• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.586-588
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• 2005

The MSC (Multi Spectral Camera) system is a remote sensing payload to obtain high resolution ground image. This system uses lossy image compression method for &Direct mission& that transmit whole image during one contact. But some image degradation occurred especially at high compression ratio. To reduce this degradation, the MSC uses NUC (Non-uniformity Correction) Unit. This unit correct CCD (Charge Coupled Device)'s high-frequency non-uniformity. So high frequency contents of image can be minimized and whole system SNR can be maximized. But NUC has some disadvantage either. It decreases entire system reliability by adding one electronic system. Adding NUC also led to difficulty of electronic design, assembly and testability. In this paper, the comparison is performed between on-orbit non-uniform correction and on ground correction. by evaluating NUC advantage for the point of view of image quality. Using real MSC parameter and proper model, considerable reference point for the system design came to possible.

• 컴퓨터교육학회논문지
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• 제3권2호
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• pp.115-127
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• 2000

기존 워드프로세서의 맞춤법 검사기는 여러 오류 단어 후보군 중에서 1개를 선택하는 오류 작업의 불편함과 60%대의 낮은 교정률 그리고 늦은 처리 속도 등의 단정을 갖고 있다. 본 연구에서는 이러한 단점들을 해결하기 위하여 1개의 교정 단어와 페이지 단위의 일괄 교정으로 교정의 불편함을 해소하고, 높은 오류 교정률과 빠른 처리 속도를 가능하게 하는 방안을 제시한다. 이를 위하여 형태소 분석 시 처리 속도를 향상시키기 위한 방법으로 양방향 최장일치법을 적용하며, 교정 시의 정확성보장과 처리 속도의 향상을 위한 사전과 여러 알고리즘(복합명사 분리, 보조용언 분리, 오타 교정 등) 등을 개발하여 적용하였다. 특히 한국어에서 모호성이 많이 발생되는 의존명사 및 접미사와 조사/어미의 구분 방안, "로써/로서, 되다" 등의 구분 처리 방안을 제시하여 교정 시스템의 신뢰성을 높였다.

• 전자공학회논문지 IE
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• 제46권3호
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• pp.12-18
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• 2009

본 논문은 LED 디스플레이 시스템에서 균일하고 부드러운 영상표출을 위하여 각각의 Red, Green, Blue LED의 휘도를 조절하여 흰색보정을 유지할 수 있는 프로세서를 설계하였다. 이 프로세서는 일반적인 LED소자의 특성곡선을 근거로 "a"를 고유특성 값, "b"를 사용시간에 따른 휘도보정 값, "X"를 구동전류 값, "Y"를 휘도 값이라 할 때, "Y=aX+b"로 설정되는 휘도 특성함수에 의하여 각 픽셀의 휘도에 따른 구동 전류 값을 산출하여 휘도를 보정하고, 또한 장시간 사용 시 LED소자의 휘도 저하 문제를 해결하기 위하여 "b"값을 조절하여 LED 디스플레이 시스템의 전체 평균 휘도 값을 상향시킬 수 있도록 구현하였다.

• 대한원격탐사학회지
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• 제14권2호
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• pp.137-148
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• 1998

본 논문에서 고해상도 위성영상의 정밀 기하학적 보정에 대하여 기술한다. 일반적으로 GCP로부터 영상과 기준 지도 사이의 다하식을 유도하는 polynomial warping 방법인 경우 원하는 정확도를 얻기위해 영상 전체를 골고루 분포된 많은 GCP를 요구하게 된다. 하지만 제안되는 알고리즘은 위성-센서-궤도-지구 간의 기하학적 모델을 바탕으로 2-3개의 GCP만으로도 전체 영상을 매우 정확히 보정할 수 있다. 개발된 알고리즘은 GCP를 순차적으로 사용하여 부정확한 초기 궤도 및 자세 정보를 정밀하게 추정하고 이러한 추정은 Kalman filter를 사용하여 이루어진다. 이 알고리즘은 현재 우리별 3호의 전처리 소프트웨어에 통합되어 구현되어 있으며 앞으로 우리별 3호 영상뿐 아니라 다목적실용위성 영상의 정밀 기하학적 보정에 사용될 예정이다.

