이미지의 의미적 이해를 위한 시각적 관계의 이용

학위논문(박사)--서울대학교 대학원 :융합과학기술대학원 융합과학부(디지털정보융합전공),2019. 8. 곽노준.이미지를 이해하는 것은 컴퓨터 비전 분야에서 가장 근본적인 목적 중 하나이다. 이러한 이해는 다양한 산업 분야의 문제를 해결 할 수 있는 혁신이 될 수 있다. 최근 딥러닝의 발전과 함께, 이미지에서 객관적인 요소를 인식하는 기술은 매우 발전되어 왔다. 그러나 시각 정보를 제대로 이해하기 위해서는 사람처럼 맥락 정보를 이해하는 것이 중요하다. 인간은 주로 직접적인 시각정보와 함께 맥락을 이해하여 의미 있는 지식 정보로 활용한다. 본 논문에서는 객체간의 의미적 관계정보를 구축하과 활용하는 방법론을 제시하여 보다 나은 이미지의 이해 방법을 연구하였다. 첫 번째로, 다이어그램에서 관계 지식을 표현하는 관계 그래프를 생성하는 알고리즘을 제안하였다. 다이어그램이 가진 정보를 축약하는 능력이 다른 형태의 지식 저장 방법에 비해 뛰어나지만, 그에 따라 해석하기에는 다양한 요소와 유연한 레이아웃 때문에 풀기 어려운 문제였다. 우리는 다이어그램에서 객체를 찾고 그것들의 관계를 찾는 통합 네트워크를 제안한다. 그리고 이러한 능동적인 그래프 생성을 위한 특수 모듈은 DGGN을 제안한다. 이 모듈의 성능을 나타내기 위해 모듈안의 활성화 게이트의 정보 역학을 비주얼라이즈 하여 분석하였다. 또한 공개된 다이어그램 데이터셋에서 기존의 알고리즘을 뛰어넘는 성능을 증명하였다.마지막으로 질의 응답 데이터셋을 이용한 실험으로 향후 다양한 응용 가능성도 증명하였다. 두 번째로, 우리는 현존하는 질의 응답 데이터셋 중 가장 복잡한 형태를 가진 교과서에서 질의응답 (TQA) 문제를 풀기위한 솔루션을 제안하였다. TQA 데이터셋은 질문 파트와 본문 파트 모두에 이미지와 텍스트 형태를 가진 데이터를 포함하고 있다. 이러한 복잡한 구조를 해결하기 위해 우리는 f-GCN이라는 다중 모달 그래프를 처리할 수 있는 모듈을 제안하였다. 이 모듈을 통해 보다 효율적으로 다중 모달을 그래프 형태로 처리하여 활용하기 쉬운 피쳐로 바꿔줄 수 있다. 그 다음으로 교과서의 경우 다양한 주제가 포함되어 있고 그에 따라 용어나 내용이 겹치지 않고 기술되어 있다. 그로인해 완전 새로운 내용의 문제를 풀어야하는 out-of-domain 이슈가 있다. 이를 해결하기위해 정답을 보지 않고 본문만으로 자가 학습을 하는 알고리즘을 제안하였다. 이 두 알고리즘을 통해 기존 연구보다 훨씬 좋은 성능을 보이는 실험 결과를 제시하였고 각각의 모듈의 기능성에 대해 검증하였다. 마지막으로, 인간과 물건의 관계정보를 활용하여 객체 검출을 약지도 학습으로 배우는 프레임워크를 제안하였다. 객체 검출 문제를 풀기위해 노동력이 많이 필요한 데이터 라벨링 작업이 필요하다. 그 중 가장 노력이 많이 필요한 위치 라벨링인데, 새로운 방법론은 인간과 물건의 관계를 이용하여 이부분을 해결하였다. 우리는 RRPN이란 모듈을 제안하여 인간의 포즈정보와 관계에 관한 동사를 이용하여 처음보는 물건의 위치를 추정할 수 있다. 이를 통해 새롭게 배우는 목표 라벨에 대해, 정답 라벨 없이 위치를 추정하여 학습할 수 있어 훨씬 적은 노력만 사용해도 된다. 또한 RRPN은 추가 방식의 구조로 다양한 태스크에 관한 네트워크에 추가 할 수 있다. HICO-DET 데이터셋을 사용하여 실험한 결과 현재의 지도학습을 대신할 가능성을 보여주었다. 또한 우리 모델이 처음 본 물건의 위치를 잘 추정하고 있음을 시각화를 통해 보여주었다.Understanding an image is one of the fundamental goals of computer vision and can provide important breakthroughs for various industries. In particular, the ability to recognize objective instances such as objects and poses has been developed due to recent deep learning approaches. However, deeply comprehending a visual scene requires higher understanding, such as is found in human beings. Humans usually exploit contextual information from visual inputs to detect meaningful features. In this dissertation, visual relation in various contexts, from the construction phase to the application phase, is studied with three tasks. We first propose a new algorithm for constructing relation graphs that contains relational knowledge in diagrams . Although diagrams contain richer information compared to individual image-based or language-based data, proper solutions for automatically understanding diagrams have not been proposed due to their innate multimodality and the arbitrariness of their layouts. To address this problem, we propose a unified diagram-parsing network for generating knowledge from diagrams based on an object detector and a recurrent neural network designed for a graphical structure. Specifically, we propose a dynamic graph-generation network that is based on dynamic memory and graph theory. We explore the dynamics of information in a diagram with the activation of gates in gated recurrent unit (GRU) cells. Using publicly available diagram datasets, our model demonstrates a state-of-the-art result that outperforms other baselines. Moreover, further experiments on question answering demonstrate the potential of the proposed method for use in various applications. Next, we introduce a novel algorithm to solve the Textbook Question Answering (TQA) task; this task describes more realistic QA (Question Answering) problems compared to other recent tasks. We mainly focus on two issues related to the analysis of the TQA dataset. First, solving the TQA problems requires an understanding of multimodal contexts in complicated input data. To overcome this issue of extracting knowledge features from long text lessons and merging them with visual features, we establish a context graph from