• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.549-554
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• 2008

Support Vector machine is the classifier which is based on the statistical training theory. Twin Support Vector Machine(TWSVM) is a kind of binary classifier that determines two nonparallel planes by solving two related SVM-type problems. The training time of TWSVM is shorter than that of SVM, but TWSVM doesn't shows worse performance than that of SVM. This paper proposes the TWSVM which is applied fuzzy membership, and compares the performance of this classifier with the other classifiers using Ionosphere radar data set.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.11
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• pp.1248-1258
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• 2010

In this paper, a method for simultaneously realizing the sum and difference patterns which are required in the monopulse radar sensor systems, is presented by using single taper array antenna with rectangular microstrip patches. The widths of patches are first determined by the voltage weights which are synthesized for the fundamental array factor patterns to be applied to the monopulse operation by using the sidelobe levels(SLLs) control technique. As the bi-directionally series-fed technique is applied and the lengths of connecting lines between patches are appropriately adjusted, the single array generates two phase-shifted beams which activates out-of-phase and in-phase ports of a $180^{\circ}$ hybrid coupler to synthesize the sum and difference patterns. The simulated results on the configuration designed at 9.5 GHz are compared with measured results showing the validity of the proposed method.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2000.11a
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• pp.29-33
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• 2000

The classical image reconstruction for stripmap SAR is based on the Fresnel approximation which utilizes deramping or chirp deconvolution in the synthetic aperture(slow-time) domain. Another approach in formulating stripmap SAR processing and imaging is based on the SAR wavefront reconsturction theory, and analysis of the SAR signal in the slow-time via the spherical wave Fourier decomposition of the radar radiation pattern. In this paper, we compare the Fresnel approximation and the wavefrong reconstruction methods using simulated stripmap SAR dada.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1347-1348
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• 2015

본 논문에서는 기상레이더 데이터에서 섞여있는 강수에코 및 비강수에코를 분류하기 위하여 Interval Type-2 FCM based RBFNN의 도움으로 사례 및 에코 분류기의 설계를 제안한다. 학습과 테스트 데이터는 현재 기상청에서 사용하는 UF radar data를 사용하였으며, 사례 분류기와 에코패턴 분류기의 데이터를 각각 생성한다. 전처리 과정인 사례 분류를 통하여 강수사례 혹은 비강수사례를 분류하여 강수사례일 경우 에코패턴분류를 진행하며, 비강수사례일 경우 데이터에 관측된 모든 반사도 값을 제거한다. 사례 및 에코 분류기는 Interval Type-2 FCM based RBFNN을 통하여 패턴분류를 진행하며, 패턴분류 성능을 확인한다. 또한 후반부 파라미터의 동정 시, 각 규칙에 파라미터를 전역적으로 구하는 LSE와 각 규칙에 대한 파라미터를 독립적으로 구하는 WSLE의 비교연구를 수행한다. 분류기의 성능을 확인하기 위하여 사례 분류 후 에코패턴분류의 결과는 현재 기상청에서 사용하고는 품질검사(QC) 데이터와 비교하여 평가하였다.

• Journal of Korea Water Resources Association
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• v.50 no.5
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• pp.335-346
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• 2017

This study generated the radar-based forecasted rainfall using spatial-scale decomposition method (SCDM) and evaluated the hydrological applicability with forecasted rainfall by KMA (MAPLE, KONOS) in terms of urban flood forecasting. SCDM is to separate the small-scale field (convective cell) and large-scale field (straitform cell) from radar rainfield. And each separated field is forecasted by translation model and storm tracker nowcasting model for improvement of QPF accuracy. As the evaluated results of various QPF for three rainfall events in Seoul and Metropolitan area, proposed method showed better prediction accuracy than MAPLE and KONOS considering the simplicity of the methodology. In addition, this study assessed the urban hydrological applicability for Gangnam basin. As the results, KONOS simulated the peak of water depth more accurately than MAPLE and SCDM, however cannot simulated the timeseries pattern of water depth. In the case of SCDM, the quantitative error was larger than observed water depth, but the simulated pattern was similar to observation. The SCDM will be useful information for flood forecasting if quantitative accuracy is improved through the adjustment technique and blending with NWP.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.545-550
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• 2013

