• Journal of the Society of Naval Architects of Korea
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• v.60 no.1
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• pp.31-37
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• 2023

In the present study, a method of performing cavitation erosion test directly on the anodized surface of the rudder model is proposed, not applying ink or paint on its surface. An image processing technique is newly developed to quantitatively evaluate the erosion damages on the rudder model surface after erosion test. The preprocessing saturation image, image smoothing, adaptive hysteresis thresholding and eroded area detection algorithms are in the image processing program. The rudder cavitation erosion tests are conducted in the rudder deflection angle range of 0° to -4°, which is used to maintain a straight course at the highest speed of the targeted navy ship. In the case of the conventional flat-type full-spade rudder currently being used in the target ship, surface erosion can occur on the model rudder surface in the above rudder deflection angle range. The bubble type of cavitation occurs on rudder surface, which is estimated to be the main reason of erosion damage on the rudder surface.

• Tunnel and Underground Space
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• v.13 no.5
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• pp.373-388
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• 2003

The investigation on the rock discontinuity geometry has been usually undergone by direct measurement on the rock exposures. But this sort of field work has disadvantages, which we, for example, restriction of surveying areas and consuming excessive times and labors. To cover these kinds of disadvantages, image processing could be regarded as an altemative way, with additional advantages such as automatic and objective tools when used under adequate computerized algorithm. This study was focused on the recognition of the rock discontinuities captured in the image of rock exposure by digital camera and the production of the discontinuity map automatically. The whole process was written using macro commands builtin image analyzer, ImagePro Plus. ver 4.1(Media Cybernetic). The procedure of image processing developed in this research could be divided with three steps, which are enhancement, recognition and extraction of discontinuity traces from the digital image. Enhancement contains combining and applying several filters to remove and relieve various types of noises from the image of rock surface. For the next step, recognition of discontinuity traces was executed. It used local topographic features characterized by the differences of gray scales between discontinuity and rock. Such segments of discontinuity traces extracted from the image were reformulated using an algorithm of computer decision-making criteria and linked to form complete discontinuity traces. To verify the image processing algorithms and their sequences developed in this research, discontinuity traces digitally photographed on the rock slope were analyzed. The result showed about 75～80% of discontinuities could be detected. It is thought to be necessary that the algorithms and computer codes developed in this research need to be advanced further especially in combining digital filters to produce images to be more acceptable for extraction of discontinuity traces and setting seed pixels automatically when linking trace segments to make a complete discontinuity trace.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.187-201
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• 2021

Recently, the demand for a 3D urban spatial information infrastructure for storing, operating, and analyzing a large number of digital data produced in cities is increasing. CityGML is a 3D spatial information data standard of OGC (Open Geospatial Consortium), which has strengths in the exchange and attribute expression of city data. Cases of constructing 3D urban spatial data in CityGML format has emerged on several cities such as Singapore and New York. However, the current ecosystem for the creation and editing of CityGML data is limited in constructing CityGML data on a large scale because of lack of completeness compared to commercial programs used to construct 3D data such as sketchup or 3d max. Therefore, in this study, a method of constructing CityGML data is proposed using commercial 3D mesh data and 2D polygons that are rapidly and automatically produced through aerial LiDAR (Light Detection and Ranging) or RGB (Red Green Blue) cameras. During the data construction process, the original 3D mesh data was geometrically transformed so that each object could be expressed in various CityGML LoD (Levels of Detail), and attribute information extracted from the 2D spatial information data was used as a supplement to increase the utilization as spatial information. The 3D city features produced in this study are CityGML building, bridge, cityFurniture, road, and tunnel. Data conversion for each feature and property construction method were presented, and visualization and validation were conducted.

• Tunnel and Underground Space
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• v.32 no.2
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• pp.131-143
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• 2022

The lunar exploration autonomous vehicle operates based on the lunar topography information obtained from real-time image characterization. For highly accurate topography characterization, a large number of training images with various background conditions are required. Since the real lunar topography images are difficult to obtain, it should be helpful to be able to generate mimic lunar image data artificially on the basis of the planetary analogs site images and real lunar images available. In this study, we aim to artificially create lunar topography images by using the location information-based style transfer algorithm known as Wavelet Correct Transform (WCT2). We conducted comparative experiments using lunar analog site images and real lunar topography images taken during China's and America's lunar-exploring projects (i.e., Chang'e and Apollo) to assess the efficacy of our suggested approach. The results show that the proposed techniques can create realistic images, which preserve the topography information of the analog site image while still showing the same condition as an image taken on lunar surface. The proposed algorithm also outperforms a conventional algorithm, Deep Photo Style Transfer (DPST) in terms of temporal and visual aspects. For future work, we intend to use the generated styled image data in combination with real image data for training lunar topography objects to be applied for topographic detection and segmentation. It is expected that this approach can significantly improve the performance of detection and segmentation models on real lunar topography images.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.2
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• pp.171-182
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• 2022

InSAR (Interferometry SAR) technique is a technique that uses complex data to obtain phase difference information from two or more SAR image data, and enables high-resolution image extraction, surface change detection, elevation measurement, and glacial change observation. In many countries, research on the InSAR technique is being conducted in various fields of study such as volcanic activity detection, glacier observation in Antarctica, and ground subsidence analysis. In this study, a case of large ground settlement due to groundwater level drawdown during tunnelling was introduced, and ground settlement analyses using InSAR technique and numerical analysis method were compared. The maximum settlement and influence radius estimated by the InSAR technique and numerical method were found to be quite similar, which confirms the reliability of the InSAR technique. Through this case study, it was found that the InSAR technique reliable to use for estimating ground settlement and can be used as a key technology to identify the long-term ground settlement history in the absence of measurement data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.1017-1023
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• 2017

