• Journal of Environmental Science International
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• v.19 no.8
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• pp.983-999
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• 2010

Wind power energy is one of the favorable and fast growing renewable energies. It is most important for exact analysis of wind to evaluate and forecast the wind power energy. The purpose of this study is to improve the performance of numerical atmospheric model by data assimilation over a complex coastal area. The benefit of the profiler is its high temporal resolution and dense observation data at the lower troposphere. Three wind profiler sites used in this study are inhomogeneously situated near south-western coastal area of Korean Peninsula. The method of the data assimilation for using the profiler to the model simulation is the three-dimensional variational data assimilation (3DVAR). The experiment of two cases, with/without assimilation, were conducted for how to effect on model results with wind profiler data. It was found that the assimilated case shows the more reasonable results than the other case compared with vertical observation and surface Automatic Weather Station(AWS) data. Although the effect of sonde data was better than profiler at a higher altitude, the profiler data improves the model performance at lower atmosphere. Comparison with the results of 4 June and 5 June suggests that the efficiency with hourly assimilated profiler data is strongly influenced by synoptic conditions. The reduction rate of Normalized Mean Error(NME), mean bias normalized by averaged wind speed of observation, on 4 June was 28% which was larger than 13% of 5 June. In order to examine the difference in wind power energy, the wind power density(WPD) was calculated and compared.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.636-641
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• 1993

Recent researches on control theory enable nonlinear state feedback which is more closer to real system without approximation. To apply nonlinear control theories, all state variables should be measured or estimated. In this paper, a technique of designing nonlinear state observer for a particular class of nonlinear system is presented. The result is applied to an aircraft model to prove the convergency of observation error.

• Proceedings of the Optical Society of Korea Conference
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• 2001.02a
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• pp.96-97
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• 2001

A 110 mm diameter aspheric metal secondary mirror for a test model of an earth observation satellite camera was fabricated by ultra-precision single point diamond turning (SPDT) . Without a conventional polishing process, the surface texture of R$\sub$a/=2.8 nm, and the form error of R$\sub$a/=0.05 λ has been stably achieved In a laboratory condition. (omitted)

• Journal of Korea Water Resources Association
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• v.44 no.7
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• pp.537-551
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• 2011

Based on optimal input data combination selected in the earlier study, Neuro-Fuzzy flood forecasting model linked Takagi-Sugeno fuzzy inference theory with neural network in Wangsukcheon and Gabcheon is established. The established model was applied to Wangsukcheon and Gabcheon and water levels for lead time of 0.5 hr, 1 hr, 1.5 hr, 2.0 hr, 2.5 hr, 3.0 hr are forecasted. For the verification of the model, the comparisons between forecasting floods and observation data are presented. The forecasted results have shown good agreements with observed data. Additionally to evaluate quantitatively for applicability of the model, various statistical errors such as Root Mean Square Error are calculated. As a result of the flood forecasting can be simulated successfully without large errors in all statistical error. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• Magazine of the Korean Society of Agricultural Engineers
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• v.18 no.1
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• pp.4021-4037
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• 1976

This paper describes the actual experiments of the error propagation and studies of analytical photogrammetry using the simulation method in which we can find the causes of the errors. These studies and the results give the valuable data which are very effective for systematically controlling the errors in aerial triangulation. The main contents in my paper are as follows: 1. Dose the scale errors in the successive models in the form of normal distribution appear when the observation errors propagate in the form of normal distribution\ulcorner 2. In what form does this scale error propagation in the actual model appear\ulcorner 3. When the causes of the scale error propagation are found, is the evaluation standard determined normally\ulcorner 4. What degree of influence is there form the constant errors\ulcorner I have done several experiments using the simulation method technique to solve the complicated error propgation of aerial triangulation which is the effective means to research the relations between cause and effect. In this paper, the studies have concentrated on the following points of simulation experiments. (1) The first part descries how we can produce the soft program of the simulation experiment. (2) The second part is the method propagating the errors in the simulation models and the kinds of errors. (3) The final part is the most important; that is the analyzing and evaluation of control during actual work. From the above-mentioned points, it is said that these studies are a very important development in the field of controlling and managing aerial photogrammetry and especially in the case of error propagation, we can clearly find the causes of the errors and steps and parts of errors generated when we use these techniques.

• Journal of Astronomy and Space Sciences
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• v.28 no.3
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• pp.193-201
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• 2011

A strategy for geostationary orbit (or geostationary earth orbit [GEO]) surveillance based on optical angular observations is presented in this study. For the dynamic model, precise analytical orbit model developed by Lee et al. (1997) is used to improve computation performance and the unscented Kalman filer (UKF) is applied as a real-time filtering method. The UKF is known to perform well under highly nonlinear conditions such as surveillance in this study. The strategy that combines the analytical orbit propagation model and the UKF is tested for various conditions like different level of initial error and different level of measurement noise. The dependencies on observation interval and number of ground station are also tested. The test results shows that the GEO orbit determination based on the UKF and the analytical orbit model can be applied to GEO orbit tracking and surveillance effectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.1
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• pp.9-15
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• 2018

Precise regional geoid heights on and around Mount Jiri were calculated and were compared to the KNGeoid14 (Korean National Geoid 2014) model. In this study, gravimetric geoid heights were calculated by using RCR (Remove-Compute-Restore) technique and then hybrid geoid heights were calculated by using the LSC (Least Square Collocation) method in the same area. In addition, gravity observation and GNSS(Global Navigation Satellite System) surveying performed in this study were utilized to determine gravimetric geoid heights and to compute hybrid geoid heights, respectively. The results of the study show that the post-fit error (mean and standard deviation) of hybrid geoid heights was evaluated as $0.057{\pm}0.020m$, while the mean and standard deviation of the differences were -0.078 and 0.085 m, respectively for KNGeoid14. Therefore, hybrid geoid heights in this study show more considerable progress than KNGeoid14.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.241-245
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• 2005

Strapdown inertial navigation system (SINS) integrated with astronavigation system (ANS) yields reliable mission capability and enhanced navigational accuracy for spacecrafts. The theory and characteristics of integrated system based on unscented Kalman filtering is investigated in this paper. This Kalman filter structure uses unscented transform to approximate the result of applying a specified nonlinear transformation to a given mean and covariance estimate. The filter implementation subsumed here is in a direct feedback mode. Axes misalignment angles of the SINS are observation to the filter. A simple approach for simulation of axes misalignment using stars observation is presented. The SINS error model required for the filtering algorithm is derived in space-stabilized mechanization. Simulation results of the integrated navigation system using a medium accuracy SINS demonstrates the validity of this method on improving the navigation system accuracy with the estimation and compensation for gyros drift, and the position and velocity errors that occur due to the axes misalignments.

• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.262-271
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• 2024

In this paper, by applying deep learning, one of the A.I. techniques, through angle information, which is optical observation data generated when observing satellites at observatories, distance information from observatories is learned to predict range data, thereby increasing the precision of satellite's orbit determination. To this end, we generated observational data from GMAT, reduced the learning data error of deep learning through preprocessing of the generated observational data, and conducted deep learning through MATLAB. Based on the predicted distance information from learning, trajectory determination was performed using an extended Kalman filter, one of the filtering techniques for trajectory determination, through GMAT. The reliability of the model was verified by comparing and analyzing the orbital determination with angular information without distance information and the orbital determination result with predicted distance information from the model.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.