• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.147-147
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• 2019

The Numerical Weather Prediction (NWP) models determine the future state of the weather by forcing current weather conditions into the atmospheric models. The NWP models approximate mathematically the physical dynamics by nonlinear differential equations; however these approximations include uncertainties. The errors of the NWP estimations can be related to the initial and boundary conditions and model parameterization. Development in the meteorological forecast models did not solve the issues related to the inevitable biases. In spite of the efforts to incorporate all sources of uncertainty into the forecast, and regardless of the methodologies applied to generate the forecast ensembles, they are still subject to errors and systematic biases. The statistical post-processing increases the accuracy of the forecast data by decreasing the errors. Error prediction of the NWP models which is updating the NWP model outputs or model output statistics is one of the ways to improve the model forecast. The regression methods (including linear, polynomial and scaling regression) are applied to the present study to improve the real time forecast skill. Such post-processing consists of two main steps. Firstly, regression is built between forecast and measurement, available during a certain training period, and secondly, the regression is applied to new forecasts. In this study, the WRF real-time forecast data, in comparison with the observed data, had systematic biases; the errors related to the NWP model forecasts were reflected in the underestimation of the meteorological data forecast by the WRF model. The promising results will indicate that the post-processing techniques applied in this study improved the meteorological forecast data provided by WRF model. A comparison between various bias correction methods will show the strength and weakness of the each methods.

• 한국컴퓨터정보학회논문지
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• 제13권5호
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• pp.163-170
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• 2008

컴포넌트에 대한 의존도 및 활용도가 증가하면서 컴포넌트의 보안성 강화를 위한 필요성이 증가하고 있다. 컴포넌트는 재사용을 통한 소프트웨어의 개발 생산성을 향상시키는 이점을 제공한다. 이러한 이점에도 불구하고 컴포넌트의 보안 취약성은 재사용에 제한을 한다. 이의 개선을 위해 컴포넌트의 보안성을 높이는 경우에 가장 문제가 되는 부분이 재사용성에 대한 제한이 확대된다는 것이다. 따라서 컴포넌트의 재사용성과 보안성을 동시에 고려하는 컴포넌트의 모델이 제공되어야 한다. 이의 해결책으로 정보은폐와 수정의 용이성을 제공하여 보안성과 재사용을 확대할 수 있도록 재사용 모델을 결합하고 포장 및 애스펙트 모델을 통합한 컴포넌트 재사용 확대를 위한 관심사의 분리보안 모델을 제안하고 응용시스템을 구축하여 모델의 적합성을 검증하였다. 이의 응용은 핵심 및 보안 관심사의 분리를 통한 컴포넌트 기능의 확장과 수정의 용이성을 제공함으로써 보안성을 높이는 동시에 재사용성을 확대한다.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2377-2386
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• 2021

To model the atmospheric dispersion of radionuclides released from nuclear accident is very important for nuclear emergency. But the uncertainty of model parameters, such as source term and meteorological data, may significantly affect the prediction accuracy. Data assimilation (DA) is usually used to improve the model prediction with the measurements. The paper proposed a parameter bias transformation method combined with Lagrangian puff model to perform DA. The method uses the transformation of coordinates to approximate the effect of parameters bias. The uncertainty of four model parameters is considered in the paper: release rate, wind speed, wind direction and plume height. And particle swarm optimization is used for searching the optimal parameters. Twin experiment and Kincaid experiment are used to evaluate the performance of the proposed method. The results show that the proposed method can effectively increase the reliability of model prediction and estimate the parameters. It has the advantage of clear concept and simple calculation. It will be useful for improving the result of atmospheric dispersion model at the early stage of nuclear emergency.

