• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.460-460
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• 2018

댐 수문학적 안전성평가는 "시설물의 안전 및 유지관리에 관한 특별벌(이하 시특법)"에 따른 댐시설물의 정밀안전진단의 안전성평가 중 가장 중요한 평가 항목으로 댐 시설물을 평가 수행 시 주요한 평가 항목이다. 기존의 수문학적 안전성평가는 가능최대강수량 발생 시 댐의 월류 및 여유고 확보여부에 대한 평가 여부만 판단하고 있으나, 본 연구에서는 기후변화를 고려하는 장기적 관점의 추가 평가항목을 도출하고자 한다. 현재 가능최대강수량으로 event적 평가를 수행하는 수문학적 안전성 평가에서 기존평가항목 뿐만 아니라, 기후변화 장기적 관점의 추가적인 기후영향인자를 도출하고 이를 함께 적용할 수 있는 평가 체계를 구축하고자한다. 장기적 관점의 기후영향인자라 함은 기상청에서 제공하는 기후변화 시나리오 결과에서 30년동안 장기적인 관점에서 대상 댐의 운영에 부담을 야기할 것으로 판단되는 인자를 말하는 것이며, 이때 기후변화 시나리오의 일자료를 활용하여 기후인자의 장기적 변동성을 추정하고자 하며, 이때 활용한 지표로는 월최대강수량, 연강우강도 및 댐 상태에 영향을 미칠 수 있는 최소기온을 사용하였다. 기후변화 시나리오의 불확실성을 최소화하기 위하여 월최대 강수량값을 산출하였고, 1년 동안 발생한 강우의 일수 및 강수량에 대한 영향을 고려하기 위하여 연강우강도값을 산출하였다. 또한 댐의 월류 및 여유고 확보여부 평가 시 댐 상태에 대하여 고려하기 때문에 댐의 외부상태에 영향을 주는 최소기온을 활용하여 댐별 평가를 수행하였다. 이때 2011~2040년(S1), 2041년~2070년(S2), 2071년~2100년(S3)기간으로 나누어 장기간 기후에 대한 영향 평가를 수행하여 1종 댐 시설물의 기후영향인자 값을 도출하였다. 도출된 기후영향인자를 기존 수문학적 안전성평가 항목과 함께 평가 될 수 있도록 AHP분석기법을 활용하여 각 인자에 대한 가중치를 재산출하였고, 기후영향인자를 고려하는 수문학적 안전성평가 체계를 구축하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7A
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• pp.710-716
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• 2007

In this paper, a new scheme of downlink co-channel interference (CCI) cancellation in OFDM cellular networks is introduced for users at the cell-edge. Coordinated symbol transmission between base stations (BS) is operated where the same symbol is transmitted from different BS on different sub-carriers. At the mobile station (MS) receiver, we introduce a soft decision maximum likelihood CCI canceler and a modified maximum ratio combining (M-MRC) to obtain an estimate of the transmitted symbols. Weights used in the combining method are derived from the channels coefficients between the cooperated BSs and the MS. Simulations show that the proposed scheme works well under frequency-selective channels and frequency non-selective channels. A gain of 9 dB and 6 dB in SIR is obtained under multipath fading and flat-fading channels, respectively.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.835-847
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• 2016

To analyze lattice or areal data, a conditionally autoregressive (CAR) model has been widely used in the eld of spatial analysis. The spatial neighborhoods within CAR model are generally formed using only inter-distance or boundaries between regions. Kyung and Ghosh (2010) proposed a new class of models to accommodate spatial variations that may depend on directions. The proposed model, a directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Properties of maximum likelihood estimators of a Gaussian DCAR are discussed. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1017-1021
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• 2009

일반적인 설계 홍수량 산정 절차는 분석하고자 하는 대상유역의 수문자료 중 홍수량 자료가 존재하지 않을 경우 강우빈도해석을 실시하고, 만약 홍수량 자료가 존재한다면 유출을 통계분석하여 홍수빈도 해석을 실시하여야 한다. 본 연구에서는 1999$^{\sim}$2008년까지 수위-유량 관측을 실시하여 유출자료를 비교적 충분히 보유하고 있는 서울시 관내 지방하천인 우이천 유역을 대상으로 수위-유량관계곡선을 작성하여 과거 호우사상을 토대로 강우-유출모형의 매개변수를 최적화하였으며, 최적화된 모형을 이용하여 기상청관할 서울지점 시간강우관측 자료를 입력 자료로 유출모의를 실시하였다. 모의된 홍수량계열과 관측유량계열을 사용하여 연최대홍수계열을 구축한 후 홍수빈도해석을 실시하였다. 분석결과 기존의 '확률강우량-단위도' 방법에 비하여 불확실성이 제거된 확률홍수량 추정치의 결과를 얻을 수 있었다.

