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잠재성 경화제를 이용한 Cycloaliphatic/DGEBA계 에폭시 블렌드 시스템의 유변학적 특성 및 경화 동력학
곽근호,박수진,이재락,김영근 한국유변학회 1998 Korea-Australia rheology journal Vol.10 No.4
잠재성 경화제인 N-benzylpyrazinium hexafluoroantimonate(BPH)를 Cycloaliphatic계 에폭시 (CAE)/DGEBA계 에폭시의 혼합물에 1 mol% 첨가 시킨 후 혼합 조성비에 따른 유변학적 특성과 경화 동력학에 대해 연구하였다. 잠재특성은 등온 DSC를 이용하여 각각 $150^{\circ}C$와 $50^{\circ}C$의 반응 온도에 대한시간의 함수로서 전화량을 구하여 측정하였다. 블렌드 시스템의 유변학적 특성은 레오미터를 사용한 등온 실험을 통하여 storage modulus (G'), loss modulus (G") 그리고 damping factor (tan$\delta$)를 구한 후 이들 데이터로부터 겔화 시간을 측정하였다. 겔화 시간과 경화 온도를 Arrenius equation에 적용시킨 결과 가교 활성화 에너지 ($E_c$)를 구할 수 있었으며 겔화 시간과 활성화 에너지 모두 DGEBA의 함량이 증가할수록 증가하였다. 경화 활성화 에너지 ($E_a$)를 동적 DSC를 이용하여 Kissinger method에 의해 구하였는데 활성화 에너지는 CAE의 함량이 증가할수록 감소함으로써 높은 반응성을 나타내었는데, 이는 짧은 반복 단위와 단순한 곁사슬기 그리고 반응 매질 내의 점도 등에 기인한다.기인한다. The effects of 1 mol% N-benzylpyrazinium hexafluoroantimonate(BPH) as a thermal latent initiator and blend compositions composed of cycloaliphatic and DGEBA epoxies were investigated in the rheological properties and cure kinetics. Latent properties were performed by measurement of the conversion as a function of reaction time using isothermal DSC at $150^{\circ}C$ and $50^{\circ}C$ Rheological properties of the blend systems were investigated in terms of isothermal experiments using a rheometer. The gelation time was obtained from the evaluation of storage modulus (G'), loss modulus (G") and damping factor (tan$\delta$)). Cross-linking activation energy ($E_c$) was also determined from the Arrhenius equation based on gel time and curing temperature. As a result, the gel time and cross-linking activation energy increased with increasing DGEBA composition. The cure activation energies ($E_a$) were obtained by Kissinger method using dynamic DSC thermograms. In this work, the cure activation energy decreased with increasing CAE concentration, which might be resulted from the short repeat units, simple side-groups and viscosity of reaction media.edia.
준감독 학습과 공간 유사성을 이용한 비접근 지역의 작물 분류 - 북한 대홍단 지역 사례 연구 -
곽근호,박노욱,이경도,최기영,Kwak, Geun-Ho,Park, No-Wook,Lee, Kyung-Do,Choi, Ki-Young 대한원격탐사학회 2017 大韓遠隔探査學會誌 Vol.33 No.5
이 논문에서는 비접근 지역의 작물 분류를 목적으로 준감독 학습에 인접 화소의 공간 유사성 정보를 결합하는 분류 방법론을 제안하였다. 적은 수의 훈련 자료를 이용한 초기 분류 결과로부터 신뢰성 높은 훈련 자료의 추출을 위해 준감독 학습 기반의 반복 분류를 적용하였으며, 새롭게 훈련 자료 추출시 인접한 화소의 분류 항목을 고려함으로써 불확실성이 낮은 훈련 자료를 추출하고자 하였다. 북한 대홍단에서 수집된 다중시기 Landsat-8 OLI 영상을 이용한 밭작물 구분의 사례 연구를 통해 제안된 분류 방법론의 적용 가능성을 검토하였다. 사례 연구 결과, 초기 분류 결과에서 나타난 작물과 산림의 오분류와 고립된 화소가 제안 분류 방법론에서 완화되었다. 또한 인접 화소의 분류 결과를 고려한 훈련 자료 추출을 통해 이러한 오분류 완화 효과가 더욱 두드러지게 나타났으며, 초기 분류 결과와 기존 준감독 학습에 비해 고립된 화소도 감소되었다. 따라서 비접근 지역으로 인해 훈련 자료의 확보가 어려울 경우 이 연구에서 제안된 방법론이 작물 분류에 유용하게 적용될 수 있을 것으로 기대된다. In this paper, a new classification method based on the combination of semi-supervised learning with spatial similarity of adjacent pixels is presented for crop classification in inaccessible areas. Iterative classification based on semi-supervised learning is applied to extract reliable training data from both the initial classification result with a small number of training data, and classification results of adjacent pixels are also considered to extract new training pixels with less uncertainty. To evaluate the applicability of the proposed method, a case study of the classification of field crops was carried out using multi-temporal Landsat-8 OLI acquired in the Daehongdan region, North Korea. From a case study, the misclassification of crops and forests, and isolated pixels in the initial classification result were greatly reduced by applying the proposed semi-supervised learning method. In addition, the combination of classification results of adjacent pixels for the extraction of new training data led to the great reduction of both misclassification results and isolated pixels, compared to the initial classification and traditional semi-supervised learning results. Therefore, it is expected that the proposed method would be effectively applied to classify areas in which it is difficult to collect sufficient training data.
