• 대한원격탐사학회지
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• 제22권1호
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• pp.25-40
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• 2006

Synthetic aperture radar (SAR) from satellites provides the opportunity to regularly incorporate microwave information into forest classification. Radar backscatter can improve classification accuracy, and SAR interferometry could provide improved thematic information through the use of coherence. This research examined the potential of using multi-temporal JERS-l SAR (L band) backscatter information and interferometry in distinguishing forest classes of mountainous areas in the Northeastern U.S. for future forest mapping and monitoring. Raw image data from a pair of images were processed to produce coherence and backscatter data. To improve the geometric characteristics of both the coherence and the backscatter images, this study used the interferometric techniques. It was necessary to radiometrically correct radar backscatter to account for the effect of topography. This study developed a simplified method of radiometric correction for SAR imagery over the hilly terrain, and compared the forest-type discriminatory powers of the radar backscatter, the multi-temporal backscatter, the coherence, and the backscatter combined with the coherence. Statistical analysis showed that the method of radiometric correction has a substantial potential in separating forest types, and the coherence produced from an interferometric pair of images also showed a potential for distinguishing forest classes even though heavily forested conditions and long time separation of the images had limitations in the ability to get a high quality coherence. The method of combining the backscatter images from two different dates and the coherence in a multivariate approach in identifying forest types showed some potential. However, multi-temporal analysis of the backscatter was inconclusive because leaves were not the primary scatterers of a forest canopy at the L-band wavelengths. Further research in forest classification is suggested using diverse band width SAR imagery and fusing with other imagery source.

• 대한원격탐사학회지
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• 제26권4호
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• pp.439-449
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• 2010

이 논문에서는 KOMPSAT-2 스테레오 영상의 PAN밴드에 존재하는 미세한 크기의 Y시차가 발생하는 원인을 규명하고 이를 개선하기 위한 일련의 실험 및 분석과정을 보고한다. 분석결과, Y시차가 발생하는 원인이 KOMPSAT-2 영상을 생성할 때 PAN밴드를 MS밴드와 일치하도록 Warping처리하는 과정에서 발생한 Resampling 오차 때문인 것으로 판단할 수 있었다. 또한 엄밀한 PAN밴드의 Warping 방식을 적용하여 Resampling 오차를 제거함으로써 Y 시차문제가 상당부분 개선될 수 있음을 확인하였다. 또한 KOMPSAT-2 영상 PAN밴드에서 관측된 밝기값 밀림현상도 엄밀한 Warping처리를 통해서 개선될 수 있음을 확인하였다. 따라서, 보다 엄밀한 Warping기법이 KOMPSAT-2 영상처리과정에 적용될 수 있다면 KOMPSAT-2 영상의 기하정확도 및 복사정확도가 많이 개선될 수 있을 것으로 기대한다.

• 스마트미디어저널
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• 제10권2호
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• pp.30-37
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• 2021

최근 웹툰이나 애니메이션을 3D 콘텐츠로 제작하는 사례가 증가하고 있다. 3D 콘텐츠 제작에서 모델링은 반드시 필요하지만 시간이 오래 걸리는 작업이다. 드로잉 기반 모델링을 사용하여 2D 카툰 원화에서 3D 모델을 생성하기 위해서는 라인 드로잉이 필요하다. 하지만 2D 카툰원화는 3D 모델의 기하학적 특성이 표현되지 않고 카툰원화의 제작 기법이 다양하여 일관성 있게 라인 드로잉 추출이 힘들다. 본 연구에서는 generative adversarial network (GAN) 모델을 사용하여 2D 카툰 원화에서 3D 모델의 기하학적 특성을 나타내는 라인 드로잉을 추출하는 방법을 제안하고 이를 실험한다.

