11 research outputs found

    ๋Œ€๊ทœ๋ชจ ๊ต๋ž€ ์ดํ›„ ์‹ ๋‘๋ฆฌ ํ•ด์•ˆ์‚ฌ๊ตฌ์˜ ํ† ์–‘โˆ™์‹์ƒโˆ™์ง€ํ˜• ๋ณ€ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์ง€๋ฆฌํ•™๊ณผ, 2022.2. ๊น€๋Œ€ํ˜„.The Sindu dunefield, one of the largest, well preserved coastal dunes in South Korea, experienced a substantial artificial disturbance. Unlike occasional vegetation removals at a small scale, the impacts of the disturbance in October 2019 resembled those of natural disturbances such as overwash. Complete devegetation and ground flattening, using excavator and bulldozer, were carried out for the area of 1.2ha at the foredune of Sindu. In this research, changes of the disturbed foredune were investigated after the disturbance. To compare the disturbed area with other undisturbed zones in geographical and ecological contexts, I monitored the Sindu dunefield at a fine temporal resolution for a year. The main uniqueness of this research is that the dune system was extensively surveyed and analyzed. Disturbed foredune studies usually have been dealing with either dune plants or landforms. In the Sindu, however, the edaphic, vegetational, and geomorphological data were widely collected. The study includes three main contents. Firstly, changes in each factor (soil, vegetation, and landforms) were investigated. Soil properties were changing at various speeds and directions. Some pioneer species were identified in the disturbed foredune. The process and the incipient stage of foredune landform regeneration were observed. Next, the original statistical methods for the data analysis were presented. Multivariate statistical methods were newly combined and interpreted for specific purposes. Lastly, appropriate field survey methods were suggested for long-term monitoring of a disturbed coastal dune. With proper study materials, collected by proper monitoring method, and analyzed with appropriate statistical methods, the dune study will provide helpful knowledge for coastal management in this era of rapid disturbance regime change.