texts and images and propose a new module f-GCN based on graph convolutional networks (GCN). Second, in the TQA dataset , scientific terms are not spread over the chapters and subjects are split. To overcome this so-called ``out-of-domain issue, before learning QA problems we introduce a novel, self-supervised, open-set learning process without any annotations. The experimental results indicate that our model significantly outperforms prior state-of-the-art methods. Moreover, ablation studies confirm that both methods (incorporating f-GCN to extract knowledge from multimodal contexts and our newly proposed, self-supervised learning process) are effective for TQA problems. Third, we introduce a novel, weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that do not have many examples. We use transferable knowledge from human-object interactions (HOI). While WSOD has lower performance than full supervision, we mainly focus on HOI that can strongly supervise complex semantics in images. Therefore, we propose a novel module called the ``relational region proposal network (RRPN) that outputs an object-localizing attention map with only human poses and action verbs. In the source domain, we fully train an object detector and the RRPN with full supervision of HOI. With transferred knowledge about the localization map from the trained RRPN, a new object detector can learn unseen objects with weak verbal supervisions of HOI without bounding box annotations in the target domain. Because the RRPN is designed as an add-on type, we can apply it not only to object detection but also to other domains such as semantic segmentation. The experimental results using a HICO-DET dataset suggest the possibility that the proposed method can be a cheap alternative for the current supervised object detection paradigm. Moreover, qualitative results demonstrate that our model can properly localize unseen objects in HICO-DET and V-COCO datasets.1. Introduction 1 1.1 Problem Definition 4 1.2 Motivation 6 1.3 Challenges 7 1.4 Contributions 9 1.4.1 Generating Visual Relation Graphs from Diagrams 9 1.4.2 Application of the Relation Graph in Textbook Question Answering 10 1.4.3 Weakly Supervised Object Detection with Human-object Interaction 11 1.5 Outline 11 2. Background 13 2.1 Visual relationships 13 2.2 Neural networks on a graph 16 2.3 Human-object interaction 17 3. Generating Visual Relation Graphs from Diagrams 18 3.1 Related Work 20 3.2 Proposed Method 21 3.2.1 Detecting Constituents in a Diagram 21 3.2.2 Generating a Graph of relationships 22 3.2.3 Multi-task Training and Cascaded Inference 27 3.2.4 Details of Post-processing 29 3.3 Experiment 29 3.3.1 Datasets 29 3.3.2 Baseline 32 3.3.3 Metrics 32 3.3.4 Implementation Details 33 3.3.5 Quantitative Results 35 3.3.6 Qualitative Results 37 3.4 Discussion 38 3.5 Conclusion 41 4. Application of the Relation Graph in Textbook Question Answering 46 4.1 Related Work 48 4.2 Problem 50 4.3 Proposed Method 53 4.3.1 Multi-modal Context Graph Understanding 53 4.3.2 Multi-modal Problem Solving 55 4.3.3 Self-supervised open-set comprehension 57 4.3.4 Process of Building Textual Context Graph 61 4.4 Experiment 62 4.4.1 Implementation Details 62 4.4.2 Dataset 62 4.4.3 Baselines 63 4.4.4 Quantitative Results 64 4.4.5 Qualitative Results 67 4.5 Conclusion 70 5. Weakly Supervised Object Detection with Human-object Interaction 77 5.1 Related Work 80 5.2 Algorithm Overview 81 5.3 Proposed Method 84 5.3.1 Training on the Source classes Ds 86 5.3.2 Training on the Target classes Dt 89 5.4 Experiment 90 5.4.1 Implementation details 90 5.4.2 Dataset and Pre-processing 91 5.4.3 Metrics 91 5.4.4 Comparison with different feature combination 92 5.4.5 Comparison with different attention loss balance and box threshold 95 5.4.6 Comparison with prior works 96 5.4.7 Qualitative results 96 5.5 Conclusion 100 6. Concluding Remarks 105 6.1 Summary 105 6.2 Limitation and Future Directions 106Docto

