• International Journal of Aeronautical and Space Sciences
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• 제17권1호
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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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• 제18권3호
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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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• 제어로봇시스템학회 2003년도 ICCAS
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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.

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2002년도 한국우주과학회보 제11권1호
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• pp.51.1-51
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• 2002

• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2008년도 한국우주과학회보 제17권2호
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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.

• 응용통계연구
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• 제27권5호
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• pp.693-703
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• 2014

실험영역을 벗어나는 다중 외삽점들에 관한 실험설계를 기획하는 경우 실험자는 종종 어느 외삽점에 더 많은 노력을 집중하여야 하는지 주어진 모형이 있다하더라도, 고민하는 경우가 있다. 본 연구에서는 이러한 상황에 관한 실험설계 문제를 다루었다. 첫 번째는 주어진 모형이 실험영역을 벗어나더라도 모형이 타당한 경우 다중 외삽점에 관한 실험설계고 다른 하나는 그렇지 않은 경우이다. 첫 번째인 경우는 비교적 기존 문헌에서 알려진 방법들이 적용될 수 있으나 그렇지 않은 경우 즉, 모형의 타당성이 의심되는 경우는 다른 실험설계기준을 제시하여야 한다, 본 연구는 이와 관련 다양한 하이브리드 방법을 제시하여 다중 외삽점에서의 문제가 어떻게 모형 불확실성하에서 전개되어야 하는지 다루어 보았다, 이를 위해 서치알고리즘의 하나인 유전알고리즘을 적용하였다. 왜냐하면 전통적인 교환알고리즘의 복잡성보다는 유전알고리즘의 효율성이 더 뛰어났다고 보기 때문이다.

• 천문학회보
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• 제37권1호
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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.

• 천문학회보
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• 제39권1호
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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.

• 항공우주시스템공학회지
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• 제16권4호
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• pp.17-27
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• 2022

본 논문에서는 에어포일의 좌표 데이터에 대해 공력 특성을 예측할 수 있는 합성곱 신경망 기반 네트워크 프레임 워크를 설계하였으며 Xfoil을 이용한 공력 데이터를 적용하여 네트워크의 가능성을 확인하였다. 이 때 에어포일의 두께 변화에 따른 공력 특성 예측을 수행하였다. 부호화 거리 함수를 이용하여 에어포일의 좌표 데이터를 이미지 데이터로 변환하였으며 받음각 정보를 반영하였다. 또한 에어포일의 압력 계수 분포를 축소 모델 기법 중 하나인 적합 직교 분해를 이용하여 축소된 데이터로 표현하였으며 이를 네트워크의 출력 데이터로 사용하였다. 제시하는 네트워크의 내삽과 외삽 성능을 평가하기 위하여 시험 데이터를 구성하였고, 결과적으로 내삽 데이터에 대한 예측 성능이 외삽에 비해 우수함을 확인하였다.

• 천문학회보
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• 제44권1호
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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.