강력범죄 발생 및 강력범죄 핫스팟 형성에 영향을 미치는 지역 특성에 대한 연구

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 농경제사회학부(지역정보학전공), 2021.8. 이성우.The primary purpose of this study is to investigate the causal inference between violent crime and spatial characteristics of local area utilizing spatial linear models and spatial discrete choice models and to propose alternative public policies for desirable urban environments in South Korea. The spatial variables adopted in this study are the proportion of hotel and restaurant businesses in an area, and road accessibility. These two variables are important factors that reflect the industrial structure and population influx. This dissertation is composed of two essays. In the first essay, the impacts of the demographic, socio-economic, and spatial factors on violent crime are analyzed utilizing sever spatial linear models. The results reveal that the increase in the number of hotel and restaurant businesses is positively associated with the incidence of crime. In addition, enhancement of the road accessibility has a positive effect on crime. The second essay analyzes the effects of the spatial variables on violent crime hot spot using spatial discrete choice models. The results show that the impact of the spatial variables that determine violent crime hot spots proves to be highly effective. The proportion of the hotel and restaurant establishments shows positive effects on determining violent crime hot spots. Furthermore, the higher the road accessibility of an area, the higher was the probability of becoming a violent crime hot spot. Regional characteristics clearly affect the level of crime incidence. Based on the findings, this thesis suggests some implications to urban planners and policymakers. Further studies on the relationship between crime and urban planning policies are necessary for crime prevention and safer urban communities. In particular, interdisciplinary research between criminology and urban planning is essential to prevent crime in urban areas.Chapter 1. Introduction 1 1.1. Objective of the Study 1 1.2. Background 3 1.3. Research Hypotheses 10 1.4. Structure of the Study 12 Chapter 2. Determinants on Violent Crime Incidence: Application of Spatial Linear Models 15 2.1. Introduction 15 2.2. Background 17 2.3. Methodology and Data 20 2.4. Results 28 2.5. Summary 39 Chapter 3. Determinats on Violent Crime Hot Spots: Application of Spatial Discrete Choice Models 40 3.1. Introduction 40 3.2. Background 42 3.3. Methodology and Data 44 3.4. Results 51 3.5. Summary 56 Chapter 4. Conclusion 58 4.1. Summary of Findings 59 4.2. Implication, Limitation, and Future Study 60 Bibliography 63 Appendix 75 Abstract in Korean 80석

