• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2001년도 학술발표회 논문집(II)
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• pp.673-678
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• 2001

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

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.413-413
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• 2018

최근 도시지역에 태풍과 집중호우로 인한 홍수발생 빈도와 그 규모가 커지고 있다. 이에 따른 재산 및 인명피해 양상도 매우 심각한 상황이다. 태풍 차바 처럼 강력한 10월 태풍의 출현은 지구 온난화의 전조로 받아들여지고 있다. 또한 10월 태풍임에도 초속 56.5m의 순간 최대풍속과 시간당 최대 116.7mm(제주 서귀포), 139mm(매곡) 등의 강수량은 지역 최대 강수량을 기록함으로써 이제 언제나 태풍 및 홍수에 대한 대비가 필요하게 되었다. 현재 재해에 대비하기 위해 다양한 대책들은 꾸준히 마련되어지고 있으며, 설계 기준 또한 강화되었다. 그러나 저류조 및 배수펌프장 등의 시설물 설치에는 막대한 예산이 필요한데다 장기간의 시간이 필요하며, 비구조적 대책도 마련되어 있으나 태풍 차바의 사례에서 경험한 것처럼 재해 발생 시 대책과 구체적인 방안의 마련이 더욱 시급해 보인다. 이에 본 연구에서는 태풍 차바 시의 호우에 대하여 UNET모형에 의한 부정류모의를 수행하였다. 부정류모의의 경계조건으로써 상류단 경계조건과 측방유입량 조건은 HEC-HMS를 이용하여 유출해석을 실시한 다음 입력 자료로 이용하였으며, 하류단 경계조건으로는 국토부 관할 수위지점의 수위를 이용하여 UNET 모형에 의한 수리학적 하도추적을 수행하였으며, 저지대 침수분석은 지형정보시스템 응용프로그램 중 하나인 ArcGIS를 활용하여 대상유역의 벡터자료를 구축하고 인접도엽의 접합 및 보정을 실시하여 수치고도자료를 생성하여 2차원 홍수범람해석을 위한 HEC-RAS 5.0을 적용하여 침수분석을 수행하였다. 본 연구의 결과를 수재해 피해저감 대책을 수립하는데 기초자료로 활용될 수 있을거라 판단된다.

• 대한원격탐사학회지
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• 제38권1호
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• pp.111-126
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• 2022

Land use and land cover (LULC) mapping is an important factor in geospatial analysis. Although highly precise ground-based LULC monitoring is possible, it is time consuming and costly. Conversely, because the synthetic aperture radar (SAR) sensor is an all-weather sensor with high resolution, it could replace field-based LULC monitoring systems with low cost and less time requirement. Thus, LULC is one of the major areas in SAR applications. We developed a LULC model using only KOMPSAT-5 single co-polarized data and digital elevation model (DEM) data. Twelve HH-polarized images and 18 VV-polarized images were collected, and two HH-polarized images and four VV-polarized images were selected for the model testing. To train the LULC model, we applied the conditional generative adversarial network (cGAN) method. We used U-Net combined with the residual unit (ResUNet) model to generate the cGAN method. When analyzing the training history at 1732 epochs, the ResUNet model showed a maximum overall accuracy (OA) of 93.89 and a Kappa coefficient of 0.91. The model exhibited high performance in the test datasets with an OA greater than 90. The model accurately distinguished water body areas and showed lower accuracy in wetlands than in the other LULC types. The effect of the DEM on the accuracy of LULC was analyzed. When assessing the accuracy with respect to the incidence angle, owing to the radar shadow caused by the side-looking system of the SAR sensor, the OA tended to decrease as the incidence angle increased. This study is the first to use only KOMPSAT-5 single co-polarized data and deep learning methods to demonstrate the possibility of high-performance LULC monitoring. This study contributes to Earth surface monitoring and the development of deep learning approaches using the KOMPSAT-5 data.

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

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회(1)
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• pp.390-391
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• 2005

• 한국방재학회:학술대회논문집
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• 한국방재학회 2009년도 정기 학술발표대회
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• pp.73.2-73.2
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• 2009

• 한국BIM학회 논문집
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• 제12권1호
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• pp.55-61
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• 2022

Recently, demand for designing own space is increasing as the rapid growth of home furnishing market. However, there is a limitation that it is not easy to compare the style between before construction view and after view. This study aims to translate real image into another style with GAN model learned with interior images. To implement this, first we established style criteria and collected modern, natural, and classic style images, and experimented with ResNet, UNet, Gradient penalty concept to CycleGAN algorithm. As a result of training, model recognize common indoor image elements, such as floor, wall, and furniture, and suitable color, material was converted according to interior style. On the other hand, the form of furniture, ornaments, and detailed pattern expressions are difficult to be recognized by CycleGAN model, and the accuracy lacked. Although UNet converted images more radically than ResNet, it was more stained. The GAN algorithm allowed us to represent results within 2 seconds. Through this, it is possible to quickly and easily visualize and compare the front and after the interior space style to be constructed. Furthermore, this GAN will be available to use in the design rendering include interior.

• 한국가시화정보학회지
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• 제21권1호
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• pp.18-25
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• 2023

In these days, high dimensional data prediction technology based on neural network shows compelling results in many different kind of field including engineering. Especially, a lot of variants of convolution neural network are widely utilized to develop pixel level prediction model for high dimensional data such as picture, or physical field value from the sensors. In this study, velocity vector field of ideal flow on ship surface is estimated on pixel level by Unet. First, potential flow analysis was conducted for the set of hull form data which are generated by hull form transformation method. Thereafter, four different neural network with a U-shape structure were conFig.d to train velocity vectors at the node position of pre-processed hull form data. As a result, for the test hull forms, it was confirmed that the network with short skip-connection gives the most accurate prediction results of streamlines and velocity magnitude. And the results also have a good agreement with potential flow analysis results. However, in some cases which don't have nothing in common with training data in terms of speed or shape, the network has relatively high error at the region of large curvature.

• 한국방재학회 논문집
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• 제6권3호
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• pp.69-76
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• 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.