• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.269-279
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• 2002

We considered the problem for confirmation of multiple outliers and leverage points. Identification of multiple outliers and leverage points is difficult because of the masking effect and swamping effect. Rousseeuw and van Zomeren(1990) identified multiple outliers and leverage points by using the Least Median of Squares and Minimum Value of Ellipsoids which are high-breakdown robust estimators. But their methods tend to declare too many observations as extremes. Atkinson(1987) suggested a method for confirming of outliers and Fung(1993) pointed out Atkinson method's limitation and proposed another method by using the add-back model. But we analyzed that Fung's method is affected by adjacent effect. In this thesis, we proposed one procedure for confirmation of outliers and leverage points and compared three example with Fung's method.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.11-23
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• 1996

본 연구에서는 선형회귀분석에서 Hadi와 Simonoff의 다중이상점 식별방법을 수정하여 새로운 알고리즘을 제시하였다. Hadi와 Simonoff의 알고리즘 첫 단계에서 이상점일 가능성이 없는 점들의 집합을 추출할 때 가장효과와 편승효과에 영향을 받을 수 있음으로, 이 첫 단계를 수정하였다. 우리는 잔차가 일정한 분산을 갖는 정규분포에 다르다는 가정하에서 잔차의 신뢰구간을 생각하고, 이 구간안에서 잔차의 MAD가 최소인 새로운 모형을 탐색하고, 이를 이상점일 가능성이 없는 점들의 집합을 추출하는데 일용하는 새로운 알로리즘을 제시하였다. 제시된 방법은 실제자료에서 다른 방법에 비해 효율적으로 이상점을 식별할 수 있었다.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.551-559
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• 2007

The maximum likelihood estimation is not robust against outliers in the logistic regression. Thus we propose an algorithm for the robust estimation, which identifies the bad leverage points and vertical outliers by the V-mask type criterion, and then strives to dampen the effect of outliers. Our main finding is that, by an appropriate selection of weights and factors, we could obtain the logistic estimates with high breakdown point. The proposed algorithm is evaluated by means of the correct classification rate on the basis of real-life and artificial data sets. The results indicate that the proposed algorithm is superior to the maximum likelihood estimation in terms of the classification.

• Journal of KIISE:Databases
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• v.41 no.4
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• pp.199-208
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• 2014

In this paper, we represent a system of identification and feature search for scanned comics in consideration of their content characteristics. For creating the feature of the scanned comics, we utilize a method of hierarchical symmetry fingerprinting. Proposed identification and search system is designed to give online service provider, such as Webhard, an immediate identification result under conditions of huge volume of the scanned comics. In simulation part, we analyze the robustness of the identification of the fingerprint to image modification such as rotation and translation. Also, we represent a structure of database for fast matching in feature point database, and compare search performance between other existing searching methods such as full-search and most significant feature search.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.541-550
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• 2010

The $L_1$-regression estimator is susceptible to the leverage points, even though it is highly robust to the vertical outliers. This article is concerned with the improvement of robustness of the $L_1$-estimator. To improve its robustness, in terms of the breakdown point, we attempt to dampen the influence of the leverage points by means of reducing the weights corresponding to the leverage points. In addition the algorithm employs the linear scaling transformation technique, for higher computational efficiency with the large data sets, to solve the linear programming problem of $L_1$-estimation. Monte Carlo simulation results indicate that the proposed algorithm yields $L_1$-estimates which are robust to the leverage points as well as the vertical outliers.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.1-11
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• 2018

Multivariate process control has become important in various applied fields. For instance, there are many situations in which the simultaneous monitoring of multivariate quality characteristics is necessary for the manufacturing industry. Despite its importance, its practical usage is not as convenient because it is difficult to identify the source of the out-of-control signal in a multivariate control chart. In this paper, we will introduce how to detect the source of the out-of-control by using confidence intervals for new observations, and will discuss the identification and interpretation of the out-of-control variable through simulation studies.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.1-13
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• 2022

With the development of network technologies, the security to protect organizational resources from internal and external intrusions and threats becomes more important. Therefore in recent years, the anomaly detection algorithm that detects and prevents security threats with respect to various security log events has been actively studied. Security anomaly detection algorithms that have been developed based on rule-based or statistical learning in the past are gradually evolving into modeling based on machine learning and deep learning. In this study, we propose a deep-autoencoder model that transforms LSTM-autoencoder as an optimal algorithm to detect insider threats in advance using various machine learning analysis methodologies. This study has academic significance in that it improved the possibility of adaptive security through the development of an anomaly detection algorithm based on unsupervised learning, and reduced the false positive rate compared to the existing algorithm through supervised true positive labeling.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.677-686
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• 2009

Stratification of population is widely used to improve the efficiency of the estimation in a sample survey. However, it causes several problems when there are some variables containing outliers. To overcome these problems, Park and Yun (2008) proposed a rather subjective method, which finds outliers before $\kappa$-means clustering for stratification. In this study, we propose the $\kappa$-spatial medians clustering method which is more robust than $\kappa$-means clustering method and also does not need the process of finding outliers in advance. We investigate the characteristics of the proposed method through a case study used in Park and Yun (2008) and confirm the efficiency of the proposed method.

• Journal of Korean Institute of Fire Investigation
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• v.9 no.1
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• pp.47-53
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• 2006

본 논문은 화재발생 이후에 화재현장의 조건에 따라 분전반의 주차단기 전원측 단자에서의 트래킹 현상 진행 가능성에 대하여, 화재현장 조사사례를 들어 고찰하였다. 화재현장 조사과정에서 분전반의 주차단기 전원측 단자에서 트래킹 형태가 식별됨에도 불구하고 그 부하측에서 전기적인 특이점이 식별되는 경우에는, 단순히 트래킹 형태가 식별되는 점만으로 발화원인을 판정하는 자세를 지양하고, 구체적인 연소형태를 검토하여 발화개소, 연소확대 경로 등의 해석 및 전체적인 전기계통의 고찰을 통하여, 분전반 주차단기에서의 발화원인 등을 판정해야되며, 또한, 화재현장의 정밀조사 없이 분전반 및 차단기의 조사와 해석만으로는 발화여부 또는 발화원인에 대하여 논단하는 것이 어렵다는 결론을 도출하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.170-172
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• 2023

최근 자동차는 자율주행차 혹은 스마트카로 진화하며 다양한 외부 통신 인터페이스를 포함하고 있습니다. 각 기능 통제를 위해 차량 소프트웨어의 복잡성과 자동차 기술 발전에 따라 통신 인터페이스의 증가로 인하여 자동차에 대한 사이버 공격 가능성 및 위험성이 꾸준히 증가하고 있습니다. 특히, 커넥티드카의 안전을 위한 V2X(Vehicle to Everything)통신이 보안 취약점을 가질 경우, 이는 탑승자의 생명에 직접적인 위협을 초래할 수 있습니다. 그러나, 지능형 교통 시스템에서는 익명성을 위해 일정 시간이 지나면 차량의 식별정보를 변경해 공격자를 찾는데 어려움이 있다. 따라서 본 논문에서는 지능형 교통 시스템 내에서 이상행위를 유발하는 차량을 탐지하기 위해 V2X에 활용되는 표준 메시지 정보를 통해 식별하여 추적하는 기술을 제안하고자 한다.