• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.123-126
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• 2023

본 논문에서는 철도 인근 주거지역, 철도 건널목, 철도 터널, 철도 교량을 위험 상황으로 설정하고, 각 상황이 철도가 아닌 다른 상황에서 발생할 때와 철도에서 발생할 때와의 위험도 차이를 분석하며 철도 위험 상황에 영향을 미치는 요인들을 분석한다. 또한, 요인별 위험도의 기준을 설정하고 설정한 위험 상황과 유사한 상황에 대한 데이터 발생 시 해당 데이터를 웹 어플리케이션에 송출하여 소비자들에게 서비스를 제공한다.

• Journal of the Korean Institute of Rural Architecture
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• v.21 no.2
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• pp.27-34
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• 2019

This study explores the emergence of a modern form of Cheongju-eup townscape in the late 1930s by re-examining the 1960s restoration model of Seongan-dong and Jungang-dong in Cheongju, one of the historic cities in South Korea. According to the acquired data from the restoration model, it is found that the construction of a new urban area during the late 1930 was resulted from the following events: the development of a railroad station located outside of the north gate of Cheongju-eup since 1921, the completion of Musimcheon embankment outside the south gate in 1932, and the construction of Chungcheongbuk provincial office outside the eastern gate in 1937. In this period of development, which the author named 'Cheongju-eup period', the streets in the old castle, consisting only of two-story financial buildings, had been expanded from the existing area at the Seongan-gil intersection to the outside the east gate of Cheongju-eup. In addition, public government buildings, which were mainly located in both Seongan-gil and Yulgok-ro in the east-west direction, were newly constructed during the late 1930s in Seokgyo-dong, a new area in which a large number of commercial buildings including department stores, clothing stores, shoes shops, and watch stores were also built along the streets. Moreover, the modern form of Cheongju-eup was to be formed by several construction projects in the area of Jungang-ro in the late 1930s. Until the 1920s, the townscape outside the northern gate of Cheongju-eup, were composed of primary, agricultural, and female schools built on a largest site of Gyoseo-ro and Daeseong-ro as well as a transportation warehouse and a railway office near the Cheongju station. Then, entering the 1930s, new school buildings and domestic industrial shops and factories were built around the area of Jungang-ro ranging from the railway outside the northern gate to Bangadari. As a result, the expansion of townscape with newly constructed buildings in the late 1930s marked the emergence of a modern form of Cheongju-eup.

• Journal of architectural history
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• v.28 no.4
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• pp.17-26
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• 2019

After opening Suwon railway station in 1905, a new road was constructed between Suwon station and Paldalmun(the South gate). It was the starting point to change urban structures of Suwon and shape the new city scape. In 1914, administrative districts of Suwon were reorganized. Suwon-myeon (township, a subdivision of Suwon-gun) was promoted to Suwon-eup(town) in 1931. Suwon-eup expanded its territory and changed the address system from 'li(里)' system to Japanese address system, 'Jeong(町)' in 1936. From 1920s, road system was changed and transformed Suwon's urban structures. A straight road was built from Jongro intersection to Janganmun(the north gate) in 1928. Another straight road was constructed between Suwon station to Padamun in the early 1930s. Public office buildings used the Hwa Seong HaengGung(華城行宮) and some of building moved to new location with new buildings. Main buildings of most schools in Suwon were reconstructed since 1930s. Commercial buildings and stores were sprung up and had their own characteristics by region. Around Suwon station, there are more hotels and restaurants than other areas. Rearranging administrative areas, developing road system and new buildings transformed Suwon's spatial structures. Constructing new roads formed a straight road passing through Suwon. After reorganizing administrative areas, this road turned to be the central axis of Suwon. Buildings in new style on the axis made the modern cityscape in Suwon.

• Journal of the Korea Safety Management & Science
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• v.23 no.1
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• pp.31-38
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• 2021

port is the intersection of highway, railway, waterway and other transportation modes, and is the key to realizing integrated transportation. There are many excellent ports around the Tumen River Region. With the obvious location advantages, Tumen River Region is an important part of Tumen River regional cooperation and development, and is the key to realizing the "borrow port to sea", which is raised in "China Tumen River regional cooperation and development planning outline -- regard Changjitu area as the development and opening leading area" (referred to as "planning outline"). This paper focus on the main ports in the Tumen River region, taking them as the research object. Furthermore, the paper makes the strategic plan for the port cluster in the Tumen River region as well as puts a collaboration scheme is proposed by analyzing the research reviews of the Tumen River region and the present situations of the main ports.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Journal of the Korean Geotechnical Society
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• v.30 no.9
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• pp.41-55
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• 2014

In performing seismic analysis of tunnels, it is a common practice to ignore the rock joints and to assume that the rock mass surrounding the tunnel is continuous. The applicability of this assumption has not yet been validated in detail. This study performs a series of pseudo-static discrete element analyses to evaluate the effect of rock joint on the seismic response of tunnels. The parameters considered are joint intersection location, joint spacing, joint stiffness, joint dip, and interface stiffness. The results show that the joint stiffness has the most critical influence on the tunnel response. The tunnel response increases with the spacing, resulting in localized concentration of moment and shear stress. The response of the tunnel is the lowest for joints dipping at $45^{\circ}$. This is because large shear stresses result in rotation of the principal planes by $45^{\circ}$. In summary, the weathered and smooth, vertical or horizontal, and widely spaced joint set will significantly increase the tunnel response under seismic loading. The tunnel linings are shown to be most susceptible to damage due to induced shear stress, and therefore should be checked in the seismic design.