• 디지털산업정보학회논문지
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• 제10권4호
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• pp.63-70
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• 2014

Method a target estimation in spatial are mobile wireless communication using network cell and GPS. It have much error that mobile wireless communication depend on cell size. GPS method can't find a target in shadow and inner area. In this paper, we estimate a target as direction of arrival method using adaptive array antenna system. Adaptive array antenna system can obtain desired signal to remove other signal This paper studied digital beamforming method in order to estimation a target. Proposed method is modified optimum weight and antenna error correction to estimation an optimal receive signal. Digital beamforming method decided a signal phase and amplitude from received signal on array antenna element. But if it is not to do error correction of received signal, system performance have decreased. Firstly, we proposed modified optimum weight in order to finding desired target. Secondly, we are error correction of antenna incident signals by optimal weight before digital beamforming method. Thirdly, throughly simulation, we showed that system performance of proposed method compare proposal method with general method. It have improved resolution of estimation target to good performance more proposed method than general method.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.159-159
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• 2023

The Amazon River basin is one of the largest basins in the world, and its ecosystem is vital for biodiversity, hydrology, and climate regulation. Thus, understanding the hydrometeorological process is essential to the maintenance of the Amazon River basin. However, it is still tricky to monitor the Amazon River basin because of its size and the low density of the monitoring gauge network. To solve those issues, remote sensing products have been largely used. Yet, those products have some limitations. Therefore, this study aims to do bias corrections to improve the accuracy of Satellite Precipitation Products (SPPs) in the Amazon River basin. We use 331 rainfall stations for the observed data and two daily satellite precipitation gridded datasets (CHIRPS, TRMM). Due to the limitation of the observed data, the period of analysis was set from 1st January 1990 to 31st December 2010. The observed data were interpolated to have the same resolution as the SPPs data using the IDW method. For bias correction, we use convolution neural networks (CNN) combined with an autoencoder architecture (ConvAE). To evaluate the bias correction performance, we used some statistical indicators such as NSE, RMSE, and MAD. Hence, those results can increase the quality of precipitation data in the Amazon River basin, improving its monitoring and management.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• 전기전자학회논문지
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• 제21권4호
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• pp.412-415
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• 2017

본 논문에서는 고해상도 $360^{\circ}$ 전방위 IP 카메라를 이용한 다중 번호판 인식 시스템을 제안한다. 제안한 시스템은 $360^{\circ}$ 원형영상의 평면 분할 부와 다중 번호판 인식 부로 구성되었다. $360^{\circ}$ 원형영상의 평면 분할 부는 고해상도 $360^{\circ}$ 전방위 IP 카메라에서 원형영상 획득, 원형영상 분할, 평면영상으로 변환, 보간법을 사용한 픽셀 보정 및 컬러보정, 에지 보정 등의 과정을 거쳐 화질이 개선된 평면영상으로 출력한다. 다중 번호판 인식 부는 평면영상에서 다중 번호판 후보영역 추출, 다중 번호판 후보영역 정규화 및 복원, 신경망을 사용한 다중 번호판 숫자, 문자 인식 과정을 거쳐 다중 번호판을 인식하게 된다. 제안된 고해상도 $360^{\circ}$ 전방위 IP 카메라를 이용한 다중 번호판 인식 시스템을 평가하기 위하여 지능형 주차관제시스템 운영 전문 업체와 공동으로 실험한 결과, 97.8%의 높은 번호판 인식률이 확인되었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.130-133
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• 2007

In order to get high quality and high resolution image data from the space-borne camera system, the image chain from the sensor to the user in the ground-station need to be designed and controlled with extreme care. The behavior of the camera system needs to be controlled by ground commands to support on-orbit calibration and to adjust imaging parameters and to perform early stage on-orbit image correction, like gain and offset control, non-uniformity correction, etc. The operation status including the temperature of the sensor needs to be transferred to the ground-station. The preparation time of the camera system for imaging with specific parameters should be minimized. The camera controller needs to synchronize the operation of cameras for every channel and for every spectral band. Detail timing information of the image data needs to be provided for image data correction at ground-station. In this paper, the design of the camera controller for the AEISS on KOMPSAT-3 will be introduced. It will be described how the image chain is controlled and which imaging parameters are to be adjusted The camera controller will have software for the flexible operation of the camera by the ground-station operators and it can be reconfigured by ground commands. A simple concept of the camera operations and the design of the camera controller, not only with hardware but also with controller software are to be introduced in this paper.

• 한국농공학회논문집
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• 제61권6호
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• pp.55-65
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• 2019

Universal Soil Loss Equation (USLE) is to estimate potential soil loss and has benefit in use with its simplicity. The equation is composed of five factors, one of the factors is the slope length and steepness factor (LS factor) that is for topographic property of fields to estimate potential soil loss. Since the USLE was developed, many equations to compute LS was suggested with field measurement. Nowadays the factor is often computed in GIS software with digital elevation model, however it was reported that the factor is very sensitive to the resolution of digital elevation model. In addition, the digital elevation model of high resolution less than 3 meter is required in small field application, however these inputs are not associate with the empirical models' backgrounds since the empirical models were derived in 22.1 meter field measurements. In the study, four equation to compute LS factor and two approaches to determine slope length and steepness were examined, and correction factor was suggested to provide reasonable precision in LS estimations. The correction factor is computed with field area and cell size of digital elevation model, thus the correction factor can be adapted in any USLE-based models using LS factor at field level.