• Communications for Statistical Applications and Methods
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• 제13권2호
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• pp.233-241
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• 2006

Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.

• 응용통계연구
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• 제7권2호
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• pp.89-100
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• 1994

본 논문에서는 평균 잔여수명의 추정에 있어서 Weibull과 gamma 분포의 평균 잔여수명을 구하는데 적분이 쉽게 되지 않으므로 부분적률에 근거한 새로운 추정량을 제시하였으며, 비록 이 추정량은 일치추정량이 아니지만 소표본인 경우에서 일치추정량인 기존의 경험적 추정량보다 평균제곱오차가 작다는 것을 몬테칼로 기법을 써서 보였다.

• 한국습지학회지
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• 제21권spc호
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• pp.90-97
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• 2019

최근 연속적인 호우사상으로 인해 홍수가 발생하고 있으며, 이로 인한 재산 및 인명피해가 증가하고 있다. 따라서 본 연구에서는 연속적인 호우사상 발생 사례를 바탕으로 거대강우 시나리오와 거대홍수를 정의하였다. 경안천 유역의 100년 빈도 확률강우사상이 연속적으로 발생한다는 가정하에 거대강우 시나리오를 생성하였으며, 거대홍수량을 산정하기 위하여 SSARR(Streamflow Synthesis and Reservoir Regulation)모형을 이용하였다. 또한, 보다 합리적인 유출해석을 수행하기 위하여 SCE_UA기법을 통해 매개변수를 추정하고, SSR(Sum of Squared of Residual)과 첨두유량 모의에 유리한 WSSR(Weighted Sum of Squared of Residual)의 목적함수를 이용하여 모형의 보정 및 검증을 수행하였다. 이를 통해 적합성 검토를 수행하였다. 그 결과, 경안천 유역의 100년 빈도 강우사상의 연속발생으로 인한 거대홍수량은 4,802㎥/s로 산정되었고, 경안천하천정비기본계획(2011)에서 산정한 100년 빈도 단일 강우사상에 의한 홍수량은 3,810㎥/s으로 산정되었다. 따라서 거대홍수량이 단일 호우사상에 의한 홍수량 보다 약 992㎥/s 만큼 증가하는 것으로 확인되었으며, 이는 향후 거대홍수를 고려할 경우, 경안천 유역의 치수방어대책 수립시 참고자료로 활용할 수 있을 것으로 기대된다.

• Ocean Science Journal
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• 제43권2호
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• pp.81-90
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• 2008

A study of estuarine flows during a neap tide was performed using 13-hour roving acoustic Doppler current profiles (ADCP) and conductivity-temperature-depth (CTD) profiles in the Altamaha River estuary, Georgia, U.S.A. The least-squared harmonic analysis method was used to fit the tidal ($M_2$) component and separate the flow into two components: the tidal and residual ($M_2$-removed) flows. We applied this method to depth-averaged data. Results show that the $M_2$ component demonstrates over 95% of the variability of observation data. As the flow was dominated by the $M_2$ tidal component in a narrow channel, the tidal ellipse distribution was essentially a back-and-forth motion. The amplitude of $M_2$ velocity component increased slightly from the river mouth (0.45 m/sec) to land (0.6 m/sec) and the phase showed fairly constant values in the center of the channel and rapidly decreasing values near the northern and southern shoaling areas. The residual flow and transport calculated from depth-averaged flow shows temporal variability over the tidal time scale. Strong landward flows appeared during slack waters which may be attributed to increased baroclinic forcing when turbulent mixing decreases.

• 한국방사선학회논문지
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• 제16권3호
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• pp.295-302
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• 2022

희박뷰 전산화단층촬영(computed tomography; CT) 영상화 기술은 피폭 방사선량을 감소시킬 수 있을 뿐만 아니라 획득한 투영상의 균일성을 유지하고 잡음을 감소시킬 수 있는 장점이 있다. 하지만 재구성 영상 내 인공물 발생으로 인하여 화질 및 피사체 구조가 왜곡되는 단점이 있다. 본 연구에서는 희박뷰 CT 영상의 인공물 감소를 위해 wavelet 변환과 잔차 학습(residual learning)을 적용한 콘볼루션 신경망(convolutional neural network; CNN) 기반 영상화 모델을 개발하고, 개발한 모델을 통한 희박뷰 CT 영상의 인공물 감소 정도를 정량적으로 분석하였다. CNN은 wavelet 변환 층, 콘볼루션 층 및 역 wavelet 변환 층으로 구성하였으며, 희박뷰 CT 영상과 잔차 영상을 각각 입출력 영상으로 설정하여 영상화 모델 학습을 진행하였다. 영상화 모델 학습을 위해 평균제곱오차(mean squared error; MSE)를 손실함수로, Adam 함수를 최적화 함수로 사용하였다. 학습된 모델을 통해 입력 희박뷰 CT 영상에 대한 예측 잔차 영상을 획득하고, 두 영상간의 감산을 통해 최종 결과 영상을 획득하였다. 또한 최종 결과 영상에 대한 시각적 특성, 최대신호대잡음비(peak signal-to- noise ratio; PSNR) 및 구조적유사성지수(structural similarity; SSIM)를 측정하였다. 연구결과 본 연구에서 개발한 영상화 모델을 통해 희박뷰 CT 영상의 인공물이 효과적으로 제거되며, 공간분해능이 향상되는 결과를 확인하였다. 또한 wavelet 변환과 잔차 학습을 미적용한 영상화 모델에 비해 본 연구에서 개발한 영상화 모델은 결과 영상의 PSNR 및 SSIM을 각각 8.18% 및 19.71% 향상시킬 수 있음을 확인하였다. 따라서 본 연구에서 개발한 영상화 모델을 이용하여 희박뷰 CT 영상의 인공물 제거는 물론 공간분해능 향상 및 정량적 정확도 향상 효과를 획득할 수 있다.

