• 지식경영연구
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• 제20권2호
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• pp.1-24
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• 2019

This tutorial introduces procedures and methods for performing structural equation modeling using R. To do this, we present advanced analysis methods based on structural equation model such as mediation effect analysis, moderation effect analysis, moderated mediation effect analysis, and multiple-group analysis with R program code using R lavaan package that supports structural equation modeling. R is flexible and scalable, unlike traditional commercial statistical packages. Therefore, new analytical techniques are likely to be implemented ahead of any other statistical package. From this point of view, R will be a very appropriate choice for applying new analytical techniques or advanced techniques that researchers need. Considering that various studies in the social sciences are applying structural equations modeling techniques and increasing interest in open source R, this tutorial is expected to be useful for researchers who are looking for alternatives to existing commercial statistical packages.

• Ocean and Polar Research
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• 제37권1호
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• pp.1-9
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• 2015

An approach based on the combined use of a 2D shallow water model and analytical 1D long wave run-up theory is proposed which facilitates the forecasting of tsunami run-up heights in a more rapid way, compared with the statistical or empirical run-up ratio method or resorting to complicated coastal inundation models. Its application is advantageous for long-term tsunami predictions based on the modeling of many prognostic tsunami scenarios. The modeling of the Chilean tsunami on February 27, 2010 has been performed, and the estimations of run-up heights are found to be in good agreement with available observations.

• Communications for Statistical Applications and Methods
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• 제23권5호
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• pp.363-383
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• 2016

The Weibull family of lifetime distributions play a fundamental role in reliability engineering and life testing problems. This paper investigates the potential usefulness of transmuted new generalized Weibull (TNGW) distribution for modeling lifetime data. This distribution is an important competitive model that contains twenty-three lifetime distributions as special cases. We can obtain the TNGW distribution using the quadratic rank transmutation map (QRTM) technique. We derive the analytical shapes of the density and hazard functions for graphical illustrations. In addition, we explore some mathematical properties of the TNGW model including expressions for the quantile function, moments, entropies, mean deviation, Bonferroni and Lorenz curves and the moments of order statistics. The method of maximum likelihood is used to estimate the model parameters. Finally the applicability of the TNGW model is presented using nicotine in cigarettes data for illustration.

• 터널과지하공간
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• 제12권1호
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• pp.19-30
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• 2002

절리 방향은 절리 크기 및 밀집도와 더불어 암반 사면 및 터널과 같은 암반구조물의 안정성에 영향을 미치는 중요한 기하학적 속성이다. 이와 같은 절리 기하학적 속성들에 대한 통계 모델링은 암반공학적 문제에 대한 확률론적 접근법을 제공할 수 있다. 암반 공학적 문제의 확률론적 모델링의 결과는 어떠한 통계 모델을 선택하느냐에 따라 많은 영향을 받는다. 따라서 , 절리 방향성 자료에 대한 대표적인 통계 모델을 정의하고 각 모델에 대한 분석적 검증과 자료의 통계적 특성에 기초한 모델링 과정의 정립은 매우 중요하다. 이에 본 연구에서는 회전대칭성 모델인 Fisher 분포와 회전 비대칭성 모델인 이변량 정규분포 모델에 대한 통계량 추정 및 검증에 대한 이론적 방법론에 대해 검토하고 , 암반 절리계 모사 및 위험도 분석에 유용하게 사용할 수 있는 인공자료 발생기 알고리즘을 제안하였다.

• 한국지능시스템학회논문지
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• 제12권6호
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• pp.577-582
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• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.

• 대한영상의학회지
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• 제83권6호
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• pp.1219-1228
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• 2022

최근 영상의학 연구 분야에서 영상 인자를 포함한 임상 예측 모형의 수요가 증가하고 있고, 특히 라디오믹스 연구가 활발하게 이루어지면서 기존의 전통적인 회귀 모형뿐만 아니라 머신러닝을 사용하는 연구들이 많아지고 있다. 본 종설에서는 영상의학 분야에서 예측 모형 연구에 사용된 통계학적 방법과 머신 러닝 방법들을 조사하여 정리하고, 각 방법론에 대한 설명과 장단점을 살펴보고자 한다. 마지막으로 예측 모형 연구에서 분석 방법 선택에서의 고려사항을 정리해 보고자 한다.