복잡하고 불확실한 환경에서 최적 의사 결정을 위한 효율적인 로봇 학습

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2021. 2. Songhwai Oh.The problem of sequential decision making under an uncertain and complex environment is a long-standing challenging problem in robotics. In this thesis, we focus on learning a policy function of robotic systems for sequential decision making under which is called a robot learning framework. In particular, we are interested in reducing the sample complexity of the robot learning framework. Hence, we develop three sample efficient robot learning frameworks. The first one is the maximum entropy reinforcement learning. The second one is a perturbation-based exploration. The last one is learning from demonstrations with mixed qualities. For maximum entropy reinforcement learning, we employ a generalized Tsallis entropy regularization as an efficient exploration method. Tsallis entropy generalizes Shannon-Gibbs entropy by introducing a entropic index. By changing an entropic index, we can control the sparsity and multi-modality of policy. Based on this fact, we first propose a sparse Markov decision process (sparse MDP) which induces a sparse and multi-modal optimal policy distribution. In this MDP, the sparse entropy, which is a special case of Tsallis entropy, is employed as a policy regularization. We first analyze the optimality condition of a sparse MDP. Then, we propose dynamic programming methods for the sparse MDP and prove their convergence and optimality. We also show that the performance error of a sparse MDP has a constant bound, while the error of a soft MDP increases logarithmically with respect to the number of actions, where this performance error is caused by the introduced regularization term. Furthermore, we generalize sparse MDPs to a new class of entropy-regularized Markov decision processes (MDPs), which will be referred to as Tsallis MDPs, and analyzes different types of optimal policies with interesting properties related to the stochasticity of the optimal policy by controlling the entropic index. Furthermore, we also develop perturbation based exploration methods to handle heavy-tailed noises. In many robot learning problems, a learning signal is often corrupted by noises such as sub-Gaussian noise or heavy-tailed noise. While most of the exploration strategies have been analyzed under sub-Gaussian noise assumption, there exist few methods for handling such heavy-tailed rewards. Hence, to overcome heavy-tailed noise, we consider stochastic multi-armed bandits with heavy-tailed rewards. First, we propose a novel robust estimator that does not require prior information about a noise distribution, while other existing robust estimators demand prior knowledge. Then, we show that an error probability of the proposed estimator decays exponentially fast. Using this estimator, we propose a perturbation-based exploration strategy and develop a generalized regret analysis scheme that provides upper and lower regret bounds by revealing the relationship between the regret and the cumulative density function of the perturbation. From the proposed analysis scheme, we obtain gap-dependent and gap-independent upper and lower regret bounds of various perturbations. We also find the optimal hyperparameters for each perturbation, which can achieve the minimax optimal regret bound with respect to total rounds. For learning from demonstrations with mixed qualities, we develop a novel inverse reinforcement learning framework using leveraged Gaussian processes (LGP) which can handle negative demonstrations. In LGP, the correlation between two Gaussian processes is captured by a leveraged kernel function. By using properties, the proposed inverse reinforcement learning algorithm can learn from both positive and negative demonstrations. While most existing inverse reinforcement learning (IRL) methods suffer from the lack of information near low reward regions, the proposed method alleviates this issue by incorporating negative demonstrations. To mathematically formulate negative demonstrations, we introduce a novel generative model which can generate both positive and negative demonstrations using a parameter, called proficiency. Moreover, since we represent a reward function using a leveraged Gaussian process which can model a nonlinear function, the proposed method can effectively estimate the structure of a nonlinear reward function.