• Communications for Statistical Applications and Methods
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• 제29권3호
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• pp.287-299
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• 2022

In radiation epidemiology, the excess relative risk (ERR) model is used to determine the dose-response relationship. In general, the dose-response relationship for the ERR model is assumed to be linear, linear-quadratic, linear-threshold, quadratic, and so on. However, since none of these functions dominate other functions for expressing the dose-response relationship, a Bayesian semiparametric method using splines has recently been proposed. Thus, we improve the Bayesian semiparametric method for the selection of the tuning parameters for splines as the number and location of knots using a Bayesian knot selection method. Equally spaced knots cannot capture the characteristic of radiation exposed dose distribution which is highly skewed in general. Therefore, we propose a nonparametric Bayesian knot selection method based on a Dirichlet process mixture model. Inference of the spline coefficients after obtaining the number and location of knots is performed in the Bayesian framework. We apply this approach to the life span study cohort data from the radiation effects research foundation in Japan, and the results illustrate that the proposed method provides competitive curve estimates for the dose-response curve and relatively stable credible intervals for the curve.

• 한국지반공학회논문집
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• 제19권4호
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• pp.299-310
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• 2003

SASW 실험으로 지반의 전단강성 구조를 구하는 해석과정에 있어서 위상속도의 계산은 SASW 실험의 신뢰도를 좌우하는 매우 중요한 단계이다. 기존의 SASW 자료 분석기법 중 위상속도의 계산은 전문가적 경험을 이용한 위상각 스펙트럼의 분석이 선행되어야 하는데, 위상각 스펙트럼 분석 과정의 난해성은 SASW 기법의 일반화에 장애가 되어 왔었다. 본 연구에서는 SASW 기법에 적용가능하고, 위상속도 계산에 전문가적 경험을 배제할 수 있으며, 자동화함으로써 효율성을 제고할 수 있는 위상속도 계산 기법을 제안하였다. 본 연구에서 제안한 기법은 다층구조 지반에서의 표면파의 주파수파수 특성을 이용하였으며, 그 개념에 기초하여 주파수파수 기법(Frequency-Wave Number Technique)이라고 하였다. 본 연구에서 제안한 기법의 신뢰성을 검증하기 위하여, SASW 수치실험을 수행하였다. 그리고 SASW 수치실험에 의해 구한 이론 전달함수로부터 위상속도를 계산하여, 위상각전개법으로 구한 위상속도와 비교 검토하였다. 또한, 경기도$\bigcirc$$\bigcirc$ 지구에서 수행한 SASW 실험자료에 대해 본 연구에서 제안한 기법을 적용하여 현장적용성 및 실용성을 확인하였다.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2006년도 춘계학술발표회 논문집(I)
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• pp.334-337
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• 2006

Generally, fragility curve has been used in predicting failure of structures due to seismic actions. In this research, the method of drawing fragility curve has been applied to evaluating success/failure of structures and satisfactory/unsatisfactory of concrete mixture performance based on material parameters. In the paper, a detailed explanation of the procedure of drawing fragility curve based on material parameter has been introduced. Fragility curve generating procedure includes generation of virtual data points from limited number of actual data points by bell curve implementation, determination of success/failure status of each data point by assigned criterion, and completion of final fragility curve. For practical applications, workability of concrete mixture content based on "unit water" has been used to obtain fragility curve. Detailed explanation of fragility curve drawing procedure for material parameters is presented.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.475-478
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• 2000

Recently encryption algorithms based on difficulties of factorization have been used with popularization. Prime number factorizations are progressed rapidly. In this paper, characteristics of elliptic curve are analyzed and generation of elliptic curves suitable for prime number factorization is discussed.

• 유체기계공업학회:학술대회논문집
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• 유체기계공업학회 2001년도 유체기계 연구개발 발표회 논문집
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• pp.143-148
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• 2001

In this study, to investigate the effect of the generalized performance curve on the performance prediction and to find the optimal ones, a systematic study is performed. For this purpose, we compared the influence of the stage performance curves with experimental data in multi-stage axial compressors. As a result, it is discovered that the optimal generalized performance curves vary according to the number of the stages in compressors. And we found that for a low-stage compressors, Muir's pressure coefficient curve gives the best prediction results at design rotational frequency regardless of the efficiency curve. On the other hand, for high-stage compressors, Stone's pressure coefficient curve gives the optimistic results about the performance prediction at design rotational frequency.

