• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.681-695
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• 2012

In this paper, a family of estimators for the finite population variance investigated by Srivastava and Jhajj (1980) is studied under two different situations of random non-response considered by Tracy and Osahan (1994). Asymptotic expressions for the biases and mean squared errors of members of the proposed family are obtained; in addition, an asymptotic optimum estimator(AOE) is also identified. Estimators suggested by Singh and Joarder (1998) are shown to be members of the proposed family. A correction to the Singh and Joarder (1998) results is also presented.

• Journal of Sensor Science and Technology
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• v.24 no.6
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• pp.368-372
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• 2015

In this paper, we proposed a post-processing technique for improving classification performance of electronic nose (E-Nose) system which may be occurred drift signals from sensor array. An adaptive radial basis function network using stochastic gradient (SG) and singular value decomposition (SVD) is applied to process signals from sensor array. Due to drift from sensor's aging and poisoning problems, the final classification results may be showed bias and fluctuations. The predicted classification results with drift are quantized to determine which identification level each class is on. To mitigate sharp fluctuations moving-averaging (MA) technique is applied to quantized identification results. Finally, quantization and some edge correction process are used to decide levels of the fluctuation-smoothed identification results. The proposed technique has been indicated that E-Nose system was shown correct odor identification results even if drift occurred in sensor array. It has been confirmed throughout the experimental works. The enhancements have produced a very robust odor identification capability which can compensate for decision errors induced from drift effects with sensor array in electronic nose system.

• Journal of the Korean Statistical Society
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• v.32 no.1
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• pp.85-92
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• 2003

We consider the partially linear model E(Y) : X$^{t}$ $\beta$＋η(Z) when the X's are measured with additive error. The semiparametric likelihood estimation ignoring the measurement error gives inconsistent estimator for both $\beta$ and η(.). In this paper we suggest the SIMEX estimator for f to correct the bias induced by measurement error, and explore its properties. We show that the rational linear extrapolant is proper in extrapolation step in the sense that the SIMEX method under this extrapolant gives consistent estimator It is also shown that the SIMEX estimator is asymptotically equivalent to the semiparametric version of the usual parametric correction for attenuation suggested by Liang et al. (1999) A simulation study is given to compare two variance estimating methods for SIMEX estimator.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.514-514
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• 2015

홍수 예측의 개선에 있어 정확한 공간 토양수분 정보는 필수적이다. 위성관측을 활용한 토양수분관측이 이루어지고 있으나 실제적 토양수분 상태와 정량적 차이가 크므로 편이보정을 통한 정량적 개선과정이 요구되는 실정이다. 따라서, 본 연구에서는 위성에서 관측한 AMSR2 토양수분과 지상관측 토양수분자료 및 다중회귀모형를 이용하여 토양수분자료를 정량적로 개선하였다. 공간 해상도가 10 km인 AMSR2 토양수분을 1 km로 상세화한 우리나라 전역의 토양수분 자료와 수자원관리종합정보시스템(WAMIS)에서 제공하는 강우관측소 556개 지점에서 관측한 강우자료, 후처리한 MODIS LST 자료, 증발산량 및 식생지수를 사용하였다. 2012년 7월부터 2013년까지 기상청 농업기상관측관서에서 관측하는 지점 중 사용 가능한 6개 토양수분관측소 자료에 대해 토양군별회귀계수를 산정하였다. 토양군별 다중회귀모형을 이용하여 편이보정한 토양수분자료는 전반적으로 과소추정되는 AMSR2 토양수분의 단점을 개선하여 위성관측 토양수분자료의 활용성을 개선하였다(Fig. 1).

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.309-309
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• 2019

기후예측모델을 통해 일단위 강수의 예측정보가 제공되고 있지만, 실제 강수량자료와 시공간적 편의로 인해 수문학적 활용은 한계가 있다. 일반적으로 기후모델의 시공간적 해석 규모 및 예측정확성을 고려할 때 계절단위에서 예측정보의 활용이 가장 현실적인 것으로 알려지고 있다. 그러나 수문해석 시 시공간적 해상도가 낮아 직접적인 활용은 어려운 상황이며, 수문해석 모형의 입력자료로 활용 시 편의보정 및 상세화 과정이 일반적으로 요구된다. 본 연구에서는 기후모델로부터 얻은 강우예측결과에 Bayesian 모델 기반의 편의보정-상세화 기법을 개발하여 강우예측정보의 활용성을 개선하고자 한다. 이 과정에서 Bayesian Copula 모델을 이용한 이변량 형태의 예측강수의 검보정 방법을 개발하였으며, 특히 기후모델 이외의 기상 상태변량인 해수면온도(sea surface temperature, SST)를 예측인자로 추가하여 Hybrid 형태의 계절 앙상블 강우예측모델을 개발하고자 한다.

