• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.589-593
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• 2013

Sea Surface Temperature (SST) is the temperature close to the ocean's surface and affects the Earth's atmosphere as an important parameter for the climate circulation and change. The SST from satellite still has biases from the error in specifying retrieval coefficients from either forward modeling or instrumental biases. So in this paper, we performed sensitivity analysis using input parameter of the SST to notice that the SST is most affected among the input parameter. We used Infrared (IR) data from the Communication, Ocean, and Meteorological Satellite (COMS)/Meteorological Imager (MI) from April 2011 to March 2012. We also used the Global Space-based Inter-Calibration System (GSICS) correction to quality of the IR data from COMS. SST was calculated by substituting the input parameters; IR data with or without the GSICS correction. The results of this sensitivity analysis, the SST was sensitive from -0.0403 to 0.2743 K when the IR data were changed by the GSICS corrections.

• The Journal of the Acoustical Society of Korea
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• v.24 no.2
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• pp.87-96
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• 2005

In matched field processing (MEP), the observed acoustic field data is basically correlated with the replica produced by the modeling. therefore the results of source localization and correlation is limited by the mismatch of the environment and sensor location. In this paper. the effects of mismatch in environment and system on the bias in estimating the source location are investigated in the context of source localization. In the Pekeris waveguide, the simulation shows that the mismatches in environment and system, can cause a significant biases in the source localization and a degradation in MFP correlation. Mismatch caused by uncertainties in array tilt and depth, bottom depth, bottom sound speed, etc. causes degradation in source localization performance.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.941-946
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• 2011

The Loran-C, a radio navigation system based on TDOA measurements is enhanced to eLoran using TOA measurements instead of TDOA measurements. Many error factors such as PF, SF, ASF, clock errors and unknown biases are included in eLoran TOA measurements. Because these error factors can cause failure in eLoran navigation algorithm, these errors must be compensated for high accuracy eLoran navigation results. Compensation of ASF and unknown biases are difficult to calculate, while the others such as PF and SF are relatively easy to eliminate. In order to compensate all errors in eLoran TOA measurements, a simple GPS aided bias compensation method is suggested in this paper. This method calculates the bias as the difference of TOA measurement and the range between eLoran transmitters and the receiver whose position is determined using GPS. The real data measured in Europe are used for verification of suggested method and navigation algorithm.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.7
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• pp.925-931
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• 2002

Gibbs sampling algorithms were implemented to the multi-trait threshold animal models with any combinations of multiple binary, ordered categorical, and linear traits and investigate the amount of bias on these models with two kinds of parameterization and algorithms for generating underlying liabilities. Statistical models which included additive genetic and residual effects as random and contemporary group effects as fixed were considered on the models using simulated data. The fully conditional posterior means of heritabilities and genetic (residual) correlations were calculated from 1,000 samples retained every 10th samples after 15,000 samples discarded as "burn-in" period. Under the models considered, several combinations of three traits with binary, multiple ordered categories, and continuous were analyzed. Five replicates were carried out. Estimates for heritabilities and genetic (residual) correlations as the posterior means were unbiased when underlying liabilities for a categorical trait were generated given by underlying liabilities of the other traits and threshold estimates were rescaled. Otherwise, when parameterizing threshold of zero and residual variance of one for binary traits, heritability estimates were inflated 7-10% upward. Genetic correlation estimates were biased upward if positively correlated and downward if negatively correlated when underling liabilities were generated without accounting for correlated traits on prior information. Residual correlation estimates were, consequently, much biased downward if positively correlated and upward if negatively correlated in that case. The more categorical trait had categories, the better mixing rate was shown.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.561-576
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• 2007

Five plotting positions are applied to the computation of probability weighted moments (PWM) on the parameters of the generalized logistic distribution. Over a range of parameter values with some finite sample sizes, the effects of five plotting positions are investigated via Monte Carlo simulation studies. Our simulation results indicate that the Landwehr plotting position frequently tends to document smaller biases than others in the location and scale parameter estimations. On the other hand, the Weibull plotting position often tends to cause larger biases than others. The plotting position (i - 0.35)/n seems to report smaller root mean square errors (RMSE) than other plotting positions in the negative shape parameter estimation under small samples. In comparison to the maximum likelihood (ML) method under the small sample, the PWM do not seem to be better than the ML estimators in the location and scale parameter estimations documenting larger RMSE. However, the PWM outperform the ML estimators in the shape parameter estimation when its magnitude is near zero. Sensitivity of right tail quantile estimation regarding five plotting positions is also examined, but superiority or inferiority of any plotting position is not observed.

