• Journal of Positioning, Navigation, and Timing
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• v.3 no.4
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• pp.155-161
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• 2014

The purpose of this study is to develop precise point positioning (PPP) algorithms based on GLONASS code-pseudorange, verify their performance and present their utility. As the basic correction models of PPP, we applied Inter Frequency Bias (IFB), relativistic effect, satellite antenna phase center offset, and satellite orbit and satellite clock errors, ionospheric errors, and tropospheric errors that must be provided on a real-time basis. The satellite orbit and satellite clock errors provided by Information-Analytical Centre (IAC) are interpolated at each observation epoch by applying the Lagrange polynomial method and linear interpolation method. We applied Global Ionosphere Maps (GIM) provided by International GNSS Service (IGS) for ionospheric errors, and increased the positioning accuracy by applying the true value calculated with GIPSY for tropospheric errors. As a result of testing the developed GLONASS PPP algorithms for four days, the horizontal error was approximately 1.4 ~ 1.5 m and the vertical error was approximately 2.5 ~ 2.8 m, showing that the accuracy is similar to that of GPS PPP.

• Survey Research
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• v.12 no.3
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• pp.25-48
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• 2011

In this paper, we overview the sample design, sampling error, non-response rate and prediction errors of the exit poll conducted for 2010 local elections and discusses how to detect a prediction bias in exit poll. To investigate the bias problem in exit poll in regional(Si-Do) level, we analyze exit poll data for 2007 presidential election and 2006 local elections as well as 2010 local elections in Korea. The measure of predictive accuracy A proposed by Martin et al.(2005) is used to assess the exit poll bias. The empirical studies based on three exit polls clearly show that there exits systematic bias in exit poll and the predictive bias of candidates affiliated to conservative party (such as Hannara-Dang) is serious in the specific regions. The result of this study on systematic bias will be very useful to improving the exit poll methodology in Korea.

• Korean Journal of Remote Sensing
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• v.34 no.1
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• pp.1-15
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• 2018

Passive microwave sea surface temperatures (SST) were validated in the Northwest Pacific using a total of 102,294 collocated matchup data between Global Precipitation Measurement (GPM) / GPM Microwave Sensor(GMI) data and oceanic in-situ temperature measurements from March 2014 to December 2016. A root-mean-square (RMS) error and a bias error of the GMI SST measurements were evaluated to $0.93^{\circ}C$ and $0.05^{\circ}C$, respectively. The SST differences between GMI and in-situ measurements were caused by various factors such as wind speed, columnar atmospheric water vapor, land contamination near coastline or islands. The GMI SSTs were found to be higher than the in-situ temperature measurements at low wind speed (<6 m/s) during the daytime. As the wind speed increased at night, SST errors showed positive bias. In addition, other factors, coming from atmospheric water vapor, sensitivity degradation at a low temperature range, and land contamination, also contributed to the errors. One of remarkable characteristics of the errors was their latitudinal dependence with large errors at high latitudes above $30^{\circ}N$. Seasonal characteristics revealed that the errors were most frequently observed in winter with a significant positive deviation. This implies that SST errors tend to be large under conditions of high wind speeds and low SSTs. Understanding of microwave SST errors in this study is anticipated to compensate less temporal capability of Infrared SSTs and to contribute to increase a satellite observation rate with time, especially in SST composite process.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1325-1328
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• 2007

When we visualize the sound field radiated from a spherical sound source, spherical acoustic holography is proper among acoustic holography methods. However, there are measurement errors due to sensor position mismatch, sensor mismatch, directivity of sensor, and background noise. These errors are amplified if one predicts the pressures close to the sources: backward prediction. The goal of this paper is to quantitatively examine the effects of the error due to sensor position mismatch on acoustic pressure estimation. This paper deals with the cases of which the measurement deviations are distributed irregularly on the hologram plane. In such cases, one can assume that the measurement is a sample of many measurement events, and the cause of the measurement error is white noise on the hologram plane. Then the bias and random error are derived mathematically. In the results, it is found that the random error is important in the backward prediction. The relationship between the random error amplification ratio and the measurement parameters is derived quantitatively in terms of their energies.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.185-190
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• 2006

The correct estimation of the ionospheric delays is very important for the precise kinematic positioning especially in case of the long baseline. In case of triple frequency system, the ionospheric delays can be estimated from the measurements, but, in case of dual frequency system, the situation is not so simple. The precision of those supplied by the external information source such as IONEX is not sufficient. The high frequency component is neglected, and the precision of the low frequency component is not sufficient for the long baseline positioning. On the other hand, the high frequency component can be estimated from the phase range measurements. If the low frequency components are estimated by using the external information source or pseudo range measurements, a more reasonable estimation of the ionospheric delays may be possible. It has already been discussed by the author that the estimation of the low frequency components by using the external information source is not sufficient but fairly effective. The estimation using the pseudo range measurements is discussed in the present paper. The accuracy is not sufficient at present because of the errors in the pseudo range measurements. It is clarified that the bias errors in the pseudo range measurements are responsible for the poor accuracy of the ionospheric delays. However, if the accuracy of the pseudo range measurements is improved in future, the method would become very promising.

