• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.64-72
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• 2016

The coverage area of a GNSS regional ionospheric delay model is mainly determined by the distribution of GNSS ground monitoring stations. Extrapolation of the ionospheric model data can extend the coverage area. An extrapolation algorithm, which combines observed ionospheric delay with the environmental parameters, is proposed. Neural network and least square regression algorithms are developed to utilize the combined input data. The bi-harmonic spline method is also tested for comparison. The IGS ionosphere map data is used to simulate the delays and to compute the extrapolation error statistics. The neural network method outperforms the other methods and demonstrates a high extrapolation accuracy. In order to determine the directional characteristics, the estimation error is classified into four direction components. The South extrapolation area yields the largest estimation error followed by North area, which yields the second-largest error.

• Korean Journal of Mathematics
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• v.18 no.3
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• pp.253-259
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• 2010

In this paper we establish approximation of contraction C-semigroups on the extrapolation space $X^C$, by showing the equicontinuity of contraction C-semigroups on $X^C$.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.156-161
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• 2003

It is an interest problem to predict substance distributions in three-dimensional space. Recently, a research field as Geostatistics is advanced. It is a kind of inter- or extrapolation mathematically. Some useful means for the inter- and extrapolation are known, in which slide window method with neural networks is hopeful one. We propose multi-dimensional extrapolation using multi-layer neural networks and the slide-window method. The multi-dimensional extrapolation is not similar to one-dimension. It has plural algorithms. We researched line predictors and local-plain predictors I two-dimensional space. The both predictors are equivalent; however, in multi-dimensional extrapolation, it is very important to find the direction of predictions. Especially, since the slide window method requires information to predict the future in sampling data, if they are not ordered appropriately in the direction, the predictor cannot operate. We tested the extrapolation for typical two-dimensional functions, and found an excellent character of slide-window method based on local-plain. By using the method, we can extrapolate the function until twice-outer regions of the definitions.

• Bulletin of the Korean Space Science Society
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• 2002.05b
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• pp.51.1-51
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• 2002

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.26.4-27
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• 2008

The completion ('initiation' de facto) of the KASI Orbit Propagator and Estimator (KASIOPEA) has been delayed for several reasons unfortunately. Due to the lack of working staffs and the Division priority rearrangement, the initial plan was dismantled and ignored for many years. However, fundamental researches regarding the essential parts of KASIOPEA has been done by author. The numerical integration module of the KASIOPEA is the most sensitive part in the precision of the final output in general. There is no silver bullet in the numerical integration in an orbit propagation as a non-stiff ODE case. Many numerical integration method like single-step methods, multi-step method, and extrapolation methods have been used in overly populated orbit propagator or estimator. In this study, several popular methods from single-step, multi-step, and extrapolation methods have been tested in numerical accuracy and stability.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.693-703
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• 2014

When setting up an experiment for extrapolation at multiple points outside the design space, we often face a difficulty in which point we should emphasize even if the polynomial model under consideration is given. In this paper we propose various methods under two possible scenarios that deal with extrapolations. One considered in this paper is the situation when the model assumed can be extended beyond the design space. In this setting, the many classical methods(including various approaches the authors proposed before) were revisited in the context of extrapolation. But the real problem arises when there is an uncertainty concerning the validity of the assumed model. Therefore, the second scenario is to develop an appropriate procedure when we have limited information about model. Consequently, a hybrid approach is suggested to deal with this issue of how to handle the multiple extrapolating under model uncertainty. A search algorithm was implemented because the classical exchange algorithm was found difficult to handle the complexity of the problem.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.1
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• pp.85.2-85.2
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• 2012

In this paper, we introduce the 3D modeling of the coronal magnetic field in the solar active region by extrapolating from the 2D observational data numerically. First, we introduce a nonlinear force-free field (NLFFF) extrapolation code based on the MHD-like relaxation method implementing the cleaning a numerical error for Div B proposed by Dedner et al. 2002 and the multi-grid method. We are able to reconstruct the ideal force-free field, which was introduced by Low & Lou (1990), in high accuracy and achieve the faster speed in the high-resolution calculation (512^3 grids). Next we applied our NLFFF extrapolation to the solar active region NOAA 10930. First of all, we compare the 3D NLFFF with the flare ribbons of Ca II images observed by the Solar Optical Telescope (SOT) aboard on the Hinode. As a result, it was found that the location of the two foot-points of the magnetic field lines well correspond to the flare ribbon. The result indicates that the NLFFF well capture the 3D structure of magnetic field in the flaring region. We further report the stability of the magnetic field by estimating the twist value of the field line and finally suggest the flare onset mechanism.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.72.1-72.1
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• 2014

In this study, we extrapolate a nonlinear force-free field (NLFFF) from an observed photospheric magnetic field to understand the three-dimensional (3D) coronal magnetic field producing a huge solar flare. The purpose of this study is to develop a NLFFF extrapolation code based on the so-called MHD relaxation method and check how accurately our model reconstructs a coronal field. Furthermore, we apply it to the photospheric magnetic field obtained by Helioseismic and Magnetic Imager (HMI) on board Solar Dynamics Observatory (SDO) to reconstruct a 3D magnetic structure. We first investigate factors in controlling the accuracy of our NLFFF code by using a semi-analytical solution obtained by Low & Lou (1990). To extend a work done by Inoue et al. (2014), we apply various boundary conditions at the side and top boundaries in order to make our solution close to a realistic solution. As a consequence, our solution has a good accuracy when three components of a reference field are all fixed at the boundaries. Furthermore, it is also found that our solution is well matched to the Low & Lou solution in the central area of a simulation domain when the three components of a potential field are fixed at side and top boundaries (this approach is close to a realistic solution). Finally, we present the 3D coronal magnetic field producing an X 1.5-class flare in the active region 11166 through the extrapolation from SDO/HMI.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.17-27
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• 2022

In this study, a deep learning-based network that can predict the aerodynamic characteristics of airfoils was designed, and the feasibility of the proposed network was confirmed by applying aerodynamic data generated by Xfoil. The prediction of aerodynamic characteristics according to the variation of airfoil thickness was performed. Considering the angle of attack, the coordinate data of an airfoil is converted into image data using signed distance function. Additionally, the distribution of the pressure coefficient on airfoil is expressed as reduced data via proper orthogonal decomposition, and it was used as the output of the proposed network. The test data were constructed to evaluate the interpolation and extrapolation performance of the proposed network. As a result, the coefficients of determination of the lift coefficient and moment coefficient were confirmed, and it was found that the proposed network shows benign performance for the interpolation test data, when compared to that of the extrapolation test data.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.52.2-52.2
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• 2019

Global map of solar surface magnetic field, such as the synoptic map or daily synchronic frame, does not tell us real-time information about the far side of the Sun. A deep-learning technique based on Conditional Generative Adversarial Network (cGAN) is used to generate farside magnetograms from EUVI $304{\AA}$ of STEREO spacecrafts by training SDO spacecraft's data pairs of HMI and AIA $304{\AA}$. Farside(or backside) data of daily synchronic frames are replaced by the Ai-generated magnetograms. The new type of data is used to calculate the Potential Field Source Surface (PFSS) model. We compare the results of the global field with observations as well as those of the conventional method. We will discuss advantage and disadvantage of the new method and future works.