• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• 제42권3호
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• pp.139-143
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• 2016

Objectives: If teeth are missing, super-eruption of teeth in the opposing arch can occur in the area and can change the occlusal plane. When missing teeth are replaced with implants, the oral surgeon must determine whether or not the super-erupted teeth need to be treated in order to normalize the occlusal plane. In this study, we evaluated the clinical prognosis of dentition after implant placement and prosthetic treatment were completed in an occlusal plane altered by super-erupted teeth in the opposing arch without additional treatment of the super-erupted teeth. Materials and Methods: Twenty-two patients (9 males, 13 females) were treated with implants and prosthetics without addressing the super-erupted opposing dentition from April 2004 to August 2012 at Seoul National University Bundang Hospital. A total of 33 implants were placed. Values of crestal bone loss, survival rates, and surgical and prosthetic complications for an average of 29.6 months after prosthetic loading were recorded. Results: In one case, the cover screw was exposed after implant surgery. The mean crestal bone loss was $0.09{\pm}0.30mm$. Of the 33 implants, 31 survived, a survival rate of 93.94%. A prosthetic complication occurred in one case but functioned well after correction. Conclusion: Favorable clinical results from prosthetic complications, crestal bone loss, and implant survival rates were exhibited in implants next to a super-erupted opposing tooth.

• 한국수자원학회논문집
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• 제48권5호
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• pp.357-365
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• 2015

결측 강수량을 보완하기 위한 방법으로 사용되고 있는 거리고도비율법에 포함되어 있는 거리와 고도차의 지수는 동일하게 제곱으로 고정되어 있으나 본 연구에서는 이 두 개의 지수를 각각 분리하여 분석하였다. 적용 사례로 한강 유역에 있는 평창을 기준 관측소로 하고 주변에 있는 5개 지표 관측소(방림, 수주, 청옥산, 진부, 영월1)의 10년(2004~2013년) 간 326개 시우량에 적용한 결과 적합한 거리와 고도의 지수 값은 각각 3.7, 0.57로 나타났다. 수정거리고도비율법의 거리와 고도의 지수는 결측 보완 또는 보간을 필요로 하는 위치의 지형공간적인 특성에 따라 맞게 적용해야 한다.

• 제어로봇시스템학회논문지
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• 제22권4호
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• pp.276-280
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• 2016

Due to the increasing interest in safety and consistent product quality over a past few decades, demand for effective quality monitoring and safe operation in the modern industry has propelled research into statistical based fault detection and diagnosis methods. This paper describes the application of Hotelling $T^2$ index based Principal Component Analysis (PCA) method for fault detection and diagnosis in industrial processes. Multivariate statistical process control techniques are now widely used for performance monitoring and fault detection. Conventional methods such as PCA are suitable only for steady state processes. These conventional projection methods causes false alarms or missing data for the systems with transient values of processes. These issues significantly compromise the reliability of the monitoring systems. In this paper, a reliable method is used to overcome false alarms occur due to varying process conditions and missing data problems in transient states. This monitoring method is implemented and validated experimentally along with matlab. Experimental results proved the credibility of this fault detection method for both the steady state and transient operations.

• 한국빅데이터학회지
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• 제5권1호
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• pp.227-239
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• 2020

본 논문에서는 산업현장에서 통신 오류에도 불구하고 최대전력수요를 예측하는 방법을 소개한다. 최근 국내의 탈원전 정책으로 전력가격상승은 불가피하며, 이에 따른 전력수요 관리를 위한 전력사용량과 최대부하관리는 중요한 문제로 부상하고 있다. 이에 따라, 피크전력을 예측하고 관리하는 것이 중요하다. 하지만 실제 산업현장에서는 각종 설비 및 센서에서 발생하는 노이즈 등으로 인해 측정된 전력데이터의 손실 및 변조 등의 문제가 발생한다. 측정된 유효전력 데이터가 손실된 경우 정확한 값을 예측하기 어렵다. 이 연구는 측정된 유효전력 데이터가 손실될 경우 이상 징후와 결측값을 예측하고 수정하는 모델을 제시한다. 본 연구에 사용된 모델은 산업현장에서 통신 오류가 발생할 경우 최대 전력수요를 예측하는 데 유용할 것으로 예상한다.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.703-713
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• 2002