• Journal of Microbiology
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• v.45 no.1
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• pp.58-63
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• 2007

In this study, the relative synonymous codon and amino acid usage biases of the broad-host range phage, KVP40, were investigated in an attempt to understand the structure and function of its proteins/protein-coding genes, as well as the role of its tRNAs. Synonymous codons in KVP40 were determined to be AT-rich at the third codon positions, and their variations are dictated principally by both mutational bias and translational selection. Further analysis revealed that the RSCU of KVP40 is distinct from that of its Vibrio hosts, V. cholerae and V. parahaemolyticus. Interestingly, the expression of the putative highly expressed genes of KVP40 appear to be preferentially influenced by the abundant host tRNA species, whereas the tRNAs expressed by KVP40 may be required for the efficient synthesis of all its proteins in a diverse array of hosts. The data generated in this study also revealed that KVP40 proteins are rich in low molecular weight amino acid residues, and that these variations are influenced primarily by hydropathy, mean molecular weight, aromaticity, and cysteine content.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.06a
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• pp.37-37
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• 2008

Hydrogenated nanocrystalline Si (nc-Si:H) thin films were prepared by plasma enhanced chemical vapor deposition (PECVD). The films were deposited with a radio frequency power of 100 W, while substrates were exposed to direct current (DC) biases in the range from 0 to -400 V. The effects of the DC bias on the formation of nanoscale Si crystallites in the films and on their optical characteristics were investigated. The size of the Si crystallites in the films ranges from ~ 1.9 to ~ 4.1 nm. The relative fraction of the crystallites in the films reached up ~ 56.5 % when the DC bias of -400 V was applied. Based on the variation in the structural, chemical, and optical features of the films with DC bias voltages, a model for the formation of nanostructures of the nc-Si:H films prepared by PECVD was suggested. This model can be utilized to understand the evolution in the size and relative fraction of the nanocrystallites as well as the amorphous matrix in the nc-Si:H films.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.155-162
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• 2016

TASS(Towed Array Sonar System) is a sonar system which tows the sensor array behind a platform. Array shape is generally assumed to be a straight line. But the array shape is often distorted by oceanic current or platform maneuvering which causes the performance loss of signal processing method like beamforming. So array shape estimation methods are needed. Typically the method based on Kalman filter using heading sensor is used. In practice, the measurement is corrupted by biases which are caused by rotation of the tow cable, varying magnetic fields and slowly varying stresses in the mechanical construction. Although they can't be calibrated but can be estimated. In this paper, we suggest the array shape estimation method based on Kalman filter compensating the sensor biases.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.598-601
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• 2006

The radio signal in GNSS was intentionally designed with two frequencies in order to combat the dispersion error caused by trans-ionospheric propagation. By measuring the path delay independently at the two, widely spaced GPS frequencies, L1 & L2, the TEC along the path from satellite to receiver can be measured directly. The issue with dual frequency measurement of the ionosphere is the calibration of L1/L2 interfrequency biases. L1/L2 interfrequency biases are generated because physical electric signal paths of L1 and L2 circuits are different from each other for both satellites and receiver. Conventionally L1/L2 interfrequency bias is estimated and broadcasted by 2D ionospheric model. In this paper, we estimated IFB (interfrequency bias) by 2D & 3D ionospheric models including real time filter methods and compared the result of those and concluded the merit of 3D tomography model to recover the problem of 2D thin shell model. We confirmed our conclusion by experimental data.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.