• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.255-268
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• 1992

We propose an iterated interpolation approach for the estimation fo time series parameters in the presence of outliers. The proposed approach iterates the parameter estimation stage and the outlier detection stage until no further outliers are detected. For the detection of outliers, interpolation diagnostic is applied, where the atypical observations by the one-step-ahead predictor instead of downweighting is also proposed. The performance of the proposed estimation methods is compared with other robust estimation methods by simulation study. It is observed that the iterated interpolation approach performs reasonably well is general, especially for single AO case and large $\phi$ in absolute values.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.375-379
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• 2010

The support vector machine (SVM) has been widely used in variety pattern recognition problems applicable to recommendation systems due to its strong theoretical foundation and excellent empirical successes. However, SVM is sensitive to the presence of outliers since outlier points can have the largest margin loss and play a critical role in determining the decision hyperplane. For robust SVM, we limit the maximum value of margin loss which includes the non-convex optimization problem. Therefore, we proposed the design method of robust SVM using genetic algorithm (GA) which can solve the non-convex optimization problem. To demonstrate the performance of the proposed method, we perform experiments on various databases selected in UCI repository.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.164-164
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• 2011

본 연구에서는 InAs 양자점 태양전지의 활성영역에 크기가 다른 양자점을 삽입하여 그 광학적 특성변화를 photoreflectance (PR)와 photoluminescence (PL)를 이용하여 연구하였다. 본 연구에 사용된 InAs 양자점 태양전지 구조는 n+-GaAs (100) 기판 위에 n+-GaAs buffer를 300 nm 성장 후 활성영역에 InAs 양자점과 40 nm 의 n-GaAs spacer를 이용하여 8층의 양자점을 삽입하였다. 그 위에 n-GaAs $1.14{\mu}m$와 p+-GaAs $0.6{\mu}m$, p+-AlGaAs window를 50 nm 성장하고 ohmic contact을 위하여 p+-GaAs 10 nm 성장하였다. 활성영역에 사용된 InAs 양자점의 크기는 InAs 조사량을 1.7 ML~3.0 ML까지 변화시키며 조절하였다. 양자점 태양전지의 활성영역에 삽입한 양자점의 크기에 따른 photoreflectance 측정에서 InAs 조사량이 0~2 ML 사이에서는 Franz-Keldysh oscillation (FKO)의 주기가 짧아지고 2.5 ML 이상에서는 일정한 값 가짐을 보였다. 이는 양자점의 크기가 커질수록 내부 응력에 의한 전기장의 변화에 의한 것으로 사료된다. 아울러 InAs 양자점 태양전지의 photoluminescence 측정 결과 상온에서 1.35 eV 근처에 발광이 관측되었으며 InAs 조사량이 증가할수록 발광중심 낮은 에너지쪽으로 이동함을 보였으며 태양전지 효율은 2.0 ML 인 경우 최고치를 나타내었다. InAs 조사량을 2.0 ML 이상 증가 시킨 경우는 효율이 점진적으로 감소하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.252-253
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• 2015

군중의 이상행동을 검출하는 것은 군중 모니터링, 보안 및 CRM 시스템의 관점에서 중요한 요소 중의 하나이다. 기존의 방법은 대다수가 옵티컬플로우를 기반으로한 검출방법으로 객체가 움직이지 않는 경우에는 객체로 인식할 수 없는 문제점이 생긴다. 또한, 많은 데이터량을 처리하기 때문에 실시간성이 보장되지 않는다는 단점이 있다. 이를 극복하기 위해서, 본 논문에서는 특징점 맵 보정과 분포분석을 통한 군중의 밀집과 대피하는 현상을 검출하는 방법을 제안한다. 먼저, 군중에서 옵티컬플로우 기반으로 움직이는 FAST 특징점을 추출하고 추출된 특징점의 분포에따라 특징점맵을 복원한다. 복원된 특징점 맵과 특징점의 분포에 기반하여 군중의 이상정도를 결정하게 된다. PETS2009 데이터베이스를 사용하여 결과를 측정하였다.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.847-858
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• 2015