์ฒœ์—ฐ๊ธฐ๋…๋ฌผ ์ œ 431ํ˜ธ๋กœ ์ง€์ •๋œ ์‹ ๋‘๋ฆฌ ํ•ด์•ˆ์‚ฌ๊ตฌ์—์„œ ์ง€๋‚œ 2019๋…„ 10์›”, ์œ ๋ก€๋ฅผ ์ฐพ์•„๋ณผ ์ˆ˜ ์—†๋Š” ๋Œ€๊ทœ๋ชจ ์ธ์œ„์  ๊ต๋ž€์ด ๋ฐœ์ƒํ•˜์˜€๋‹ค. ์ „์‚ฌ๊ตฌ ์•ฝ 1.2ha์— ๋‹ฌํ•˜๋Š” ๊ตฌ์—ญ์— ๋Œ€ํ•ด ์™„์ „ํ•œ ์‹์ƒ ์ œ๊ฑฐ ๋ฐ ์ง€ํ˜• ํ‰ํƒ„ํ™” ์ž‘์—…์ด ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•ด๋‹น ์‚ฌ๊ฑด์˜ ๊ฒฐ๊ณผ๊ฐ€ washover๊ณผ ๊ฐ™์€ ์ž์—ฐ์  ๊ต๋ž€์˜ ๊ฒฐ๊ณผ์™€ ์œ ์‚ฌํ•˜๋‹ค๊ณ  ํŒ๋‹จํ•˜์˜€๊ณ , ๊ต๋ž€ ์ดํ›„ ํ•ด์•ˆ์‚ฌ๊ตฌ ์‹œ์Šคํ…œ์ด ๋ณ€ํ™”ํ•˜๋Š” ๊ณผ์ •์„ ์ถ”์ ํ•˜์˜€๋‹ค. ๊ต๋ž€ ์ดํ›„ ๊ต๋ž€๋œ ์ „์‚ฌ๊ตฌ์˜ ์ƒํƒœ์ โˆ™์ง€ํ˜•์  ๋ณ€ํ™”๋ฅผ ์—ฐ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด ๊ต๋ž€๋˜์ง€ ์•Š์€ ๋Œ€์กฐ๊ตฐ์„ ์„ค์ •ํ•˜์˜€๊ณ , ์—ฐ๊ตฌ์ง€์—ญ์—์„œ 1๋…„ ๊ฐ„ ํ† ์–‘โˆ™์‹์ƒโˆ™์ง€ํ˜• ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ํ•ด์•ˆ์‚ฌ๊ตฌ์—์„œ์˜ ์ผ๋ฐ˜์ ์ธ ๊ต๋ž€ ์—ฐ๊ตฌ๋Š” ์‹์ƒ ํ˜น์€ ์ง€ํ˜•์—๋งŒ ๊ด€์‹ฌ์„ ๋‘์ง€๋งŒ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•ด์•ˆ์‚ฌ๊ตฌ ์‹œ์Šคํ…œ์„ ๊ตฌ์„ฑํ•˜๋Š” ์ค‘์š”ํ•œ ์„ธ ๊ฐ€์ง€ ์š”์ธ๋“ค์„ ๋ชจ๋‘ ๊ณ ๋ คํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํฌ๊ฒŒ ์„ธ ๊ฐ€์ง€ ๋‚ด์šฉ์„ ์ œ์‹œํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ ๊ฐ ์š”์ธ๋“ค์˜ ๋ณ€ํ™”๋ฅผ ์„ค๋ช…ํ•˜์˜€๋‹ค. ๊ต๋ž€๋œ ์ „์‚ฌ๊ตฌ์˜ ํ† ์–‘ ์„ฑ์งˆ๋“ค์€ ๋‹ค์–‘ํ•œ ์†๋ ฅ๊ณผ ๋ฐฉํ–ฅ์œผ๋กœ ๋ณ€ํ™”ํ•˜์˜€๋‹ค. ์‹์ƒ ์ž๋ฃŒ ๋ถ„์„ ๊ฒฐ๊ณผ, ๊ต๋ž€ ์ดํ›„ ์„ธ ๊ฐœ์˜ ์ดˆ๊ธฐ ์ฒœ์ด์ข…์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ ๊ธฐ๊ฐ„์ด ์งง์•„ ์ „์‚ฌ๊ตฌ ์ง€ํ˜•์ด ์™„์ „ํžˆ ํšŒ๋ณต๋˜๋Š” ๊ฒƒ์„ ๊ด€์ฐฐํ•˜์ง€๋Š” ๋ชปํ–ˆ์ง€๋งŒ, ์ง€ํ˜• ํšŒ๋ณต์— ํ•„์š”ํ•œ ์ƒ๋ฌผ์ง€ํ˜•ํ•™์  ํ”„๋กœ์„ธ์Šค์™€ ์ง€ํ˜• ํšŒ๋ณต์˜ ์ดˆ๊ธฐ ๋‹จ๊ณ„๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜๋Š” ํ†ต๊ณ„์  ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์—ฌ๋Ÿฌ๊ฐ€์ง€ ๋‹ค๋ณ€๋Ÿ‰ ๋ถ„์„ ๊ธฐ๋ฒ•๋“ค์„ ์ƒˆ๋กญ๊ฒŒ ์กฐํ•ฉํ•˜๊ณ  ํ•ด์„ํ•˜์—ฌ ๊ต๋ž€๋œ ํ•ด์•ˆ์‚ฌ๊ตฌ์—์„œ ์ƒˆ๋กœ์šด ํ•จ์˜๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ณธ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„๋ฅผ ๊ณ ๋ คํ•œ ์ƒˆ๋กœ์šด ์žฅ๊ธฐ๊ฐ„์˜ ๋ชจ๋‹ˆํ„ฐ๋ง ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ด์ฒ˜๋Ÿผ ์ ์ ˆํ•œ ๋Œ€์ƒ(ํ† ์–‘โˆ™์‹์ƒโˆ™์ง€ํ˜•)์„ ์ ์ ˆํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜์—ฌ ์ ์ ˆํ•˜๊ฒŒ ๋ถ„์„ํ•œ๋‹ค๋ฉด, ๊ธฐํ›„๋ณ€ํ™”์˜ ๋งฅ๋ฝ ์†์—์„œ ๊ต๋ž€์˜ ์œ„ํ˜‘์„ ๋ฐ›๋Š” ํ•ด์•ˆ ์‹œ์Šคํ…œ์„ ๋ณด์ „ํ•˜๊ณ  ๊ด€๋ฆฌํ•˜๋Š” ๋ฐ ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค.Chapter 1. Introduction 1 1.1. Study background 1 1.2. Purpose of the research 4 Chapter 2. Literature Review 5 2.1. Studies on the Sindu coastal dune 5 2.2. Coastal dune and disturbance 6 2.3. Knowledge gap 8 Chapter 3. Materials and Methods 10 3.1. Site description 10 3.2. Fieldwork design and data collection 15 3.2.1. Fieldwork design 15 3.2.2. Analysis of soil properties 16 3.2.3. Vegetation survey 21 3.2.4. Geomorphological survey 22 3.3. Statistical analysis 24 3.3.1. Soil property data analysis 24 3.3.2. Vegetation data analysis 25 Chapter 4. Results 28 4.1. Soil property 28 4.1.1. Soil property of coastal dune system 28 4.1.2. Changes in soil properties โ€“ an individualistic approach 28 4.1.3. Changes in soil property โ€“ a comprehensive approach 38 4.2. Plant species composition 41 4.2.1. Vegetation of coastal dune system 41 4.2.2. Indicator species analysis 43 4.2.3. Comparing the plant species compositions of multiple groups 43 4.3. Landforms 48 Chapter 5. Discussion 52 5.1. Changes in soil, vegetation, and landforms 52 5.1.1. Various directions and speeds of changes in soil properties 52 5.1.2. Pioneer species of the disturbed foredune 57 5.1.3. Regeneration of foredune landforms 59 5.2. Monitoring methods for a disturbed coastal dune system 61 5.2.1. Three factors of a coastal dune system 61 5.2.2. Suggestions on proper monitoring methods 63 Chapter 6. Conclusion 66 Bibliography 69 Abstract (in Korean) 84์„

    ์ˆ˜์†Œ ์ƒ์‚ฐ ๋ฐ ์ˆ˜์†ก์‹œ์Šคํ…œ์˜ ์•ˆ์ „ํ•œ ๋””์ž์ธ ๋ฐ ๊ด€๋ฆฌ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€(์—๋„ˆ์ง€ํ™˜๊ฒฝ ํ™”ํ•™์œตํ•ฉ๊ธฐ์ˆ ์ „๊ณต), 2020. 8. ์ด์›๋ณด.International demand for hydrogen is increasing. In particular, after the spread of electric vehicles, hydrogen has been connected not only with chemical plants, but also with peoples living life. In this paper, the safe design of a hydrogen refueling station for electronic vehicle and the prediction of the corrosion damage of a pipe defect for the safe management of a hydrogen underground buried pipe is