• 한국CDE학회논문집
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• 제4권1호
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• pp.32-42
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• 1999

Approximate lofting or skinning is one of practical surface modeling techniques well used in CAD and reverse engineering applications. Presented in this paper is a method for approximately lofting a given set of curves wihin a specified tolereance. It is based on refitting input curves simultaneously on a common knot vector and interpolating them to get a resultant NURBS surface. A concept of reducing the number of interior knots of the common knot vector is well adopted to acquire more compact representation for the resultant surface. Energy minimization is newly introduced in curve refitting process to stabilize the solution of the fitting problem and get more fair curve. The proposed approximate lofting provides more smooth surface models and realizes more efficient data reduction expecially when the parameterization and compatibility of input curves are not good enough. The method has been successfully implemented in a new CAD/CAM product VX Vision? of Varimetrix Corporation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.445-448
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• 2003

This paper presents a novel iris recognition method using wavelet transform and curve simplification. One-dimensional signals, which are calculated over circles on the iris, are decomposed into a multiple frequency bands. Each decomposed signal is approximated by a piecewise linear curve connecting node points. The curve is simplified by progressively removing unimportant node points while keeping the shape of the curve. Finally, a small number of node points represent features of each signal. Experiment results show that the presented method results in good performance in various noise environments.

• 한국안전학회지
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• 제21권4호
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• pp.42-48
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• 2006

The strategies of maintenance and operation are usually established based on the number of flaws and their size distribution obtained from nondestructive inspection in order to preserve safety of the plant. But non destructive inspection results are different from the physical flaws which really exist in the equipments. In case of a single inspection, it is easy to estimate the number of physical flaws using the POD curve. However, we may be faced with some difficulties in obtaining the number of physical flaws from the periodic in-service non destructive inspection data. In this study a simple method for estimating the number of physical flaws from periodic in-service nondestructive inspection data was proposed. In order to obtain the flaw growth history, the flaw growth was simulated using the Monte Carlo method and the flaw size and the corresponding POD value were obtained for each flaw at each periodic inspection time. The flaw growth rate used in the simulation was statistically calculated from the in-service inspection data. By repeating the simulation numerous flaw growth data could be generated and the effective POD curve was obtained as a function of flaw size. From the effective POD curve the number of physical flaws was obtained. The usefulness and convenience of the proposed method was evaluated from several applications and satisfactory results were obtained.

• 한국수자원학회논문집
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• 제42권4호
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• pp.297-307
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• 2009

신뢰도 높은 수질 모의를 위해서는 유역 내 정확한 유출 모의가 반드시 선행되어야 한다. 본 연구에서는 연속방정식과 운동파 근사에 의한 Manning의 식이 결합된 비선형 저류방정식에 근거한 하도추적법과 금일 강수량을 고려하여 시간적으로 가중 평균된 유출곡선지수를 산정하도록 개선된 지표유출계산 모듈이 수질 모의에 미치는 영향을 분석하였다. 이를 대표적 준분포형 모형인 SWAT에 탑재하여 충주댐 유역에 적용하여 각 개선모듈별 독립적인 분석과 전체 개선의 효과를 개선 전 후로 분석하였다. 각 개선 모듈별 수질 모의의 기여도를 분석한 결과 지표유출계산 모듈의 개선보다는 하도추적의 개선이 더 많은 영향을 미치는 것으로 분석되었다. 이는 비점오염원의 특성 상 하도추적의 개선으로 인한 유출 지체 현상의 개선이 부하량의 배출에 가장 큰 요인으로 작용하였기 때문이라고 판단된다.

• International journal of advanced smart convergence
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• 제10권3호
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• pp.51-58
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• 2021

In this paper, we propose a deep learning classifier for estimating the number of layers in the Earth's structure. When installing a grounding system, knowledge of the subsurface in the area is absolutely necessary. The subsurface structure can be modeled by the earth parameters. Knowing the exact number of layers can significantly reduce the amount of computation to estimate these parameters. The classifier consists of a feedforward neural network. Apparent resistivity curves were used to train the deep learning classifier. The apparent resistivity at 20 equally spaced log points in each curve are used as the features for the input of the deep learning classifier. Apparent resistivity curve data sets are collected either by theoretical calculations or by Wenner's measurement method. Deep learning classifiers are coded by Keras, an open source neural network library written in Python. This model has been shown to converge with close to 100% accuracy.