• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.684-688
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• 2008

레이더 강우와 관련한 대부분의 연구나 실무적용이 제한을 받는 이유는 레이더 반사도 등의 원시자료를 획득하기가 어려울 뿐만 아니라 이를 처리하여 수문해석에 적용하는 과정이 간단하지 않기 때문이다. 이를 해결하기 위하여 다음과 같은 내용을 연구하였다. (1) 레이더 영상자료를 실용적으로 활용하기 위한 '레이더 영상 디지털 변환법(RAIDOM)'을 연구 개발하였다. 또한 오프라인상에서도 기상청 레이더 합성 CAPPI 이미지 자료를 디지털 강우자료로 직접 변환할 수 있는 방법을 제시하였다. 이러한 기술은 앞으로 레이더 강우 연구와 레이더 강우의 활용성을 넓히는데 크게 기여할 것으로 기대된다. (2) RAIDOM 레이더 강우와 연계한 분포형 강우유출모형을 구축하였다. 본 연구에서는 DEM, 토지피복도, 토양도로부터 분포형 강우-유출모형의 매개변수를 산정하는 방법을 상세히 연구하여 제시하였다. 이러한 연구결과는 앞으로 분포형 강우유출모형에 대한 연구와 활용성을 넓히는데 기여할 것으로 기대된다. (3) 주요 관측 레이더 호우사상을 이용하여 RAIDOM 강우와 구축된 분포형 모형의 적용성을 검증하였다. 이를 위하여 먼저 강우유출자료가 체계적으로 관리되고 있는 평창강 국제수문개발계획(IHP) 시범유역의 자료를 이용하여 모형의 매개변수 보정을 수행하였다. 강우 전 하천의 기저유량과 유역의 초기함수조건을 제외한 나머지 매개변수는 유역특성을 나타내는 인자들이므로 모든 강우사상에 대하여 일정한 것으로 가정하여 매개변수 보정을 수행하였다. 6개 주요 호우사상에 대하여 보정한 결과 4개의 호우사상에 대하여 강우-유출과정을 거의 완벽하게 재현하였으며, 2개의 호우사상에 대해서는 수문곡선의 상승과 하강은 비교적 일치하나 첨두부에서 다소차이가 발생하였다. (4) 보정된 분포형 모형을 2006년 7월에 발생한 국지성 집중호우와 한강유역 중상류지역에 걸쳐 큰 홍수량을 발생시킨 2개의 호우사상에 대하여 레이더 강우자료를 적용하여 검토하였다. 검토결과 임진강유역 3개 수위관측소와 우이천수위관측소 및 중랑교수위관측소에서 모의된 홍수수문곡선은 실측치와 잘 일치하는 것으로 나타나 본 연구에서 제시한 RAIDOM과 이를 적용한 분포형 모형이 강우유출 모의를 위하여 활용될 수 있음을 보여주었다. 앞으로 태풍에 수반된 강우와 장마전선 등을 포함한 다양한 유형의 여러 가지 강우에 대한 적용을 통하여 모형의 검증과 보완을 수행하여 RAIDOM 레이더 강우와 분포형 강우유출모형을 연계한 홍수 예보 시스템으로 발전시켜 나갈 예정이다.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.131-144
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• 2015

This research proposes a prototype for visualizing radar rainfall data using the unity 3D engine. The mashup of radar data with topographic information is necessary for the 3D visualization of the radar data with high quality. However, the mashup of a huge amount of radar data and topographic data causes the overload of data processing and low quality of the visualization results. This research utilized the Unitiy 3D engine, a widely used engine in the game industry, for visualizing the 3D topographic data such as the satellite imagery/the DEM(Digital Elevation Model) and radar rainfall data. The satellite image segmentation technique and the image texture layer mashup technique are employed to construct the 3D visualization system prototype based on the topographic information. The developed protype will be applied to the disaster-prevention works by providing the radar rainfall data with the 3D visualization based on the topographic information.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.6
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• pp.34-40
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• 2011

From the present paper researched about the Design of Image Rejection SSB Modulator for X-Band Monopulse RADAR using Waveguide Hybrid Coupler. Generally, SSB modulator mixes IF(RF) and LO signals, and then it converts to RF(IF) frequency band. In this case, in order to transmit one sideband from RF band, SSB modulator is demanded the removal of image and LO signal. The balanced mixer was designed using waveguide hybrid coupler and crystal mixer diode to mix LO and IF signal. And also the IF Amplifier was designed for IF(+) and IF(-) signal generation which have $90^{\circ}$ phase differences which are suitable in two crystal mixer diode inputs. In order to maintain a high electric reliability from high frequency band the waveguide and IF amplifier's case were manufactured with aluminum using deep brazing techniques. The test result of SSB modulator, LO and sideband signal rejection ratio were 14.2dB and 18.5dB respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1860-1866
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• 2016

Helicopter-related collision accidents with structures mostly occur at landing, especially in a limited visibility environment, which necessitates some secondary equipment like a radar that can generate stationary clutter image. In this paper, we propose an algorithm that makes an image of stationary ground clutter in two dimensional range and azimuth angle domain. We present a mathematical model for the received signals from each clutter patch in the iso range ring and analyze their clutter and Doppler characteristics, assuming that a helicopter-borne radar has a rotational antenna. We propose a filter structure, which suppresses side lobe signal components while extracting a main lobe signal component, and suggest a solution for a problem stemmed from the filtering process. Finally, by conducting a simulation we show the performance of the suggested imaging algorithm on a two dimensional virtual scenario of the topographic clutter.