• The Korean Journal of Applied Statistics
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• v.18 no.3
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• pp.543-553
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• 2005

Linear logistic regression is one of the most widely used method for credit scoring in credit risk management. This paper deals with credit scoring using splines based on Logistic regression. Linear splines and an automatic basis selection algorithm are adopted. The final model is an example of the generalized additive model. A simulation using a real data set is used to illustrate the performance of the spline method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.41-47
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• 2024

With the advancement and proliferation of the Internet of Things (IoT), a wide range of location-based services are being offered, and various ranging methods are being researched to meet the objectives of the required services. Conventional ranging methods involve the direct exchange of signals between tags and anchors to estimate distance, presenting a limitation in efficiently utilizing communication resources in large-scale IoT environments. To overcome these limitations, active-passive ranging methods have been proposed. However, there is a lack of theoretical convergence guarantees against clock drift errors and a detailed analysis of the characteristics of ranging estimation techniques, making it challenging to derive precise positioning results. In this paper, an improved active-passive ranging method that accounts for clock drift errors is proposed for precise positioning in large-scale IoT environments. The simulation results confirmed that the proposed active-passive ranging method can enhance distance estimation performance by up to 94.4% and 14.4%, respectively, compared to the existing active-passive ranging methods.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.2
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• pp.197-204
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• 2011

RCM is used to clarify the complex phenomena of engine combustion. In order to describe engine combustion, several significant experimental studies are considered. Prediction of the peak pressure is very important since it has a significant influence on engine combustion. In addition, peak-temperature variation can be calculated from the measured peak pressure by using the fundamental thermodynamic relation. When the RCM is in operation, heat transfer occurs through the cylinder wall. Because of this phenomenon, it is difficult to determine the peak pressure without employing the case by case experimental method. The goal of this study is to evaluate the peak pressure analytically. We conduct an experiment to confirm the relationship between the peak pressure and some parameters. Using the results of the peak pressure variation experiment, we develop a general equation that be used to calculate the peak pressure as a function of operation time and compression ratio.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.8
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• pp.95-104
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• 2016

Subspace-based direction-of-arrival (DOA) estimation algorithms have a difficulty in dealing with coherent signals caused by multi-path environment. As one of attempts to solve this problem, a spatial differencing method is known to be useful for not only estimating DOAs of the coherent signals but also improving the number of resolvable wavefronts even more than the number of antenna elements. However, since the conventional spatial differencing method uses only the partial statistics of the observed data, this method suffers from the performance degradation in estimation accuracy caused by the residual correlation between the uncorrelated signals. To cope with this problem, in this paper, a generalized spatial differencing method is proposed. Unlike the conventional method, the proposed method utilizes the entire statistics of the received signals. Therefore, the additional performance enhancement in both estimation accuracy and the number of resolvable wavefronts can be achieved. The performance analyses with computer simulations show that the proposed method outperforms the conventional method in terms of the estimation accuracy and the number of resolvable wavefronts.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.587-598
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• 2016

In predicting an outcome of election using a variety of methods ahead of the election, non-response is one of the major issues. Therefore, to address the non-response issue, a variety of methods of non-response imputation may be employed, but the result of forecasting tend to vary according to methods. In this study, in order to improve electoral forecasts, we studied a model based method of non-response imputation attempting to apply the Monte Carlo Expectation Maximization (MCEM) algorithm, introduced by Wei and Tanner (1990). The MCEM algorithm using maximum likelihood estimates (MLEs) is applied to solve the boundary solution problem under the non-ignorable non-response mechanism. We performed the simulation studies to compare estimation performance among MCEM, maximum likelihood estimation, and Bayesian estimation method. The results of simulation studies showed that MCEM method can be a reasonable candidate for non-response model estimation. We also applied MCEM method to the Korean presidential election exit poll data of 2012 and investigated prediction performance using modified within precinct error (MWPE) criterion (Bautista et al., 2007).

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.2
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• pp.122-133
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• 2021

Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.