• Journal of the Korean Society for Precision Engineering
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• v.9 no.3
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• pp.157-164
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• 1992

This study concerns about the orbit prediction and orbit determination of Korean future communication satellite, called 'Moogunghwa", which will be motioned in the geo-stationary orbit. Perturbation effect on the satellite orbit due to nonspherical gravitation of the earth, gravitation of the sun and moon, radiation of sun, drag of the atmosphere was investigated. Cowell's method is used for orbit prediction. Orbit determination was performed by using Extended Kalman Filter which is suitable for real-time orbit determination. The result shows that the chacteristics of the satellite orbit has east-west and south-north drift. So the periodic control time and control value in the view of the periodic of error can be provided. The orbit determination demonstrated the effectiveness since the convergence performance on the positon and velocity error, and state error standard deviation is reasonable.able.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.692-697
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• 1992

This study concerns about the orbit prediction and orbit determination of Korean future communication satellite, called "Moogunghwa", which will be motioned in the geo-stationary orbit. Perturbation effect on the satellite orbit due to nonspherical geopotential term, lunar and solar gravity, drag force of the atmosphere and solar radiation pressure was investigated. Cowell's method is used for orbit prediction. Orbit determination was performed by using EKF which is suitable for real-time orbit determination. The result shows that the characteristics of the satellite orbit has drift. So the periodic control time and control value in the view of the periodic of error can be provided. The orbit determination demonstrated the effectiveness since the convergence performance on the position and velocity error , and state error standard deviation is reasonable.easonable.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.200-205
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• 1992

This study concerns about the orbit prediction and orbit determination of Korean future connumication satellite, called "Moogunghwa" , which will be motioned in the geo-stationary orbit. Perturbation effect on the satellite orbit due to nonspherical term, lunar and solar gravity, drag force of the atmospher, and solar radiation pressure was investigated. Cowell's method is used for orbit prediction. Orbit determination was performed by using Extended Kalman Filter which is suitable for real-time orbit determination. The result shows that the chacteristics of the satellite orbit has east-west and south-north drift. So the periodic control time and control value in the view of the periodic of error can be provided. The orbit determination demonstrated the effectiveness since the convergence performance on the positon and velocity error, and state error standard deviation is reasonable.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.52-61
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• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.3
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• pp.439-445
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• 2001

In this paper, we propose a method that can minimize time-error when a commercial rubidium atomic clock is used as a portable reference clock. A linear interpolation method which was widely used is not based upon long-term stability, but our new method is considered to reduce time error. The comparison results between two method have shown that time error of our new approach considering with long-term stability is better than that of linear interpolation method within observation duration about one and half days. In addition, when the role of a rubidium atomic clock as a portable reference clock is completed within 12 hours, our new method can provide at most maximum time-error of 10 ns which is shorter than 15 ns in conventional method.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.523-533
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• 2018

The objective of this study is to analyze uncertainties of ensemble-based streamflow prediction method for model parameters and input data. ESP (Ensemble Streamflow Prediction) and BAYES-ESP (Bayesian-ESP) based on ABCD rainfall-runoff model were selected as streamflow prediction method. GLUE (Generalized Likelihood Uncertainty Estimation) was applied for the analysis of parameter uncertainty. The analysis of input uncertainty was performed according to the duration of meteorological scenarios for ESP. The result showed that parameter uncertainty was much more significant than input uncertainty for the ensemble-based streamflow prediction. It also indicated that the duration of observed meteorological data was appropriate to using more than 20 years. And the BAYES-ESP was effective to reduce uncertainty of ESP method. It is concluded that this analysis is meaningful for elaborating characteristics of ESP method and error factors of ensemble-based streamflow prediction method.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.4
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• pp.996-1007
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• 1996

In this paper, a blind adaptation method for a decision feedback equalizer (DFE) is proposed to deal with nominimum phase channels. This equalizer is composed of a linear transversal filter and a prediction error filter which are trained separately using constant modulus and decision feedback prediction algorithms, respectively, during the learnign time. The proposed algorithm guaranetees the DFE to converge to a suboptimal point on the condition that a linear transversal of the proposed scheme is illustrated and the performance is compared with conventional blind equlization algorithms.

• International Journal of Ocean System Engineering
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• v.3 no.3
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• pp.116-130
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• 2013

The prediction of ocean currents in real time over the warning times of a few hours or days is required in planning many operation-related activities in the ocean. Traditionally this is done through numerical models which are targeted toward producing spatially distributed information. This paper discusses a complementary method to do so when site-specific predictions are desired. It is based on the use of a recurrent type of neural network as well as the statistical tool of model tree. The measurements made at a site in Indian Ocean over a period of 4 years were used. The predictions were made over 72 time steps in advance. The models developed were found to be fairly accurate in terms of the selected error statistics. Among the two modeling techniques the model tree performed better showing the necessity of using distributed models for different sub-domains of data rather than a unique one over the entire input domain. Typically such predictions were associated with average errors of less than 2.0 cm/s. Although the prediction accuracy declined over longer intervals, it was still very satisfactory in terms of theselected error criteria. Similarly prediction of extreme values matched with that of the rest of predictions. Unlike past studies both east-west and north-south current components were predicted fairly well.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.