• Journal of Radiation Protection and Research
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• 제33권2호
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• pp.67-76
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• 2008

본 연구에서는 국내 4개 원전 부지에서 가동 중인 20기에 대한 보건영향을 평가하였다. 이를 위해 국제원자력기구가 최근 개발하여 보급하고 있는 원전에 대한 평가도구인 '뉴크팩스(NukPacts) 모형'을 활용하였다. 국내 원전의 부지별 피폭 경로에 따른 중대 영향인자를 분석하고 보건영향 발생 빈도를 비교하며, 보건영향의 연간 피해비용을 산출하여 발전량당 피해비용을 유럽 국가의 산출 결과와 비교하였다. 동일 배출량 조건 하에서의 상대적 중요도, 피폭 경로의 상대적 중요도 및 연도별 경향 분석 등을 통해 부지별로 가장 크게 영향을 미치는 방사성물질을 분석하여 최소 비용으로 그 효과를 극대화할 수 있는 방안을 도출하였다. 주요 입력 파라미터의 변화에 따른 영향을 분석하기 위하여 인구 밀도, 유효 배출 고도 등에 대한 민감도 분석을 수행하였다.

• 한국도서관정보학회지
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• 제30권3호
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• pp.97-116
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• 1999

The purpose of this paper is to develop a model of the strategic management of university library in Korea. The model from this study is summarized as follows: 1. External environment is divide into opportunities and threats. 2) Internal environment is divide into strengths and weakness. We can formulate a management strategy on the basis of library environment element from environment analysis and library situation. 2. Formulation of strategic management are formulated through three stages. 1) Library strategy: $\circled1$Growth strategy $\circled2$Stability $\circled3$Retrenchment 2) Business(competition) strategy 3) Functional strategy: $\circled1$Material organization strategy $\circled2$Collection strategy $\circled3$Reference service strategy $\circled4$Human resource management strategy $\circled5$marketing strategy 3. Implementation of strategic management 1) The chief of strategy implementation 2) Programs, budgets and procedures 3) Structure and strategy implementation 4) culture and strategy implementation 4. Evaluation and control of strategy implementation.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2006년도 전기학술대회논문집
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• pp.85-86
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• 2006

In this paper, a nonlinear controller based on adaptive sliding-mode method which has a sliding surface vector including new boundary function is proposed and applied to a two-wheeled voiding mobile robot (WMR). This controller makes the welding point of WMR achieve tracking a reference point which is moving on a smooth curved welding path with a desired constant velocity. The mobile robot is considered in view of a kinematic model and a dynamic model in Cartesian coordinates. The proposed controller can overcome uncertainties and external disturbances by adaptive sliding-mode technique. To design the controller, the tracking error vector is defined, and then the new sliding is proposed to guarantee that the error vector converges to zero asymptotically. The stability of the dynamic system will be shown through the Lyapunov method. The simulations is shown to prove the effectiveness of the proposed controller.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2009년도 춘계학술대회 논문집
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• pp.940-945
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• 2009

This paper presents the design method of low order controller for the active pantograph of electric train system. The pantograph is the most playa role to supply constant current to the train. The design objectives are to have good tracking performance about reference contact force despite the stiffness variation that is like sinusoidal function concerned in train speed or span length of contact wire. In this paper, we consider stiffness variation from external disturbance of active pantograph to simplify model equation, and propose simple second-order controller which is designed by Characteristic ratio assignment(CRA) control method. Finally, we verify time response appling to model equation of real system and frequency response about parameter uncertainty like stiffness variation. it is performed by Matlab version 6.5 and Matlab simulink simulation.