• 방송공학회논문지
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• 제24권1호
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• pp.25-31
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• 2019

360 비디오는 VR 응용의 확산과 함께 몰입형 미디어로 주목받고 있으며, JVET(Joint Video Experts Team)에서 post-HEVC로 진행중인 VVC(Versatile Video Coding) 표준화에서 360 비디오 부호화도 함께 고려되고 있다. 360 비디오 부호화를 위하여 2D로 투영된 영상에는 투영 면(face) 경계의 불연속성과 비활성 영역이 존재할 수 있으며 이는 부호화 효율을 저하시키고 시각적 아티팩트(visual artifact)를 발생시킬 수 있다. 본 논문에서는 2D 투영 기법 중 SSP(Segmented Sphere Projection)에서의 이러한 불연속성과 비활성 영역을 줄이는 효율적인 기하학적 패딩(padding) 기법을 제시한다. 실험결과, 제안 기법은 복사에 의한 패딩을 사용하는 기존 SSP 대비 미미한 부호화 효율 저하는 있지만 주관적 화질이 향상된 것을 확인할 수 있었다.

• Journal of Korean Neurosurgical Society
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• 제64권1호
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• pp.13-22
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• 2021

Objective : High precision and accuracy are expected in gamma knife radiosurgery treatment. Because of the requirement of clinically applying complex radiation and dose gradients together with a rapid radiation decline, a dedicated quality assurance program is required to maintain the radiation dosimetry and geometric accuracy and to reduce all associated risk factors. This study investigates the validity of Leksell Gamma plan (LGP)10.1.1 system of 5th generation Gamma Knife Perfexion as modified variable ellipsoid modeling technique (VEMT) method. Methods : To verify LGP10.1.1 system, we compare the treatment plan program system of the Gamma Knife Perfexion, that is, the LGP, with the calculated value of the proposed modified VEMT program. To verify a modified VEMT method, we compare the distributions of the dose of Gamma Knife Perfexion measured by Gafchromic EBT3 and EBT-XD films. For verification, the center of an 80 mm radius solid water phantom is placed in the center of all sectors positioned at 16 mm, 4 mm and 8 mm; that is, the dose distribution is similar to the method used in the x, y, and z directions by the VEMT. The dose distribution in the axial direction is compared and analyzed based on Full-Width-of-Half-Maximum (FWHM) evaluation. Results : The dose profile distribution was evaluated by FWHM, and it showed an average difference of 0.104 mm for the LGP value and 0.130 mm for the EBT-XD film. Conclusion : The modified VEMT yielded consistent results in the two processes. The use of the modified VEMT as a verification tool can enable the system to stably test and operate the Gamma Knife Perfexion treatment planning system.

• 한국수학교육학회지시리즈A:수학교육
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• 제60권2호
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• pp.133-157
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• 2021

본 연구에서는 학생들의 생활과 밀접한 공간을 기반으로 한 비구조화된 문제를 개발하고 수업에 적용하였다. 이 과정에서 6학년 학생들의 공간 추론 능력으로는 외적 추론에 비해 내적 추론에서 어려움을 표했으며, 공간 추론이 수와 연산, 측정 등의 영역과 연계되어 활용될 때 그 수준이 더 높게 나타났다. 문제 해결 능력에서는 반성 요소가 미흡하게 나타났으며 초등 현장에서 온라인 환경에서의 협력과 수학적 모델링 학습이 적용 가능하다는 결과를 얻었다. 이를 통해 수학 교육 현장에 공간 학습과 실생활 문제 해결에 관한 의미 있는 시사점을 도출할 것으로 기대된다.

• 정보보호학회논문지
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• 제15권5호
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• pp.93-103
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• 2005