본 학위 논문에서는 시범과 보상함수를 기반으로한 로봇 학습 문제를 다룬다. 로봇 학습 방법은 불확실하고 복잡 업무를 잘 수행 할 수 있는 최적의 정책 함수를 찾는 것을 목표로 한다. 로봇 학습 분야의 다양한 문제 중에, 샘플 복잡도를 줄이는 것에 집중한다. 특히, 효율적인 탐색 방법과 혼합 시범으로 부터의 학습 기법을 개발하여 적은 수의 샘플로도 높은 효율을 갖는 정책 함수를 학습하는 것이 목표이다. 효율적인 탐색 방법을 개발하기 위해서, 우리는 일반화된 쌀리스 엔트로피를 사용한다. 쌀리스 엔트로피는 샤논-깁스 엔트로피를 일반화한 개념으로 엔트로픽 인덱스라는 새로운 파라미터를 도입한다. 엔트로픽 인덱스를 조절함에 따라 다양한 형태의 엔트로피를 만들어 낼 수 있고 각 엔트로피는 서로 다른 레귤러라이제이션 효과를 보인다. 이 성질을 기반으로, 스파스 마르코프 결정과정을 제안한다. 스파스 마르코프 결정과정은 스파스 쌀리스 엔트로피를 이용하여 희소하면서 동시에 다모드의 정책 분포를 표현하는데 효과적이다. 이를 통해서 샤논-깁스 엔트로피를 사용하였을때에 비해 더 좋은 성능을 갖음을 수학적으로 증명하였다. 또한 스파스 쌀리스 엔트로피로 인한 성능 저하를 이론적으로 계산하였다. 스파스 마르코프 결정과정을 더욱 일반화시켜 일반화된 쌀리스 엔트로피 결정과정을 제안하였다. 마찬가지로 쌀리스 엔트로피를 마르코프 결정과정에 추가함으로써 생기는 최적 정책함수의 변화와 성능 저하를 수학적으로 증명하였다. 나아가, 성능저하를 없앨 수 있는 방법인 엔트로픽 인덱스 스케쥴링을 제안하였고 실험적으로 최적의 성능을 갖음을 보였다. 또한, 헤비테일드 잡음이 있는 학습 문제를 해결하기 위해서 외란(Perturbation)을 이용한 탐색 기법을 개발하였다. 로봇 학습의 많은 문제는 잡음의 영향이 존재한다. 학습 신호안에 다양한 형태로 잡음이 들어있는 경우가 있고 이러한 경우에 잡음을 제거 하면서 최적의 행동을 찾는 문제는 효율적인 탐사 기법을 필요로 한다. 기존의 방법론들은 서브 가우시안(sub-Gaussian) 잡음에만 적용 가능했다면, 본 학위 논문에서 제안한 방식은 헤비테일드 잡음을 해결 할 수 있다는 점에서 기존의 방법론들보다 장점을 갖는다. 먼저, 일반적인 외란에 대해서 리그렛 바운드를 증명하였고 외란의 누적분포함수(CDF)와 리그렛 사이의 관계를 증명하였다. 이 관계를 이용하여 다양한 외란 분포의 리그렛 바운드를 계산 가능하게 하였고 다양한 분포들의 가장 효율적인 탐색 파라미터를 계산하였다. 혼합시범으로 부터의 학습 기법을 개발하기 위해서, 오시범을 다룰 수 있는 새로운 형태의 가우시안 프로세스 회귀분석 방식을 개발하였고, 이 방식을 확장하여 레버리지 가우시안 프로세스 역강화학습 기법을 개발하였다. 개발된 기법에서는 정시범으로부터 무엇을 해야 하는지와 오시범으로부터 무엇을 하면 안되는지를 모두 학습할 수 있다. 기존의 방법에서는 쓰일 수 없었던 오시범을 사용 할 수 있게 만듦으로써 샘플 복잡도를 줄일 수 있었고 정제된 데이터를 수집하지 않아도 된다는 점에서 큰 장점을 갖음을 실험적으로 보였다.1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . 4 2 Background 5 2.1 Learning from Rewards . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 Multi-Armed Bandits . . . . . . . . . . . . . . . . . . . . . 7 2.1.2 Contextual Multi-Armed Bandits . . . . . . . . . . . . . . . 7 2.1.3 Markov Decision Processes . . . . . . . . . . . . . . . . . . 9 2.1.4 Soft Markov Decision Processes . . . . . . . . . . . . . . . . 10 2.2 Learning from Demonstrations . . . . . . . . . . . . . . . . . . . . 12 2.2.1 Behavior Cloning . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.2 Inverse Reinforcement Learning . . . . . . . . . . . . . . . . 13 3 Sparse Policy Learning 19 3.1 Sparse Policy Learning for Reinforcement Learning . . . . . . . . . 19 3.1.1 Sparse Markov Decision Processes . . . . . . . . . . . . . . 23 3.1.2 Sparse Value Iteration . . . . . . . . . . . . . . . . . . . . . 29 3.1.3 Performance Error Bounds for Sparse Value Iteration . . . 30 3.1.4 Sparse Exploration and Update Rule for Sparse Deep QLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.1.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 Sparse Policy Learning for Imitation Learning . . . . . . . . . . . . 46 3.2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.2.2 Principle of Maximum Causal Tsallis Entropy . . . . . . . . 50 3.2.3 Maximum Causal Tsallis Entropy Imitation Learning . . . 54 3.2.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4 Entropy-based Exploration 65 4.1 Generalized Tsallis Entropy Reinforcement Learning . . . . . . . . 65 4.1.1 Maximum Generalized Tsallis Entropy in MDPs . . . . . . 71 4.1.2 Dynamic Programming for Tsallis MDPs . . . . . . . . . . 74 4.1.3 Tsallis Actor Critic for Model-Free RL . . . . . . . . . . . . 78 4.1.4 Experiments Setup . . . . . . . . . . . . . . . . . . . . . . . 79 4.1.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . 84 4.1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.2 E cient Exploration for Robotic Grasping . . . . . . . . . . . . . . 92 4.2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.2.2 Shannon Entropy Regularized Neural Contextual Bandit Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4.2.3 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . 99 4.2.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . 104 4.2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 5 Perturbation-Based Exploration 113 5.1 Perturbed Exploration for sub-Gaussian Rewards . . . . . . . . . . 115 5.1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 115 5.1.2 Heavy-Tailed Perturbations . . . . . . . . . . . . . . . . . . 117 5.1.3 Adaptively Perturbed Exploration . . . . . . . . . . . . . . 119 5.1.4 General Regret Bound for Sub-Gaussian Rewards . . . . . . 