• 한국수자원학회논문집
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• 제32권1호
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• pp.3-13
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• 1999

본 연구에서는 공간 분포의 해석을 위하여 일반적으로 사용되어 오던 Thiessen 또는 Kriging 법들을 대체할 수 있는 새로운 모형인 SANN(Spatial-Analysis Neural-Network)을 소개한다. 이 모델은 신경망 기법을 이용한 비매개 변수법의 일종으로 미측정 기점의 평균값 뿐만 아니라 분산, 왜도 등의 고차 통계치를 제공하여 준다. 또한 어떤 기점에서의 공간변수의 값이 그 심각도에 따른 미리 지정된 여러 분류들 중 각각의 분류에 속할 확률값과 전체 공간을 각 분류에 따라 가장 최적하게 분류경제(class boundary)를 선정하여줄 수 있는 Bayesian 계급분류기(Classifier)를 제공하는 의사결정(decision-making) 역할도 수행할 수 있다. 본 연구에서는 제안된 SANN모형의 외삽기(interpolator)를 사용하여 관측 기점의 연평균 강우량을 대상 유역 전체의 공간적으로 분포시키고 또한 각 지점의 예측 오류를 산정하며, Bayesian 분류기를 사용하여 대상유역을 가장 적절하게 건조, 보통, 습윤 지역으로 분류하는 방법을 제시하여 본다. 본 연구에서는 39개 강우 계측 지점을 이용하여 우리나라의 연평균 강우의 공간 해석에 응용하여 본다. 결과적으로 연평균 강우량의 공간 분포, 표준편차, 그리고 확률도를 얻었다. 더불어 우리나라 전역을 건조, 보통, 습윤 지역으로 분류하여 보았다.

• 한국전기전자재료학회논문지
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• 제24권12호
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• pp.949-954
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• 2011

In this study, the characteristics and error ranges of the mechanical bonding strength were analyzed according to before and after thermal shock test for various chips of automotive application component using Sn-3.0Ag-0.5Cu solder. In the after thermal shock test, the mechanical bonding strengths tend to decrease, meanwhile decreasing rates of mechanical strengths were less then 12% at specimen's bonding area below 3.5$mm^2$, and were from 17 to 21% at specimen's bonding area above 12 $mm^2$. On the other hand, Specimen's mean deviation rates were about 5% at specimen's bonding area more than 12 $mm^2$. Inversely, at specimen's bonding area is less then 3.5 $mm^2$, mean deviation rates were increased to about 8%. It means that the smaller device size is, the larger mean deviation rate. In addition, error ranges and deviation rates of the mechanical bonding strengths may differ slightly depending on their bonding area. Furthermore, process conditions as well as method of mechanical reliability evaluation should be established to reduce the error ranges of bonding strength.

• Wind and Structures
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• 제31권3호
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• pp.269-285
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• 2020

The statistical characteristics of typhoon wind speed records tend to have a considerable time-varying trend; thus, the stationary wind model may not be appropriate to estimate the wind characteristics of typhoon events. Several nonstationary wind speed models have been proposed by pioneers to characterize wind characteristics more accurately, but comparative studies on the applicability of the different wind models are still lacking. In this study, three landfall typhoons, Ampil, Jongdari, and Rumbia, recorded by ultrasonic anemometers atop the Shanghai World Financial Center (SWFC), are used for the comparative analysis of stationary and nonstationary wind characteristics. The time-varying mean is extracted with the discrete wavelet transform (DWT) method, and the time-varying standard deviation is calculated by the autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model. After extracting the time-varying trend, the longitudinal wind characteristics, e.g., the probability distribution, power spectral density (PSD), turbulence integral scale, turbulence intensity, gust factor, and peak factor, are comparatively analyzed based on the stationary wind speed model, time-varying mean wind speed model and time-varying standard deviation wind speed model. The comparative analysis of the different wind models emphasizes the significance of the nonstationary considerations in typhoon events. The time-varying standard deviation model can better identify the similarities among the different typhoons and appropriately describe the nonstationary wind characteristics of the typhoons.

• 대한산업공학회지
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• 제30권2호
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• pp.159-164
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• 2004

Lot-streaming is the process of splitting a job (lot) into a number of smaller sublots and then scheduling these sublots in order to accelerate the completion of jobs in a multi-stage production system. Anew genetic algorithm (NGA) is proposed for an-job, m-machine, equal-size sublot lot-streaming flow shop scheduling problem with delivery windows in which the objective is to minimize the mean weighted absolute deviation of job completion times from due dates. The performance of NGA is compared with that of an adjacent pairwise interchange (API) method and the results of computational experiments show that NGA works well for this type of problem.

