• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.850-863
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• 2008

Business Process is a set of interrelated business functions, which are defined by various business process rule and that will lead a company to accomplishing a specific organizational goal. Many business organizations are using process modeling methods for their business process management, but mostly these methods are accomplished by Human-Based activities. These human-based activities cause unexpected design anomalies in modeling phase. If process engine executed without design anomalies detection, that will be lead to huge loss on costs. To ensure that there is no design anomalies in modeling phase and to detect anomalies of predefined actions within modeling tools are important issues in business process management. In this paper, we provide specific types of design anomalies, which can effectively use to detect design anomalies in business process modeling phase.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.399-408
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• 2016

There is a need to detect heteroscedasticity in a regression analysis; however, it invalidates the standard inference procedure. The diagnostics on heteroscedasticity may be distorted when both outliers and heteroscedasticity exist. Available heteroscedasticity detection methods in the presence of outliers usually use robust estimators or separating outliers from the data. Several approaches have been suggested to identify outliers in the heteroscedasticity problem. In this article conventional tests on heteroscedasticity are modified by using a sequential outlier detection methods to separate outliers from contaminated data. The performance of the proposed method is compared with original tests by a Monte Carlo study and examples.

• Communications for Statistical Applications and Methods
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• v.18 no.2
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• pp.171-177
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• 2011

In order to construct approximation functions for real data, it is necessary to remove the outliers from the measured raw data before constructing the model. Conventionally, visualization and maximum residual error have been used for outlier detection, but they often fail to detect outliers for nonlinear functions with multidimensional input. Although the standard support vector regression based outlier detection methods for nonlinear function with multidimensional input have achieved good performance, they have practical issues in computational cost and parameter adjustments. In this paper we propose a practical approach to outlier detection using support vector regression that reduces computational time and defines outlier threshold suitably. We apply this approach to real data examples for validity.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.307-315
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• 2014

An exact distribution of the test statistic to test for multiple outlier candidates does not generally exist; therefore, tests of individual outliers (or tests using simulated critical-values) are usually conducted instead of testing for groups of outliers. This article is on procedures to test outlying observations. We suggest a method that can be applied to arbitrary observations or multiple outlier candidates detected by an outlier detecting method. A Monte Carlo study performance is used to compare the proposed method with others.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10d
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• pp.128-131
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• 2006

IPv6 프로토콜은 현재 인터넷 프로토콜로 사용되고 있는 IPv4 프로토콜이 가지고 있는 주소 부족 문제, 미흡한 QoS의 제공, 다양한 보안 문제 등을 해결하도록 설계된 차세대 인터넷 표준이다. IPv4에서 IPv6로의 전환이 이루어지고 있는 과정이지만, 아직까지 IPv6가 많이 사용되고 있지는 않고 있어 IPv6 트래픽 모니터링 도구 및 침입대응 장비도 많이 나와 있지 않다. 그러나, IPv6 네트워크가 점진적으로 등장하고 전환이 됨에 따라 IPv6에서 발생할 수 있는 각종 인터넷 침해사고에 대한 대비가 필요하다. 이미 IPv6 프로토콜의 허점을 이용한 서비스 거부공격, 디폴트 라우터 위장공격 등 IPv4에서 발생했던 이상트래픽, IPv6 확장헤더를 이용한 이상트래픽 및 IPv6-over-IPv4 터널링 등의 이상트래픽 발생이 보고되고 있다. 이에 본 논문은 IPv6 프로토콜에서 발생할 수 있는 이상트래픽에 대해 살펴보고, 이러한 이상트래픽의 탐지를 위해 IETF 표준인 IPFIX 템플릿을 이상 트래픽 탐지가 가능하게 제안한다. 제안된 IPFIX 플로우 메시지를 이용하여 간단하게 IPv6 이상 트래픽을 분류하는 방법도 제시하였다.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.699-706
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• 2016

Outlier detection methods without performing a test often do not succeed in detecting multiple outliers because they are structurally vulnerable to a masking effect or a swamping effect. This paper considers testing procedures supplemented to a clustering-based method of identifying the group with a minority of the observations as outliers. One of general steps is performing a variety of t-test on individual outlier-candidates. This paper proposes a sequential procedure for searching for outliers by changing cutoff values on a cluster tree and performing a test on a set of outlier-candidates. The proposed method is illustrated and compared to existing methods by an example and Monte Carlo studies.

• Journal of the Korean Society for Library and Information Science
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• v.37 no.2
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• pp.269-289
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• 2003

The purpose of this study is a basic research to formulate missions and visions in libraries. Results of this study are as follows. First of all, conceptions and characters of terms related to planning, strategic management organizing culture dealt with missions and visions of an organization are surveyed. Second, the importance of formulating missions and visions in libraries are explained. Third, missions and visions of libraries in Korea and other countries are surveyed and analyzed. Fourth, several basic functions of library formed the foundation on formulating missions and visions in libraries are discussed. Fifth, this study is to provide the desirable direction and expectation of management centering around missions and visions of libraries in Korea.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.69-75
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• 2022

Due to the increase in the demand for anomaly detection according to the ageing of power generation facilities, the need for developing an anomaly detection method that can provide high-reliability turbine blade anomaly detection performance has been continuously raised. Additionally, the false detection results caused by a human error accelerates the increase of the need. In this paper, we propose an anomaly detection technique for turbine blades in power plants using deep neural networks. Experimental results prove that the proposed technique achieves stable anomaly detection performance while minimizing human factor intervention.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.127-136
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• 2024

This study explores the correlation between system anomalies and large-scale logs within the Spark cluster environment. While research on anomaly detection using logs is growing, there remains a limitation in adequately leveraging logs from various components of the cluster and considering the relationship between anomalies and the system. Therefore, this paper analyzes the distribution of normal and abnormal logs and explores the potential for anomaly detection based on the occurrence of log templates. By employing Hadoop and Spark, normal and abnormal log data are generated, and through t-SNE and K-means clustering, templates of abnormal logs in anomalous situations are identified to comprehend anomalies. Ultimately, unique log templates occurring only during abnormal situations are identified, thereby presenting the potential for anomaly detection.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1455-1461
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• 2014

In this study, using flux data measured in Deokgokje reservoir watershed near Deokyu mountain in May, June, and July 2011, statistical analysis was conducted for outlier detection and replacement for vertical wind speed in the measurement of evapotranspiration based on eddy covariance method. To statistically analyze the outliers of vertical wind speed, the outlier detection method based on interquartile range (IQR) in boxplot was employed and the detected outliers were deleted or replaced with mean. The comparison was conducted for the measured evapotranspiration before and after the outlier replacement. The study results showed that there is a difference between evapotranspiration before outlier replacement and evapotranspiration after outlier replacement, especially during the rainy day. Therefore, based on the study results, the outliers should be deleted or replaced in the measurement of evapotranspiration.