• 한국군사과학기술학회지
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• 제22권2호
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• pp.189-196
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• 2019

The inertial sensor errors in SDINS(Strapdown Inertial Navigation System) can be compensated by rotating the inertial measurement unit and it is called RINS(Rotational Inertial Navigation System). It is assumed that the error of the inertial sensor in RINS is a static bias. However, the error of the inertial sensor actually developed and produced is not a static bias due to the change of the temperature applied to the sensor and the influence of the earth's gravity acceleration. In this paper, we propose a six-position gyro bias calibration method to evaluate the gyro bias required for RINS and present the test results of applying it to a ring laser gyro inertial navigation system under development.

• 대기
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• 제32권4호
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• pp.277-295
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• 2022

The global weather prediction model, Korean Integrated Model (KIM), has been in operation since April 2020 by the Korea Meteorological Administration. This study assessed the performance of heat waves (HWs) in Korea in 2020. Case experiments during 2018-2020 were conducted to support the reliability of assessment, and the factors which affect predictability of the HWs were analyzed. Simulated expansion and retreat of the Tibetan High and North Pacific High during the 2020 HW had a good agreement with the analysis. However, the model showed significant cold biases in the maximum surface temperature. It was found that the temperature bias was highly related to underestimation of downward shortwave radiation at surface, which was linked to cloudiness. KIM tended to overestimate nighttime clouds that delayed the dissipation of cloud in the morning, which affected the shortage of downward solar radiation. The vertical profiles of temperature and moisture showed that cold bias and trapped moisture in the lower atmosphere produce favorable conditions for cloud formation over the Yellow Sea, which affected overestimation of cloud in downwind land. Sensitivity test was performed to reduce model bias, which was done by modulating moisture mixing parameter in the boundary layer scheme. Results indicated that the daytime temperature errors were reduced by increase in surface solar irradiance with enhanced cloud dissipation. This study suggested that not only the synoptic features but also the accuracy of low-level temperature and moisture condition played an important role in predicting the maximum temperature during the HWs in medium-range forecasts.

• 천문학회지
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• 제38권2호
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• pp.175-178
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• 2005

We show that next-generation galaxy surveys such as KAOS (the Kilo-Aperture Optical Spectro-graph)will constrain dark energy even if the baryon oscillations are missing from the monopole power spectrum and the bias is scale- and time-dependent KAOS will accurately measure the quadrupole power spectrum which gives the leading anisotropies in the power spectrum in redshift space due to peculiar velocities, the finger of God effect, as well as the Alcock-Paczynski effect. The combination of monopole and quadrupole power spectra powerfully breaks the degeneracy between the bias parameters and dark energy and, in the complete absence of baryon oscillations ($\Omega$b = 0), leads to a roughly $500\%$ improvement in constraints on dark energy compared with the monopole spectrum alone. As a result, for KAOS the worst case with no oscillations has dark energy errors only mildly degraded relative to the ideal case, providing insurance on the robustness of KAOS constraints on dark energy. We show that nonlinear effects are crucial in correctly evaluating the quadrupole and significantly improving the constraints on dark energy when we allow for multi-parameter scale-dependent bias.

• Journal of Astronomy and Space Sciences
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• 제34권1호
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• pp.19-30
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• 2017

The optical wide-field patrol network (OWL-Net) is a Korean optical surveillance system that tracks and monitors domestic satellites. In this study, a batch least squares algorithm was developed for optical measurements and verified by Monte Carlo simulation and covariance analysis. Potential error sources of OWL-Net, such as noise, bias, and clock errors, were analyzed. There is a linear relation between the estimation accuracy and the noise level, and the accuracy significantly depends on the declination bias. In addition, the time-tagging error significantly degrades the observation accuracy, while the time-synchronization offset corresponds to the orbital motion. The Cartesian state vector and measurement bias were determined using the OWL-Net tracking data of the KOMPSAT-1 and Cryosat-2 satellites. The comparison with known orbital information based on two-line elements (TLE) and the consolidated prediction format (CPF) shows that the orbit determination accuracy is similar to that of TLE. Furthermore, the precision and accuracy of OWL-Net observation data were determined to be tens of arcsec and sub-degree level, respectively.

