• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2013년도 추계학술발표대회
• /
• pp.638-639
• /
• 2013

Anymon Plus(ver 3.0)은 통합 로그 분석 시스템으로 대용량 로그 및 빅데이터의 실시간 수집 저장 분석할 수 있는 제품(초당 40,000 이벤트 처리)으로서, 방화벽 로그 분석을 통한 비정상 네트워크 행위 탐지, 웹 로그 분석을 통한 사용 패턴 분석, 인터넷 쇼핑몰 사기 주문 분석 및 탐지, 내부 정부 유출 분석 및 탐지 등과 같은 다양한 분야로 응용이 확대되고 있다. 본 논문에서는 보안관련 인프라 로그를 분석하고 예측하여 예상 보안사고 시기에 집중적 경계를 통한 선제적 대응을 모색하기 위해 통계적 이론에 기반한 통합 로그 분석 시스템을 개발하기 위해, 회귀분석 및 시계열 분석이 가능한 예측 엔진 시스템을 설계하고 구현한다.

• 한국물환경학회지
• /
• 제24권4호
• /
• pp.442-451
• /
• 2008

We analyzed characteristics of rainfall-runoff for the channel of Yongwon area made by a new port construction. And we conducted inundation analysis on the region of lower elevation near the coast. SWMM5 was calibrated with the storm produced by the typhoon Megi from August 19 to August 20 in 2004, and was verified with the storm from August 22 to August 22 in 2004. We performed hydraulic channel routing of Yongwon channel about typhoon Megi from August 19 to August 20 in 2004 by UNET model which is a hydraulic channel routing. The simulated runoff hydrographs were added to the new stream as lateral inflow hydrographs and a watershed runoff hydrograph was the upstream boundary condition. The downstream boundary condition data were estimated by the measured stage hydrographs. The maximum stage that was calculated by hydraulic channel routing was higher than the levee of inundated region in typhoon Megi. Thus we can suppose an inundation to have been occurred. We performed inundation analysis about typhoon Megi from August 19 to August 20 in 2004 and flood discharge of return period 10~150 years. And we estimated each inundation area. The inundation areas by return periods of storms were estimated by 3.4~5.7 ha. The causes of inundation are low heights of levee crests (D.L. 2.033~2.583 m), storm surges induced by typhoons and reverse flow through the coastal sewers (D.L. -0.217~0.783 m). A result of this study can apply to establish countermeasure of a flood disaster in Yongwon.

• Computers and Concrete
• /
• 제32권6호
• /
• pp.615-623
• /
• 2023

Crack detection is an essential method to ensure the safety of dam concrete structures. Low-quality crack images of dam concrete structures limit the application of neural network methods in crack detection. This research proposes a modified attentional mechanism model to reduce the disturbance caused by uneven light, shadow, and water spots in crack images. Also, the focal loss function solves the small ratio of crack information. The dataset collects from the network, laboratory and actual inspection dataset of dam concrete structures. This research proposes a novel method for crack detection of dam concrete structures based on the U-Net neural network, namely AF-UNet. A mutual comparison of OTSU, Canny, region growing, DeepLab V3+, SegFormer, U-Net, and AF-UNet (proposed) verified the detection accuracy. A binocular camera detects cracks in the experimental scene. The smallest measurement width of the system is 0.27 mm. The potential goal is to achieve real-time detection and localization of cracks in dam concrete structures.