• Korean Journal of Radiology
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• 제23권3호
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• pp.343-354
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• 2022

Objective: To develop and evaluate a deep learning-based artificial intelligence (AI) model for detecting skull fractures on plain radiographs in children. Materials and Methods: This retrospective multi-center study consisted of a development dataset acquired from two hospitals (n = 149 and 264) and an external test set (n = 95) from a third hospital. Datasets included children with head trauma who underwent both skull radiography and cranial computed tomography (CT). The development dataset was split into training, tuning, and internal test sets in a ratio of 7:1:2. The reference standard for skull fracture was cranial CT. Two radiology residents, a pediatric radiologist, and two emergency physicians participated in a two-session observer study on an external test set with and without AI assistance. We obtained the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity along with their 95% confidence intervals (CIs). Results: The AI model showed an AUROC of 0.922 (95% CI, 0.842-0.969) in the internal test set and 0.870 (95% CI, 0.785-0.930) in the external test set. The model had a sensitivity of 81.1% (95% CI, 64.8%-92.0%) and specificity of 91.3% (95% CI, 79.2%-97.6%) for the internal test set and 78.9% (95% CI, 54.4%-93.9%) and 88.2% (95% CI, 78.7%-94.4%), respectively, for the external test set. With the model's assistance, significant AUROC improvement was observed in radiology residents (pooled results) and emergency physicians (pooled results) with the difference from reading without AI assistance of 0.094 (95% CI, 0.020-0.168; p = 0.012) and 0.069 (95% CI, 0.002-0.136; p = 0.043), respectively, but not in the pediatric radiologist with the difference of 0.008 (95% CI, -0.074-0.090; p = 0.850). Conclusion: A deep learning-based AI model improved the performance of inexperienced radiologists and emergency physicians in diagnosing pediatric skull fractures on plain radiographs.

• Asian Journal of Atmospheric Environment
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• 제11권4호
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• pp.330-343
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• 2017

Climate change is an important issue, with many researches examining not only future climatic conditions, but also the interaction of climate and air quality. In this study, a new version of the emissions processing software tool - Python-based PRocessing Operator for Climate and Emission Scenarios (PROCES) - was developed to support climate and atmospheric chemistry modeling studies. PROCES was designed to cover global and regional scale modeling domains, which correspond to GEOS-Chem and CMAQ/CAMx models, respectively. This tool comprises of one main system and two units of external software. One of the external software units for this processing system was developed using the GIS commercial program, which was used to create spatial allocation profiles as an auxiliary database. The SMOKE-Asia emissions modeling system was linked to the main system as an external software, to create model-ready emissions for regional scale air quality modeling. The main system was coded in Python version 2.7, which includes several functions allowing general emissions processing steps, such as emissions interpolation, spatial allocation and chemical speciation, to create model-ready emissions and auxiliary inputs of SMOKE-Asia, as well as user-friendly functions related to emissions analysis, such as verification and visualization. Due to its flexible software architecture, PROCES can be applied to any pregridded emission data, as well as regional inventories. The application results of our new tool for global and regional (East Asia) scale modeling domain under RCP scenario for the years 1995-2006, 2015-2025, and 2040-2055 was quantitatively in good agreement with the reference data of RCPs.

• 한국지하수토양환경학회지:지하수토양환경
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• 제20권3호
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• pp.51-64
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• 2015

In the present study, we develop two history matching techniques based on Markov chain Monte Carlo method where radial basis function and Gaussian distribution generated by unconditional geostatistical simulation are employed as the random walk transition kernels. The Bayesian inverse methods for aquifer characterization as the developed models can be effectively applied to the condition even when the targeted information such as hydraulic conductivity is absent and there are transient hydraulic head records due to imposed stress at observation wells. The model which uses unconditional simulation as random walk transition kernel has advantage in that spatial statistics can be directly associated with the predictions. The model using radial basis function network shares the same advantages as the model with unconditional simulation, yet the radial basis function network based the model does not require external geostatistical techniques. Also, by employing radial basis function as transition kernel, multi-scale nested structures can be rigorously addressed. In the validations of the developed models, the overall predictabilities of both models are sound by showing high correlation coefficient between the reference and the predicted. In terms of the model performance, the model with radial basis function network has higher error reduction rate and computational efficiency than with unconditional geostatistical simulation.