• Journal of Korea Water Resources Association
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• v.42 no.6
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• pp.493-503
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• 2009

Estimation of the mean-field bias of radar rainfall is to determine the difference between the areal means of radar and rain gauge rainfall, where the rain gauge rainfall is assumed to be the truth. To exactly determine this bias, the variance of the difference between two observations must be small enough, thus, enough number of observations is indispensible. So, the problem becomes to determine the number of rain gauges to satisfy the level of variance of the difference between two observations. Especially, this study focuses on the case when the rain gauges are disproportionate spatially. This is the problem for the Ganghwa rain radar for the observation of rainfall within the Imjin river basin and the same problem also occurs when a radar is located in between land and ocean. This study considered the Imjin river basin, and compared two cases when rain gauges are available only within the downstream part, about one third of the whole basin, and over the whole basin. Based on the results derived, the rain gauge density within the downstream part of the Imjin river basin was proposed to secure the same accuracy obtained when the rain gauges are available over the whole Imjin river basin.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.3
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• pp.293-301
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• 2011

The ionospheric delay is the largest error source in GPS positioning after the SA effect has been turned off in May, 2000. In this study, we used 44 permanent GPS stations being operated by National Geographic Information Institute (NGII) to estimate Total Electron Content (TEC) based on pseudorange measurements phase-leveled by a linear combination with carrier phases. The Differential Code Bias (DCB) of GPS satellites and receivers was estimated and applied for an accurate estimation of the TEC. To validate our estimates of DCB, changes of TEC values after DCB application were investigated. As a result, the RMS error went down by about an order of magnitude; from 35~45 to 3~4 TECU. After the DCB correction, ionospheric TEC maps were produced at a spatial resolution of $1^{\circ}{\times}1^{\circ}$. To analyze the effect of the number of sites used for map generation on the accuracy of TEC values, we tried 10, 20, 30, and 44 stations and the RMS error was computed with the Global Ionosphere Map as the truth. While the RMS error was 5.3 TECU when 10 sites are used, the error reduced to 3.9 TECU for the case of 44 stations.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.1
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• pp.23-32
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• 2008

According to recent frequent local flash flood due to climate change, the very short-term rainfall forecast using remotely sensed rainfall like radar is necessary to establish. This research is to evaluate the feasibility of GIS-based distributed model coupled with radar rainfall, which can express temporal and spatial distribution, for multipurpose dam operation during flood season. $Vflo^{TM}$ model was used as physically based distributed hydrologic model. The study area was Yongdam dam basin ($930\;km^2$) and the 3 storm events of local convective rainfall in August 2005, and the typhoon.Ewiniar.and.Bilis.collected from Jindo radar was adopted for runoff simulation. Distributed rainfall consistent with hydrologic model grid resolution was generated by using K-RainVieux, pre-processor program for radar rainfall. The local bias correction for original radar rainfall shows reasonable results of which the percent error from the gauge observation is less than 2% and the bias value is $0.886{\sim}0.908$. The parameters for the $Vflo^{TM}$ were estimated from basic GIS data such as DEM, land cover and soil map. As a result of the 3 events of multiple peak hydrographs, the bias of total accumulated runoff and peak flow is less than 20%, which can provide a reasonable base for building operational real-time short-term rainfall-runoff forecast system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.4
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• pp.143-154
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• 2012

As demand of water resources and attentions to changes in climate (e.g., due to ENSO) increase, long/short term prediction of precipitation is getting necessary in water planning. This research evaluated the ability of MM5 to predict precipitation in the Tampa Bay region over 23 year period from 1986 to 2008. Additionally MM5 results were statistically bias-corrected using observation data at 33 stations over the study area using CDF-mapping approach and evaluated comparing to raw results for each ENSO phase (i.e., El Ni$\tilde{n}$o and La Ni$\tilde{n}$a). The bias-corrected model results accurately reproduced the monthly mean point precipitation values. Areal average daily/monthly precipitation predictions estimated using block-kriging algorithm showed fairly high accuracy with mean error of daily precipitation, 0.8 mm and mean error of monthly precipitation, 7.1 mm. The results evaluated according to ENSO phase showed that the accuracy in model output varies with the seasons and ENSO phases. Reasons for low predictions skills and alternatives for simulation improvement are discussed. A comprehensive evaluation including sensitivity to physics schemes, boundary conditions reanalysis products and updating land use maps is suggested to enhance model performance. We believe that the outcome of this research guides to a better implementation of regional climate modeling tools in water management at regional/seasonal scale.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1269-1279
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• 2021

Geostationary Ocean Color Imager II (GOCI-II), which are now operated successfully since its launch in 2020, acquires local area images with 12 Level 1B slot images that are sequentially acquired in a 3×4 grid pattern. The boundary areas between the adjacent slots are prone to discontinuity in radiance, which becomes even more clear in the following Level 2 data, and this warrants the precise analysis and correction before the distribution. This study evaluates the relative radiometric biases between the adjacent slots images, by exploiting the overlapped areas across the images. Although it is ideal to derive the statistics from humongous images, this preliminary analysis uses just the scenes acquired at a specific time to understand its general behavior in terms of bias and variance in radiance. Level 1B images of February 21st, 2021 (UTC03 = noon in local time) were selected for the analysis based on the cloud cover, and the radiance statistics were calculated only with the ocean pixels. The results showed that the relative bias is 0~1% in all bands but Band 1 (380 nm), while Band 1 exhibited a larger bias (1~2%). Except for the Band 1 in slot pairs aligned North-South, biases in all direction and in all bands turned out to have biases in the opposite direction that the sun elevation would have caused.