• Current Optics and Photonics
• /
• 제8권3호
• /
• pp.215-224
• /
• 2024

In medium- and long-range optical imaging systems, atmospheric turbulence causes blurring and distortion of images, resulting in loss of image information. An image-restoration method based on an adaptive feature-fusion multi-input-multi-output (MIMO) dense U-shaped network (Unet) is proposed, to restore a single image degraded by atmospheric turbulence. The network's model is based on the MIMO-Unet framework and incorporates patch-embedding shallow-convolution modules. These modules help in extracting shallow features of images and facilitate the processing of the multi-input dense encoding modules that follow. The combination of these modules improves the model's ability to analyze and extract features effectively. An asymmetric feature-fusion module is utilized to combine encoded features at varying scales, facilitating the feature reconstruction of the subsequent multi-output decoding modules for restoration of turbulence-degraded images. Based on experimental results, the adaptive feature-fusion MIMO dense U-shaped network outperforms traditional restoration methods, CMFNet network models, and standard MIMO-Unet network models, in terms of image-quality restoration. It effectively minimizes geometric deformation and blurring of images.

• 천문학회보
• /
• 제45권1호
• /
• pp.50.1-50.1
• /
• 2020

In this topic, I will introduce a recent work on reconstructing the cosmic density field based on the GAN. I will show the performance of the GAN compared to the traditional Unet architecture. I'd also like to discuss a 3-channels-based 2D datasets for the training to recover the 3D density field. Finally, I will present some performance tests based on the test datasets.

• 인터넷정보학회논문지
• /
• 제21권3호
• /
• pp.103-111
• /
• 2020

Liver cancer is the most fatal cancer that occurs worldwide. In order to diagnose liver cancer, the patient's physical condition was checked by using a CT technique using radiation. Segmentation was needed to diagnose the liver on the patient's abdominal CT scan, which the radiologists had to do manually, which caused tremendous time and human mistakes. In order to automate, researchers attempted segmentation using image segmentation algorithms in computer vision field, but it was still time-consuming because of the interactive based and the setting value. To reduce time and to get more accurate segmentation, researchers have begun to attempt to segment the liver in CT images using CNNs, which show significant performance in various computer vision fields. The pixel value, or numerical value, of the CT image is called the Hounsfield Unit (HU) value, which is a relative representation of the transmittance of radiation, and usually ranges from about -2000 to 2000. In general, deep learning researchers reduce or limit this range and use it for training to remove noise and focus on the target organ. Here, we observed that the range of HU values was limited in many studies but different in various liver segmentation studies, and assumed that performance could vary depending on the HU range. In this paper, we propose the possibility of considering HU value range as a hyper parameter. U-Net and ResUNet were used to compare and experiment with different HU range limit preprocessing of CHAOS dataset under limited conditions. As a result, it was confirmed that the results are different depending on the HU range. This proves that the range limiting the HU value itself can be a hyper parameter, which means that there are HU ranges that can provide optimal performance for various models.

• 한국방재학회 논문집
• /
• 제6권3호
• /
• pp.69-76
• /
• 2006

본 연구에서는 침수위험이 큰 도시하천내에 건설된 도로의 침수로 인한 피해를 미연에 방지하고 적절한 교통통제계획, EAP 등을 수립하기 위한 수문, 수리학적 분석과정을 제시하였다. 연구대상지역으로 우리나라의 대표적 도시하천인 중랑천 유역의 좌우안에 위치한 동부간선도로를 선정하고 비상대처계획 수립을 위한 기초자료를 작성하였다. HEC-HMS에 의하여 유출 해석을 실시하였으며, UNET을 이용하여 주요 지점 및 구간별 침수특성을 분석하였다. 이문철교부근(하구에서 약 9.5 km)과 월릉교부근(하구에서 약 11.5 km)에서 침수위험이 가장 높아 이문철교부근은 10년 빈도시에, 월릉교부근의 좌안도로는 20년 빈도시에 각각 침수가 되는 것으로 나타났다. 누가강우량과 지속시간을 고려한 침수특성 분석결과 강우 지속시간 7시간 이하에서 누가강우량이 250 mm이상일 경우에는 월계1교지점의 위험홍수위(EL.17.84 m)를 초과하는 것으로 분석되었다. 한강의 배수위를 고려하지 않은 경우에는 C2(월계1교-중랑교, 좌안), C1(월계1교-중랑교, 우안), D(중랑교-군자교)구간순으로 침수위험이 높은 것으로 나타났으나 배수위를 고려한 경우에는 D2(중랑교-군자교, 좌안), E(군자교-용비교)구간의 침수위험이 오히려 높은 것으로 분석되었다.

