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    ์ค‘ํ•™์ƒ์˜ ์ฝ”๋”ฉ ์ˆ˜์ค€ ์ฐจ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ AI์œตํ•ฉ๊ต์œกํ•™๊ณผ, 2023. 8. ์กฐ์ •ํšจ.์—ฌ๋Ÿฌ ๋‚˜๋ผ์—์„œ ์ธ๊ณต์ง€๋Šฅ ๊ธฐ์ˆ ์˜ ๊ฒฝ์Ÿ๋ ฅ์„ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•ด ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ๋ฅผ ํ•จ์–‘ํ•  ์ˆ˜ ์žˆ๋Š” ๊ต์œก๊ณผ์ •์„ ๊ณต๊ต์œก๊ณผ์ •์— ๋„์ž…ํ•˜๊ณ  ์žˆ๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ ๋˜ํ•œ ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ๋ฅผ ๊ณต๊ต์œก์— ๋„์ž…ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ ์ค‘ ํ•˜๋‚˜์ธ ์ปดํ“จํŒ… ์‚ฌ๊ณ ๋ ฅ์„ ํ•จ์–‘์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๊ต์œก์„ ๊ฐ•์กฐํ•˜๊ณ  ์žˆ๋‹ค. ๊ต์œก๋ถ€์—์„œ๋Š” ์ปดํ“จํŒ… ์‚ฌ๊ณ ๋ ฅ์„ ํ•จ์–‘ํ•˜๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ์œผ๋กœ SW ๊ต์œก ํ•„์ˆ˜ ์ด์ˆ˜ ์‹œ๊ฐ„์„ ์ •ํ•˜์˜€์œผ๋ฉฐ, 2025๋…„๋ถ€ํ„ฐ๋Š” ์ดˆยท์ค‘ํ•™๊ต ์ •๋ณด ๊ต๊ณผ ์‹œ์ˆ˜๋ฅผ ํ™•๋Œ€ํ•˜์—ฌ ์ดˆ๋“ฑ์€ 34์‹œ๊ฐ„ ์ด์ƒ, ์ค‘๋“ฑ์€ 68์‹œ๊ฐ„ ์ด์ƒ ํ•„์ˆ˜๋กœ ์ด์ˆ˜ํ•ด์•ผ ํ•œ๋‹ค๋Š” ๋‚ด์šฉ์„ ๋‹ด์€ ๋””์ง€ํ„ธ ์ธ์žฌ์–‘์„ฑ ์ข…ํ•ฉ๋ฐฉ์•ˆ์„ ๋ฐœํ‘œํ•˜์˜€๋‹ค. ์„ ํ–‰์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ธ๊ณต์ง€๋Šฅ์„ ํ™œ์šฉ, ์œตํ•ฉํ•œ ๊ต์œก์„ ํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด ๊ต์œก์˜ ์„ ํ–‰์ด ํ•„์ˆ˜์ ์ด๋‹ค.๋ผ๋Š” ์˜๊ฒฌ๊ณผ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด์— ๋Œ€ํ•œ ์ง€์‹์ด ์—†์–ด๋„ ์ธ๊ณต์ง€๋Šฅ ํ™œ์šฉ, ์œตํ•ฉ ๊ต์œก์ด ํšจ๊ณผ์ ์ด์—ˆ๋‹ค.๋ผ๋Š” ๋‘ ๊ฐ€์ง€์˜ ๋Œ€๋ฆฝ๋œ ์˜๊ฒฌ์„ ๊ฐ€์ง€๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ธ๊ณต์ง€๋Šฅ์„ ํ™œ์šฉํ•œ ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต์„ 10์ฐจ์‹œ๋กœ ๊ตฌ์„ฑํ•˜์—ฌ ์ค‘ํ•™๊ต 1ํ•™๋…„ ํ•™์ƒ 43๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด ํ™œ์šฉ ๋Šฅ๋ ฅ์— ๋”ฐ๋ผ ์ง‘๋‹จ์„ ๋‘ ๊ฐœ๋กœ ๋ถ„๋ฅ˜ํ•œ ํ›„ ์ ์šฉํ•ด๋ณด์•˜๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ์ˆ˜์—… ์ „, ํ›„ ์‹ค์‹œํ•œ ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ ๊ฒ€์‚ฌ๋ฅผ ํ†ตํ•ด ์ธ๊ณต์ง€๋Šฅ ํ™œ์šฉ ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต ํ”„๋กœ๊ทธ๋žจ์˜ ํšจ๊ณผ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ํ•™์Šต์ž์˜ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด ํ™œ์šฉ ๋Šฅ๋ ฅ์„ ์—”ํŠธ๋ฆฌ์—์„œ ์ œ๊ณตํ•˜๋Š” ์—”ํŠธ๋ฆฌ๋ด‡ ๋ฏธ์…˜ ๋‹ฌ์„ฑ ๋‹จ๊ณ„๋ฅผ ๊ธฐ์ค€์œผ๋กœ ๋‘ ๊ทธ๋ฃน์œผ๋กœ ๋‚˜๋ˆ„์–ด ์ธ๊ณต์ง€๋Šฅ ํ™œ์šฉ ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต ์ „ํ›„ ๊ทธ๋ฃน ๊ฐ„ ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ ์ ์ˆ˜ ํ‰๊ท  ์ฐจ์ด๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ง‘๋‹จ์˜ ํฌ๊ธฐ๊ฐ€ ์ž‘์€ ์—ฐ๊ตฌ ์„ค๊ณ„๊ฐ€ ๊ฐ–๋Š” ํ•œ๊ณ„๋ฅผ ๋ณด์™„ํ•˜๊ณ , ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด ํ™œ์šฉ ๋Šฅ๋ ฅ์˜ ์ฐจ์ด์— ๋”ฐ๋ผ ์ธ๊ณต์ง€๋Šฅ์„ ํ™œ์šฉํ•œ ๊ต์œก ํ”„๋กœ๊ทธ๋žจ์˜ ์ ์šฉ ํšจ๊ณผ ๋ฐ ํฅ๋ฏธ๋„ ๋ถ€๋ถ„์„ ์–ด๋–ป๊ฒŒ ์ธ์ง€ํ•˜๋Š”์ง€ ์‚ดํŽด๋ณด๊ธฐ ์œ„ํ•ด ์„œ๋ฉด ์„ค๋ฌธ๊ณผ ๋ฉด๋‹ด์„ ํ†ตํ•œ ์งˆ์  ์—ฐ๊ตฌ ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ์ธ๊ณต์ง€๋Šฅ์„ ํ™œ์šฉํ•œ ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต ํ›„ ํ•™์Šต์ž์˜ ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ๊ฐ€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•˜๊ฒŒ ํ–ฅ์ƒ๋˜์—ˆ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค(p<.000). ํ•™์ƒ๋“ค์˜ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด ํ™œ์šฉ ๋Šฅ๋ ฅ์„ ์ƒ, ํ•˜๋กœ ๋‚˜๋ˆ„์–ด ํ”„๋กœ๊ทธ๋žจ ์ ์šฉ ์ „ํ›„ ๊ทธ๋ฃน ๊ฐ„ ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ ํ‰๊ท  ์ฐจ์ด๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ํ”„๋กœ๊ทธ๋žจ ์ ์šฉ ์ „์—๋Š” ํ‰๊ท ์˜ ์ฐจ์ด๊ฐ€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•˜์˜€์œผ๋‚˜(p<0.05) ํ”„๋กœ๊ทธ๋žจ ์ ์šฉ ํ›„์—๋Š” ์ƒ, ํ•˜ ๊ทธ๋ฃน ๊ฐ„ ํ‰๊ท  ์ฐจ์ด๊ฐ€ ์œ ์˜๋ฏธํ•˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•™์ƒ๋“ค์ด ์ธ๊ณต์ง€๋Šฅ ํ™œ์šฉ ์ˆ˜์—…์„ ๋”ฐ๋ผ๊ฐ€๊ณ  ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๋ฐ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด ํ™œ์šฉ ๋Šฅ๋ ฅ์ด ์ค‘์š”ํ•œ ์š”์ธ์€ ์•„๋‹ ์ˆ˜ ์žˆ๋‹ค๋Š” ์‹œ์‚ฌ์ ์„ ์ œ์–ธํ•˜์˜€๋‹ค.Various countries are introducing AI literacy into their public education curricula to secure competitiveness in AI technology. Korea is also incorporating AI literacy into its public education system, with an emphasis on fostering computational thinking, one aspect of AI literacy. The Ministry of Education has established mandatory hours for software (SW) education to cultivate computational thinking skills. Starting from 2025, the number of hours dedicated to information education in elementary and middle schools will be expanded, requiring elementary students to complete at least 34 hours and middle school students to complete at least 68 hours, as outlined in the "Comprehensive Plan for Digital Talent Development." Previous studies have presented contrasting opinions regarding the necessity of prior programming language education for effective AI-integrated education. One opinion emphasizes the essentiality of prior programming language education, while the other suggests that AI-integrated education can be effective even without prior knowledge of programming languages. Therefore, this study implemented a problem-based learning program utilizing AI over a span of 10 sessions. The program was applied to 43 1st-grade middle school students, who were divided into two groups based on their proficiency in programming language utilization. Through the AI literacy assessment conducted before and after the classes, the effectiveness of the AI-integrated problem-based learning program was confirmed. The average difference in AI literacy scores before and after the