The Presence of Anti-ribonucleoprotein at Diagnosis Is Associated with the Flare during the First Follow-up Year in Korean Patients with Systemic Lupus Erythematosus

Objective : The aim of this study was to examine whether the presence of anti-ribonucleoprotein (anti-RNP) antibodies at diagnosis is associated with systemic lupus erythematosus (SLE) flares in newly diagnosed patients during the first year of follow-up. Methods : The medical records of 71 newly diagnosed SLE patients without other concomitant autoimmune disease, serious infection, or malignancy were reviewed retrospectively. SLE flares were defined according to the SLE Disease Activity Index 2000. Patients were divided into 2 groups according to the presence or absence of anti-RNP, and variables were compared between the groups. Results : During the first year of follow-up, SLE patients with anti-RNP at diagnosis more frequently presented with mucosal ulcers (p=0.003), rash (p=0.001), and arthritis (p=0.007), compared to those without anti-RNP. The SLE flare incidence was remarkably higher in patients with anti-RNP than in those without anti-RNP (62.5% vs. 23.1%, p=0.001). SLE patients with anti-RNP at diagnosis had a significantly higher risk of ever experiencing a SLE flare during the first year of follow-up, compared to those without anti-RNP (odds ratio=8.250). Conclusion : In conclusion, SLE patients with anti-RNP at diagnosis were more than 8-fold more likely to experience an SLE flare during the first year of follow-up.ope

