• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.3-3
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• 2019

최근 기후변화로 인한 집중호우, 가뭄 등 예측하기 어려운 사태가 발생하면서 깨끗하고 안정적인 용수공급 기술의 필요성이 대두되고 있다. 이에 IoT와 기존 물관리시스템을 결합한 스마트워터그리드 출범은 실시간으로 수요와 공급량의 정보를 취득하여 물 관리 효율성을 제고 할 수 있게 되었다. 실시간 수요량 자료를 이용하여 물 수요량 예측을 통한 최적의 물 공급량을 결정할 수 있다. 이 때 스마트워터그리드의 핵심 기술은 실시간으로 취득한 자료의 품질관리라 할 수 있다. 본 연구 대상지역인 영종도 112 블록에는 528개 AMI 스마트 미터를 이용하여 1시간 단위의 물 수요량 자료를 원격 검침하고 있다. 각 수용가에 설치된 AMI 센서를 통해 수집된 자료에는 오류를 포함할 수 있는데 통신 장애, 미터기 고장 및 교체 등으로 발생된다. 결측된 수요량 자료는 상수관망 수리해석에 사용되는 기본자료로서 비표본오차를 증가시켜 검정력과 정확성을 결여시키는 문제가 있다. 이에 본 연구에서는 수집된 자료를 가용할 수 있는 자료로 정제하고 대체하기 위해 완전히 관찰된 자료(complete data)만을 이용하여 각 시간에 따른 관경별, 용도별 그리고 요일별 수요패턴을 추정한다. 결측된 자료는 기존에 사용되는 평균대체법과 핫덱 대체(hot deck imputation) 등과 비교 검증한다.

• Steel and Composite Structures
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• v.4 no.5
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• pp.385-402
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• 2004

Nearly 400 composite railway bridge decks of a new kind belonging to the trough type with U-shaped cross section have been constructed in Belgium over the last fifteen years. The construction of these bridge decks is rather complex with the preflexion of precambered steel girders, the prestressing of a concrete slab and the addition of a 2nd phase concrete. Until now, they have been designed with a classical computation method using a pseudo-elastic analysis with modular ratios. Globally, they perform according to the expectations but variability has been observed between the measured and the computed camber of these bridge decks just after the transfer of prestressing and also at long-term. A statistical analysis of the variability of the relative difference between the measured camber and the computed camber is made for a sample of 36 bridge decks using no less than 10 variables. The most significant variables to explain this variability at prestressing are the ratio between the maximum tensile stress reached in the steel girders during the preflexion and the yield strength and the type of steel girder. For the same sample, the long-term camber under permanent loading is computed by two methods and compared with measurements taken one or two years after the construction. The camber computed by the step-by-step method shows a better agreement with the measured camber than the camber computed by the classical method. The purpose of the paper is to report on the statistical analysis which was used to determine the most significant parameters to consider in the modeling in order to improve the prediction of the behaviour of these composite railway bridge decks.

• Survey Research
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• v.11 no.1
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• pp.1-18
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• 2010

We compare various imputation methods for compensating wave nonresponse that are commonly adopted in many panel surveys. Unlike the cross-sectional survey, the panel survey is involved a time-effect in nonresponse in a sense that nonresponse may happen for some but not all waves. Thus, responses in neighboring waves can be used as powerful predictors for imputing wave nonresponse such as in longitudinal regression imputation, carry-over imputation, nearest neighborhood regression imputation and row-column imputation method. For comparison, we carry out a simulation study on a few income data from the Korean Welfare Panel Study based on two performance criteria: predictive accuracy and estimation accuracy. Our simulation shows that the ratio and row-column imputation methods are much more effective in terms of both criteria. Regression, longitudinal regression and carry-over imputation methods performed better in predictive accuracy, but less in estimation accuracy. On the other hand, nearest neighborhood, nearest neighbor regression and hot-deck imputation show higher performance in estimation accuracy but lower predictive accuracy. Finally, the mean imputation shows much lower performance in both criteria.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.377-390
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• 2017

When conducting a survey, item nonresponse occurs if the respondent does not respond to some items. Since analysis based only on completely observed data may cause biased results, imputation is often conducted to analyze data in its complete form. The panel study is a survey method that examines changes of responses over time. In panel studies, there has been a preference for using information from response values of previous waves when the imputation of item nonresponses is performed; however, limited research has been conducted to support this preference. Therefore, this study compares the performance of imputation methods according to whether or not information from previous waves is utilized in the panel study. Among imputation methods that utilize information from previous responses, we consider ratio imputation, imputation based on the linear mixed model, and imputation based on the Bayesian linear mixed model approach. We compare the results from these methods against the results of methods that do not use information from previous responses, such as mean imputation and hot deck imputation. Simulation results show that imputation based on the Bayesian linear mixed model performs best and yields small biases and high coverage rates of the 95% confidence interval even at higher nonresponse rates.