• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.1138-1142
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• 2005

The tidal river is a river affected by tide, which causes the water level to rise and fall two times everyday periodically. The local velocity across the river could be very fast because of the cross-sectional characteristics of the river even though it's not a rainy season. Therefore extreme local scour could take place around hydraulic structures such as piers and caissons due to backward flow velocity. For the construction of pier foundation of Ilsan-bridge In the Han River, the field observations were performed to get the velocity and water level. The numerical analysis was performed by HEC-RAS(UNET). The relationship between measured maximum velocity and calculated mean velocity is achieved, which is used to estimate the velocity and water level as the construction is proceeding. Countermeasures for scour were designed with the results of the hydraulic analysis to avoid potential damage during construction work. According to the results of monitoring, the velocity increase after temporary road embankment was negligible, from which it is considered that the degradation of main channel compensated for the constriction of cross-section by embankment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2333-2345
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• 2024

Facial wrinkles are widely used to evaluate skin condition or aging for various fields such as skin diagnosis, plastic surgery consultations, and cosmetic recommendations. In order to effectively process facial wrinkles in facial image analysis, accurate wrinkle segmentation is required to identify wrinkled regions. Existing deep learning-based methods have difficulty segmenting fine wrinkles due to insufficient wrinkle data and the imbalance between wrinkle and non-wrinkle data. Therefore, in this paper, we propose a new facial wrinkle segmentation method based on a UNet++ model. Specifically, we construct a new facial wrinkle dataset by manually annotating fine wrinkles across the entire face. We then extract only the skin region from the facial image using a facial landmark point extractor. Lastly, we train the UNet++ model using both dice loss and focal loss to alleviate the class imbalance problem. To validate the effectiveness of the proposed method, we conduct comprehensive experiments using our facial wrinkle dataset. The experimental results showed that the proposed method was superior to the latest wrinkle segmentation method by 9.77%p and 10.04%p in IoU and F1 score, respectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05b
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• pp.673-678
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• 2001

• Computers and Concrete
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• v.32 no.6
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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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.

• Korean Journal of Remote Sensing
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• v.38 no.1
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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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• v.8 no.3
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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.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09a
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• pp.390-391
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• 2005

• 한국방재학회:학술대회논문집
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• 2009.02b
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• pp.73.2-73.2
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• 2009

• Journal of KIBIM
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• v.12 no.1
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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.