• 한국초전도ㆍ저온공학회논문지
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• 제7권1호
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• pp.21-24
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• 2005

The problem of fluctuation-induced digital errors in a rapid single flux quantum (RSFQ) circuit has been very important issue. So in this experiment, we calculated error rate of RSFQ switch in superconductiyity ALU, The RSFQ switch should have a very low error rate in the optimal bias. We prepared two circuits Placed in parallel. One was a 10 Josephson transmission lines (JTLs) connected in series, and the other was the same circuit but with an RSFQ switch placed in the middle of the 10 JTLs. We used a splitter to feed the same input signal to the both circuits. The outputs of the two circuits were compared with an RSFQ XOR to measure the error rate of the RSFQ switch. By using a computerized bit error rate test setup, we measured the bit error rate of 2.18$\times$$10^{12}$ when the bias to the RSFQ switch was 0.398mh that was quite off from the optimum bias of 0.6mA.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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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.

• 응용통계연구
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• 제22권5호
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• pp.971-979
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• 2009

이 논문에서는 GARCH 모형에서 변환-역변환 방법을 통해 예측값을 추정할 때 발생하는 편향을 줄이기 위한 방법을 소개한다. 모수적 붓스트랩을 활용하여 본래 척도에서의 최소평균제곱오차 예측값인 조건부 기대값을 계산한다. KOSPI와 KOSDAQ 수익률 분석을 통해 제안한 방법이 편향을 줄여주는 것을 확인하였다.

• International Journal of Aeronautical and Space Sciences
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• 제1권1호
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• pp.70-80
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• 2000

The attitude knowledge error model is formulated for specifically KOMPSAT attitude determination system using the Lefferts/Markley/Shuster method, and the attitude determination(AD) error analysis is performed so as to investgate the on-board attitude determination capability of KOrea Multi-Purpose SATellite(KOMPSAT) using the covariance analysis method. Analysis results show there is almost no initial value effect on Attitude Determination (AD) error and the sensor noise effects on AD error are drastically decreased as is predicted because of the inherent characteristic of Kalman filter structure. However, it shows that the earth radiance effect of IR-sensor(earth sensor) and the bias effects of both IR-sensor and fine sun sensor are the dominant factors degrading AD error and gyro rate bias estimate error in AD system. Analysis results show that the attitude determination errors of roll, pitch and yaw axes are 0.056, 0.092 and 0.093 degrees, respectively. These numbers are smaller than the required values for the normal mission of KOMPSAT. Also, the selected on-orbit data of KOMPSAT is presented to demonstrate the designed AD system.

• Journal of Astronomy and Space Sciences
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• 제23권2호
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• pp.161-166
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• 2006

이 논문에서는 위성 자세제어용으로 사용되는 RLG(Ring Lager Gyroscope) 전류 안정화 회로에 대해서 설명한다. RLG는 헬륨-네온레이저의 공진기 내에서 샤냑효과를 이용한다. 두 방전전류의 차이는 자이로 바이어스 오차의 한 부분을 차지한다. 전류 안정화회로의 이론적 배경과 설계과정에 대해서 기술하고, 입력전압 시험, 온도시험을 통하여 검증하였다.

• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.315-319
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• 2007

A technique to model plasma processes was presented. This was accomplished by combining the backpropagation neural network (BPNN) and genetic algorithm (GA). Particularly, the GA was used to optimize five training factor effects by balancing the training and test errors. The technique was evaluated with the plasma etch data, characterized by a face-centered Box Wilson experiment. The etch outputs modeled include Al etch rate, AI selectivity, DC bias, and silica profile angle. Scanning electron microscope was used to quantify the etch outputs. For comparison, the etch outputs were modeled in a conventional fashion. GABPNN models demonstrated a considerable improvement of more than 25% for all etch outputs only but he DC bias. About 40% improvements were even achieved for the profile angle and AI etch rate. The improvements demonstrate that the presented technique is effective to improving BPNN prediction performance.