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.1
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• pp.98-105
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• 2013

This paper presents a finite-difference time-domain(FDTD) simulation and a data processing technique for radar sensing of the internal structure of a wall using an ultra-wide band antenna. We first designed an ultra-wide band anti-podal vivaldi antenna with a frequency range of 0.3~7 GHz which is chosen to be relatively low after considering the characteristics of wave attenuation, wall penetration, and range resolution. In this study the two-dimensional FDTD technique was used to simulate a wall-penetration-radar experiment under practical conditions. The next, the measured radiation pattern of the practical antenna is considered as an equivalent source in the FDTD simulation, and the reflection data of a concrete wall and targets are obtained by using the simulation. Then, a data processing technique has been applied to the FDTD reflection data to get a radar image for remote sensing of the internal structure of the wall. We compared the two different source excitations in the FDTD simulation; (1) commonly-used isotropic point sources and (2) polynomial curve fitting sources of the measured radiation pattern. As a result, when we apply the measured antenna pattern into the FDTD simulation, we could obtain about 2.5 dB higher signal to noise level than using a plane wave incidence with isotropic sources.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.194-196
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• 2022

When electromagnetic wave transmitted from the radar antenna hits the target, a specific type of electric field is formed according to the electrical characteristics and appearance of the target, which is called an RCS pattern. There are various commercial programs that calculate the RCS of the target and plot the RCS pattern. For the analysis of large targets such as warships or ships, a program with a high frequency analysis method, which has a faster calculation speed than the low frequency analysis method is suitable. XGTD, a high-frequency analysis program, can quickly plot a two-dimensional RCS pattern for 360 degrees of a target, but a three-dimensional RCS pattern cannot be obtained. In this paper, it is proposed that a method of plotting two-dimensional RCS patterns of a target from various angles using XGTD, generating a three-dimensional RCS pattern using this, and converting it into a three-dimensional antenna pattern file.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.886-888
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• 2004

본 논문에서는 현재 프레임 블록의 인접 블록으로부터 예측된 움직임 정보를 구하여, 이를 탐색 원점으로 하여 한 방향으로 진행하는 수정된 다이아몬드 지역 레이더 패턴으로 블록정합을 수행하는 수정된 다이아몬드 지역탐색 알고리즘을 제안한다. 실험 결과 제안된 방식은 기존의 방식들에 비해 PSNR 간에 있어서 평균적으로 0.02~0.37(㏈) 개선되고 속도면에서 우수한 결과를 나타내었다. 또한 기존의 다이아몬드 지역탐색 알고리즘보다 탐색 속도면에서 14~24%의 성능향상을 보이면서 화질면에서는 거의 근접하게 나타났다. 제안된 방식은 정량적인 결과뿐만 아니라 부호화후 복호화한 영상의 화질에 있어서도 다른 고속 탐색 알고리즘보다 월등히 우수한 화질을 제공한다.

• Journal of Korea Water Resources Association
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• v.50 no.3
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• pp.155-167
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• 2017

Accurate QPE (Quantitative Precipitation Estimation) and the quality of the rainfall data for hydrological analysis are very important factors. Especially, the quality has a great influence on flood runoff result. It needs to know characteristics of the uncertainties in radar QPE for the reliable flood analysis. The purpose of this study is to present a probabilistic approach which defines the range of possible values or probabilistic distributions rather than a single value to consider the uncertainties in radar QPE and evaluate its applicability by applying it to radar rainfall. This study generated radar rainfall ensemble for the storms by the typhoon 'Sanba' on Namgang dam basin, Korea. It was shown that the rainfall ensemble is able to simulate well the pattern of the rain-gauge rainfall as well as to correct well the overall bias of the radar rainfall. The suggested ensemble technique represented well the uncertainties of radar QPE. As a result, the rainfall ensemble model by a probabilistic approach can provide various rainfall scenarios which is a useful information for a decision making such as flood forecasting and warning.