• 전기전자학회논문지
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• 제27권4호
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• pp.456-463
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• 2023

본 논문에서는 사용자의 개입없이 고해상도 위성 영상을 활용하여 정밀한 토지피복분류를 위해 U-Net 네트워크 모델에 SPADE 구조를 결합한 SSResUNet 모델을 제안한다. 제안하는 네트워크는 위성 영상의 공간적 특성을 보존하여 복잡도가 높은 환경에서도 강인한 분류모델이라는 장점이 있다. 다목적실용위성 3A 영상을 통해 학습한 결과 기존 U-Net, U-Net++ 대비 뛰어난 결과를 보였으며 평균 IoU 76.10, Dice 86.22의 성능을 도출하였다.

• Journal of Forest and Environmental Science
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• 제40권1호
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• pp.15-23
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• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• Smart Structures and Systems
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• 제31권4호
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• pp.335-349
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• 2023

Bridges constantly undergo deterioration and damage, the most common ones being concrete damage and exposed rebar. Periodic inspection of bridges to identify damages can aid in their quick remediation. Likewise, identifying components can provide context for damage assessment and help gauge a bridge's state of interaction with its surroundings. Current inspection techniques rely on manual site visits, which can be time-consuming and costly. More recently, robotic inspection assisted by autonomous data analytics based on Computer Vision (CV) and Artificial Intelligence (AI) has been viewed as a suitable alternative to manual inspection because of its efficiency and accuracy. To aid research in this avenue, this study performs a comparative assessment of different architectures, loss functions, and ensembling strategies for the autonomous segmentation of bridge components and damages. The experiments lead to several interesting discoveries. Nested Reg-UNet architecture is found to outperform five other state-of-the-art architectures in both damage and component segmentation tasks. The architecture is built by combining a Nested UNet style dense configuration with a pretrained RegNet encoder. In terms of the mean Intersection over Union (mIoU) metric, the Nested Reg-UNet architecture provides an improvement of 2.86% on the damage segmentation task and 1.66% on the component segmentation task compared to the state-of-the-art UNet architecture. Furthermore, it is demonstrated that incorporating the Lovasz-Softmax loss function to counter class imbalance can boost performance by 3.44% in the component segmentation task over the most employed alternative, weighted Cross Entropy (wCE). Finally, weighted softmax ensembling is found to be quite effective when used synchronously with the Nested Reg-UNet architecture by providing mIoU improvement of 0.74% in the component segmentation task and 1.14% in the damage segmentation task over a single-architecture baseline. Overall, the best mIoU of 92.50% for the component segmentation task and 84.19% for the damage segmentation task validate the feasibility of these techniques for autonomous bridge component and damage segmentation using RGB images.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.203-211
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• 2021

Image tampering detection and localization have become an active area of research in the field of digital image forensics in recent times. This is due to the widespread of malicious image tampering. This study presents a new method for image tampering detection and localization that combines the advantages of dilated convolution, residual network, and UNET Architecture. Using the UNET architecture as a backbone, we built the proposed network from two kinds of residual units, one for the encoder path and the other for the decoder path. The residual units help to speed up the training process and facilitate information propagation between the lower layers and the higher layers which are often difficult to train. To capture global image tampering artifacts and reduce the computational burden of the proposed method, we enlarge the receptive field size of the convolutional kernels by adopting dilated convolutions in the residual units used in building the proposed network. In contrast to existing deep learning methods, having a large number of layers, many network parameters, and often difficult to train, the proposed method can achieve excellent performance with a fewer number of parameters and less computational cost. To test the performance of the proposed method, we evaluate its performance in the context of four benchmark image forensics datasets. Experimental results show that the proposed method outperforms existing methods and could be potentially used to enhance image tampering detection and localization.

• 한국전산구조공학회논문집
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• 제37권2호
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• pp.143-149
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• 2024

이미지 분석을 통한 재료의 상 구분은 재료의 미세구조 분석을 위해 필수적이다. 이미지 분석에 주로 사용되는 마이크로-CT 이미지는 대체로 재료를 구성하고 있는 상에 따라 회색조 값이 다르게 나타나므로 이미지의 회색조 값 비교를 통해 상을 구분한다. 순환골재의 고체상은 수화된 시멘트풀과 천연골재로 구분되는데, 시멘트풀과 천연골재는 CT이미지 상에서 유사한 회색조 분포를 보여 상을 구분하기 어렵다. 본 연구에서는 Unet-VGG16 네트워크를 활용하여 순환골재 CT 이미지로부터 천연골재를 분할하는 자동화 방법을 제안하였다. 딥러닝 네트워크를 활용하여 2차원 순환골재 CT 이미지로부터 천연골재 영역을 분할하는 방법과 이를 3차원으로 적층하여 3차원 천연골재 이미지를 얻는 방법을 제시하였다. 선별된 3차원 천연골재 이미지에서 각각의 골재 입자를 분할하기 위해 이미지 필터링을 사용하였다. 골재 영역 분할 성능을 정확도, 정밀도, 재현율 F1 스코어를 통해 검증하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2013년도 추계학술발표대회
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• pp.638-639
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• 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
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• 제24권4호
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• pp.442-451
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• 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.

• 방송공학회논문지
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• 제28권3호
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• pp.267-274
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• 2023

의료영상 공개 데이터는 수집에 한계가 있어 데이터셋의 양이 부족하다는 문제점이 있다. 때문에 기존 연구들은 공개 데이터셋에 과적합 되었을 우려가 있다. 본 논문은 실험을 통해 8개의 (Unet, X-Net, HarDNet, SegNet, PSPNet, SwinUnet, 3D-ResU-Net, UNETR) 의료영상 분할 모델의 성능을 비교함으로써 기존 모델의 성능을 재검증하고자 한다. 뇌졸중 진단 공개 데이터 셋인 Anatomical Tracings of Lesions After Stroke(ATLAS) V1.2과 ATLAS V2.0에서 모델들의 성능 비교 실험을 진행한다. 실험결과 대부분 모델은 V1.2과 V2.0에서 성능이 비슷한 결과를 보였다. 하지만 X-net과 3D-ResU-Net는 V1.2 데이터셋에서 더 높은 성능을 기록했다. 이러한 결과는 해당 모델들이 V1.2에 과적합 되었을 것으로 해석할 수 있다.

• 대한토목학회논문집
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• 제31권6B호
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• pp.523-529
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• 2011

감조하천은 조석의 영향을 받는 하천으로, 하루에 두 번 수위를 상승 및 하강시킨다. 감조하천에서는 홍수시 하천유속 보다 비홍수기 조석에 의한 유속이 구조물 설계에 보다 지배적인 인자가 될 수 있다. 본 연구는 한강 하류부 감조구간에서 비홍수기에 발생된 유속 및 수위 관측을 실시하고 수치해석의 검증자료로 활용하였다. 흐름해석을 위해 부정류 해석이 가능한 UNET모형을 이용하였다. 신곡수중보 아래 감조구간의 조도계수 추정을 위해 통계적인 방법이 사용되었다. 통계적 방법으로 실측수위와 계산수위 간의 불일치율을 이용하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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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.