• Smart Structures and Systems
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• 제31권2호
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• pp.113-130
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• 2023

Hybrid simulation (HS) has attracted community attention in recent years as an efficient and effective experimental technique for structural performance evaluation in size-limited laboratories. Traditional hybrid simulations usually take deterministic properties for their numerical substructures therefore could not account for inherent uncertainties within the engineering structures to provide probabilistic performance assessment. Reliable structural performance evaluation, therefore, calls for stochastic hybrid simulation (SHS) to explicitly account for substructure uncertainties. The experimental design of SHS is explored in this study to account for uncertainties within analytical substructures. Both computational simulation and laboratory experiments are conducted to evaluate the pseudo-random Sobol sequence for the experimental design of SHS. Meta-modeling through polynomial chaos expansion (PCE) is established from a computational simulation of a nonlinear single-degree-of-freedom (SDOF) structure to evaluate the influence of nonlinear behavior and ground motions uncertainties. A series of hybrid simulations are further conducted in the laboratory to validate the findings from computational analysis. It is shown that the Sobol sequence provides a good starting point for the experimental design of stochastic hybrid simulation. However, nonlinear structural behavior involving stiffness and strength degradation could significantly increase the number of hybrid simulations to acquire accurate statistical estimation for the structural response of interests. Compared with the statistical moments calculated directly from hybrid simulations in the laboratory, the meta-model through PCE gives more accurate estimation, therefore, providing a more effective way for uncertainty quantification.

• Membrane and Water Treatment
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• 제9권5호
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• pp.363-371
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• 2018

In this work, it was investigated the usability of artificial bee colony (ABC) and genetic algorithm (GA) in modeling adsorption of Co(II) onto drinking water treatment sludge (DWTS). DWTS, obtained as inevitable byproduct at the end of drinking water treatment stages, was used as an adsorbent without any physical or chemical pre-treatment in the adsorption experiments. Firstly, DWTS was characterized employing various analytical procedures such as elemental, FT-IR, SEM-EDS, XRD, XRF and TGA/DTA analysis. Then, adsorption experiments were carried out in a batch system and DWTS's Co(II) removal potential was modelled via ABC and GA methods considering the effects of certain experimental parameters (initial pH, contact time, initial Co(II) concentration, DWTS dosage) called as the input parameters. The accuracy of ABC and GA method was determined and these methods were applied to four different functions: quadratic, exponential, linear and power. Some statistical indices (sum square error, root mean square error, mean absolute error, average relative error, and determination coefficient) were used to evaluate the performance of these models. The ABC and GA method with quadratic forms obtained better prediction. As a result, it was shown ABC and GA can be used optimization of the regression function coefficients in modeling adsorption experiments.

• 한국전산구조공학회논문집
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• 제32권6호
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• pp.417-423
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• 2019

본 논문에서는 OpenSees 프로그램을 이용한 콘크리트 교량의 지진취약성 분석 방법에 대한 고찰을 제시한다. 교각 및 휨 부재 분산 비선형(distributed plasticity) 요소를 적용한 해석모델을 활용하여 지진에 대한 응답을 구하고 이를 통계적으로 처리하여 확률론적 지진취약성 분석을 수행한다. 응답 통계는 세기가 같은 지진파의 집단을 단계별로 scaling하는 stripe 방법과 다양한 세기를 가진 지진파 집단을 선정하는 cloud방법을 적용하고 이 두 방법에 의한 분석결과의 차이를 비교한다. 한계상태에는 교각의 휨변형과 교좌장치의 변위를 기준으로 산정한 다단계 한계상태를 적용하고, 여러 가지 한계상태를 취합한 시스템 취약성을 도출한다. 지진응답의 통계적 처리 방법과 교량의 손상 정의가 지진취약성 곡선에 주는 영향을 고찰한다.

• ETRI Journal
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• 제39권5호
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• pp.718-728
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• 2017

Empirical modeling of wireless fading channels using common schemes such as autoregression and the finite state Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal-to-noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that a first-order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed and proven to be less demanding compared to the conventional FSMC approach based on the level crossing rate. Simulations confirm the analytical results and promising performance of the new channel model based on the Tauchen approach without extra complexity costs.