• 대한기계학회논문집B
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• 제36권3호
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• pp.343-350
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• 2012

본 연구에서는 외부 개질기에 열원을 공급하기 위한 시스템 내에 가용한 열에너지의 활용 및 확보에 대한 해석을 위해서 외부 개질기를 연계한 평판형 SOFC 시스템의 해석 모델을 구축하고자 한다. 이러한 해석을 위한 모델 구축을 위해 Matlab simulink$^{(R)}$ 기반의 ThermoLib module을 사용하였으며, 구축된 해석 모델을 통하여 시스템의 성능 향상을 위한 구성 기법에 대해서 연구를 하였다. 시스템 구성 방법은 기존 시스템의 layout을 바꾸기 위해 공기극 출구가스 재순환 및 외부개질기와 촉매연소기를 통합한 개질반응시스템 적용, 개질기에 공급되는 혼합연료의 예열, 연료극 출구가스의 응축을 통한 연료 농도 향상 등을 고려하였다. 시뮬레이션의 해석 결과에서는 SOFC 시스템에 있어서 일반 연소기를 적용한 기준 시스템에 비하여 촉매 연소기를 사용한 시스템의 전기 효율이 12.13% 향상되었으며, 연료극 출구 가스를 응축시켜 버너로 연소시킨 시스템에서는 열효율이 76.12%로 가장 높았다.

• 접착 및 계면
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• 제13권1호
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• pp.9-16
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• 2012

접착용 테이프 박리거동에 미치는 외부효과를 알아보고, 이를 설명하는 준강체 실린더 체인(semi-rigid body cylinder chain) 모델을 제안하였다. 먼저 평면으로부터 테이프를 떼어낼 때, 테이프의 거동을 정성적으로 파악하고, 이를 토대로 준강체 실린더 체인 모형을 도식화하였다. 그 다음 테이프를 떼어내는 힘에 미치는 여러 요소들(온도, 표면거칠기, 떼어내는 속도, 각도 의존성, 테이프 폭 등)의 효과와 이들의 민감도를 분석하여 동일한 실험 기준조건을 설정하고, 이 조건에서 평면으로부터 테이프를 떼어내는 힘(벡터)의 각도 의존성을 $10^{\circ}$ 간격으로 측정하였다. 실험결과는 본 연구에서 확립한 모형이론과 실험오차범위 내에서 잘 일치하였고, 다른 요소들의 효과는 현상론적 입장에서 정성적으로 잘 설명되었다. 이러한 이유로, 본 결과는 PSA 테이프의 접착력을 시험 평가하는 모델로 활용될 수 있을 것으로 기대된다.

• Composites Research
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• 제33권3호
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• pp.108-114
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• 2020

Piecewise Integrated Composite (PIC) 보는 하중 유형에 따라 구간을 나누어, 각 구간마다 하중 유형에 강한 복합재료의 적층 순서를 배열한 보이다. 본 연구는 PIC 보의 구간을 머신 러닝의 일종인 k-NN(k-Nearest Neighbor) 분류를 통해 나누어 기존에 제시되었던 PIC 보에 비해 우수한 굽힘 특성을 갖게 하는 것이 목적이다. 먼저, 알루미늄 보의 3점 굽힘 해석을 통하여 참조점에서의 3축 특성(Triaxiality) 값 데이터를 얻었고, 이를 통해 인장, 전단, 압축의 레이블을 가진 학습 데이터가 만들어진다. 학습 데이터를 통해 각 면마다 독립적인 k-NN 분류 모델을 구성하는 방법(Each plane)과 전체 면에 대한 k-NN 분류 모델을 구성하는 방법(one part)을 이용하여 k-NN 분류 모델을 생성하였고, 하이퍼파라미터의 튜닝을 통하여 다양한 하중 충실도를 도출하였다. 가장 높은 하중 충실도를 가진 k-NN 분류 모델을 기반으로 보를 매핑(mapping)하였고, PIC 보에 대하여 유한요소 해석을 진행한 결과, 기존에 제시되었던 PIC 보에 비해 최대하중과 흡수 에너지가 커지는 특성을 보였다. 하중 충실도를 수동으로 조절하여 100%로 만든 PIC 보와 비교하였을 때, 최대하중과 흡수에너지가 미소한 차이가 나타났으며 이는 타당한 하중 충실도로 보여진다.