다중 원인 이상 감지 및 진단을 위한 데이터 기반 방법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부, 2018. 2. 이종민.Fault detection and diagnosis (FDD) has been an important issue in chemical industry for optimal operation and process safety. FDD has three different approaches which are model-based approach, knowledge-based approach and data-driven approach. Recent advances in data acquisition and storage techniques have enabled high-frequency sampling and processing of sensor signals. Therefore, the data-driven methods can handle the limitations of the traditional FDD method. To improve the FDD performance, three advanced FDD schemes were proposed. The first proposed method was the combination of model-based and data-driven approaches. If the unknown parameters of the process model is inaccurate, the result of FDD with model-based approach can be poor. In addition, since some processes, such as pharmaceutical process, are hard to collect measurement data, the robust parameter estimation with limited data is necessary. In this reason, Bayesian inference was introduced to estimate the unknown parameters of physiologically based pharmacokinetic (PBPK) model with a small number of data. With the proposed estimation scheme, the estimation result was more robust than the least squares method. In addition, the model mismatch was reduced by introducing the drug dissolution model (DDM) into the PBPK model. With these results, FDD performance of model-based approach can be improved. When the abundant data collection is possible, faulty state data can be classified by the differences between the normal data sets and fault data sets. To describe the data differences, Support vector machine (SVM) which is one of the machine learning technique was introduce to help the transient analysis of water pipe network to diagnose the partial blockage. The time domain transient data were convert to the frequency domain data to find the differences between the normal pipe and blocked pipe. With test experiences with various sizes of the blockage, normal, small blockage, medium blockage and harsh blockage transient data were collected. SVM structures of four cases of blockages were constructed with converted transient data. Finally, SVM structures can classify the blocked pipe and its blockage size automatically with the transient analysis data. The data-based model is accurate when the learning data describes the characteristic of the process perfectly. Usually, it is impossible to collect perfect learning data from the operating process. Therefore, knowledge-based model can help to reduce model mismatch of the data-based model with prior information of the process and intuition of the expert engineer. Bayesian belief network (BBN) is data-based model which describes the causality between the measurements of the process. To construct BBN structure with imperfect data, weight matrix from the signed digraph (SDG), which is one of the knowledge-based model, was proposed and applied to the structure learning algorithm. In addition, the root cause of the pre-defined fault scenario also introduced into the BBN with prior information of the process. Three case studies was conducted to verify the FDD performance of BBN-based fault diagnosis method with single fault scenarios and multiple fault scenarios. The BBN-based method was effective for all case studies compared with the traditional PCA-based method. Moreover, the fault diagnosis rate of the BBN-based method was better than the PCA-based method for not only single fault cases but multiple faults cases. Consequently, the BBN-based fault diagnosis method, which is the combination of knowledge-based and data-driven approaches, can improve the FDD performance compared with the traditional data-based approaches. With the three proposed ways to improve the traditional FDD approaches, accurate and real-time process monitoring is possible. Therefore, the proposed methods can help to maintain the process when the failures occur and remain the process with optimal operation condition.1. Introduction 1 1.1 Fault Detection and Diagnosis 1 1.1.1 Model-based approaches 4 1.1.2 Knowledge-based approaches 9 1.1.3 Data-driven approaches 13 1.2 Objective & Outlook 16 2. Methodologies 23 2.1 Parameter estimation techniques 23 2.1.1 Least squares method 23 2.1.2 Parameter estimation via maximum a posteriori principle 24 2.2 Multivariate analysis methods 29 2.2.1 Principal component analysis 29 2.2.2 Partial least squares 31 2.2.3 Hotellings T-squared and squared prediction error 32 2.3 Machine learning techniques 36 2.3.1 Support vector machine 36 2.3.2 Bayesian belief network 40 3. Model description & Simulation 45 3.1 Model-based approach for drug delivery system 45 3.1.1 Model description 45 3.1.2 Simulation 55 3.2 Data-driven approach for water pipe network 56 3.2.1 Water pipe network 56 3.2.2 Experiments & Simulation 59 3.3 Data-driven approach using Bayesian network 65 3.3.1 Continuous stirred-tank reactors 65 3.3.2 Wet gas compressor 70 3.3.3 Penicillin batch process 74 4. Simulation results 79 4.1 Robust parameter estimation for drug delivery system 79 4.2 Diagnosis of partial blockage in water pipe network 87 4.3 Fault detection & diagnosis with Bayesian network 92 4.3.1 Continuous stirred tank reactors 92 4.3.2 Wet gas compressor 99 4.3.3 Penicillin batch process 108 5. Discussions & Concluding remarks 119 5.1 Robust parameter estimation for drug delivery system 119 5.2 Diagnosis of partial blockage in water pipe network 121 5.3 Fault detection & diagnosis with Bayesian network 123 5.4 Summary & Suggested future works 129 Bibliography 132Docto