studied. First, the safe design of a hydrogen refueling station targets a process that produces hydrogen from natural gas-derived material, which is known to be the most economical. This is a comparison of three processes: the first is to load hydrogen produced from the outside of the station carried by a high-pressure trailer, and then transform its pressure to meet the demand. The second is to produce hydrogen from gaseous NG(natural gas) through steam reforming reaction, and the last is the process of producing hydrogen by steam reforming reaction through LPG. All three processes is found to exceed tolerable risk levels in areas with some population density under currently known process conditions. Therefore, it is possible to safely design the process by changing the conditions of the process units that most affect the risk to mitigate the risk, or lower the frequency of failure event occurring by constructing additional firewalls. On the other hand, off-site pipelines placed to transport the produced hydrogen going out of the hydrogen station or the incoming hydrogen from the outside are mainly installed in a buried form. Buried piping is an inevitable structure for the utilization of the ground area, but it is difficult to check the condition frequently due to the limitations of drilling costs and human resources to directly check the condition of the piping. Therefore, more attention should be paid to safety management. In particular, buried piping accidents in areas close to the population, such as Kaohsiung in Taiwan or San Bruno in the United States, can cause personal injury, so evaluate and predict whether the risk or reliability of piping is safe and secure in the future. It is important to do. There have been many studies predicting the defect depth distribution of pipes due to external corrosion. Predictive modeling of the previous papers were well predicted defect depths measured in the soil environments. However, the external corrosion of piping is affected by various environmental factors, so a well-made model may be inaccurate in other environments. This is because