• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.169.3-169
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• 2001

This paper introduces a new approach to analysis of error convergence for a class of iterative learning control systems. First, a nonlinear plant is represented using a Takagi-Sugeno(T-S) fuzzy model. Then each iterative learning controller is designed for each linear plant in the T-S fuzzy model. From the view point of two-dimensional(2-D) system theory, we transform the proposed learning systems to a 2-D error equation, which is also established in the form of T-S fuzzy model. We analysis the error convergence in the sense of induced 2 L -norm, where the effects of disturbances and initial conditions on 2-D error are considered. The iterative learning controller design problem to guarantee the error convergence can be reduced to linear matrix inequality problems. In comparison with others, our learning algorithm ...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.352-355
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• 1995

In this paper, estimation error bounds of the optimal FIR (Finite Impulse Response) filter, which is proposed by Kwon et al.[1, 2], are presented in discrete-time systems with the model uncertainty. Performance bounds are here represented by the upper bounds on the difference of the estimation error covariances between the nominal and real values in case of the systems with the noise or model parameter uncertainty. The estimation error bounds of the discrete-time optimal FIR filter is compared with those of the Kalman filter via a numerical example applied to the simulation problem by Toda and Patel[3]. Simulation results show that the former has robuster performance than the latter.

• Communications for Statistical Applications and Methods
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• 제22권4호
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• pp.349-359
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• 2015

We study a semiparametric Bayesian approach to small area estimation under a nested error linear regression model with area level covariate subject to measurement error. Consideration is given to radial basis functions for the regression spline and knots on a grid of equally spaced sample quantiles of covariate with measurement errors in the nested error linear regression model setup. We conduct a hierarchical Bayesian structural measurement error model for small areas and prove the propriety of the joint posterior based on a given hierarchical Bayesian framework since some priors are defined non-informative improper priors that uses Markov Chain Monte Carlo methods to fit it. Our methodology is illustrated using numerical examples to compare possible models based on model adequacy criteria; in addition, analysis is conducted based on real data.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 봄 학술논문 발표대회
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• pp.223-224
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• 2021

Recently, the demand for high fluidity concrete has been increased due to skyscrapers. However, it has its own limits. First of all, high fluidity concrete has large variation and through trial & error it costs lots of money and time. Neural network model has repetitive learning process which can solve the problem while training the data. Therefore, the purpose of this study is to predict optimum mix design of 40MPa, 60MPa high fluidity concrete by using neural network model and verifying compressive strength by applying real data. As a result, comparing collective data and predicted compressive strength data using MATLAB, 40MPa mix design error rate was 1.2%~1.6% and 60MPa mix design error rate was 2%~3%. Overall 40MPa mix design error rate was less than 60MPa mix design error rate.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.25-25
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• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.

• 제어로봇시스템학회논문지
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• 제8권6호
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• pp.524-532
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• 2002

This paper presents a new in-flight alignment method for an SDINS under large initial heading error. To handle large heading error, a new attitude error model is introduced. The attitude errors are divided into heading error and leveling errors using a newly defined horizontal frame. Some navigation error dynamic models are derived from the attitude error model for indirect feedback filtering of the in-flight alignment system. A Kalman filter with Position measurement is designed to estimate navigation errors as the indirect feedback filter Simulation results show that the proposed in-flight alignment method reduces the heading error very quickly from more than 40deg to about 5deg so as to apply a refined navigation filter. The total alignment process including leveling mode and navigation mode in addition to the proposed one allows large initial values not only in heading error but also in leveling errors.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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• pp.889-893
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• 1995

This paper verified error of STL file and presented a new application of reformed division method for reducing to appeared error by using Delaunay triangulation method that used to modify error of verified data. First, it is analyzed each vertices in hole error and classified follow case that is 1. case of plan or slope, 2. case of edge, 3. case of apex and 4. case of rapid curve, and reduced volume tolerance between original model and converted model after convert STL file.

• Journal of Positioning, Navigation, and Timing
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• 제4권1호
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• pp.1-7
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• 2015

In the case of an unmanned aerial vehicle (UAV) equipped with a GNSS sensor, a boundary line where the vehicle can actually exist can be calculated using a navigation error model, and safe navigation (e.g., precise landing and collision prevention) can be supported based on this boundary line. Therefore, for the safe operation of UAV, a model for the position error of UAV needs to be established in advance. In this study, the multipath error of a GNSS sensor installed at UAV was modeled through a flight test, and this was analyzed and compared with the error model of an existing manned aircraft. The flight test was conducted based on a scenario in which UAV performs hovering at an altitude of 40 m, and it was found that the multipath error value was well bound by the error model of an existing manned aircraft. This result indicates that the error model of an existing manned aircraft can be used in operation environments similar to the scenario for the flight test. Also, in this study, a scenario for the operation of multiple UAVs was considered, and the correlation between the multipath errors of the UAVs was analyzed. The result of the analysis showed that the correlation between the multipath errors of the UAVs was not large, indicating that the multipath errors of the UAVs cannot be canceled out.

• 한국수자원학회논문집
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• 제39권8호
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• pp.677-690
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• 2006

본 연구에서는 LH-OAT (Latin Hypercube Ore factor At a Time) 민감도분석 방법과 SCE-UA (Shuffled Complex Evolution at University of Arizona) 최적화 기법을 적용하여 보청천 유역에서 SWAT모형에 대한 자동보정 방법을 제시하였다. LH-OAT 방법은 전역 민감도분석과 부분 민감도 분석의 장점을 조합하여 가용매개변수 공간에 대하여 효율적으로 매개변수의 민감도 분석이 가능하게 하였다. LH-OAT민감도 분석으로부터 결정된 매개변수의 민감도 등급은 SWAT 모형의 자동보정 과정에서 요구되는 보정대상 매개변수의 선택에 유용하게 적용될 수 있다. SCE-UA 방법을 적용한 SWAT모형의 자동보정 해석결과는 보정자료, 보정매개변수, 통계적 오차의 선택에 따라서 모형의 성능이 좌우되었다. 보정기간과 보정매개변수가 증가함에 따라 검증기간에 대한 RMSE (Root Mean Square Error), NSEF (Nash-Sutcliffe Model Efficiency), RMAE (Relative Mean Absolute Error), NMSE (Normalized Mean Square Error) 등의 모형오차는 감소하였지만, NAE (Normalized Average Error) 및 SDR(Standard Deviation Ratio)은 개선되지 않았다. SWAT모형의 보정에 적용되는 보정자료, 보정매개변수 및 모형평가를 위한 통계적 오차 선택이 해석결과에 미치는 복잡한 영향을 이해하기 위하여 다양한 대표유역을 대상으로 추가적인 연구가 필요하다.

• Journal of the Korean Statistical Society
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• 제30권4호
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• pp.541-550
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• 2001

This paper considers a panel data regression model in which the disturbances follow a nested error components with serial correlation. Given this model, this paper derives several Lagrange Multiplier(LM) testis for the presence of serial correlation as well as random individual effects, nested effects, and for existence of serial correlation given random individual and nested effects.