• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.327-336
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• 2004

Many researchers have used the robust variogram to reduce the effect of outliers in spatial data analysis. Recently it is known that estimating the variogram after replacing outliers is more efficient. In this paper, we suggest a new data cleaner for geostatistic data analysis and compare the efficiency of outlier cleaners.

• Journal of the Korean Statistical Society
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• v.36 no.4
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• pp.457-469
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• 2007

Many procedures are available to identify a single outlier or an isolated influential point in linear regression and logistic regression. But the detection of influential points or multiple outliers is more difficult, owing to masking and swamping problems. The multiple outlier detection methods for logistic regression have not been studied from the points of direct procedure yet. In this paper we consider the direct methods for logistic regression by extending the $Pe\tilde{n}a$ and Yohai (1995) influence matrix algorithm. We define the influence matrix in logistic regression by using Cook's distance in logistic regression, and test multiple outliers by using the mean shift model. To show accuracy of the proposed multiple outlier detection algorithm, we simulate artificial data including multiple outliers with masking and swamping.

• Journal of the Korean Statistical Society
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• v.29 no.3
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• pp.285-294
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• 2000

In analyzing bivariate date the sample correlation coefficient is often used, and it is quite sensitive to one or few isolated cases. In this article we derive a formula for the effect of $textsc{k}$ observations on the samples correlation coefficient by the deletion method. To give a reference value for the isolated cases the asymptotic distribution fo the formula is derived. Also, we give some interpretations on several types of isolated cases and an example based on a real data set.

• The Journal of the Acoustical Society of Korea
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• v.6 no.4
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• pp.31-43
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• 1987

As a study on Speaker-Independent Isolated Word Recognition, a Modified ISODATA clustering method is proposed. This method simplifies the outlier processing and the splitting procedure in conventional ISODATA algorithm, and eliminates the lumping procedure. Through this method, we could find cluster centers precisely and automatically. When this method applied to 11 digits by 10 males and 4 females, its recognition rates of $84.42\%$ for K=4 were better than those of the latest Modified K-means, $82.5\%$. Judging from these results, we proved this method the best method in finding cluster centers precisely.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.95-97
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• 2019

본 논문에서는 ZigBee 기반의 WSN과 노드 연결을 위한 새로운 기법을 제안한다. 이 기법은 통신 노드 간에 격리된 노드의 수를 최소화하기 위해 수신신호강도(RSSI) 샘플에 적용된 딕슨 테스트(Dixon's test)를 사용하여 ZigBee 기반의 WSN을 위한 새로운 노드 연결 구조로써 특이점(outlier)을 제거하여 적은 수의 RSSI 값으로도 정확한 노드 연결이 가능하다. 본 논문에서는 시뮬레이션을 통하여 제안하는 기법이 기존의 RSSI 기반의 기법보다 더 높은 정확도를 유지하면서 처리 시간은 줄어든 것을 증명하였다.

• Smart Structures and Systems
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• v.29 no.1
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• pp.29-39
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• 2022

Crack detection plays an important role in the maintenance and protection of steel box girder of bridges. However, since the cracks only occupy an extremely small region of the high-resolution images captured from actual conditions, the existing methods cannot deal with this kind of image effectively. To solve this problem, this paper proposed a novel three-stage method based on deep learning technology and morphology operations. The training set and test set used in this paper are composed of 360 images (4928 × 3264 pixels) in steel girder box. The first stage of the proposed model converted high-resolution images into sub-images by using patch-based method and located the region of cracks by CBAM ResNet-50 model. The Recall reaches 0.95 on the test set. The second stage of our method uses the Attention U-Net model to get the accurate geometric edges of cracks based on results in the first stage. The IoU of the segmentation model implemented in this stage attains 0.48. In the third stage of the model, we remove the wrong-predicted isolated points in the predicted results through dilate operation and outlier elimination algorithm. The IoU of test set ascends to 0.70 after this stage. Ablation experiments are conducted to optimize the parameters and further promote the accuracy of the proposed method. The result shows that: (1) the best patch size of sub-images is 1024 × 1024. (2) the CBAM ResNet-50 and the Attention U-Net achieved the best results in the first and the second stage, respectively. (3) Pre-training the model of the first two stages can improve the IoU by 2.9%. In general, our method is of great significance for crack detection.