120 5.1.5 Regret Bounds for Speci c Perturbations with sub-Gaussian Rewards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 5.1.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.2 Perturbed Exploration for Heavy-Tailed Rewards . . . . . . . . . . 128 5.2.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 130 5.2.2 Sub-Optimality of Robust Upper Con dence Bounds . . . . 132 5.2.3 Adaptively Perturbed Exploration with A p-Robust Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 5.2.4 General Regret Bound for Heavy-Tailed Rewards . . . . . . 136 5.2.5 Regret Bounds for Speci c Perturbations with Heavy-Tailed Rewards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 5.2.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 144 5.2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 6 Inverse Reinforcement Learning with Negative Demonstrations149 6.1 Leveraged Gaussian Processes Inverse Reinforcement Learning . . 151 6.1.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 152 6.1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 6.1.3 Gaussian Process Regression . . . . . . . . . . . . . . . . . 156 6.1.4 Leveraged Gaussian Processes . . . . . . . . . . . . . . . . . 159 6.1.5 Gaussian Process Inverse Reinforcement Learning . . . . . 164 6.1.6 Inverse Reinforcement Learning with Leveraged Gaussian Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 6.1.7 Simulations and Experiment . . . . . . . . . . . . . . . . . 175 6.1.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 7 Conclusion 185 Appendices 189 A Proofs of Chapter 3.1. 191 A.1 Useful Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 A.2 Sparse Bellman Optimality Equation . . . . . . . . . . . . . . . . . 192 A.3 Sparse Tsallis Entropy . . . . . . . . . . . . . . . . . . . . . . . . . 195 A.4 Upper and Lower Bounds for Sparsemax Operation . . . . . . . . . 196 A.5 Comparison to Log-Sum-Exp . . . . . . . . . . . . . . . . . . . . . 200 A.6 Convergence and Optimality of Sparse Value Iteration . . . . . . . 201 A.7 Performance Error Bounds for Sparse Value Iteration . . . . . . . . 203 B Proofs of Chapter 3.2. 209 B.1 Change of Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 209 B.2 Concavity of Maximum Causal Tsallis Entropy . . . . . . . . . . . 210 B.3 Optimality Condition of Maximum Causal Tsallis Entropy . . . . . 212 B.4 Interpretation as Robust Bayes . . . . . . . . . . . . . . . . . . . . 215 B.5 Generative Adversarial Setting with Maximum Causal Tsallis Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 B.6 Tsallis Entropy of a Mixture of Gaussians . . . . . . . . . . . . . . 217 B.7 Causal Entropy Approximation . . . . . . . . . . . . . . . . . . . . 218 C Proofs of Chapter 4.1. 221 C.1 q-Maximum: Bounded Approximation of Maximum . . . . . . . . . 223 C.2 Tsallis Bellman Optimality Equation . . . . . . . . . . . . . . . . . 226 C.3 Variable Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 C.4 Tsallis Bellman Optimality Equation . . . . . . . . . . . . . . . . . 230 C.5 Tsallis Policy Iteration . . . . . . . . . . . . . . . . . . . . . . . . . 234 C.6 Tsallis Bellman Expectation (TBE) Equation . . . . . . . . . . . . 234 C.7 Tsallis Bellman Expectation Operator and Tsallis Policy Evaluation235 C.8 Tsallis Policy Improvement . . . . . . . . . . . . . . . . . . . . . . 237 C.9 Tsallis Value Iteration . . . . . . . . . . . . . . . . . . . . . . . . . 239 C.10 Performance Error Bounds . . . . . . . . . . . . . . . . . . . . . . 241 C.11 q-Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 D Proofs of Chapter 4.2. 245 D.1 In nite Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 D.2 Regret Bound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 E Proofs of Chapter 5.1. 255 E.1 General Regret Lower Bound of APE . . . . . . . . . . . . . . . . . 255 E.2 General Regret Upper Bound of APE . . . . . . . . . . . . . . . . 257 E.3 Proofs of Corollaries . . . . . . . . . . . . . . . . . . . . . . . . . . 266 F Proofs of Chapter 5.2. 279 F.1 Regret Lower Bound for Robust Upper Con dence Bound . . . . . 279 F.2 Bounds on Tail Probability of A p-Robust Estimator . . . . . . . . 284 F.3 General Regret Upper Bound of APE2 . . . . . . . . . . . . . . . . 287 F.4 General Regret Lower Bound of APE2 . . . . . . . . . . . . . . . . 299 F.5 Proofs of Corollaries . . . . . . . . . . . . . . . . . . . . . . . . . . 302Docto