a large amount of data is required and it is generally difficult to apply to changing soils. To overcome this, the Adaptive Bayesian methodology is needed. Predicting Defect Depth well can be said to have established a model for how quickly the defect depth is growing. Defect Depth Growth rate model, that is, prediction model for External Corrosion rate, has also been studied. Like Defect Depth, since it is affected by various environmental variables, the Adaptive model is effective for general prediction. Therefore, through this, it was possible to study a more accurate prediction model of the defect depth for the safe design of the hydrogen filling station and the reliability measurement of the pipe that transports hydrogen to the outside of the filling station. It is a demand for a more careful and safe design for the hydrogen charging station in the vicinity of a person, and it is expected that through the above study, a safe hydrogen storage will be installed and managed.์ˆ˜์†Œ์— ๋Œ€ํ•œ ๊ตญ์ œ ์ˆ˜์š”๊ฐ€ ์ ์ฐจ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์ „๊ธฐ์ž๋™์ฐจ์˜ ๋ณด๊ธ‰ ์ดํ›„, ์ˆ˜์†Œ๋Š” ํ™”ํ•™ํ”Œ๋žœํŠธ์—์„œ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๋„์‹œ์—์„œ ์žˆ๋Š” ์‚ฌ๋žŒ๋“ค์˜ ์ƒํ™œ๊ถŒ๊ณผ๋„ ๋งž๋‹ฟ์•„ ์žˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ „๊ธฐ์ž๋™์ฐจ์— ์ˆ˜์†Œ๋ฅผ ๊ณต๊ธ‰๋ฐ›๊ธฐ ์œ„ํ•œ ์ˆ˜์†Œ์ถฉ์ „์†Œ์˜ ์•ˆ์ „ํ•œ ์„ค๊ณ„์™€ ํ•ด๋‹น ์ˆ˜์†Œ์ถฉ์ „์†Œ์˜ ์™ธ๋ถ€๋กœ, ํ˜น์€ ์™ธ๋ถ€์—์„œ ์ˆ˜์†Œ๊ฐ€ ์ด์†ก๋  ๊ฒฝ์šฐ ์ด์šฉํ•˜๊ฒŒ ๋  ์ˆ˜์†Œ ์ง€ํ•˜๋งค์„ค๋ฐฐ๊ด€์˜ ์•ˆ์ „ํ•œ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•œ ๋ฐฐ๊ด€๊ฒฐํ•จ์˜ ์†์ƒ๋„ ์˜ˆ์ธก์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋จผ์ € ์ˆ˜์†Œ์ถฉ์ „์†Œ์— ๋Œ€ํ•œ ์•ˆ์ „ํ•œ ์„ค๊ณ„๋Š” ์ˆ˜์†Œ๋ฅผ ๊ฐ€์žฅ ๊ฒฝ์ œ์ ์œผ๋กœ ์ƒ์‚ฐํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ์•Œ๋ ค์ง„ ์ฒœ์—ฐ๊ฐ€์Šค๋กœ๋ถ€ํ„ฐ ์ƒ์‚ฐํ•˜๋Š” ๊ณต์ •์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ๋‹ค. ์ด๋Š” 3๊ฐ€์ง€ ๊ณต์ •์„ ๋น„๊ตํ•  ์ˆ˜ ์žˆ๋Š”๋ฐ, ์ฒซ๋ฒˆ์งธ๋Š” ์™ธ๋ถ€์—์„œ ์ƒ์‚ฐ๋œ ์ˆ˜์†Œ๋ฅผ ๊ณ ์•• ํŠธ๋ ˆ์ผ๋Ÿฌ๋กœ ์‹ฃ๊ณ  ์˜จ ํ›„, ์ˆ˜์š”์— ๋งž๊ฒŒ ๋ณ€์••ํ•˜๋Š” ๊ณต์ •์ด๊ณ , ๋‘๋ฒˆ์งธ๋Š” ๊ธฐ์ฒด์ƒํƒœ์˜ NG์—์„œ ์ˆ˜์†Œ๋ฅผ Steam Reforming Reaction์œผ๋กœ ์ƒ์‚ฐํ•˜๋Š” ๊ณต์ •, ๋งˆ์ง€๋ง‰์œผ๋กœ LPG์—์„œ ์ˆ˜์†Œ๋ฅผ Steam Reformingํ•˜์—ฌ ์ƒ์‚ฐํ•˜๋Š” ๊ณต์ •์ด๋‹ค. ์„ธ ๊ณต์ • ๋ชจ๋‘ ํ˜„์žฌ ์•Œ๋ ค์ง„ ๊ณต์ • ์กฐ๊ฑด์—์„œ๋Š” ์ธ๊ตฌ๋ฐ€๋„๊ฐ€ ์–ด๋Š ์ •๋„ ์žˆ๋Š” ์ง€์—ญ์—์„œ ๋ชจ๋‘ Tolerableํ•œ ์œ„ํ—˜๋„ ์ˆ˜์ค€์„ ๋„˜์–ด์„œ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ ์œ„ํ—˜๋„์— ๊ฐ€์žฅ ๋งŽ์ด ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ณต์ • ์œ ๋‹›๋“ค์˜ ์กฐ๊ฑด๋“ค์„ ์กฐ๊ธˆ์”ฉ ๋ฐ”๊ฟ”๊ฐ€๋ฉฐ ์œ„ํ—˜๋„๋ฅผ ๋‚ฎ์ถ”๋Š” ๊ณต์ • ์ˆ˜์ •์„ ํ•˜์—ฌ ์•ˆ์ „ํ•œ ๊ณต์ •์„ค๊ณ„๋ฅผ ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œํŽธ, ์ˆ˜์†Œ์ถฉ์ „์†Œ ์™ธ๋ถ€๋กœ ๋‚˜๊ฐ€๋Š” ์ƒ์‚ฐ๋œ ์ˆ˜์†Œ, ํ˜น์€ ์™ธ๋ถ€์—์„œ ๋“ค์–ด์˜ค๋Š” ์ˆ˜์†Œ๋ฅผ ์ด์†กํ•˜๊ธฐ ์œ„ํ•ด ๋†“์—ฌ์ง€๋Š”Off-site ํŒŒ์ดํ”„๋ผ์ธ๋“ค์€ ์ฃผ๋กœ ๋งค์„ค๋œ ํ˜•ํƒœ๋กœ ์„ค์น˜๊ฐ€ ๋œ๋‹ค. ๋งค์„ค๋ฐฐ๊ด€์€ ์ง€์ƒ๋ฉด์ ์˜ ํ™œ์šฉ์„ ์œ„ํ•ด ํ•„์—ฐ์ ์ธ ๊ตฌ์กฐ๋ฌผ์ด์ง€๋งŒ, ๋ฐฐ๊ด€ ์ƒํƒœ๋ฅผ ์ง์ ‘ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•œ ๊ตด์ฐฉ๋น„์šฉ ๋ฐ ์ธ์  ์ž์›์˜ ํ•œ๊ณ„ ๋“ฑ์œผ๋กœ ์ž์ฃผ ์ƒํƒœ๋ฅผ ํ™•์ธํ•˜๊ธฐ ํž˜๋“ค๋‹ค. ๋”ฐ๋ผ์„œ ์•ˆ์ „๊ด€๋ฆฌ์— ๋”์šฑ ์œ ์˜ํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ํŠนํžˆ ๋Œ€๋งŒ์˜ ๊ฐ€์˜ค์Š(Kaohsiung)์ด๋‚˜ ๋ฏธ๊ตญ์˜ ์‚ฐ ๋ธŒ๋ฃจ๋…ธ(San Bruno) ์‚ฌ๊ณ ์ฒ˜๋Ÿผ ์ธ๊ตฌ ๋ฐ€์ ‘ ์ง€์—ญ์—์„œ์˜ ๋งค์„ค๋ฐฐ๊ด€์‚ฌ๊ณ ๋Š” ์ธ๋ช…ํ”ผํ•ด๋ฅผ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ์–ด, ํ˜„์žฌ ๋ฐ ํ–ฅํ›„์— ๋ฐฐ๊ด€์˜ ์œ„ํ—˜๋„๋‚˜ ์‹ ๋ขฐ๋„๊ฐ€ ์•ˆ์ „ํ•œ ์ˆ˜์ค€์ธ์ง€ ํ‰๊ฐ€ํ•˜๊ณ  ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์™ธ๋ถ€๋ถ€์‹์— ๋”ฐ๋ฅธ ๋ฐฐ๊ด€์˜ Defect Depth ๋ถ„ํฌ๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ์—ฐ๊ตฌ๋“ค์ด ๋งŽ์ด ์žˆ์–ด์™”๋‹ค. ์„ ํ–‰ ๋…ผ๋ฌธ๋“ค์˜ ์˜ˆ์ธก ๋ชจ๋ธ๋ง๋“ค์€ ํ•ด๋‹น ํ† ์–‘ํ™˜๊ฒฝ๋“ค์—์„œ ์ง์ ‘ ์ธก์ •ํ•œ Defect Depth๋“ค์„ ์ž˜ ์˜ˆ์ธกํ•œ ๋ชจ๋ธ๋“ค์ด์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋ฐฐ๊ด€์˜ ์™ธ๋ถ€๋ถ€์‹์€ ์—ฌ๋Ÿฌ๊ฐ€์ง€์˜ ํ™˜๊ฒฝ์š”์†Œ์— ์˜ํ–ฅ์„ ๋ฐ›๊ณ , ๋”ฐ๋ผ์„œ ์ž˜ ๋งŒ๋“ค์–ด์ง„ ๋ชจ๋ธ๋„ ๋‹ค๋ฅธ ํ™˜๊ฒฝ์—์„œ๋Š” ๋ถ€์ •ํ™•ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋Œ€๋Ÿ‰์˜ ๋ฐ์ดํ„ฐ๊ฐ€ ํ•„์š”ํ•˜๊ณ , ๋ณ€ํ™”ํ•˜๋Š” ํ† ์–‘์— ์ผ๋ฐ˜์ ์œผ๋กœ ์ ์šฉํ•˜๊ธฐ ํž˜๋“ค๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ด๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด Adaptive Bayesian ๋ฐฉ๋ฒ•๋ก ์ด ํ•„์š”ํ•˜๋‹ค. Defect Depth๋ฅผ ์ž˜ ์˜ˆ์ธกํ•œ๋‹ค๋Š” ๊ฒƒ์€ defect depth๊ฐ€ ์–ผ๋งˆ๋‚˜ ๋นจ๋ฆฌ ์„ฑ์žฅํ•˜๊ณ  ์žˆ๋Š”์ง€์— ๋Œ€ํ•œ ๋ชจ๋ธ์„ ์ž˜ ์„ธ์› ๋‹ค๊ณ ๋„ ํ•  ์ˆ˜ ์žˆ๋‹ค. Defect Depth Growth rate ๋ชจ๋ธ, ์ฆ‰ External Corrosion rate์— ๋Œ€ํ•œ ์˜ˆ์ธก๋ชจ๋ธ ์—ญ์‹œ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์žˆ์–ด์™”๋‹ค. Defect Depth์™€ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์—ฌ๋Ÿฌ ํ™˜๊ฒฝ๋ณ€์ˆ˜์˜ ์˜ํ–ฅ์„ ๋ฐ›์œผ๋ฏ€๋กœ, ์ด ์—ญ์‹œ ์ผ๋ฐ˜์ ์ธ ์˜ˆ์ธก์„ ์œ„ํ•ด Adaptive ๋ชจ๋ธ์ด ํšจ๊ณผ์ ์ด์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋ฅผ ํ†ตํ•ด ์ˆ˜์†Œ ์ถฉ์ „์†Œ์˜ ์•ˆ์ „ํ•œ ์„ค๊ณ„ ๋ฐ ์ถฉ์ „์†Œ ์™ธ๋ถ€๋กœ ์ˆ˜์†Œ๋ฅผ ์ด์†กํ•˜๋Š” ๋ฐฐ๊ด€์˜ ์‹ ๋ขฐ๋„ ์ธก์ •์„ ์œ„ํ•œ Defect Depth์˜ ๋ณด๋‹ค ์ •ํ™•ํ•œ ์˜ˆ์ธก๋ชจ๋ธ์„ ์—ฐ๊ตฌํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์‚ฌ๋žŒ์ด ์ธ์ ‘ํ•œ ๊ณณ์˜ ์ˆ˜์†Œ ์ถฉ์ „์†Œ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•˜์—ฌ, ๋”์šฑ ์‹ ์ค‘ํ•˜๊ณ , ์•ˆ์ „ํ•œ ์„ค๊ณ„๊ฐ€ ์š”๊ตฌ๋˜๋Š” ์ˆ˜์š”์ฒ˜์ด๋ฉฐ, ์œ„ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์•ˆ์ „ํ•œ ์ˆ˜์†Œ ์ €์žฅ์†Œ ์„ค์น˜ ๋ฐ ๊ด€๋ฆฌ๊ฐ€ ๋  ๊ฒƒ์„ ๊ธฐ๋Œ€ํ•œ๋‹ค.Chapter1. Introduction 1 1.1. Research motivation 1 Chapter2. Safe design for onsite hydrogen refueling station 5 2.1. Background 5 2.2. Process description 9 2.2.1. Hydrogen production process modeling 9 2.3. Quantitative risk assessment procedure 47 2.4. Layout of the hydrogen refueling station 50 2.5. Result and discussion 52 2.5.1. Risk assessment result before process modification 52 2.5.1. Proposed process modification for risk mitigation 70 2.6. Conclusion 74 Chapter3. Adaptive approach for estimation of pipeline corrosion defects via Bayesian inference 75 3.1. Introduction 75 3.2. Adaptive estimation of corrosion defect depth 81 3.2.1. Time-dependent GEV distribution for corrosion defect depth distribution 81 3.2.2. Adaptive estimation framework using Bayesian inference 84 3.3. Implementation 89 3.4. Visualization and discussion 93 3.4.1. Case 1 Direct inspection 93 3.4.1. Case 2 indirect inspection 96 3.4.1. Case 3 sudden changes in hidden depth distribution 100 3.5. Conclusion 108 Chapter4. Concluding remarks 110 Reference 112Docto

