위나 시수, 리드타임 등의 생산관리 관점에서는 적용에 어려움을 겪고 있다. 특히 생산 리드타임이라는 기준정보는 다양한 공정변수로 인한 변동성이 크기 때문에 이를 해결하기 위해서 현실적으로 한계가 있는 영역에 대해 Causation에서 Correlation 관점에 따른 연구가 필요하다고 본다. 따라서 본 논문에서는 생산 리드타임이라는 시간요소에 대한 기준정보 체계 개선을 위해 기계학습 및 딥러닝 알고리즘을 적용한 예측모델을 구축하였다. 기존에 관리되는 조선소의 표준 리드타임에 대비하여 제품의 다양한 속성을 고려한 변동 리드타임을 예측하기 위해 여러 조선소의 데이터를 수집하였고 공정에 따른 생산 리드타임을 예측하기 위한 다양한 기계학습 및 딥러닝 알고리즘을 적용하였다. 데이터를 분석하기 위해서 R과 Python 언어 등의 오픈소스를 활용하였으며 알고리즘에 따른 리드타임 예측모델을 생성하였다. 분석 알고리즘에 따라 생성된 예측모델의 평가를 위해 여러 가지 평가지표를 활용하였다. 또한, 기존연구 결과에 비해 기계학습과 딥러닝 알고리즘에 따른 유의미한 결과를 비교하여 조선소에서의 활용성을 검토해보고 다양한 공정변수에 따른 기준정보 수립에 대한 의사결정 지원을 가능하도록 하였다.1. 서 론 1 1.1 연구 배경 1 1.1.1 조선소의 생산관리 3 1.1.2 빅데이터 방법론 4 1.2 관련 연구 동향 7 1.2.1 제조업 빅데이터 연구 사례 7 1.2.2 조선업 빅데이터 연구 사례 8 2. 분석 알고리즘 및 적용방안 10 2.1 기계학습 알고리즘 10 2.1.1 회귀분석 13 2.1.2 인공신경망 13 2.1.3 의사결정나무 14 2.2 딥러닝 알고리즘 16 2.2.1 Multi-Layer Perceptron (MLP) 18 2.2.2 Recurrent Neural Network (RNN) 19 2.2.3 딥러닝 라이브러리 20 2.3 데이터 분석과정 23 2.3.1 데이터 수집 25 2.3.2 데이터 처리 25 2.3.3 모델 구축 30 2.3.4 데이터 평가 30 3. 예측모델 구축을 위한 조선소 데이터 분석 32 3.1 블록 절단공정 중일정 계획 데이터 분석 34 3.2 블록 탑재공정 실적 데이터 분석 39 3.3 해양플랜트 배관재 공급망 데이터 분석 47 4. 예측모델 결과 분석 56 4.1 기계학습 모델링 결과분석 56 4.2 딥러닝 모델링 결과분석 62 4.3 알고리즘에 따른 결과분석 68 5. 결론 73 5.1 연구 결론 73 5.2 향후 과제 74 참고문헌 75 부록 A 77Maste

고용량 pitavastatin이 경동맥 탄성에 주는 영향을 반점 추적 영상을 이용하여 분석한 연구

학위논문 (석사)-- 서울대학교 대학원 : 임상의과학과, 2017. 2. 김용진.Background: Two-dimensional speckle-tracking strain imaging has been introduced for the precise assessment of arterial mechanics. The objective of this study was to evaluate the short-term effects of pitavastatin on carotid artery elasticity measured by speckle tracking methods. Methods: This study included 30 statin-naïve patients (age, 61.6 ± 7.6 years26.7% male) with hypercholesterolemia. Circumferential carotid artery strain (CAS) was measured using speckle-tracking imaging before and after 3 months of high-dose pitavastatin treatment (4 mg daily). Results: After 3 months, circumferential CAS was significantly increased compared to baseline (from 2.73 ± 1.17% to 3.27 ± 1.53%, p = 0.029). Among conventional carotid elasticity metrics, strain measured by B-mode improved significantly after statin therapy. No significant change in carotid intima-media thickness was observed after pitavastatin treatment (from 0.73 ± 0.18 to 0.71 ± 0.16 mm, p = 0.913). Conclusions: Short-term treatment with high-dose pitavastatin improved carotid artery elasticity measured by speckle-tracking methods. These speckle-tracking imaging-based measurements may allow the early noninvasive assessment of favorable effects of medical intervention in patients with hypercholesterolemia.Introduction 1 Methods 3 Results 7 Discussion 15 Conclusion 19 References 20 국문초록 24Maste

"일상의 기적들": 버지니아 울프의 『등대로』에 드러난 순간의 미학

학위논문 (석사)-- 서울대학교 대학원 : 영어영문학과, 2016. 2. 손영주.본 논문은 버지니아 울프가 그녀의 여러 에세이 속에서 진실한 삶은 무엇인가에 대해 끊임없이 고민한 작가라는 점에 착안하여 울프가 『등대로』에서 그려낸 진실한 삶은 무엇인지 탐구한다. 구체적으로 본 논문은 『등대로』에서 올바른 인식의 방식이 곧 온전한 삶을 사는 것으로 이어진다는 것을 밝힌다. 곧 『등대로』에서 존재론과 인식론은 불가분의 관계에 있다는 것을 밝히는 것이다. 1장에서는 인물들의 자유로운 인식을 가로막는 가부장제의 규범들에 대해 살펴본다. 가부장제의 규범들은 개인에게 역할과 임무를 부여함으로써 개인을 정의한다. 가부장제가 부과한 인식의 틀 속에서 개인의 자기 자신과 남, 그리고 세상 전반에 대한 이해는 제한되고 왜곡된다. 성역할을 비롯한 다양한 역할들로 인해 그 역할에 맞도록 자신의 내적 자아를 억압하거나, 타인과의 관계를 유연하지 못한 방식으로 맺는 것이 문제가 된다. 1장에서는 가부장제가 부여하는 단선적인 시선이 온전한 삶을 방해하는 요소로서 다루어진다. 2장에서는 『등대로』에서 현실이 어떻게 구현되고 있는 지를 다루면서 개인이 현실을 인식하는 태도가 삶에서의 경험과 밀접한 관계가 있음을 밝힌다. 유동적이어서 쉽게 이해하거나 환원하기가 불가능한 현실 앞에서 인물은 불안감을 느끼고 그 결과 현실의 유동성을 인정하지 않는 억압적인 세계관으로 불안감을 해소하려 하거나 반대로 현실 앞에서의 실망과 좌절, 분노 등으로 인해 허무주의나 비관주의에 빠지는 모습을 보여준다. 2장에서는 이러한 현실과 개인 간의 갈등 양상을 다루며 현실을 억압하거나 현실에 위축되는 태도가 모두 자유롭고 풍요로운 경험을 제한한다는 측면에서 문제가 됨을 밝힌다. 3장에서는 『등대로』가 현실을 억압하지도, 현실에 억눌리지도 않는 절묘한 균형의 지점을 찾고 있으며, 그 균형은 순간을 사는 것을 통해서 도달될 수 있다는 것을 밝힌다. 『등대로』에서는 순간을 사는 것을 통해 인식 상의 문제에 근거한 삶의 제한을 뚫고 생생한 경험을 할 수 있음이 드러난다. 현실과 개인 간의 갈등은 상당부분 현실의 유동성을 인정하지 않는 단선적인 시각에 기인하기 때문에, 일상 속의 순간을 살며 현실의 유동성을 섬세하게 인식함으로써 개인은 보다 자유롭고 풍요로운 경험을 할 수 있게 된다.1. 서론 1 본론1. 가부장제와 인식의 왜곡 26 본론2. 현실 인식과 삶에 대한 태도 45 본론3. 위대한 계시에서 일상의 기적들로 65 5. 결론 82 인용문헌 84 Abstract 89Maste