• 한국측량학회지
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• 제36권1호
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• pp.9-15
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• 2018

Precise regional geoid heights on and around Mount Jiri were calculated and were compared to the KNGeoid14 (Korean National Geoid 2014) model. In this study, gravimetric geoid heights were calculated by using RCR (Remove-Compute-Restore) technique and then hybrid geoid heights were calculated by using the LSC (Least Square Collocation) method in the same area. In addition, gravity observation and GNSS(Global Navigation Satellite System) surveying performed in this study were utilized to determine gravimetric geoid heights and to compute hybrid geoid heights, respectively. The results of the study show that the post-fit error (mean and standard deviation) of hybrid geoid heights was evaluated as $0.057{\pm}0.020m$, while the mean and standard deviation of the differences were -0.078 and 0.085 m, respectively for KNGeoid14. Therefore, hybrid geoid heights in this study show more considerable progress than KNGeoid14.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• 제4권4호
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• pp.241-247
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• 2000

This paper reports on an experimental study of the absorption of COS in a secondary amine diethanolamine. The primary objectives were to investigate an analogy between $N_2$O and COS, thereby allowing an estimation of the physical solubility and diffusivity of the sulfur gases in the reacting amine solutions. The solubilities and diffusivities of $N_2$O and COS in 5~25% aqueous polyethylene glycol at $25^{\circ}C$ were measured. The results appeared to verify the use of an $N_2$O-COS analogy for estimating the solubility and diffusivity of COS in aqueous solutions of alkanolamines up to an approximate 25 weight % concentration. The mean deviation of the $N_2$O analogy relative to the measured solubilities was 3.7%, and the mean deviation between the analogy and the measured diffusion coefficients using the experimental values for Hcos/PEG was 14.6%.

• Computational Structural Engineering : An International Journal
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• 제2권1호
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• pp.1-10
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• 2002

Information on the distribution of the basic random variables is essential for the accurate evaluation of structural reliability. The usual method for determining the distribution is to fit a candidate distribution to the histogram of available statistical data of the variable and perform appropriate goodness-of-fit tests. Generally, such candidate distributions would have two parameters that may be evaluated from the mean value and standard deviation of the statistical data. In the present paper, a-parameter Gamma distribution, whose parameters can be directly defined in terms of the mean value, standard deviation and skewness of available data, is suggested. The flexibility and advantages of the distribution in fitting statistical data and its significance in structural reliability evaluation are identified and discussed. Numerical examples are presented to demonstrate these advantages.

• 경영과학
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• 제24권2호
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• pp.73-80
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• 2007

Robust Design(RD) is a cost-effective methodology to determine the optimal settings of control factors that make a product performance insensitive to the influence of noise factors. To better facilitate the robust design optimization, a dual response surface approach, which models both the process mean and standard deviation as separate response surfaces, has been successfully accepted by researchers and practitioners. However, the construction of response surface approximations has been limited to problems with only a few variables, mainly due to an excessive number of experimental runs necessary to fit sufficiently accurate models. In this regard, an innovative response surface approach has been proposed to investigate robust design optimization problems with larger number of variables. Response surfaces for process mean and standard deviation are partitioned and estimated based on the multi-level approximation method, which may reduce the number of experimental runs necessary for fitting response surface models to a great extent. The applicability and usefulness of proposed approach have been demonstrated through an illustrative example.

• Journal of the Optical Society of Korea
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• 제9권3호
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• pp.107-110
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• 2005

An optical method of measuring hematocrit noninvasively is presented. An LED Light with multiple wavelengths was irradiated on fingernail and transmitted light from the finger was measured to predict hematocrit. A finger probe contained an LED array and detector. Our previous experience showed that prediction accuracy was sensitive to reliability of the finger probe hardware and we optimized the finger probe parameters such as the internal color, detector area and the emission area of a light source based on Design of Experiment. Using the optimized finger probe, we developed a hematocrit monitoring system and tested with 549 persons. For the calibration model with 368 persons, a regression coefficient of 0.74 and a standard deviation of 3.67 and the mean percent error of $8\%$ were obtained. Hematocrits for 181 persons were predicted. We achieved a mean percent error of $8.2\%$ where the regression coefficient was 0.68 and the standard deviation was 3.69.

• Nuclear Engineering and Technology
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• 제46권5호
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• pp.619-632
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• 2014

The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.