• 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 for structural maintenance and inspection
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• v.19 no.2
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• pp.92-98
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• 2015

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring (SHM) technique is increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and influenced by various external loads. "Abnormal behavior point" is a moment when the structure starts vibrating abnormally and this can be detected by comparing between before and after abnormal behavior point. In other words, anomalous behavior is a sign of damage on structures and estimating the abnormal behavior point can be directly related to the safety of structure. Abnormal behavior causes damage on structures and this leads to enormous economic damage as well as damage for humans. This study proposes an estimating technique to find abnormal behavior point using Hilber-Huang Transform which is a time-frequency signal analysis technique and the proposed algorithm has been examined through laboratory tests with a bridge model using a shaking table.

• Journal of the Korean Data and Information Science Society
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• v.21 no.5
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• pp.841-852
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• 2010

An integrated process control (IPC) procedure is a scheme which simultaneously applies the engineering control procedure (EPC) and statistical control procedure (SPC) techniques to reduce the variation of a process. In the IPC procedure, the observed deviations are monitored during the process where adjustments are repeatedly done by its controller. Because the effects of the noise, the special cause, and the adjustment are mixed, the use and properties of the SPC procedure for the out-of-control process are complicated. This paper considers efficiency of EWMA charts for detecting special causes in an ARMA(1,1) noise model with a minimum mean squared error adjustment policy. And we propose the readjustment procedure after having a true signal. This procedure can be considered when the elimination of the special cause is not practically possible.

• Environmental Analysis Health and Toxicology
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• v.24 no.1
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• pp.43-52
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• 2009

Fullerene의 유전독성을 평가하기 위하여 Chinese hamster유래의 난소유아세포(CHO-K1 cell)를 이용하여 직접법(-S9)과 대사활성화법(+S9 mix)의 염색체이상시험을 실시하였다. 시험물질은 1% CMC 나트륨염의 현탁액(1% CMC 용액)에 희석하여 조제하였다. 대사활성화를 시키지 않은 직접법의 염색체이상시험에서 24시간 투여군은 8단계의 농도(0.078, 0.156, 0.313, 0.625, 1.25, 2.5, 5, 10 mM)로 투여하여 실시하였다. 투여 농도 증가에 따른 염색체이상의 빈도가 증가하는 양상이 나타나지 않았다. 48시간의 투여군에서는 8단계의 농도(0.078, 0.156, 0.313, 0.625, 1.25, 2.5, 5, 10 mM)로 투여하여 실시하였는데 투여 농도 증가에 따른 염색체이상의 빈도가 증가하는 양상이 나타나지 않았다. 배수체의 염색체이상은 직접법에서 관찰되지 않았다. 대사활성화법을 이용하여 6시간 시험물질을 투여한 시험에 있어서는 8단계의 용량단계(0.078, 0.156, 0.313, 0.625, 1.25, 2.5, 5, 10mM)를 설정하였는데 투여 농도가 증가함에 따른 염색체이상빈도의 증가양상이 관찰되지 않았다. 이상의 결과를 종합할 때 본 시험물질은 본 시험 조건하에서 CHO-K1세포에서 대사활성화를 시켰을 때 염색체이상을 유발하지 않는 것으로 판단된다.

• Journal of the Korean GEO-environmental Society
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• v.16 no.5
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• pp.43-53
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• 2015

In this study, detection methods of outlier in various monitoring data that fit into big data category were developed and outlier detections were conducted for both artificial data and real field monitoring data. Rule-based methods applied rate of change and probability of error for monitoring data are effective to detect a large-scale short faults and constant faults having no change within a certain period. There are however, problems with misjudgement that consider the normal data with a large scale variation as outlier caused by using independent single dataset. Rule-based methods for noise faults detection have a limit to application of real monitoring data due to the problem with a choice of proper window size of data and finding of threshold for outlier judgment. A correlation analysis among different two datasets were very effective to detect localized outlier and abnormal variation for short and long-term monitoring dataset if reasonable range of training data could be selected.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.3
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• pp.615-625
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• 2015

This paper proposes new methods and examples for improving fraud detection rules based on banking customer's transaction behaviors focused on anomaly detection method. This study investigates real example that FDS(Fraud Detection System) regards fraudulent transaction as legitimate transaction and figures out fraudulent types and transaction patterns. To understanding the cases that FDS regard legitimate transaction as fraudulent transaction, it investigates all transactions that requied additional authentications or outbound call. We infered additional facts to refine detection rules in progress of outbound calling and applied to existing detection rules to improve. The main results of this study is the following: (a) Type I error is decreased (b) Type II errors are also decreased. The major contribution of this paper is the improvement of effectiveness in detecting fraudulent transaction using transaction behaviors and providing a continuous method that elevate fraud detection rules.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.377-383
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• 2018

Internet of Things (IoT) is producing various data as the smart environment comes. The IoT data collection is used as important data to judge systems's status. Therefore, it is important to monitor the anomaly state of the sensor in real-time and to detect anomaly data. However, it is necessary to convert the IoT data into a normalized data structure for anomaly detection because of the variety of data structures and protocols. Thus, we can expect a good quality effect such as accurate analysis data quality and service quality. In this paper, we propose an anomaly detection system based on big data from collected sensor data. The proposed system is applied to ensure anomaly detection and keep data quality. In addition, we applied the machine learning model of support vector machine using anomaly detection based on time-series data. As a result, machine learning using preprocessed data was able to accurately detect and predict anomaly.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.657-663
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• 2012

There are very small values and/or very big values which get out of the normal range for survey data in various fields. The reasons of occurrence for outlier are two. One of them is the error in process of data input and the other is the strange response of the respondent. If the data has outliers, then the summary statistics such as the mean and the variance produce misleading information. Therefore, researcher should be careful in detecting the outlier in data. In particular, it is very important problem for clinical fields because the cost of experiment is very high. This article introduce the Grubb test and Cochran test to detect outliers in the data and we apply this method for clinical data.

• Resources Recycling
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• v.13 no.2
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• pp.3-8
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• 2004

A basic study on the recovery of heavy metals such as Zn, Ni, Cu and Fe ions from wastewater was carried out with the spent iron oxide catalyst, which was used in the Styrene Monomer(SM) production company. The heavy metals could be recovered more than 98％ with the spent iron oxide catalyst. The alkaline components of the spent catalyst could be precipitated the metal ions of the wastewater as metal hydroxides at the higher pH 10.6 in Ni, pH 8.0 in Cu, pH 6.5 in Fe, pH 8.5 in Zn. But the metal ions are adsorbed physically on the surface of the spent catalyst in the range of the pH of the metal hydroxides and pH 3.0, which is the isoelectric point of the iron oxide catalyst.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.67-77
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• 2004

A treatment of outlier has been discussed. Outliers disrupt the reliability of information systems and they should be eliminated prior to the information and/or data fusion. A time series-based elimination algorithm were proposed and prediction interval, as a criterion of acceptable value width, was obtained with the model. Ten actual link values were used and the best model was identified as IMA(1,1). Although the actual verification was difficult in a sense that the matching process between the eliminated data and model data was not readily available, the proposed model can be successfully used in practice with some calibration efforts.