Effects of Monetary Policy Shocks on Farm Prices and Exchange Rate

학위논문 (박사)-- 서울대학교 대학원 : 농경제사회학부, 2017. 2. 노재선.The farm financial crisis following the monetary regime change in the early 1980s of the U.S. was an important historical episode that made many economists reevaluate the effect of macroeconomic events such as monetary policy shocks on agricultural markets. From the experience of the farm financial crisis, many economists realized that farm prices are affected not only by shocks in agricultural markets but also by shocks in monetary policy and macroeconomic condition. Since then, many researchers investigated the effects of monetary shocks on farm prices. In contrast with the traditional view based on neutrality of money and flexible prices, the overshooting theory is developed, which suggests that the short-run responses of farm prices overshoot the long run level in the presence of a sticky non-farm price under monetary policy shocks and thus the real farm or relative prices may change in the short-run in the presence of monetary policy shocks. Many empirical studies on the effects of monetary policy shocks on farm prices, especially in the U.S., were conducted but the empirical evidence was mixed. In addition, the studies on emerging/developing countries such as Korea are very rare. To document further evidence and help to resolve the controversy in the literature, this study empirically analyzes the effects of monetary policy shocks on farm prices in the U.S and South Korea (for the post Asian crisis period in which many regulations in agricultural markets are deregulated.) by applying a recently developed empirical method in the VAR framework. This study identifies monetary policy shocks by imposing sign restrictions on impulse responses. Further, this study analyzes the effects on exchange rate and farm prices together, differently from past studies that focus on either exchange rate or farm prices. The empirical model for the U.S. follows the detailed specifications of Uhlig (2005) that also analyzes the effects of monetary policy shocks in the U.S. In addition to farm prices and the exchange rate, the key macro variables such as product, non-borrowed reserves, the Federal Funds rate, the price level, and a price variable that is likely to reflect the expectation on the price level are included in the model. To identify negative interest shocks, we use the following sign restrictions on impulse responses. The Federal Funds rate increases, the price level and the price variable decrease, and non-borrowed reserves decrease. The predictions of most theories on the effects of monetary policy shocks are consistent with these effects. The empirical model for Korea incorporates small open economy features, as suggested by Kim and Lim (2015). The key macro variables such as production, the price level, the short-term interest rate, and monetary base are included in addition the exchange rate and to farm prices. Furthermore, the U.S. Federal Funds rate, the U.S. output, the U.S. price level, and VIX are included as exogenous variables in the empirical model to reflect the small open economy feature in which US macroeconomic condition and international financial market condition are important factors. The following sign restrictions, that are similar to those used in the U.S. model, are imposed. The short-term rate increases, the price level and monetary base decrease. The main empirical results for the U.S. can be described as follows. First, contractionary monetary policy shocks have significant negative effects on real farm prices, which suggest that farm prices respond to monetary policy shocks more than the general price level. This is against the traditional view based on the neutrality of money assumption. Second, the effects of monetary policy shocks on farm prices are stronger than the effects of monetary policy shocks on exchange rate. The former is as large as or greater than the latter even in the floating exchange rate regime period. The result is interesting since exchange rate is often thought of as a variable that is substantially affected by monetary policy shocks. Third, farm price dynamics under monetary policy shocks show delayed overshooting as exchange rate dynamics under monetary policy shocks do. These results imply that the equilibrium condition between the interest rate and the expected return on holding farm products and the uncovered interest parity (UIP) condition do not hold conditional on monetary policy shocks. The main empirical results for Korea are as follows. First, (contractionary) monetary policy shocks have significant negative effects on real farm prices, which suggest that farm prices respond to monetary policy shocks more than the general price level. This is against the traditional view based on the neutrality of money. The effect on real farm prices in Korea is less persistent than in the U.S. This result may be explained by farm price stabilization policy and strong regulation in farm prices in Korea. Second, the effects of monetary policy shocks on farm prices are far stronger than the effects of monetary shocks on exchange rate. This tendency is even more clear in Korea than in the United States. This result may be explained by the fact that exchange rate is less flexible in Korea than in the United States. Farm price responses are short-lived and not inconsistent with the overshooting theory. The results in this study suggest that macroeconomic shocks such as monetary policy shocks can affect farm prices significantly and generate volatility in farm prices, not only in the U.S. but also in Korea. Although micro factors mostly explain farm price dynamics, considering macro factors can also helpful in understanding farm price dynamics and implementing farm price stabilization policies. However, the current study has a limitation in that it does not explicitly consider various important factors of farm price determination, such as weather and micro factors.Chapter 1: Introduction 1 Chapter 2: Methodology 7 2.1. VAR Models 7 2.1.1 VAR Models on Monetary Policy 7 2.1.2 SVAR with Sign Restriction 10 2.2. Model Specification and Data: U.S. 13 2.3. Model Specificaion and Data: Korea 20 Chapter 3: Empirical Results 27 3.1. U.S 27 3.1.1 Baseline Model 27 3.1.2 Extended Experiments 35 3.2. Korea 37 3.2.1. Baseline Model 37 3.2.2. Extended Experiments 45 Chapter 4: Conclusion 46 References 90 국문초록 98Docto