곽근호,박노욱,Phaedon C. Kyriakidis 대한원격탐사학회 2018 大韓遠隔探査學會誌 Vol.34 No.1
Spatial downscaling is often applied to coarse scale satellite products with high temporal resolution for environmental monitoring at a finer scale. An area-to-point regression kriging (ATPRK) algorithm is regarded as effective in that it combines regression modeling and residual correction with areato- point kriging. However, an open source tool or package for ATPRK has not yet been developed. This paper describes the development and code organization of an R-based spatial downscaling tool, named R4ATPRK, for the implementation of ATPRK. R4ATPRK was developed using the R language and several R packages. A look-up table search and batch processing for computation of ATP kriging weights are employed to improve computational efficiency. An experiment on spatial downscaling of coarse scale land surface temperature products demonstrated that this tool could generate downscaling results in which overall variations in input coarse scale data were preserved and local details were also well captured. If computational efficiency can be further improved, and the tool is extended to include certain advanced procedures, R4ATPRK would be an effective tool for spatial downscaling of coarse scale satellite products.
광학 영상의 구름 제거를 위한 조건부 생성적 적대 신경망과 회귀 기반 보정의 결합
곽근호,박소연,박노욱,Kwak, Geun-Ho,Park, Soyeon,Park, No-Wook 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.
회귀 크리깅을 이용한 무인기 영상 기반의 갯벌 표층 퇴적상 분포도 작성
곽근호,김근용,이진교,유주형 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
The distribution characteristics of tidal flat sediment components are used as an essentialdata for coastal environment analysis and environmental impact assessment. Therefore, a reliableclassification map of surface sedimentary facies is essential. This study evaluated the applicability ofregression kriging to generate a classification map of the sedimentary facies of tidal flats. For this aim,various factors such as the number of field survey data and remote sensing-based auxiliary data, theeffect of regression models on regression kriging, and the comparison with other prediction methods(univariate kriging and regression analysis) on surface sedimentary facies classification were investigated. To evaluate the applicability of regression kriging, a case study using unmanned aerial vehicle (UAV)data was conducted on the Hwang-do tidal flat located at Anmyeon-do, Taean-gun, Korea. As a resultof the case study, it was most important to secure an appropriate amount of field survey data and to usetopographic elevation and channel density as auxiliary data to produce a reliable tidal flat surface sedimentfacies classification map. In addition, regression kriging, which can consider detailed characteristics ofthe sediment distributions using ultra-high resolution UAV data, had the best prediction performancecompared to other prediction methods. It is expected that this result can be used as a guideline to producethe tidal flat surface sedimentary facies classification map.