• 의학교육논단
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• 제23권1호
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• pp.56-67
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• 2021

Educational program evaluation can improve the quality of the curriculum, instructional methods, and resources and provide useful data for making educational decisions and policies. Developing and implementing a program evaluation system is essential in competency-based medical education. The purpose of this study was to explore and establish an educational program evaluation system adapting an integrated program evaluation model to promote competency-based medical education. First, an Educational Evaluation Committee was organized, consisting of faculty, staff members, and students. The committee established an integrated program evaluation model, combining Stufflebeam's Context, Input, Process, and Product (CIPP) model of a process-oriented approach and Kirkpatrick's four-level model of an outcome-oriented approach. Kirkpatrick's model was applied to the product evaluation of the CIPP model. The committee then developed evaluation criteria, indicators, and data collection methods according to the components of the CIPP model and the four levels (reaction, learning, behavior, and results) of Kirkpatrick's model, and collected and analyzed data. Finally, the committee reported the results of evaluation to a Medical Education Quality Improvement Committee, and the results were used to improve the curriculum and student selection. To enhance the quality of education, identifying educational deficiencies and developing various elements of education in a balanced way through educational evaluation will be needed. Furthermore, it will be necessary to listen to opinions of various stakeholders, work with all members involved in education, and communicate with decision-makers in the process of educational evaluation.

• 한국재난정보학회 논문집
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• 제18권2호
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• pp.364-373
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• 2022

연구목적: 본 논문은 지하공동구의 초기 화재 감지를 위해 CCTV를 활용한 AI 연기 객체 감지 모델을 개발하는데 목적이 있다. 연구방법:비정형성이 높은 연기 객체의 감지 성능을 제고하기 위해 화재 감지에 특화된 딥러닝 객체 감지 모델을 지하공동구 연기 감지에 특화되도록 학습시켰고, 학습데이터셋의 정제 및 학습 중 Gradient explosion 완화 등 감지 성능 개선을 위한 방법들을 적용해 모델 결과를 비교하였다. 연구결과: 결과는 제안된 방법을 통해 모델 성능을 향상시켰고 mAP 등의 지표를 평가를 통해 개발 모델이 우수한 성능을 보유하고 있음을 보여준다. 최종 모델은 지하공동구 환경의 연기에 대해 미탐이 낮은 반면 오탐이 다수 발견되는 성능을 보였다. 결론: 본 논문의 모델은 지하공동구 관리시스템과 연계를 통해 보완함으로써 지하공동구의 연기 객체 감지에 활용할 수 있을 것으로 판단된다.

• Korean Journal of Radiology
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• 제22권3호
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• pp.415-424
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• 2021

Objective: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). Materials and Methods: We retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. Results: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. Conclusion: NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.

• 대한지역사회영양학회:학술대회논문집
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• 대한지역사회영양학회 1997년도 추계학술대회:건강증진을 위한 영양서비스 접근방향
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• pp.1-18
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• 1997

no Abstract, See Full Text

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2016년도 춘계학술논문요약집
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• pp.45-46
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• 2016

• 한국경영과학회지
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• 제30권1호
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• pp.75-94
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• 2005

Research in the decision sciences has continued to develop a variety of mathematical models as well as software tools supporting corporate decision-making. Yet. in spite of their potential usefulness, the models are little used in real-world decision making since the model solution processes are too complex for ordinary users to get accustomed. This paper proposes an intelligent and flexible model-solver integration framework that enables the user to solve decision problems using multiple models and solvers without having precise knowledge of the model-solution processes. Specifically, for intuitive model-solution, the framework enables a decision support system to suggest the compatible solvers of a model autonomously without direct user intervention and to solve the model by matching the model and solver parameters intelligently without any serious conflicts. Thus, the framework would improve the productivity of institutional model solving tasks by relieving the user from the burden of leaning model and solver semantics requiring considerable time and efforts.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.258-258
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• 2018

In this study, a hydraulic flow model and an error correction model are combined to improve the flood simulation accuracy. First, the hydraulic flow model is calibrated by optimizing the Manning's roughness coefficient that considers spatial and temporal variability. Then, an error correction model were used to correct the systematic errors of the calibrated hydraulic model. The error correction model is developed using Artificial Neural Networks (ANNs) that can estimate the systematic simulation errors of the hydraulic model by considering some state variables as inputs. The input variables are selected using parital mutual information (PMI) technique. It was found that the calibrated hydraulic model can simulate flood water levels with good accuracy. Then, the accuracy of estimated flood levels is improved further by using the error correction model. The method proposed in this study can be used to the flood control and water resources management as it can provide accurate water level eatimation.