약물 용해 모델이 포함된 생리학적 약동학 모델의 베이즈 접근을 통한 강건한 변수 추정

학위논문 (석사)-- 서울대학교 대학원 : 화학생물공학부, 2014. 2. 이종민.Physiologically based pharmacokinetics (PBPK) model can predict absorption, degradation, execration and metabolism in drug delivery system. Thus, it can be useful for regulating dose and estimating drug concentration at a particular time during the clinical demonstration. While PBPK model is generally expressed as a set of ordinary differential equations with a large number of parameters, in-vivo experimental data are often noisy and sparse. This makes it difficult to estimate parameters with conventional least squares approaches. Therefore, maximum a posteriori method from Bayesian approach that is the robust parameter estimation technique can be used to estimate parameters of PBPK model. However, the scheme of maximum a posteriori method by using Markov Chain Monte Carlo sampling is hard to use for parameter estimation of PBPK model because of the large number of parameters. This work introduces the Bayesian approach estimation method for parameter estimation of PBPK model. In addition, a scheme of maximum a posteriori method is proposed to find maximum of the posterior distribution without using Markov Chain Monte Carlo sampling.\\ To regulate the concentration of drug and prevent side effect, the studies of drug dosage form are developed. However, since PBPK models and drug dissolution models are studied independently, there is no consideration of the drug dissolution dynamics in PBPK model. Therefore, accurate description of oral administrated drug delivery system requires an improved PBPK model. This work proposes a PBPK model that can describe orally administrated drug dissolution model by combining the drug dissolution model and PBPK model.\\ This thesis simulates parameter estimation of PBPK model to compare the performance of least squares method and maximum a posteriori method. In addition, the case study for Tegafur delivery system is conducted with in-vivo data and drug dissolution model included PBPK model to predict concentration profile of Tegafur, and to evaluate the proposed PBPK model.Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 1 2. Background . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1 Physiologically Based Pharmacokinetics Model . . . 4 2.2 Drug Dissolution Model . . . . . . . . . . . . . . . . 6 2.3 Least Squares Method . . . . . . . . . . . . . . . . . 7 2.4 Bayesian Estimation Methods . . . . . . . . . . . . . 8 2.4.1 Maximum Likelihood Method . . . . . . . . . 9 2.4.2 Maximum a Posteriori Method . . . . . . . . . 9 3. Drug Dissolution Included PBPK Model for Tegafur Delivery System . . . . . . . . . . . . . . . . . . . . . . 10 4. Parameters Estimation Method for PBPK Model . . . 18 4.1 Parameter Estimation Schemes of Least Squares, MLE, and MAP Method . . . . . . . . . . . . . . . . . . . 18 4.1.1 Least Squares Method . . . . . . . . . . . . . 19 4.1.2 MLE Method . . . . . . . . . . . . . . . . . . 20 4.1.3 MAP Method . . . . . . . . . . . . . . . . . . 21 4.2 Comparison Between Least Squares Method and MAP Method . . . . . . . . . . . . . . . . . . . . . . . . . 23 5. Comparison Between The PBPK Model and DDM Included PBPK Model . . . . . . . . . . . . . . . . . . . 27 6. Concluding Remarks . . . . . . . . . . . . . . . . . . . 35 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . 37Maste

The Development and Transition of Ten Schools in Chosun Dynasty from 14C-15C

이 연구에서는 조선전기부터 『경국대전』정착시기까지 십학(十學)이라는 관리 계속교육 제도가 설치되고 정 착하는 과정을 검토하였다. 이에 따르면, 조선은 태종 대부터 십학(十學)이라 하여 전문 분야를 열개의 영역으로 구분하여, 관리 및 생도들을 교육하는 제도를 설치하였다. 십학에서는 현직관리들을 교육하면서 정기적으로 평 가하였고, 그 결과를 이들의 승진에 반영하였다. 십학의 운영 방식은 각 학문 영역별로 관련 관청에 교육담당관 인 제조와 참좌관 등을 두어 교육을 주관하게 하는 형태였다. 십학 제조들의 주관하에 분기별로 1회씩 취재가 실시되었으며, 취재 시의 성적에 따라 승진이 이루어지는 역량 선발 원칙이 적용되었다. 십학의 운영 과정에서 문제가 되었던 점은 두 가지였다. 우선 십학의 하나였던 유학(儒學)의 취재 절차에 대한 반발이 나타났다. 그 이유는 유학 분야의 경우 관련 관청인 삼관(三館)에 별도로 독자적인 관리교육과 평가 제도 가 존재하고 있었기 때문이었다. 다른 논란은 십학 취재가 표방하고 있는 역량 위주 선발 원칙에 대한 반발이었 다. 이 원칙에 대하여 구임관(久任官)의 우대와 직무의 성실성을 평가해야 한다는 주장이 제기되어, 역량과 함께 재직기간이나 근무태도를 고려하는 방식으로 취재 방식이 수정되었다. 십학의 존재는 조선정부가 초기부터 관리 들의 자질을 높이고 일정 수준을 유지하려는 의지를 가지고 있었음을 보여준다. In Chosun Dynasty, the government enforced the system of Ten schools which educate and estimate government officials from 14C. Ten Schools were comprised of curriculums for Confucianism, military science, foreign language, mathematics, jurisprudence, music and so on. Confucianism was most important educative value, but other professional science and various arts did not underestimated by the government. The officials had to make a good academic result in order to be promoted to higher echelons of government. Ten Schools were the institution for continuing education and training of all government officials and education officer of Ten schools educated them in each administrative office related to the science and arts. Ten schools estimated officials four times every year and they were promoted or demoted by the results. The system is to place emphasis on individual competence as professional officials. The process of establishment of this system revealed two issues by the officials. First was propounded by Confucian officials. They insisted that the system presses and disregarded the outstanding confucian officials who had been selected by the examination(科擧 文科). Second, officials who was worked shortly and lazy, were earlier promoted by the results of estimation. The Continuing Education System of officials by Ten Schools are maintained until late in the fifteenth century