본 논문에서는 멀티미디어 저작권 보호를 위한 회전, 크기, 이동 (RST: Rotation, Scale, Translation) 변환 공격에 견디는 강인한 로고 삽입 방법을 제안한다. 기하학적인 처리는 영상의 화질을 많이 훼손하지 않으면서 워터마크의 탐지 과정을 매우 복잡하고 어렵게 한다. 정규화된 영상 (Normalized image)에 워터마크를 삽입하는 방법은 영상의 정규화 과정에서 보간에 의해 평탄화 (Smoothing effect) 현상이 발생하는 단점이 있다. 이것은 워터마크를 정규화된 영상에 직접 삽입하는 대신, 영상 정규화를 변환 파라미터를 계산하는데 사용함으로써 해결할 수 있다. RST 변환에 대응하기 위한 기존의 방법은 주로 전체 영상에 대해 DFT 변환을 수행한다. 그러나 이 방법은 전체 영상에 변환을 취함으로써 효과적인 마스킹 방법의 적용이 어려운 단점이 있다. 따라서 본 논문에서는 $8\times8$ 블록 DCT (Discrete Cosine Transform)를 채용하고 $8\times8$ 블록 DCT 계수의 공간-주파수 국부화 특성을 이용한 마스킹 방법을 사용한다. 실험결과, 제안된 방법이 영상 압축과 기하학적 처리를 포함한 다양한 공격에 강인한 특성을 보였다.

• Journal of Radiation Protection and Research
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• 제47권3호
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• pp.111-133
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• 2022

The exciting advancement related to the "modeling of digital human" in terms of a computational phantom for radiation dose calculations has to do with the latest hype related to deep learning. The advent of deep learning or artificial intelligence (AI) technology involving convolutional neural networks has brought an unprecedented level of innovation to the field of organ segmentation. In addition, graphics processing units (GPUs) are utilized as boosters for both real-time Monte Carlo simulations and AI-based image segmentation applications. These advancements provide the feasibility of creating three-dimensional (3D) geometric details of the human anatomy from tomographic imaging and performing Monte Carlo radiation transport simulations using increasingly fast and inexpensive computers. This review first introduces the history of three types of computational human phantoms: stylized medical internal radiation dosimetry (MIRD) phantoms, voxelized tomographic phantoms, and boundary representation (BREP) deformable phantoms. Then, the development of a person-specific phantom is demonstrated by introducing AI-based organ autosegmentation technology. Next, a new development in GPU-based Monte Carlo radiation dose calculations is introduced. Examples of applying computational phantoms and a new Monte Carlo code named ARCHER (Accelerated Radiation-transport Computations in Heterogeneous EnviRonments) to problems in radiation protection, imaging, and radiotherapy are presented from research projects performed by students at the Rensselaer Polytechnic Institute (RPI) and University of Science and Technology of China (USTC). Finally, this review discusses challenges and future research opportunities. We found that, owing to the latest computer hardware and AI technology, computational human body models are moving closer to real human anatomy structures for accurate radiation dose calculations.

• KEPCO Journal on Electric Power and Energy
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• 제8권2호
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• pp.103-110
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• 2022

Ultrafine Particle number and size distributions were simultaneously measured at rural area around the power plant in Dangjin, South Korea. New Particle formation and growth events were frequently observed during January, 2021 and classified based on their strength and persistence as well as the variation in geometric mean diameter(GMD) on January 12, 21 and 17. In this study, we investigated mechanisms of new particle growth based on measurements using a high resolution time of flight aerosol mass spectrometer(HR-ToF-AMS) and a scanning mobility particle sizer(SMPS). On Event days(Jan 12 and 21), the total average growth rate was found to be 8.46 nm/h~24.76 nm/hr. These growth rate are comparable to those reported for other urban and rural sites in South Korea using different method. Comparing to the Non-Event day(Jan 17), New Particle Growth mostly occurred when solar radiation is peaked and relative humidity is low in daytime, moreover enhanced under the condition of higher precusors, NO₂ (39.9 vs 6.2ppb), VOCs(129.5 vs 84.6ppb), NH₃(11 vs 4.7ppb). The HR-ToF-AMS PM_1.0 composition shows Organic and Ammoniated nitrate were dominant species effected by emission source in domestic. On the other hand, The Fraction of Ammoniated sulfate was calculated to be approximately 16% and 31% when air quality is inflow from China. Longer term studies are needed to help resolve the relative contributions of each precusor species on new particle growth characteristics.

• Journal of Information Processing Systems
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• 제19권3호
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• pp.289-301
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• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.