In longitudinal studies, outcomes are repeatedly measured over time for each subject. It is common to have missing values or dropouts for longitudinal data. In this study time trend in longitudinal data with dropouts is of concern. The confounding of time trend with dropout process is investigated through simulation studies. Some simulation results are reported for binary responses as well as continuous responses with patterns of dropouts varying. It has been found that time trend is not confounded with random dropout process for binary responses when it is estimated using GEE.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.243-251
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• 2000

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause the inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.

• 응용통계연구
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• 제24권6호
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• pp.1103-1113
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• 2011

Gene expression data is obtained through many stages of an experiment and errors produced during the process may cause missing values. Due to the distinctness of the data so called 'small n large p', genes have to be selected for statistical analysis, like classification analysis. For this reason, imputation and gene selection are important in a microarray data analysis. In the literature, imputation, gene selection and classification analysis have been studied respectively. However, imputation, gene selection and classification analysis are sequential processing. For this aspect, we compare the performance of classification methods after imputation and gene selection methods are applied to microarray data. Numerical simulations are carried out to evaluate the classification methods that use various combinations of the imputation and gene selection methods.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 봄 학술발표논문집 Vol.30 No.1 (B)
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• pp.446-448
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• 2003

실세계의 많은 데이터는 결측 값들을 포항하고 있기 때문에 데이터 마이닝 시스템에 완벽한 데이터를 제공하기는 불가능하다. 또한 결측 값이 존재하는 대용량의 데이터를 추천시스템에 적용하여 분석하고자 할 경우, 정확성이 떨어지는 결과를 초래할 수 있다. 따라서 데이터에 결측 값이 존재할 경우 입력 데이터를 사전에 보간하는 전처리 방법이 필요하다. 이러한 기존의 보간 전처리 방법에는 결측 값 속성을 삭제하거나 대치하는 방범이 대표적이나. 삭제 방법은 결측 값이 존재하는 데이터를 제거하는 방법으로 중요속성 삭제 및 데이터 손실을 유발하는 단점이 있어 일반적으로 결측 값을 다른 값으로 처리하는 대치 방범이 널리 사용된다. 본 논문에서는 전처리 방법 중 결측 값을 처리하는 가장 일반적인 대치 방법과 신경망을 이용한 평가 예측 처리 방법을 소개한다. 또한 신경망을 이용 결측 값을 대치하는 새로운 모델을 제안하고, 각각의 결측 값 처리방법을 비교 분석한다.

• 한국지구과학회지
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• 제38권6호
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• pp.427-441
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• 2017

Theory of Earth's free oscillation is revisited. Firstly, we summarized the underlying formulations, such as the equation of motion and its conversion into numerically integrable form and then explained computational procedures including the treatment of inner core-outer core boundary and core-mantle boundary, while the latter information has not been explicitly given in most publications. Secondly, we re-calculated the periods of Earth's free oscillation modes (period >200 s) for PREM model. In doing so we acquired the values of modes missing in Dziewonski and Anderson (1981). As a case observation, one seismogram after 2011 Tohoku earthquake recorded at Daejeon, Korea (KIGAM seismic station) was briefly analyzed to identify free oscillation mode excitations on its spectra. The material in this article will be most clear guide for those on calculating the Earth's free oscillation mode.

• 한국지능시스템학회논문지
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• 제11권5호
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• pp.441-447
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• 2001

In general, Rough Set theory is used for classification, inference, and decision analysis of incomplete data by using approximation space concepts in information system. Information system can include quantitative attribute values which have interval characteristics, or incomplete data such as multiple or unknown(missing) data. These incomplete data cause tole inconsistency in information system and decrease the classification ability in system using Rough Sets. In this paper, we present various types of incomplete data which may occur in information system and propose INcomplete information Processing System(INiPS) which converts incomplete information system into complete information system in using Rough Sets.