• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.220-220
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• 2021

2020년 장마는 6월 중순부터 8월 중순까지 전국적으로 평균 687 mm의 강수가 내려, 1973년 이후 역대 2위 강수량을 기록하였으며, 연이은 태풍으로 큰 인명 및 재산 피해가 발생하였다. 특히, 섬진강 및 한탄천 등에서 계획홍수위를 초과하는 홍수로 인해 상당수의 제방이 월류로 인해 붕괴된 것으로 나타났다. 따라서, 향후 기후변화에 따른 연평균 강수량이 증가할 것으로 전망되는 가운데 집중호우로 인한 제방 붕괴 피해를 최소화하기 위한 고도화된 기술 개발을 통한 선제적 재발 방지대책이 필요한 시점이다. 한국건설기술연구원은 바이오폴리머라는 새로운 친환경 신소재를 이용하여 제방의 안정성 평가 기술 개발 연구를 수행하고 있다. 이에 안동하천연구센터에서는 실규모에 준하는 제방모형(높이 3 m, 사면경사 1:2, 길이 10 m 이상)을 제작하고, 제방 표면에 바이오폴리머 신소재를 처리하여 전방 월류 흐름 유도에 따른 실규모 제방붕괴실험을 수행하였다. 또한, 신소재 보강 및 무보강 조건에 따른 영상분석 기반 붕괴지연효과를 정량적으로 분석하여 신소재의 성능을 평가하였다. 하지만, 기존에 수행된 실험은 댐 붕괴 흐름과 같이 홍수파가 발생하여 제내지로 퍼져 나가는 형태로 진행되어, 보강공법의 검증에 있어 실제 하천에서 발생하는 횡월류 흐름을 재현하지 못한다는 한계를 가지고 있다. 본 연구에서는 횡월류 흐름(0.6 m³/s 이상)을 발생시켜 수리실험에 따른 축척효과(scale effect)를 최소화하고, 현장에 대한 충분한 자연성을 재현하는 것을 목표로 하여 실험을 수행하였다. 실험 조건은 1) 신소재가 처리된 식생 제방, 2) 신소재가 처리되지 않은 식생 제방으로 각각의 조건에 따른 횡월류 흐름 및 제방 붕괴를 유도하여 영상분석 기법(이미지 픽셀분석 및 3D 포인트 클라우드 모델링)을 통한 침식 저항에 관한 분석결과를 제시하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1229-1238
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• 2023

Digital twin is a technology that creates a virtual space that replicates the real world and manages the real world efficiently by integrating the real and virtual spaces. The digital twin concept for water facilities is to effectively manage water pipes in the real world by implementing them in a virtual space and augmenting them to the interior space of the building. In the proposed method, the Unity 3D game engine is used to implement the application of digital twin technology in the interior of a building. The AR Foundation toolkit based on ARCore is used as the augmented reality technology for our Digital Twin implementation. In digital twin applications, it is essential to match the real and virtual worlds. In the proposed method, 2D image markers are used to match the real and virtual worlds. The Unity shader program is also applied to make the augmented objects visually realistic. The implementation results show that the proposed method is simple but accurate in placing water pipes in real space, and visually effective in representing water pipes on the wall.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.140-153
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• 2019

Cloase-range image information from drones and ground-based camera has been frequently used in the field of disaster mitigation with 3D modeling and mapping. In addition, the utilization of virtual reality(VR) is being increased by implementing realistic 3D models with the VR technology simulating disaster circumstances in large scale. In this paper, we created a VR training program by extracting realistic 3D models from close-range images from unmanned aircraft and digital camera on hand and observed several issues occurring during the implementation and the effectiveness in the case of a VR application in training for disaster mitigation. First of all, we built up a scenario of disaster and created 3D models after image processing with the close-range imagery. The 3D models were imported into Unity, a software for creation of augmented/virtual reality, as a background for android-based mobile phones and VR environment was created with C#-based script language. The generated virtual reality includes a scenario in which the trainer moves to a safe place along the evacuation route in the event of a disaster, and it was considered that the successful training can be obtained with virtual reality. In addition, the training through the virtual reality has advantages relative to actual evacuation training in terms of cost, space and time efficiencies.

• Information Systems Review
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• v.23 no.4
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• pp.45-67
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• 2021

As the transaction volume of the C2C second-hand market is growing, the number of frauds, which intend to earn unfair gains by sending products different from specified ones or not sending them to buyers, is also increasing. This study explores the model that can identify frauds in the online C2C second-hand market by examining the postings for transactions. For this goal, this study collected 145,536 field data from actual C2C second-hand market. Then, the model is built with the characteristics from postings such as the topic and the linguistic characteristics of the product description, and the characteristics of products, postings, sellers, and transactions. The constructed model is then trained by the machine learning algorithm XGBoost. The final analysis results show that fraudulent postings have less information, which is also less specific, fewer nouns and images, a higher ratio of the number and white space, and a shorter length than genuine postings do. Also, while the genuine postings are focused on the product information for nouns, delivery information for verbs, and actions for adjectives, the fraudulent postings did not show those characteristics. This study shows that the various features can be extracted from postings written in C2C second-hand transactions and be used to construct an effective model for frauds. The proposed model can be also considered and applied for the other C2C platforms. Overall, the model proposed in this study can be expected to have positive effects on suppressing and preventing fraudulent behavior in online C2C markets.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.101-107
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• 2016

Due to the recent advancement in digital imaging technology, development of intervention equipment has become generalize. Video arbitration procedure is a process to insert a tiny catheter and a guide wire in the body, so in order to enhance the effectiveness and safety of this treatment, the high-quality of x-ray of image should be used. However, the increasing of radiation has become the problem. Therefore, the studies to improve the performance of x-ray detectors are being actively processed. Moreover, this intervention is based on the reference of the angiographic imaging and 3D medical image processing. In this paper, we propose a guidance system to support this intervention. Through this intervention, it can solve the problem of the existing 2D medical images based vessel that has a formation of cerebrovascular disease, and guide the real-time tracking and optimal route to the target lesion by intervention catheter and guide wire tool. As a result, the system was completely composed for medical image acquisition unit and image processing unit as well as a display device. The experimental environment, guide services which are provided by the proposed system Brain Phantom (complete intracranial model with aneurysms, ref H+N-S-A-010) was taken with x-ray and testing. To generate a reference image based on the Laplacian algorithm for the image processing which derived from the cerebral blood vessel model was applied to DICOM by Volume ray casting technique. $A^*$ algorithm was used to provide the catheter with a guide wire tracking path. Finally, the result does show the location of the catheter and guide wire providing in the proposed system especially, it is expected to provide a useful guide for future intervention service.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1045-1055
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• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.