• Journal of the Korea Society of Computer and Information
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• v.26 no.2
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• pp.27-37
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• 2021

In this paper, we propose Automatic detection system of underground pipe which automatically detects underground pipe to help experts. Actual location of underground pipe does not match with blueprint due to various factors such as ground changes over time, construction discrepancies, etc. So, various accidents occur during excavation or just by ageing. Locating underground utilities is done through GPR exploration to prevent these accidents but there are shortage of experts, because GPR data is enormous and takes long time to analyze. In this paper, To analyze 3D GPR data automatically, we use 3D image segmentation, one of deep learning technique, and propose proper data generation algorithm. We also propose data augmentation technique and pre-processing module that are adequate to GPR data. In experiment results, we found the possibility for pipe analysis using image segmentation through our system recorded the performance of F1 score 40.4%.

• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.2342-2344
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• 2005

초광대역 임펄스를 이용한 비파괴 지중 매설물 탐지용 지반 탐사 레이더(Ground penetrating image radar: GPR)를 개발하였다. 최대 탐사 깊이를 고려하여, 900 picosecond(ps) 상승 시간을 갖는 초광대역 임펄스를 설계하였고, 임펄스 발생기의 주파수 특성을 고려하여, 소형 평판형 다이폴 안테나가 설계되었다. 또한, 지중으로부터 반사되는 신호를 수신하기 위해서 고속의 A/D를 사용하였다. 측정은 송수신 안테나의 간격을 고정한 Bistatic 방식을 사용하였으며, 지중 매설물의 영상처리 판별을 위해 마이그레이션(migration) 기법을 사용하였다. 개발된 시스템은 금속 물체와 비금속 물체가 매설된 실증 시험장에서 시험되었고, 평면 해상도 및 깊이에 대한 해상도가 우수함을 보였다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.10 no.1 s.20
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• pp.1-7
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• 2004

RADARSAT is one of many possible data sources that can play an important role in marine surveillance including ship detection because radar sensors have the two primary advantages: all-weather and day or night imaging. However, atmospheric effects on SAR imaging can not be bypassed and any remote sensing image has various geometric distortions, In this study, radiometric and geometric calibrations for RADARSAT/SAT data are tried using SGX products georeferenced as level 1. Even comparison of the near vs. far range sections of the same images requires such calibration Radiometric calibration is performed by compensating for effects of local illuminated area and incidence angle on the local backscatter, Conversion method of the pixel DNs to beta nought and sigma nought is also investigated. Finally, automatic geometric calibration based on the 4 pixels from the header file is compared to a marine chart. The errors for latitude and longitude directions are 300m and 260m, respectively. It can be concluded that the error extent is acceptable for an application to open sea and can be calibrated using a ground control point.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.91-97
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• 2019

When a radar tracks the warhead of a ballistic missile, decoys of a ballistic missile put a heavy burden on the radar resource management tracking the targets. To reduce this burden, it is necessary to be able to separate the signal of the warhead from the received dynamic radar cross section (RCS) signal on the radar. In this paper, we propose the method of separating the dynamic RCS of each target from the received signal by the Hough transform which extracts straight lines from the image. The micro motion of the targets was implemented using a 3D CAD model of the warhead and decoys. Then, we calculated the dynamic RCS from the 3D CAD model having micromotion and verified the performance by applying the proposed algorithm. Simulation results show that the proposed method can separate the signals of the warhead and decoys at the signal-to-noise ratio (SNR) of 10dB.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.897-907
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• 2023

Purpose: In this study, We aim to estimate a untrained person's three postures using a 2D CNN model which is trained with minimal FFT data collected by a 24GHz FMCW radar. Method: In an indoor space, we collected FFT data for three distinct postures (standing, sitting, and lying) from three different individuals. To apply this data to a 2D CNN model, we first converted the collected data into 2D images. These images were then trained using the 2D CNN model to recognize the distinct features of each posture. Following the training, we evaluated the model's accuracy in differentiating the posture features across various individuals. Result: According to the experimental results, the average accuracy of the proposed scheme for the three postures was shown to be a 89.99% and it outperforms the conventional 1D CNN and the SVM schemes. Conclusion: In this study, we aim to estimate any person's three postures using a 2D CNN model and a 24GHz FMCW radar for disastrous situations in indoor. it is shown that the different posture of any persons can be accurately estimated even though his or her data is not used for training the AI model.