AI-integrated problem-based learning program was analyzed by dividing the groups based on the students' achievement levels in programming language utilization, as determined by their completion of missions in the "Entry Bot" platform. Additionally, to compensate for the limitations of a small sample size and examine how participants perceive the applied AI education program's effectiveness and level of interest, qualitative research data were collected through written surveys and interviews. The research results revealed that after participating in AI-integrated problem-based learning, students' AI literacy significantly improved (p < .000). Analyzing the average difference in AI literacy scores between the groups divided into higher and lower programming language utilization levels before and after program implementation, a statistically significant difference in averages was observed before program implementation (p < 0.05). However, after program implementation, there was no significant difference in average scores between the higher and lower proficiency groups. Based on these findings, this study suggests that proficiency in programming may not be a critical determinant for students to effectively engage in and address challenges within artificial intelligence-focused classes.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ ๋ฌธ์ œ 6 ์ œ 2 ์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 7 ์ œ 1 ์ ˆ ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต 7 1. ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต์˜ ๊ฐœ๋… 7 2. ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต์˜ ํŠน์ง•์  ์š”์†Œ 8 3. ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต ๋ชจํ˜• 12 ์ œ 2 ์ ˆ ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ 13 1. ๋ฆฌํ„ฐ๋Ÿฌ์‹œ์˜ ๊ฐœ๋… 13 2. ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ 14 ์ œ 3 ์ ˆ ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ ๊ต์œก์„ ์œ„ํ•œ ๊ตญ๋‚ด์™ธ ๊ต์œก๊ณผ์ • ๋™ํ–ฅ 16 1. ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ ๊ต์œก์„ ์œ„ํ•œ ์™ธ๊ตญ ๊ต์œก๊ณผ์ • ๋™ํ–ฅ 16 2. ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ ๊ต์œก์„ ์œ„ํ•œ ์šฐ๋ฆฌ๋‚˜๋ผ ๊ต์œก๊ณผ์ • ๋™ํ–ฅ 19 ์ œ 4 ์ ˆ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ์–ธ์–ด ํ™œ์šฉ ์ˆ˜์ค€์— ๋”ฐ๋ฅธ ์ธ๊ณต์ง€๋Šฅ ํ™œ์šฉ ๊ต์œก 25 ์ œ 3 ์žฅ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 29 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ 29 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์ ˆ์ฐจ 29 ์ œ 3 ์ ˆ ์ธ๊ณต์ง€๋Šฅ์„ ํ™œ์šฉํ•œ ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต ์ˆ˜์—…์˜ ๊ฐœ๋ฐœ ๋ฐ ์ ์šฉ 30 ์ œ 4 ์ ˆ ์ž๋ฃŒ ์ˆ˜์ง‘ ๋ฐ ๋ถ„์„ ๋ฐฉ๋ฒ• 32 1. ์ž๋ฃŒ ์ˆ˜์ง‘์„ ์œ„ํ•œ ๊ฒ€์‚ฌ ๋„๊ตฌ ๋ฐ ์„œ๋ฉด ์„ค๋ฌธ, ๋ฉด๋‹ด ๋ฐฉ๋ฒ• 32 2. ๋ถ„์„ ๋ฐฉ๋ฒ• 36 ์ œ 4 ์žฅ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 38 ์ œ 1 ์ ˆ ์ธ๊ณต์ง€๋Šฅ์„ ํ™œ์šฉํ•œ ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต ํ›„, ์ค‘ํ•™์ƒ์˜ ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ ํ–ฅ์ƒ์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ 38 1. ์ธ๊ณต์ง€๋Šฅ ๊ธฐ์ดˆ ์ง€์‹ 39 2. ์ธ๊ณต์ง€๋Šฅ ๊ฐœ๋ฐœ ์—ญ๋Ÿ‰ 40 3. ์ธ๊ณต์ง€๋Šฅ ํ™œ์šฉ ๋Šฅ๋ ฅ 41 4. ์ธ๊ณต์ง€๋Šฅ ์œค๋ฆฌ์  ๊ฐ€์น˜๊ด€ 42 ์ œ 2 ์ ˆ ์ธ๊ณต์ง€๋Šฅ์„ ํ™œ์šฉํ•œ ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต ํ›„, ํ•™์Šต์ž์˜ ์ฝ”๋”ฉ ๋Šฅ๋ ฅ์˜ ์ฐจ์ด์™€ ์ธ๊ณต์ง€๋Šฅ ๋ฆฌํ„ฐ๋Ÿฌ์‹œ ํ–ฅ์ƒ๋„์˜ ๊ด€๊ณ„ 44 ์ œ 3 ์ ˆ ์ธ๊ณต์ง€๋Šฅ ํ™œ์šฉ ๋ฌธ์ œ๊ธฐ๋ฐ˜ํ•™์Šต์— ๋Œ€ํ•œ ํ•™์Šต์ž ๋ฐ˜์‘ 50 ์ œ 5 ์žฅ ๋…ผ์˜ ๋ฐ ๊ฒฐ๋ก  53 ์ฐธ๊ณ ๋ฌธํ—Œ 57 ๋ถ€๋ก 61 Abstract 72์„

    ๋ณด์„ธ์‚ฌ์ œ๋„ ๊ฐœ์„  ๋ฐฉ์•ˆ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ABSTRACT A Study on the Development Plan of Bonded Goods Caretaker System Sung, Ji-Hyen Department of International Customs Graduate School of Korea Maritime and Ocean University Due to the liberalization and opening of global economy, trade volume is steadily increasing and the import and export logistics volume has rapidly increased. Accordingly, countries have been putting efforts to improve the system to facilitate international logistics flow, but since the 9/11 attack in the U.S., the trend of global logistics has changed from beingโ€˜quick and smoothโ€™ toโ€˜safeโ€™. In case of Korea that has adopted in bond system, customs officers were dispatched in the beginning to bonded area for strict management of in bond shipment directly by the government, but with the rapid increase in global trade volume, self-managed bonded area and bonded good caretaker systems were adopted for rapid and efficient logistics support in an indirect management method. For safe international trading, fair business conduct by bonded goods caretakers, who handle bonded goods on behalf of customs officers, is especially important, but they are sometimes involved in smuggling that they are disrupting the order in bonded area and threatening the safety of society. Therefore, the purpose of this study is to present the course of improvement in bonded goods caretaker system by examining such problems. This study examined previous study data in Korea and analyzed publications and statistical data by relevant organizations such as Korea Customs Service and Korea Customs Logistics Association. In addition, interviews and surveys were conducted on bonded goods caretakers that are in the actual operations. As a result, problems including the dependence according to the employment relationship between bonded goods caretakers and bonded area managers, level of difficulty in bonded goods caretaker screening that