• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.487-499
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• 2009

Atmospheric correction of satellite measurements is a major step to estimate accurate surface reflectance of solar spectrum channels. In this study, Simplified Method for the Atmospheric Correction (SMAC) radiative transfer model used to retrieve surface reflectance from MODIS (MODerate resolution Imaging Spectrometer) top of atmosphere (TOA) reflectance. It is fast and simple atmospheric correction method, so it uses for work site operation in various satellite. This study attempts a test of accuracy of SMAC through a sensitivity test to detected error sources and to improve accuracy of surface reflectance using SMAC. The results of SMAC as compared with MODIS surface reflectance (MOD09) was represented that low accuracy ($R^2\;=\;0.6196$, Root Means Square Error (RMSE) = 0.00031, bias = - 0.0859). Thus sensitivity analysis of input parameters and coefficients was conducted to searching error sources. Among the input parameters, Aerosol Optical Depth (AOD) is the most influence input parameter. In order to modify AOD term in SMAC code, Stepwise multiple regression was performed with testing and remove variable in three stages with independent variables of AOD at 550nm, solar zenith angle, viewing zenith angle. Surface reflectance estimation by using Newly proposed AOD term in the study showed that improve accuracy ($R^2\;=\;0.827$, RMSE = 0.00672, bias = - 0.000762).

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.3
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• pp.179-187
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• 2008

In this paper, we carried out the performance evaluation of depolarized fiber optic gyroscope(D-FOG) that was designed and fabricated with the low-cost optical communication single mode fiber and depolarizer. In order to reduce the phase error of D-FOG, the circuit of stabilized current and temperature of the light source was made and the performance was analyzed. The current and the temperature stability of the fabricated stabilization circuit were less than $200{\mu}A$ and $0.0098^{\circ}C$, respectively. Also, the D-FOG's experimental result showed that the value of the dynamic range of rotated rate, the scale factor error with a good linearity, and the zero bias drift were ${\pm}50^{\circ}/s$, 2.8881%, and $19.49^{\circ}/h$, respectively. The results indicated that a low-cost FOG was able to fabricate which was more cost effective than conventional FOG with a high-cost high-birefringent polarization maintaining fiber.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.4
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• pp.8-16
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• 1993

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dim in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.

• 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.

• Journal of KIISE
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• v.44 no.5
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• pp.486-495
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• 2017

Collaborative Filtering is limited because of the cost that is required to perform the recommendation (such as the time complexity and space complexity). The RFWBP (Rating Frequency Weight-based Baseline Predictor) that approximates the precision of the existing methods is one of the efficiency methods to reduce the cost. But, the following issues need to be considered regarding the RFWBP: 1) It does not reduce the error because the RFWBP does not learn for the recommendation, and 2) it recommends all of the items because there is no condition for an appropriate recommendation list when only the RFWBP is used for the achievement of efficiency. In this paper, the BBP (Bias-Based Predictor) is proposed to solve these problems. The BBP reduces the error range, and it determines some of the cases to make an appropriate recommendation list, thereby forging a recommendation list for each case.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.91-94
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• 2001

Recently a constant-norm constraint equation-error method was proposed to solve the bias problem in adaptive IIR filtering. However, the method adopts a fixed step-size and thus results in slow convergence for a small step-size and significant misadjustment error for a largestep-size. In this paper, we propose a variable step-size (VSS) algorithm that greatly improves convergence properties of the constant-norm constraint equation-error method. The analysis and the simulation results show that the proposed method indeed achieves both fast convergence and small misadjustment error.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.4
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• pp.352-357
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• 2004

This paper studies the errors that associated with particle velocity and intensity in a space. We theoretically derived their bias error and random error. The analysis shows that the more samples do not always guarantee the better results. The random error of the velocity and intensity are increased when we have many samples. The characteristics of the amplification of the random error are analyzed in terms of the sample spacing. The amplification was found to be related to the spatial differential of random noise. The numerical simulations are performed to verify theoretical results.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.299-303
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• 1993

In an underwater vehicle, the navigation error is mostly caused by the initial misalignment, the bias of a gyro and an accelerometer, and the sea current. Therefore, it is important that these error sources are estimated and compensated in order to reduce the navigation error. In this paper, the E.M.Log aided SDINS is designed by using the E.M.Log which measures the forward velocity of a vehicle. And the system error state equation and the measurement equation are derived and the suboptimal Kalman Filter is established for this aided SDINS. The simulation result showed that this had an important role in estimating and compensating these error sources, thus reducing the navigation error of an underwater vehicle.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.739-745
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• 2006

This paper presents performance improvement of the INS velocity-adided GPS carier tracking loop. To this end, INS velocity-aided GPS carrier tracking loop was modeled as a feedfoward and a feedback loop system. In the phase tracking loop, it was shown that the tracking error caused by the dynamic motion of the vehicle can be compensated with the aiding of the INS information irrespective of the loop order and bandwidth. However, the signal trcking error increases as the INS error increases. It was also shown that in order to remove the tracking error caused by INS bias error, more than or equal to 2nd order PLL should be used. Experiments were carried out and the experimental results were compared with the analysis results.

• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.47-55
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• 2019

It is important to identify informative variables in high dimensional data analysis; however, it becomes a challenging task when covariates are contaminated by measurement error due to the bias induced by measurement error. In this article, we present a two-step approach for variable selection in the presence of measurement error. In the first step, we directly select important variables from the contaminated covariates as if there is no measurement error. We then apply, in the following step, orthogonal regression to obtain the unbiased estimates of regression coefficients identified in the previous step. In addition, we propose a modification of the two-step approach to further enhance the variable selection performance. Various simulation studies demonstrate the promising performance of the proposed method.