The kinetics and reaction mechanism of titanium dioxide and silicon negative electrodes for lithium-ion batteries

학위논문 (박사)-- 서울대학교 대학원 : 화학생물공학부(에너지환경 화학융합기술전공), 2015. 2. 성영은.Lithium-ion batteries (LIBs) are energy storage•conversion devices, which utilize reversible electrochemical reactions on anode and cathode, storing chemical energy and converting it to electrical energy. Until now, LIBs usage has been limited to energy sources for small IT equipments. LIBs for the future, however, have far more possibilities to be applied in extended fields, such as electric vehicles, and energy storage systems. Thus, LIBs energy density and rate performance should be enhanced. To achieve these goals, methods to use the materials with high energy densities like Si more effectively, such as improving the rate performances by coating conductive material and controlling structure of active materials, have been studied extensively. Other areas of studies, for example, understanding the reaction mechanism or segmentizing the components which can affect kinetics, are also necessary. Nevertheless, these kinds of studies are less common in the research area. In the first part of study, Li ion diffusion in electrolyte and its effect of kinetics was investigated. The mesoporous TiO2 was chosen because mesoporous structure is known to have facile Li ion diffusion through electrolyte, and because other parameters related to volume expansion along the reaction can be eliminated with TiO2 system, facilitating investigation of Li ion diffusion. 3-dimmensional mesoporous TiO2 particles with three different pore sizes and two different particle sizes were synthesized using nano-silica as a template. Moreover, the structure of synthesized materials was analyzed with transmission electron microscopy, x-ray diffraction, small angle x-ray diffraction, and nitrogen adsorption/desorption isotherm. Using the synthesized materials, electrochemical experiments were conducted. There was no difference in electrochemical performances between the synthesized materials with 1 M concentration electrolyte. On the other hand, Li ion depletion occurs inside of the active materials depending on the size of the active materials and the pore sizes with 0.1 M concentration electrolyte. Such result reflects that the particle size and pore size are influencial parameter that affects Li ion diffusion, but their effects are negligible in 1 M concentration electrolyte. Based on the acquired results, the mathematical model with Thiele modulus was set up, and the boundaries of Li ion depletion inside of the particle was predicted using the model. In the second part of this study, the reaction mechanism and the kinetics of Si, which has high theoretical capacity, were investigated. The reaction between Si and Li was widely reported to progress through solid-solution reaction. Recently, however, there were some reports that insist that the reaction of Li-Si is two-phase reaction, and the reaction mechanism of Li-Si requires some clarification. The main obstacle for analyzing the reaction mechanism of Li-Si is due to the amorphous structure which silicon transforms into during the reaction. Thus, routinely used analytic methods have limitations to be used in Li-Si system. In this study, the reaction between Li and Si was analyzed with electrochemical method and thermodynamic relations, which is independent of the structure of material. The result concludes that the reaction between Li and Si proceeds through two steps of two-phase reaction. This result was confirmed by showing the trend of the diffusion coefficient change of Li in Si is similar to that of the materials with two-phase reaction. Also, it is logically more plausible to understand the reaction between Li and Si with two-phase reaction. The electrochemical performances for Si were measured with various current densities, and it was discovered that two different two-phase reactions had different kinetic properties. The cause was investigated using electrochemical impedance spectroscopy. Two resistances related to charge transfer and solid-electrolyte interphase were extracted from the impedance data, and it was found that these two resistances are not dominant parameters that determine the kinetics of two reactions. Based on these evidences, among the three kinetic parameters, diffusion in electrolyte, surface reaction, and bulk diffusion, this study concluded that the rate determining step is the diffusion in bulk phase. Furthermore, the strategy to use Si more effectively was proposed, and confirmed with experiments. Through this study, the reaction mechanism of active material and the important kinetic parameters were clarified. These findings give some clues to effectively designing active materials depending on the environment of the reactions.Abstract i List of Tables vi List of Figures vii Chapter 1. Introduction 1 1.1.Lithium-ion batteries (LIBs) 1 1.1.1. Background of LIBs 1 1.1.1. Past, present and future of LIBs 6 1.2. Recent issues on LIBs 10 1.2.1. About energy density 10 1.2.2. About rate performance 14 1.2.3. About reaction mechanism 17 1.3. Objectives of this dissertation 21 Chapter 2. Li ion diffusion through the pores in meso-porous structures 24 2.1. Introduction 24 2.2. Experimental 27 2.2.1. Materials synthesis 27 2.2.2. Characterizations and electrochemical test 29 2.3. Results and discussion 31 2.3.1. Preparation and characterization of 3DOm titania 31 2.3.2. Electrochemical performance 46 2.3.3. Mathematical approach using Thiele modulus 58 2.4. Conclusion 65 Chapter 3. Reaction mechanism and kinetics in Li-Si system 67 3.1. Introduction 67 3.1.1. General information on Si 67 3.1.2. Scheme of this study 71 3.2. Experimental section 72 3.3. Results and discussion 76 3.3.1 Reaction mechanism of Li-Si system 76 3.3.2. How the kinetic of two regions is different? 91 3.3.3. Application 107 3.4. Conclusion 110 References 112 국문초록 124Docto