• 대한원격탐사학회지
• /
• 제36권6_2호
• /
• pp.1579-1590
• /
• 2020

본 논문에서는 고해상도의 위성영상을 활용하여 도시의 변화 양상을 분석하기 위하여 SPADE기반의 U-Net과 객체 영역기반 변화탐지 방법을 제안한다. 제안하는 네트워크는 기존의 U-Net에서 공간 정보를 잃지 않기 위해 SPADE를 사용했다. 고해상도 위성영상을 활용한 변화탐지 방법은 계획, 예측 등 다양한 도시 문제를 해결하기 위해 활용할 수 있다. IR-MAD 등 전통적인 방법인 화소 기반의 변화탐지를 수행할 경우, 다중 시기 영상 간의 기후, 계절 변화 등에 의해 화소의 변화가 민감하기 때문에 미변화 지역들이 변화 지역으로 오탐지될 가능성이 매우 크다. 이에 본 논문에서는 시계열 위성영상에서 도시를 구성하는 객체에 대한 변위를 정확하게 탐지하기 위해 도시를 구성하는 주요 공간 객체를 정의하고, 딥러닝 기반 영상 분할을 통해 추출한 후 영역 간의 변위 오차를 분석하여 변화탐지를 수행한다. 변화 양상을 분석하기 위한 공간 객체로 건축물, 도로, 농경지, 비닐하우스, 산림 영역, 수변 영역의 6개로 정의하였다. KOMPSAT-3A 위성영상으로 학습한 각 네트워크 모델을 시계열 KOMPSAT-3 위성영상에 대한 변화탐지를 수행한다. 객관적인 성능 평가를 위한 변화탐지 지표는 F1-score, Kappa를 사용한다. 제안하는 변화탐지 기법은 U-Net, UNet++ 대비 뛰어난 결과를 보이며, 평균 F1 score는 0.77, kappa는 77.29의 성능을 확인할 수 있다.

• 천문학회보
• /
• 제44권2호
• /
• pp.53.4-53.4
• /
• 2019

We reconstruct the underlying dark matter (DM) density distribution of the local universe within 20Mpc/h cubic box by using the galaxy position and peculiar velocity. About 1,000 subboxes in the Illustris-TNG cosmological simulation are used to train the relation between DM density distribution and galaxy properties by using UNet-like convolutional neural network (CNN). The estimated DM density distributions have a good agreement with their truth values in terms of pixel-to-pixel correlation, the probability distribution of DM density, and matter power spectrum. We apply the trained CNN architecture to the galaxy properties from the Cosmicflows-3 catalogue to reconstruct the DM density distribution of the local universe. The reconstructed DM density distribution can be used to understand the evolution and fate of our local environment.

• 한국멀티미디어학회논문지
• /
• 제25권2호
• /
• pp.206-214
• /
• 2022

The way to measure the extent of damage from floods and droughts is to identify changes in the extent of water systems. In order to effectively grasp this at a glance, satellite images are used. KOMPSAT-5 uses Synthetic Aperture Radar (SAR) to capture images regardless of weather conditions such as clouds and rain. In this paper, various deep learning models are applied to perform semantic segmentation of the water system in this SAR image and the performance is compared. The models used are U-net, V-Net, U2-Net, UNet 3+, PSPNet, Deeplab-V3, Deeplab-V3+ and PAN. In addition, performance comparison was performed when the data was augmented by applying elastic deformation to the existing SAR image dataset. As a result, without data augmentation, U-Net was the best with IoU of 97.25% and pixel accuracy of 98.53%. In case of data augmentation, Deeplab-V3 showed IoU of 95.15% and V-Net showed the best pixel accuracy of 96.86%.