• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.31-37
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• 2023

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.

• Korean Journal of Construction Engineering and Management
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• v.16 no.1
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• pp.35-43
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• 2015

It is important to have enough design and construction duration for infrastructure projects. However, recent water resource project in Korea shows several problems caused by their fast-tract schedule. National Audit Committee report several water resource projects have quality problems caused by insufficient project duration. Especially, water resource projects such as dam and water pipeline construction should have proper time to secure their structure quality. Normal project duration for these projects should be estimated based on previous similar projects' historical data analysis. However there is no standard model which can estimate normal project duration for water resource projects in Korea. There are several normal project duration estimation models for building project developed by public(LH) and private construction companies. However, there is no proper model for water resource projects. So, this study developed normal project duration model for dam and water pipeline projects using historical data and show application of models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.872-877
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• 2018

STEAM is an acronym for Science, Technology, Engineering, Arts, and Mathematics. It is considered important to equip students with a creative thinking ability and the core competences required in future society, helping them devise new ideas emerging from branches of study. This study is about the convergence of instructional design in private finance for the life sciences, which aims to foster talent through problem-based learning (PBL). Skills like collaboration, creativity, critical thinking, and problem solving are part of any STEAM PBL, and are needed for students to be effective. STEAM projects give students a chance to problem-solve in unique ways, because they are forced to use a variety of methods to solve problems that pop up during these types of activities. The results of this study are as follows. First is the structured process of convergence lessons. Second is the convergence lesson process. Third is the development of problems in the introduction of private finance and the life sciences for a convergence lesson at Dornod University. Learning motivation shows the following results: understanding of learning content (66.6%), effectiveness (63.3%), self-directed learning (59.9%), motivation (63.2%), and confidence (63.3%). To make an effective model, studies applying this instructional design are to be implemented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.182-199
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• 2023

Since 2021, government departments have been promoting Automated Driving Technology Development and Innovation Project as national research and development(R&D) project. The automated vehicles and service technologies developed as part of these projects are planned to be subsequently provided to the public at the selected Living Lab City. Therefore, it is important to determine a spatial area and operation section that enables safe and stable automated driving, depending on the purpose and characteristics of the target service. In this study, the static Operational Design Domain(ODD) elements for Level 4 automated driving services were reclassified by reviewing previously published papers and related literature surveys and investigating field data. Spatial analysis techniques were used to consider the reclassified ODD elements for level 4 in the real area of level 3 automated driving services because it is important to reflect the spatial factors affecting safety related to real automated driving technologies and services. Consequently, a total of six driving mode changes(disengagement) were derived through spatial information analysis techniques, and the factors affecting the safety of automated driving were crosswalk, traffic light, intersection, bicycle road, pocket lane, caution sign, and median strip. This spatial factor analysis method is expected to be useful for determining special areas for the automated driving service.

• Journal of Mushroom
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• v.17 no.4
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• pp.211-217
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• 2019

Currently, cultivation of mushrooms using the Information and Communication Technology (ICT)-based smart farming technique is increasing rapidly. The main environmental factors for growth of mushrooms are temperature, humidity, carbon dioxide (CO₂), and light. Among all the mentioned factors, currently, only temperature has been maintained under automatic control. However, humidity and ventilation are controlled using a timer, based on technical experience.Therefore, in this study, a Pleurotus eryngii first-generation smart farm model was set up that can automatically control temperature, humidity, and ventilation. After installing the environmental control system and the monitoring device, the environmental condition of the mushroom cultivation room and the growth of the fruiting bodies were studied. The data thus obtained was compared to that obtained using the conventional cultivation method.In farm A, the temperature during the primordia formation stage was about 17℃, and was maintained at approximately 16℃ during the fruiting stage. The humidity was initially maintained at 95%, and the farm was not humidified after the primordia formation stage. There was no sensor for CO₂ management, and the system was ventilated as required by observing the shape of the pileus and the stipe. It was observed that, the concentration of CO₂ was between 700 and 2,500 ppm during the growth period. The average weight of the mushrooms produced in farm A was 125 g, and the quality was between that of the premium and the first grade.In farm B. The CO₂ sensor was in use for measurement purposes only; the system was ventilated as required by observing the shape of the pileus and the stipe. During the growth period, the CO₂ concentration was observed to be between 640 and 4,500 ppm. The average weight of the mushrooms produced in farm B was 102 g.These results indicate that the quality of the king oyster mushroom is determined by the environmental conditions, especially by the concentration of CO₂. Thus, the data obtained in this study can be used as an optimal smart farm model, where, by improving the environmental control method of farm A, better quality mushrooms were obtained.