한국의 담도폐쇄증의 역학연구

학위논문 (석사)-- 서울대학교 대학원 : 임상의과학과, 2017. 2. 고재성.Introduction: Biliary atresia (BA) is the major cause of cholestasis and the leading indication for liver transplantation (LT). However, the incidence of BA in Korea has not been reported. The aim of this study was to investigate the incidence and clinical outcomes of BA in Korea. Methods: We used the Korean universal health insurance database and extracted data regarding BA patients younger than 18 years of age admitted between 2011 and 2015. The incidence of BA was calculated by dividing the number of BA patients by the number of live births. Results: Two hundred forty infants were newly diagnosed with BA. A total of 963 BA patients younger than 18 years of age were followed up for 5 years. The overall incidence of BA was 1.06 cases per 10,000 live births. The incidence of BA was 1.4 times higher for female patients than for male patients. Additionally, significant seasonal variation was observedin particular, the incidence of BA was two times higher from June through August than from December through February. Congenital anomalies were found in 38 out of 240 patients (15.8%). Congenital heart diseases were major associated congenital anomalies (6.3%). Several complications developed during the study period, including cholangitis (24.0%), varix (6.2%) and gastrointestinal bleeding (4.4%). Three hundred one of the 963 BA patients under 18 years of age (31.3%) received LT for BA. Conclusions: The incidence of BA is higher in Korea than that in Western countries. We also report significant gender-associated differences and seasonal variation with respect to the incidence of BA.1.Introduction . 1 2. Materials and Methods . 2 3. Results . 4 3.1 Incidence . 4 3.2 Congenital anomalies . 7 3.3 Complications . 9 3.4 Liver transplantation . 9 4. Discussion 11 5. References . 14 6. Abstract in Korean . 18Maste

Methodology for analyzing market performance of cultural goods : an analysis of box office performance using bayesian network

학위논문(박사)--서울대학교 대학원 :협동과정 기술경영전공,2007.Docto

(A) study on narrative poetics of Han sul ya`s novels

학위논문(박사) --서울대학교 대학원 :국어국문학과,2008.8.Docto

Numerical Modeling for Temperature and Pressure Distributions in Gas Hydrate Bearing Sediments while Drilling