朝鮮朝 書院 講學 活動의 性格 : 會講과 講會를 中心으로

학위논문(석사)--서울대학교 대학원 :교육학과 교육학전공,2001.Maste

Hermeneutical Reflection of Interreligious Pains and Liquid Religion

The basis ideal of religion is peace and love. However, if the true spirit of religion is faded by religion conflicts, violence, murder, and war, it will bring unimaginable to human beings. No matter how the psychological feature of the pain is bigger than the physical feature, the pain caused by religions includes both of them. The masses who recognize the symptoms of pain in religion will strongly feel the death of God more than the Gods presence. This is definitely a dysfunction of religion. Ham Suk-Hun refuses the religion being fixed or solidified, and claims Liquid religion which emphasizes the flexibility between religions. Liquidization, the method that does not fear inter-penetration, is one way to the peace of religion. In addition, if it becomes Liquid religion, the reconstruction of metaphysics of postmetaphysics is possible. Furthermore, consideration and care between religions generate emotional empathy and it recognizes the others as neighbors rather than strangers. They even do not stand against any violence but share religious imagination producing mutual happiness. In addition, in order to overcome the pain and suffering of the rhetoric of the language, the pain should be understood not only with the social and self-reflective language, but also with the religious language, and the religion should be the true religion for the people to recognize the Gods presence.종교의 근본 이상은 사랑과 평화이다. 그러나 그 바탕을 실현하지 못하고 외려 종교간의 갈등, 폭력, 살해, 전쟁 등으로 그 진정한 정신을 왜곡, 퇴색시킨다면 인류에게 크나큰 고통이 될 수밖에 없다. 고통이 아무리 물리적인 성격보다는 심리적인 성격이 강하다 하더라도, 종교 간에서 빚어진 고통은 그 둘을 모두 포함한다. 이러한 종교적 고통의 현상을 인식하는 대중들은 종교 안에서 신의 현존보다는 신의 죽음을 더 강하게 느끼게 될 것이다. 분명히 이것은 종교의 역기능이다. 이에 대해 함석헌은 종교가 고착화되거나 고체화되는 것을 거부하고 종교 간 유연성을 가져야 한다는 유동적 종교를 역설한다. 거리와 간격의 폭력에서 벗어나 상호 침투와 스밈을 두려워하지 않는 액성화(liquidization)는 종교 간 평화로 갈 수 있는 하나의 방법이다. 더불어 유동적 종교가 된다면 신의 죽음에서 신의 있음이라는 탈형이상학의 형이상학의 재건을 가능케 할 것이다. 더 나아가 종교 간에 서로 배려하고 돌보는 정서적 공감 공동체는 타자를 이방인으로 보지 않고 이웃으로 인식하기 때문에, 어떠한 폭력에도 맞서지 않고 상호 행복을 생산하는 종교적 상상력을 공유하게 된다. 이와 더불어 고통의 수사학을 극복하기 위해서는 고통을 신앙적 언어뿐만 아니라 사회적 관계의 언어, 반성의 언어로 보고 대중들이 종교 안에서 신의 현존을 알아차릴 수 있도록 참의 종교가 되어야 할 것이다

A Study on development of a simulation model for rural key villages planning using geographic information system and multi-criteria evaluation method

학위논문(박사)--서울대학교 대학원 :농공학과 농업토목전공,1999.Docto