Deep-sea fishery in the Antarctic Ocean has been actively progressed by the developed countries including Korea. In order to prevent the environmental destruction of the Antarctic Ocean, related countries have established the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) and have monitored any illegal unreported or unregulated fishing. Fishing of tooth fish, an expensive fish, in the Antarctic Ocean has increased recently and high catches per unit effort (CPUE) of fishing boats, which is suspicious for an illegal activity, have been frequently reported. The data of CPUEs in a fishing area of the Antarctic Ocean often show an extreme Distribution or a mixture of two extreme distributions. This paper proposes an algorithm to detect an outlier of CPUEs by using the mixture of two extreme distributions. The parameters of the mixture distribution are estimated by the EM algorithm. Log likelihood value and posterior probabilities are used to detect an outlier. Experiments show that the proposed algorithm to detect outlier of the data can be adopted instead of simple criteria such as a CPUE is greater than 1.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.631-644
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• 2022

This paper considers robust estimation of the sparse vector autoregressive model (sVAR) useful in high-dimensional time series analysis. First, we generalize the result of Xu et al. (2008) that the adaptive lasso indeed has robustness in sVAR as well. However, adaptive lasso method in sVAR performs poorly as the number and sizes of outliers increases. Therefore, we propose new robust estimation methods for sVAR based on least absolute deviation (LAD) and Huber estimation. Our simulation results show that our proposed methods provide more accurate estimation in turn showed better forecasting performance when outliers exist. In addition, we applied our proposed methods to power usage data and confirmed that there are unignorable outliers and robust estimation taking such outliers into account improves forecasting.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.107-107
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• 2012

본 연구에서는 GaAs p-i-n 태양전지구조에 InAs 양자점을 삽입하여 계면의 전기장 변화를 Photoreflectance (PR) 방법으로 연구하였다. InAs/GaAs 양자점 태양전지구조는 n-GaAs 기판위에 p-i-n 구조의 태양전지를 분자선박막성장 장치를 이용하여 제작하였다. GaAs p-i-n 태양전지와 p-QD(i)-n 양자점 태양전지를 제작하여 계면전기장의 변화를 PR 신호에 나타난 Franz-Keldysh oscillation (FKO)으로부터 측정하였다. 기본적인 p-i-n 구조에서 두 가지 전기장성분을 검출 하였고 양자점 태양전지구조에서는 39 kV/cm 이상의 내부전기장이 존재함을 관측하였다. 이러한 내부전기장은 양자점 주변에 형성된 국소전기장의 효과로 추측하였다. 아울러 양자점을 AlGaAs 양자우물 구조에 삽입하여 케리어의 구속에 의한 FKO의 변화를 관측하였으며 양자점 태양전지의 구조적 변화에 따른 효율을 측정하여 비교 분석하였다.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.403-418
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• 2004

Recently space-time model in spatial data analysis is widly used. In this paper we applied this model to analysis of greenhouse whitefly. For handling time component, we used ARMA model and autoregressive error model and for outliers, we adapted Mugglestone's method. We compared space-time models and geostatistic model with MSE and MAPE.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.225-237
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• 1994

A generalized likelihood ratio test is developed to detect an outlier associated with monitoring nuclear proliferation. While the classical outlier detection methods consider continuous variables only, our approach allows both continuous and discrete variables or a mixture of continuous and discrete variables to be used. In addition, our method is free of the normality assumption, which is the key assumption in most of the classical methods. The proposed test is constructed by applying the bootstrap to a generalized likelihood ratio. We investigate the performance of the test by studying the power with simulations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.3
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• pp.197-203
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• 1999

Depending on geographical features and error sources in the survey field, inaccurate data is inevitable in GPS kinematic survey for positioning with feature codes. In this study, the trimmed mean and the first order differential equation are used to develop an inaccurate positioning data detection algorithm, and a cubic spline curve and a linear polynomial are used to interpolate the inaccurate data. Based on interpolated data, a digital map for 30 km range of rural highway is produced and a corresponding GIS coverage map is obtained by analyzing and solving the problem associated with the map.