• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.979-986
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• 2011

A linear regression method is commonly used to analyze data because of its simplicity and applicability; however, it is well known that data may contain some outliers and influential cases that may have a harmful effect on a statistical analysis. Thus detection and examination of outliers or influential cases are important parts of data analysis. In detecting multiple outliers, masking effects usually occur and make it difficult to identify the true outliers. We propose to use dynamic plots as a method resistant to masking effect. The procedure using dynamic plots is useful to find appropriate basic sets with which a dependent outliers detection method start and detect a true outliers set. Examples are given to demonstrate the effectiveness of the suggested idea.

• Journal of Broadcast Engineering
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• v.6 no.2
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• pp.160-168
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• 2001

In this paper, we propose a new practical motion model that can exploit the general properties of camera motion in constructing a panorama. accounting for panning. tilting, and evert the change in focal length of the camera. We also present an efficient algorithm to handle moving objects or noose in the scene based on outliers rejection. Spatial and temporal statistical properties of motion field are exploited to detect the outliers. The proposed algorithm removes moving objects or noise from the panoramic Image so that mode clear and complete view of the background Image can be obtained. This method does not require assumptions or a priors knowledge of the scene. The entire process is fully automatic as this method does not require any manual correction in the process of constructing a Panorama. The proposed algorithm is tested on the broadcasting images of soccer games. Oun simulation result shows that this method is superior to conventional image mosaicing algorithms.

• 한국생물공학회:학술대회논문집
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• 2004.07a
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• pp.47-84
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• 2004

Principal Component Analysis나 Partial Least Squares와 같은 다변량 통계 기법은 변수간의 correlation structure로부터 공정의 variance를 설명할 수 있는 latent variable를 얻고 이를 이용하여 공정을 효과적으로 modeling할 수 있는 방법으로 최근 들어 많은 관심을 얻고 있다. 하지만 PLS는 공정이 stationary state에 있다고 가정하기 때문에, 생물학적 공정의 non-stationary and time-varying behavior를 설명하기에 부적절하다. 본 논문에서는 PLS 알고리즘의 혐기성 폐수처리 공정에의 적용에 있어, 이와 같은 문제를 해결하기 위해서 adaptive PLS 알고리즘을 사용함으로써 변화하는 공정의 특성에 대응하여 모델을 update하는 방법을 이용하였다. 하지만 실시간 데이터로부터 adaptive PLS 방법을 적용하는 데에는 많은 어려움이 존재하며, 특히 outlier나 abnormal disturbance에 모델이 부적절하게 adaptation하는 문제가 발생할 수 있다. 따라서 이의 해결을 위해 adaptive PLS를 적용하는데 있어 robustness를 향상시키기 위해 monitoring index를 이용하여 abnormal data에 weight를 주고 안정적인 모델의 update가 가능하게 하는 방법을 제안하였으며, 이를 적용하여 성공적으로 혐기성 폐수처리 공정의 Output을 예측하고 효과적으로 공정을 모니터링할 수 있었다. 만들어진 PLS 모델은 산업폐수를 처리하기 위한 industrial plan에서 측정된 실제 데이터에 적용하여 그 효용성을 입증하였으며, 그 결과는 mechanistic model을 적용하기 힘든 실공정에 비교적 쉽게 implementation할 수 있는 장점이 있다.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.6
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• pp.672-677
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• 2010

Recently, the efficient management system for large-scale sensor data has been required due to the increasing deployment of large-scale sensor networks. In this paper, we propose a cloud-based sensor data management system with low cast, high scalability, and efficiency. Sensor data in sensor networks are transmitted to the cloud through a cloud-gateway. At this point, outlier detection and event processing is performed. Transmitted sensor data are stored in the Hadoop HBase, distributed column-oriented database, and processed in parallel by query processing module designed as the MapReduce model. The proposed system can be work with the application of a variety of platforms, because processed results are provided through REST-based web service.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.5
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• pp.565-576
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• 2012