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    Spatial Point Pattern Analysis of Riparian Tree Distribution After the 2020 Summer Extreme Flood in the Seomjin River

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    2020๋…„ ์—ฌ๋ฆ„, ํ•œ๋ฐ˜๋„ ๋‚จ๋ถ€๋ฅผ ๊ฐ•ํƒ€ํ•œ ๋Œ€ํ™์ˆ˜๋Š” ์„ฌ์ง„๊ฐ• ํ•˜์ฒœ ์ƒํƒœ๊ณ„๋ฅผ ํฌ๊ฒŒ ๊ต๋ž€์‹œ์ผฐ๋‹ค. ํ™์ˆ˜์˜ ํ”ผํ•ด๋ฅผ ์ž…์€ ํ•˜์ฒœ ์ˆ˜๋ชฉ์ค‘ ์ผ๋ถ€๋Š” ๊ณ ์‚ฌํ•˜์˜€๊ณ , ๋˜ ๋‹ค๋ฅธ ์ผ๋ถ€๋Š” ์‹œ๊ฐ„์ด ์ง€๋‚จ์— ๋”ฐ๋ผ ์–ด๋Š ์ •๋„ ํšŒ๋ณต๋˜๋Š” ๋ชจ์Šต์„ ๋ณด์˜€๋‹ค. ๋”๋ถˆ์–ด ํ™์ˆ˜ ํ›„ ์ƒˆ๋กœ ์ž๋ผ๋‚œ ์„ค๋ช…ํ•˜์˜€๋‹ค. ๊ณต๊ฐ„ ์  ํŒจํ„ด ๋ถ„์„ (spatial point pattern analysis) ๊ฒฐ๊ณผ, ์ƒˆ๋กœ ๋ฐœ์ƒํ•œ ์ˆ˜๋ชฉ๊ณผ ํ™์ˆ˜ ์ด์ „๋ถ€ํ„ฐ ์กด์žฌํ•˜์˜€๋˜ ์ˆ˜๋ชฉ์€์„œ๋กœ ๊ณต๊ฐ„์ ์œผ๋กœ ๊ตฐ์ง‘ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•˜์ง€๋งŒ ๋ณด๋‹ค ์„ธ๋ฐ€ํ•œ ๋ถ„์„์„ ํ†ตํ•ด ์ƒˆ๋กœ ๋ฐœ์ƒํ•œ ์ˆ˜๋ชฉ์€ ๊ธฐ์กด ์ˆ˜๋ชฉ ์ค‘ ํšŒ๋ณตํ•œ ์ˆ˜๋ชฉ๋ณด๋‹คํšŒ๋ณต๋˜์ง€ ์•Š์€ ์ˆ˜๋ชฉ๊ณผ ๋” ๊ฐ•ํ•˜๊ฒŒ ๊ตฐ์ง‘ํ•œ๋‹ค๋Š” ์‚ฌ์‹ค์ด ๋ฐํ˜€์กŒ์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ์ด‰์ง„์  (facilitative) ์ƒํ˜ธ์ž‘์šฉ ์†์— ๊ฐ€๋ ค์ง„ ๊ฒฝ์Ÿ์ (competitive) ์ƒํ˜ธ์ž‘์šฉ์„ ํฌ์ฐฉํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๊ธฐ์กด ์ˆ˜๋ชฉ์— ์˜ํ•œ ์„œ์‹์ฒ˜ ๊ฐœ์„  ํšจ๊ณผ๋Š” ์ƒˆ๋กœ ๋ฐœ์ƒํ•œ ์ˆ˜๋ชฉ์˜ ์ƒ์žฅ์— ๊ธ์ •์ ์ธ ์—ญํ• ์„ํ–ˆ์„ ๊ฒƒ์ด๋‚˜, โ€œ์‚ด์•„ ์žˆ๋Š”โ€ ๊ธฐ์กด ์ˆ˜๋ชฉ์€ ์ƒˆ๋กœ ๋ฐœ์ƒํ•œ ์ˆ˜๋ชฉ๊ณผ ํ•œ์ •๋œ ์ž์›์„ ๋‘๊ณ  ๊ฒฝ์Ÿํ–ˆ์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์ „์ง€๊ตฌ์ ์ธ ๊ธฐํ›„๋ณ€ํ™”์˜ ๋งฅ๋ฝ ์†์—์„œ ๊ตญ๋‚ด ํ•˜์ฒœ ์ƒํƒœ๊ณ„๊ฐ€ ๋งˆ์ฃผํ•  ๊ฐ•ํ•˜๊ณ  ๋นˆ๋ฒˆํ•œ ์ž์—ฐ์  ๊ต๋ž€์— ๋Œ€๋น„ํ•  ์ƒˆ๋กœ์šด ์ง€์‹์„ ์ œ๊ณตํ•  ์ˆ˜์žˆ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ํ•™๊ณ„์—์„œ๋Š” ํ™˜๊ฒฝ ์š”์ธ์„ ์„ค๋ช… ๋ณ€์ˆ˜๋กœ ํ•˜๋Š” ์ƒํƒœ ๋ชจ๋ธ๋ง์ด ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, ์ˆ˜๋ชฉ ๊ฐœ์ฒด๋“ค ๊ฐ„ ์ƒ๋Œ€์  ์œ„์น˜๊ด€๊ณ„๋กœ ๋Œ€ํ‘œ๋  ์ˆ˜ ์žˆ๋Š” ๊ฐœ์ฒด ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ ์—ญ์‹œ ์ƒํƒœ๊ณ„ ๋ณ€ํ™”๋ฅผ ์„ค๋ช…ํ•˜๋Š” ๋ฐ ์žˆ์–ด์„œ ์ค‘์š”ํ•œ ์š”์ธ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค.N
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