• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.223-231
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• 2001

다변량분석 기법을 위해서는 자료가 정규성(normality)가정을 만족해야한다. 본 연구에서는 GUI환경에서 일변량 및 다변량자료의 정규성검정, 이상치제거 및 변수변환을 하는 시스템을 Visual Basic 언어로서 구축하여 사용자들이 보다 편리하게 사용할 수 있음을 소개 하고자 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.876-880
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• 2008

Outliers are the observations which are very larger or smaller than most observations in the given data set. These are shown by some sources. The result of the analysis with outliers may be depended on them. In general, we do data analysis after removing outliers. But, in data mining applications such as fraud detection and intrusion detection, outliers are included in training data because they have crucial information. In regression models, simple and multiple regression models need to eliminate outliers from given training data by standadized and studentized residuals to construct good model. In this paper, we use support vector regression(SVR) based on statistical teaming theory to analyze data with outliers in regression. We verify the improved performance of our work by the experiment using synthetic data sets.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.589-599
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• 2008

Accurate software effort estimation has always been a challenge for the software industrial and academic software engineering communities. Many studies have focused on effort estimation methods to improve the estimation accuracy of software effort. Although data quality is one of important factors for accurate effort estimation, most of the work has not considered it. In this paper, we investigate the influence of outlier elimination on the accuracy of software effort estimation through empirical studies applying two outlier elimination methods(Least trimmed square regression and K-means clustering) and three effort estimation methods(Least squares regression, Neural network and Bayesian network) associatively. The empirical studies are performed using two industry data sets(the ISBSG Release 9 and the Bank data set which consists of the project data collected from a bank in Korea) with or without outlier elimination.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.6
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• pp.33-42
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• 2010

Travel speed is an important parameter in measurement of road traffic. UTIS(Urban Traffic Information System) was developed as a type of section detector. However, UTIS incur errors caused by irregular vehicle trajectories, wireless communication range and so on. This paper suggests a new model that use an error-filtering algorithm to improve the accuracy of travel speed measurements. In the field test, the variance of the percent errors measured by the new model was reduced. Therefore, it can be concluded that the proposed model significantly improves travel speed measuring accuracy.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.15-21
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• 2020

This paper presents the outlier detection algorithm in the estimation method of a source location and velocity based on two-step weighted least-squares method using time difference of arrival(TDOA) and frequency difference of arrival(FDOA) data. Since the accuracy of the estimated location and velocity of a moving source can be reduced by the outliers of TDOA and FDOA data, it is important to detect and remove the outliers. In this paper, the method to find the minimum inlier data and the method to determine whether TDOA and FDOA data are included in inliers or outliers are presented. The results of numerical simulations show that the accuracy of the estimated location and velocity is improved by removing the outliers of TDOA and FDOA data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2745-2752
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• 2014

In recent years, Transport Demand Management has been conducted for the efficient management of transport. In ITS applications in particular, the prerequisite is accurate and reliable positioning. However, the major problems are satellite signal outage, and multipath. This paper proposes that outage and multipath measurement can be detected and estimated using elevation angle and signal to noise ratio data association relation in stand-alone GPS. In order to verify the performance of the proposed method, it is then evaluated by the car test. the evaluation test environment has low accuracy and unreliable positioning because of signal outage or multipath such as steep hill and high buildings. In the evaluation test result, 918times abnormal signal occurred and it was confirmed that the proposed method showed more improved 9.48m(RMS) horizontal positioning error than without proposed method.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.27-35
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• 2017

In near future, IoT data is expected to be a major portion of Big Data. Moreover, sensor data is expected to be major portion of IoT data, and its' research is actively carried out currently. However, processed results may not be trusted and used if outlier data is included in the processing of sensor data. Therefore, method for detection and deletion of those outlier data before processing is studied in this paper. Moreover, we used Spark which is memory based distributed processing environment for fast processing of big sensor data. The detection and deletion of outlier data consist of four stages, and each stage is implemented with Mapper and Reducer operation. The proposed method is compared in three different processing environments, and it is expected that the outlier detection and deletion performance is best in the distributed Spark environment as data volume is increasing.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.333-338
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• 2006

This paper proposes an improved do-noising method using multi-thresholding function based on translation-invariant (W) wavelet proposed by Donoho et al. for underwater radiated noise measurement. The traditional wavelet thresholding de-noising method causes Pseudo-Gibbs phenomena near singularities due to discrete wavelet transform. In order to suppress Pseudo-Gibbs Phenomena, a do-noising method combining multi-thresholding function with the translation-invariant wavelet transform is proposed in this paper. The multi-thresholding function is a modified soft-thresholding to each node according to the discriminated threshold so as to reject かon external noise and white gaussian noise. It is verified by numerical simulation. And the experimental results are confirmed through sea-trial using multi-single sensors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.21-30
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• 2008

The travel speed collected by the prove-car equipped with the GPS has the problems, which are the data's stability and finding out the representative travel speed, by the influence of the traffic signal and etc. at the interrupted traffic. This study was conducted to develop the method of filtering the outlier data from the data collected by the prove-car. The method to remove the outlier data from the serial data which were collected by the prove-car was adapted to each of the quartile deviation statistics model and the management graphic statistics model. The rate of removing the outlier data by the quartile deviation method was $0{\sim}3.7%$ while the rate by the management graphic statistic methods was $0.3{\sim}7.2%$. Both methods show the low removal rate at the dawn time when the traffic is inactivity, on the other hand the remove rate is high during the daytime. However, both methods have the problem such that the threshold level for removing the outlier data was established at the low bound in the case as good as the statistics model. Therefore, it is required for the experience calibration.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.493-496
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• 2006

본 논문에서는 원자 시계를 이용한 위성 시계 감시 기법을 제안하고 실시간으로 구현하였다. GPS 수신기 측정치에는 궤도 오차, 이온층 지연, 대류층 지연, 다중경로, 수신기 시계 오차들이 포함되어 있어 감시국에서는 위성 시계 오차가 이러한 오차보다 커지기 전에는 검출하기가 어렵다. 따라서 천천히 변화하는 위성시계 오차를 검출하는데 긴 시간이 소요된다. 이러한 문제를 해결하기 위하여 본 논문에서는 원자시계를 이용하여 수신기 시계오차를 최소화하고, 이중 주파수 측정치를 이용하여 전리층 지연을 제거하는 등 위성 시계 오차 외의 나머지 오차 성분들을 효과적으로 제거하고 남은 오차의 특성으로부터 위성시계의 이상을 감시하는 방법을 제안하였다. 제안한 기법은 윈도우 기반 GUI형태의 소프트웨어로 구현하였고, 원자시계로부터 시각을 제공받는 GPS 수신기로 실시간으로 데이터를 수신하여 그 타당성을 확인하였다. 수신기에 원자시계를 이용함으로써 이상판별을 위한 임계치를 낮출 수 있어 천천히 변화하는 이상을 빨리 검출할 수 있어 이를 일반 사용자가 방송함으로써 사용자의 안전성을 향상시킬 수 있을 것이다.