Cost estimates are necessary for government acquisition program to support decisions about funding, to develop annual budget requests and to validate resource requirements at key decision points. Many researches have been done about cost estimating technique recently. Parametric cost estimating models based on CERs(Cost Estimating Relationships) have been mainly used using regression method with historical data. However, there are many restrictions in developing Korean version CERs because the number of data points are too small. Specially, data collection and data management system are unstable in Korean defense environment, when developing CERs. In this research, we analyzed the historical data, and found some cost drivers in guided weapon system area. We developed the Acquisition Unit Cost CER using the regression to remove multicollinearity in the historical data. So we could overcome the restriction of the insufficient sample number. This research as a first attempt is meaningful in terms of obtaining our own Acquisition Unit Cost CER using historical cost and physical characteristic in Korean development environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.342-344
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• 2018

This paper shows the possibility to substitute statistical methods such as mean imputation, correlation coefficient analysis, graph correlation analysis for the proposed algorithm, and replace statistician for processing various abnormal data measured in the water treatment process with it. In addition, this study aims to model a data-filtering system based on a recent fractile pattern and a deep learning-based LSTM algorithm in order to improve the reliability and validation of the algorithm, using the open-sourced libraries such as KERAS, THEANO, TENSORFLOW, etc.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.10
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• pp.717-727
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• 2022

An algorithm is realized for estimating the component fault and health diagnosis such as a deterioration. Based on the turbofan engine health diagnosis model, from the health parameters which are estimated by a real time tracking filter, the outliers are eliminated efficiently by an effective median filter to minimize an false alarm. The difference between the fault and deterioration trends is identified by the detection measure for abrupt change, thereby the clear diagnosis classifying the fault and the health condition is possible. The effectiveness of the algorithm for fault and health diagnosis is verified from the simulated results of engine component faults and deterioration.

• ETRI Journal
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• v.45 no.3
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• pp.448-461
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• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.96-96
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• 2020

In order to establish the risk criteria of inundation due to typhoons or heavy rainfall, research is underway to predict the limit rainfall using basin characteristics, limit rainfall and artificial intelligence algorithms. In order to improve the model performance in estimating the limit rainfall, the learning data are used after the pre-processing. When 50.0% of the entire data was removed as an outlier in the pre-processing process, it was confirmed that the accuracy is over 90%. However, the use rate of learning data is very low, so there is a limitation that various characteristics cannot be considered. Accordingly, in order to predict the limit rainfall reflecting various watershed characteristics by increasing the use rate of learning data, the watersheds with similar characteristics were clustered. The algorithms used for clustering are K-Means, Agglomerative, DBSCAN and Spectral Clustering. The k-Means, DBSCAN and Agglomerative clustering algorithms are clustered at the impervious area ratio, and the Spectral clustering algorithm is clustered in various forms depending on the parameters. If the results of the clustering algorithm are applied to the limit rainfall prediction algorithm, various watershed characteristics will be considered, and at the same time, the performance of predicting the limit rainfall will be improved.

• Restorative Dentistry and Endodontics
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• v.46 no.3
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• pp.37.1-37.11
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• 2021

Objectives: This systematic review evaluated the efficacy of the supplementary use of the XP-endo Finisher on bacteria content reduction in the root canal system. Materials and Methods: In-vitro studies evaluating the use of the XP-endo Finisher on bacteria content were searched in four databases in July 2020. Two authors independently screened the studies for eligibility. Data were extracted, and risk of bias was assessed. Data were meta-analyzed by using random-effects model to compare the effect of the supplementary use (experimental) or not (control) of the XP-endo Finisher on bacteria counting reduction, and results from different endodontic protocols were combined. Four studies met the inclusion criteria while 1 study was excluded from the meta-analysis due to its high risk of bias and outlier data. The 3 studies that made it to the meta-analysis had an unclear risk of bias for at least one criterion. Results: No heterogeneity was observed among the results of the studies included in the meta-analysis. The study excluded from the meta-analysis assessing the bacteria counting deep in the dentin demonstrated further bacteria reduction upon the use of the XP-endo Finisher. Conclusions: This systematic review found no evidence supporting the supplementary use of the XP-endo Finisher on further bacteria counting the reduction in the root canal.