• Communications for Statistical Applications and Methods
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• 제12권3호
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• pp.557-587
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• 2005

This paper elucidates the limiting Gaussian distribution of a class of rank order statistics {$T_N$} for two sample problem pertaining to empirical processes of the squared residuals from two independent samples of GARCH processes. A distinctive feature is that, unlike the residuals of ARMA processes, the asymptotics of {$T_N$} depend on those of GARCH volatility estimators. Based on the asymptotics of {$T_N$}, we empirically assess the relative asymptotic efficiency and effect of the GARCH specification for some GARCH residual distributions. In contrast with the independent, identically distributed or ARMA settings, these studies illuminate some interesting features of GARCH residuals.

• Journal of the Korean Statistical Society
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• 제34권2호
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• pp.161-172
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• 2005

In this paper a nonparametric method is proposed for detecting conditionally heteroscedastic errors in a nonparametric time series regression model where the observation points are equally spaced on [0,1]. It turns out that the first-order sample autocorrelation of the squared residuals from the kernel regression estimates provides essential information. Illustrative simulation study is presented for diverse errors such as ARCH(1), GARCH(1,1) and threshold-ARCH(1) models.

• ETRI Journal
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• 제42권3호
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• pp.333-340
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• 2020

We consider a hybrid combiner design for downlink massive multiple-input multiple-output systems when there is residual inter-user interference and each user is equipped with a limited number of radio frequency (RF) chains (less than the number of receive antennas). We propose a hybrid combiner that minimizes the mean-squared error (MSE) between the information symbols and the ones estimated with a constant amplitude constraint on the RF combiner. In the proposed scheme, an iterative alternating optimization method is utilized. At each iteration, one of the analog RF and digital baseband combining matrices is updated to minimize the MSE by fixing the other matrix without considering the constant amplitude constraint. Then, the other matrix is updated by changing the roles of the two matrices. Each element in the RF combining matrix is obtained from the phase component of the solution matrix of the optimization problem for the RF combining matrix. Simulation results show that the proposed scheme performs better than conventional matrix-decomposition schemes.

• Steel and Composite Structures
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• 제36권2호
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• pp.143-161
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• 2020

In nanosized structures as the surface area to the bulk volume ratio increases the classical continuum mechanics approaches fails to investigate the mechanical behavior of such structures. In perforated nanobeam structures, more decrease in the bulk volume is obtained due to perforation process thus nonclassical continuum approaches should be employed for reliable investigation of the mechanical behavior these structures. This article introduces an analytical methodology to investigate the size dependent, surface energy, and perforation impacts on the nonclassical bending behavior of regularly squared cutout nanobeam structures for the first time. To do this, geometrical model for both bulk and surface characteristics is developed for regularly squared perforated nanobeams. Based on the proposed geometrical model, the nonclassical Gurtin-Murdoch surface elasticity model is adopted and modified to incorporate the surface energy effects in perforated nanobeams. To investigate the effect of shear deformation associated with cutout process, both Euler-Bernoulli and Timoshenko beams theories are developed. Mathematical model for perforated nanobeam structure including surface energy effects are derived in comprehensive procedure and nonclassical boundary conditions are presented. Closed forms for the nonclassical bending and rotational displacements are derived for both theories considering all classical and nonclassical kinematics and kinetics boundary conditions. Additionally, both uniformly distributed and concentrated loads are considered. The developed methodology is verified and compared with the available results and an excellent agreement is noticed. Both classical and nonclassical bending profiles for both thin and thick perforated nanobeams are investigated. Numerical results are obtained to illustrate effects of beam filling ratio, the number of hole rows through the cross section, surface material characteristics, beam slenderness ratio as well as the boundary and loading conditions on the non-classical bending behavior of perforated nanobeams in the presence of surface effects. It is found that, the surface residual stress has more significant effect on the bending deflection compared with the corresponding effect of the surface elasticity, Es. The obtained results are supportive for the design, analysis and manufacturing of perforated nanobeams.

• Communications for Statistical Applications and Methods
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• 제18권6호
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• pp.733-739
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• 2011

The linear absolute shrinkage and selection operator(Lasso) method improves the low prediction accuracy and poor interpretation of the ordinary least squares(OLS) estimate through the use of $L_1$ regularization on the regression coefficients. However, the Lasso is not robust to outliers, because the Lasso method minimizes the sum of squared residual errors. Even though the least absolute deviation(LAD) estimator is an alternative to the OLS estimate, it is sensitive to leverage points. We propose a robust Lasso estimator that is not sensitive to outliers, heavy-tailed errors or leverage points.