• Atmosphere
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• v.32 no.4
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• pp.263-276
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• 2022

The observation error of satellite radiation data that assimilated into the Korean Integrated Model (KIM) was diagnosed by applying the Hollingsworth and Lönnberg and Desrozier techniques commonly used. The magnitude and correlation of the observation error, and the degree of contribution for the satellite radiance data were calculated. The observation errors of the similar device, such as Advanced Technology Microwave Sounder (ATMS) and Advanced Microwave Sounding Unit-A shows different characteristics. The model resolution accounts for only 1% of the observation error, and seasonal variation is not significant factor, either. The observation error used in the KIM is amplified by 3-8 times compared to the diagnosed value or standard deviation of first-guess departures. The new inflation value was calculated based on the correlation between channels and the ratio of background error and observation error. As a result of performing the model sensitivity evaluation by applying the newly inflated observation error of ATMS, the error of temperature and water vapor analysis field were decreased. And temperature and water vapor forecast field have been significantly improved, so the accuracy of precipitation prediction has also been increased by 1.7% on average in Asia especially.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.64-69
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• 2004

The work presented here deals with controller design using error model constructed with proportional control ramp response. The design aims at the improvement of transient response, steady-state error reduction with stability preservation, generation of the consistent contour error through the proportional gain regulation of a mismatched system. The first step is to generate tracking-error curve with proportional control only and decide the added error signal shape on the error curve. The next is to construct a table for the steady-state loop gain with step input. The table is used for selecting the proportional gain. The effectiveness of the proposed controller is confirmed through the simulation and experiment.

• Atmosphere
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• v.22 no.3
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• pp.309-319
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• 2012

It is important to characterize and quantify the inherent error in the radar rainfall to make full use of the radar rainfall. This study verified the error structure of the reflectivity and corrected the range dependent error in the CAPPI using a VPR (vertical profile of reflectivity) model. The error of the CAPPI to display the reflectivity data becomes bigger for the range longer than 100 km. This range dependent error, however, is significantly improved by corrected the CAPPI data using the VPR model.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.551-560
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• 2010

This paper considers a Bayesian approach to modeling a flexible regression function under structural measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under structural measurement error model without a semiparametric component.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.215-218
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• 1998

In this paper, we design the $H_{\infty}$ controllers satisfying robust stability and performance for underwater vehicle. The underwater vehicle has computations delay time and input delay. In addition, there exist parameter uncertainties by the roll motion coefficient error, buoyance error, and gravity error. We design the $H_{\infty}$ controllers using model-matching method and check the performance of the proposed controller by nonlinear simulation which includes time delay model, sensor error model, and actuator model.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.296-302
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• 1995

When heteroscedasticity occurs in random effects linear model, the error variance may depend on the values of one or more of the explanatory variables or on other relevant quantities such as time or spatial ordering. In this paper we derive a score test as a diagnostic tool for detecting non-constant error variance in random effefts linear model based on the model expansion on error variance. This score test is compared to loglikelihood ratio test.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.379-385
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• 2010

This paper considers Bayesian approach to modeling a flexible regression function under functional measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under functional measurement error model without semiparametric component.

• Journal of Distribution Science
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• v.12 no.2
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.852-860
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• 2003

We introduce the error models for an attitude determination system(ADS) with gyroscopes and stellar sensor. The ADS error models are derived according to the definition of the reference frame and of the attitude error. The equivalent error models applicable to the attitude determination system with large attitude errors are presented. The simulation results show that the proposed error models improve performance of the attitude determination system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.676-680
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• 2000

In this paper, an extended volumetric error model considering backlash in a three-axis machine tool was proposed and utilized for calculating the volumetric error of the machine tool at any position in three-dimensional workspace. Backlashes are interrelated; i.e. the angular backlash affects the straightness errors which then affect the calculated squareness errors. Therefore, a new concept was introduced to define the backlash of squareness errors to incorporate the backlash of squareness error into the volumetric error, and the characteristics of the backlash of squareness error were investigated. The effects of backlash errors were assessed, by experiments, fur 21 geometric errors of a machine tool. The backlash error was shown to be one of the systematic errors of a machine tool. Based on this volumetric error model, a computer-aided volumetric error analysis system was developed for a three-axis machine tool in this paper. Then the volumetric error at an arbitrary position can be obtained, and displayed in a three-dimensional graphic form.