학위논문 (석사)-- 서울대학교 대학원 : 에너지시스템공학부, 2011.2. 최종근.Maste

Changes of speech rate and dysfluency of pre-school children with stuttering according to conversational partner's speech rate modification

언어병리학 협동과정/석사[한글]학령전기 말더듬아동의 치료에 있어서는 부모의 상호작용 방식과 말속도 변화 등을 이용한 상호작용치료가 사용되고 있다. 이러한 상호작용치료 기법 중의 한가지인 대화 상대자의 말속도 조절에 대한 선행연구의 경우, 말더듬아동의 말더듬 발생비율은 감소하였다고 보고하였으나 대화 상대자의 말속도 조절 폭에 따라 아동의 말속도 변화에 있어서는 서로 다른 결과를 보고하였다./이에, 본 연구에서는 대화상대자인 연구자가 아동보다 빠른 정상 성인의 조음속도, 아동과 유사한 조음속도, 아동보다 느린 조음속도로 상호작용을 하였을 경우 학령전기 말더듬아동의 조음속도와 전체말속도, 말더듬 발생비율의 변화를 살펴보았다. 결과를 요약하면 다음과 같다.//1. 대화 상대자의 조음속도 변화시, 학령전기 말더듬아동의 조음속도와 전체말속도 사이에는 유의한 차이가 없었다. /2. 학령전기 말더듬아동의 말더듬 발생비율은 대화 상대자의 조음속도에 따라 유의한 변화가 있었다. 대화 상대자의 세가지 조음속도 상황 중에서, 아동보다 빠른 조음속도 상황과 아동보다 느린 조음속도 상황 사이, 아동과 유사한 조음속도 상황과 아동보다 느린 조음속도 상황 사이에는 학령전기 말더듬아동의 말더듬 발생비율에서는 유의한 차이가 나타났다. 그러나 대화 상대자의 조음속도가 아동보다 빠른 상황과 아동과 유사한 상황 간에는 학령전기 말더듬아동의 말더듬 발생비율의 차이가 유의하게 나타나지 않았다. / /이상의 결과는 대화 상대자가 말더듬아동의 말속도보다 느리게 조절하여 상호작용할 경우, 비록 학령전기 말더듬아동의 조음속도와 전체말속도는 변화하지 않아도 말더듬 발생비율은 감소하였다는 것을 보여준다. /후속 연구로는 대화 상대자의 말속도 조절에 대하여 학령전기 말더듬아동과 정상아동이 보이는 반응에 대한 비교와, 말더듬아동 발화의 복잡성, 반응 시간 등의 변화에 대한 연구 역시 필요하다. [영문]Indirect therapies including modification of interaction syles and speech rate have been used to treat pre-school children with stuttering. Previous studies on conversational partner''s modification of speech rate, which is a part of interaction therapy, show reduction of dysflueny rate of pre-school children with stuttering but diverse results in children''s speech rate change according to the degree of conversational partner''s modification of speech rate./This study investigated the changes of articulation rate, overall speech rate and dysfluency frequencies of pre-school children with stuttering according to conversational partner''s modifications of speech rate, which are adults'' normal articulation rate, articulation rate similar to children''s, slower speech rate than children''s./The results are as follows://1. There was no significant change in articulation rate and overall speech rate of pre-school children with stuttering even with the modification of conversational partner''s modified articulation rate. /2. Dysfluency frequencies of pre-school children with stuttering decreased as conversational partner slowed down his speech. Out of three conversational partner''s articulation rate situations, there was significant decrease in dysfluency frequencies between conversational partner''s normal speech rate and slower speech rate, speech rate similar to children''s and slower speech rate situation. There was no significant decrease in normal speech rate and speech rate similar to children''s situation. //These results show slower speech rate of conversational partner''s articulation rate is effective in enhancing fluencies of pre-school children with stuttering without changes in children''s articulation rate and overall speech rate. /Comparison between responses of pre-school children with and without stuttering to conversational partner''s modification of speech rate is needed. Also study on changes in utterance complexity and response time etc. of pre-school children with stuttering should be followed according to conversational partner''s modification of speech rate./ope