Duration induced by freeway crashes has a critical influence on traffic congestion. In general, crash duration composes detection and verification, response, and clearance time. Of these, the crash clearance time determined by a crash clearance team has attracted considerable attention in the freeway congestion management since the interest of the first two time stages faded away with increasing ubiquitous mobile phone users. The objective of this study is to identify the critical factors that affect freeway crash clearance time using a Cox's proportional hazard model. In total, 6,870 crash duration data collected from 30 major Korean freeways in 2013 were used. As a result, it was found that crashes during the night, with trailer or larger size truck, and in tunnel section contribute to increasing clearance time. Crashes associated with fatality, completed damage of crashed vehicle (s), and vehicles' fire or rollover after crash also lead to increasing clearance time. Additionally, an increase in the number of vehicles involved resulted in longer clearance time. On the other hand, crashes in the vicinity of tollgate, by passenger car, during spring, on flat section, and of car-facility type had longer clearance time. On the basis of the results, this paper suggested some strategic plans and mitigation measures to reduce crash clearance time on Korean freeway systems.

• Tunnel and Underground Space
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• v.30 no.5
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• pp.462-472
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• 2020

Rock classification is fundamental discipline of exploring geological and geotechnical features in a site, which, however, may not be easy works because of high diversity of rock shape and color according to its origin, geological history and so on. With the great success of convolutional neural networks (CNN) in many different image-based classification tasks, there has been increasing interest in taking advantage of CNN to classify geological material. In this study, a feasibility of the deep CNN is investigated for automatically and accurately identifying rock types, focusing on the condition of various shapes and colors even in the same rock type. It can be further developed to a mobile application for assisting geologist in classifying rocks in fieldwork. The structure of CNN model used in this study is based on a deep residual neural network (ResNet), which is an ultra-deep CNN using in object detection and classification. The proposed CNN was trained on 10 typical rock types with an overall accuracy of 84% on the test set. The result demonstrates that the proposed approach is not only able to classify rock type using images, but also represents an improvement as taking highly diverse rock image dataset as input.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.5
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• pp.128-134
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• 2014

Recently, design and construction of street tunnels tend to focus on cost reduction and preservation of nature. Accordingly, research is actively being carried out to quickly detect fires when they occur in tunnels, which have partially closed structures. Among such research, fire detection methods using optical fiber sensors have a wide bandwidth and fast transmission speed, while using light as a medium. Therefore, it does not receive electrical interference and there is almost no loss of information during transmission, while also having little noise as well. In relation to this, a fire monitoring system that can accurately detect the location of fires in real time using shape memory alloy and optical cables was developed in this study. In order to verify the developed method, light loss measurement test was conducted according to indoor temperature changes, while also conducting fire simulation tests by installing test beds in common underground zones with different external environments of temperature and distance. Upon carrying out experiments, the fire monitoring system developed in this study was found to be able to detect fires in long distance sections in real time.

• Journal of the Korean Geotechnical Society
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• v.23 no.4
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• pp.15-24
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• 2007

Two kinds of temperature monitoring technology have been introduced in this study, which can measure coincidently temperatures at many points along a single length of cable. One is to use a thermal sensor cable comprizing of addressable thermal sensors. The other is to use an optic fiber sensor with Distributed Temperature Sensing (DTS) system. The differences between two technologies can be summarized as follows: A thermal sensor cable has a concept of "point sensing" that can measure temperature only at a predefined position. The accuracy and resolution of temperature measurement are up to the capability of the individual thermal sensor. On the other hand, an optic fiber sensor has a concept of "distributed sensing" because temperature is measured practically at all points along the fiber optic cable by analysing the intensity of Raman back-scattering when a laser pulse travels along the fiber. Thus, the temperature resolution depends on the measuring distance, measuring time and spatial resolution. The purpose of this study is to investigate the applicability of two different temperature monitoring techniques in technical and economical sense. To this end, diverse experiments with two techniques were performed and two techniques are applied under the same condition. Considering the results, the thermal sensor cable will be well applicable to the assessment of groundwater flow, geothermal distribution and grouting efficiency within about loom distance, and the optic fiber sensor will be suitable for long distance such as pipe line inspection, tunnel fire detection and power line monitoring etc.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.218-230
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• 2024

Recently, many studies have been conducted for safety management in construction sites by incorporating computer vision. Anchor box parameters are used in state-of-the-art deep learning-based object detection and segmentation, and the optimized parameters are critical in the training process to ensure consistent accuracy. Those parameters are generally tuned by fixing the shape and size by the user's heuristic method, and a single parameter controls the training rate in the model. However, the anchor box parameters are sensitive depending on the type of object and the size of the object, and as the number of training data increases. There is a limit to reflecting all the characteristics of the training data with a single parameter. Therefore, this paper suggests a method of applying multiple parameters optimized through data split to solve the above-mentioned problem. Criteria for efficiently segmenting integrated training data according to object size, number of objects, and shape of objects were established, and the effectiveness of the proposed data split method was verified through a comparative study of conventional scheme and proposed methods.