• Atmosphere
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• v.25 no.2
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• pp.205-219
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• 2015

In this study, the simulation results of temperature by regional climate model (Reg- CM4) over South Korea were corrected by Hempel et al. (2013)'s method (Hempel method), and evaluated with the observation data of 50 stations from Korea Meteorological Administration. Among the 30 years (1981~2010) of simulation data, 20 years (1981~2000) of simulation data were used as a training data, and the remnant 10 years (2001~2010) data were used for the evaluation of correction. In general, the Hempel method and parametric quantile mapping show a reasonable correction both in mean and extreme climate of temperature. As the results, the systematic underestimation of mean temperature was greatly reduced after bias correction by Hempel method. And the overestimation of extreme climate, such as the number of TN5% and freezing day, was significantly recovered. In addition to that, the Hempel method better preserved the temporal trend of simulated temperature than other bias correction methods, such as the quantile mapping. However, the overcorrection of the extreme climate related to the upper quantile, such as TX5% and hot days, resulted in the exaggeration of the simulation errors. In general, the Hempel method can reduce the systematic biases embedded in the simulation results preserving the temporal trend but it tends to overcorrect the non-linear biases, in particular, extreme climate related to the upper percentile.

• Bulletin of the Korean Chemical Society
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• v.35 no.12
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• pp.3482-3488
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• 2014

Matrix-induced mass bias and its effect on the accuracy of isotope ratio measurements have been examined for a quadrupole-based inductively coupled plasma-mass spectrometer (Q ICP-MS). Matrix-induced mass bias effect was directly proportional to % mass difference, and its magnitude varied for element and nebulizer flow rate. For a given element and conditions in a day, the effect was consistent. The isotope ratio of Cd106/Cd114 under $200{\mu}g\;g^{-1}$ U matrix deviated from the natural value significantly by 3.5%. When Cd 111 and Cd114 were used for the quantification of Cd with isotope dilution (ID) method, the average of differences between the calculated and measured concentrations was -0.034% for samples without matrix ($0.076{\mu}g\;g^{-1}$ to $0.21{\mu}g\;g^{-1}$ for the period of 6 months). However, the error was as large as 1.5% for samples with $200{\mu}g\;g^{-1}$ U. The error in ID caused by matrix could be larger when larger mass difference isotopes are used.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.4
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• pp.331-338
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• 2004

This paper describes capability of utilizing ground control points(GCPs) obtained from 1:1,000 and 1:5,000 digital vector maps to correct image coordinates which have errors due to bais rational polynomial coefficient(RPC) of IKONOS Geo-level stereo images. The accuracy of the bias-corrected images was improved to approximately 4m and 2m in planimetry and height, respectively. The accuracy was also compared with results from using GCPs obtained by GPS surveying. In consequence, bias-compensated IKONOS sereo imagery was evaluated to satisfy generating topographic map 1:10,000.

• ETRI Journal
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• v.34 no.3
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• pp.379-387
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• 2012

In this paper, an error compensation technique for a dead reckoning (DR) system using a magnetic compass module is proposed. The magnetic compass-based azimuth may include a bias that varies with location due to the surrounding magnetic sources. In this paper, the DR system is integrated with a Global Positioning System (GPS) receiver using a finite impulse response (FIR) filter to reduce errors. This filter can estimate the varying bias more effectively than the conventional Kalman filter, which has an infinite impulse response structure. Moreover, the conventional receding horizon Kalman FIR (RHKF) filter is modified for application in nonlinear systems and to compensate the drawbacks of the RHKF filter. The modified RHKF filter is a novel RHKF filter scheme for nonlinear dynamics. The inverse covariance form of the linearized Kalman filter is combined with a receding horizon FIR strategy. This filter is then combined with an extended Kalman filter to enhance the convergence characteristics of the FIR filter. Also, the receding interval is extended to reduce the computational burden. The performance of the proposed DR/GPS integrated system using the modified RHKF filter is evaluated through simulation.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.821-840
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• 2009

This article addresses the problem of estimating a family of general population parameter ${\theta}_{({\alpha},{\beta})}$ using auxiliary information in the presence of measurement errors. The general results are then applied to estimate the coefficient of variation $C_Y$ of the study variable Y using the knowledge of the error variance ${\sigma}^2{_U}$ associated with the study variable Y, Based on large sample approximation, the optimal conditions are obtained and the situations are identified under which the proposed class of estimators would be better than conventional estimator. Application of the main result to bivariate normal population is illustrated.