depreciates the qualitative value of professional license, absence of institutional strategy to reinforce and maintain the professionalism and ethics of bonded goods caretakers, and lack of social incentives at work were drawn. In order to solve these problems, the level of difficulty in screening needs to be heightened or the number of qualified candidates should be limited to increase the scarcity value first. Second, regular training should be institutionalized to reinforce and maintain bonded goods caretakersโ€™ professionalism and ethics. Therefore, legal rights of bonded goods caretakers should be expanded and establishment of their organizations should be supported to enhance their social stature. Lastly, to secure independence of bonded goods caretakers from their employers, establishing bonded goods caretakersโ€™corporation and cancelling the qualification upon illegal acts may be options. The bonded goods caretaker system is the system that only exists in Korea. The number of bonded goods caretakers will continue to grow in the current global trading environment. Therefore, the role of bonded goods caretakers is crucial in securing fast logistics and safe shipment management. For safe management of bonded shipments, reduced administrative power and development of logistics, continuous attention and research on bonded goods caretaker system are needed. ์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์  1 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„์™€ ๋ฐฉ๋ฒ• 3 1. ์—ฐ๊ตฌ ๋ฒ”์œ„ 3 2. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 4 ์ œ 2 ์žฅ ๋ณด์„ธ๊ตฌ์—ญ๊ณผ ๋ณด์„ธ์‚ฌ์ œ๋„ 5 ์ œ1์ ˆ ๋ณด์„ธ๊ตฌ์—ญ 5 1. ๋ณด์„ธ๊ตฌ์—ญ์˜ ์˜์˜ 5 2. ๋ณด์„ธ๊ตฌ์—ญ์˜ ์ข…๋ฅ˜ 7 3. ์šฐ๋ฆฌ๋‚˜๋ผ ๋ณด์„ธ๊ตฌ์—ญ ์šด์˜ํ˜„ํ™ฉ 10 ์ œ2์ ˆ ์ž์œจ๊ด€๋ฆฌ๋ณด์„ธ๊ตฌ์—ญ 11 1. ์ž์œจ๊ด€๋ฆฌ๋ณด์„ธ๊ตฌ์—ญ์˜ ๋„์ž…๋ฐฐ๊ฒฝ 11 2. ์ž์œจ๊ด€๋ฆฌ๋ณด์„ธ๊ตฌ์—ญ์˜ ์—ฐํ˜ 13 3. ์ž์œจ๊ด€๋ฆฌ๋ณด์„ธ๊ตฌ์—ญ์˜ ์ฃผ์š”๋‚ด์šฉ๊ณผ ์šด์˜ํ˜„ํ™ฉ 17 ์ œ3์ ˆ ๋ณด์„ธ์‚ฌ์ œ๋„ 24 1. ๋ณด์„ธ์‚ฌ์ œ๋„์˜ ๋„์ž…๋ฐฐ๊ฒฝ 24 2. ๋ณด์„ธ์‚ฌ ์ž๊ฒฉ ๊ฐœ์š” 26 3. ๋ณด์„ธ์‚ฌ์˜ ์—ญํ• ๊ณผ ์˜๋ฌด 29 ์ œ 3 ์žฅ ๋ณด์„ธ์‚ฌ์ œ๋„์˜ ํ˜„ํ™ฉ๊ณผ ๋ฌธ์ œ์  32 ์ œ1์ ˆ ๋ณด์„ธ์‚ฌ์ œ๋„์˜ ํ˜„ํ™ฉ๋ถ„์„ 32 1. ๋ณด์„ธ์‚ฌ์ œ๋„์˜ ์ผ๋ฐ˜ํ˜„ํ™ฉ 32 2. ๋ณด์„ธ์‚ฌ์ œ๋„์˜ ์šด์˜ํ˜„ํ™ฉ 33 ์ œ2์ ˆ ๋ณด์„ธ์‚ฌ์ œ๋„์— ๋Œ€ํ•œ ์ธ์‹ ๋ถ„์„ 35 1. ์„ค๋ฌธ์กฐ์‚ฌ ๋ชฉ์ ๊ณผ ๊ตฌ์„ฑ 35 2. ์‘๋‹ต์ž์˜ ํŠน์„ฑ 36 3. ๋ถ„์„๊ฒฐ๊ณผ 37 ์ œ3์ ˆ ๋ณด์„ธ์‚ฌ์ œ๋„์˜ ๋ฌธ์ œ์  43 1. ๋ณด์„ธ์‚ฌ์˜ ์ข…์†์„ฑ ๋ฌธ์ œ 43 2. ๋ณด์„ธ์‚ฌ ์ „ํ˜•์˜ ๋ณ€๋ณ„๋ ฅ ๋ถ€์กฑ 45 3. ๋ณด์„ธ์‚ฌ์˜ ์ „๋ฌธ์„ฑ ์œ ์ง€ ๋ฐฉ์•ˆ ๋ถ€์žฌ 46 4. ๋ณด์„ธ์‚ฌ์— ๋Œ€ํ•œ ์‚ฌํšŒ์  ์ธ์‹๊ณผ ์ง€์œ„ ๋ฌธ์ œ 48 ์ œ 4 ์žฅ ๋ณด์„ธ์‚ฌ์ œ๋„์˜ ๊ฐœ์„ ๋ฐฉ์•ˆ 50 ์ œ1์ ˆ ๋ณด์„ธ์‚ฌ ์ž๊ฒฉ์˜ ์งˆ์  ํ–ฅ์ƒ 50 ์ œ2์ ˆ ๋ณด์„ธ์‚ฌ์˜ ์ „๋ฌธ์„ฑ ์œ ์ง€๋ฅผ ์œ„ํ•œ ์ œ๋„์  ์žฅ์น˜ ๋งˆ๋ จ 52 1. ๋ณด์„ธ์‚ฌ ์ง๋ฌด๋Šฅ๋ ฅ ํ–ฅ์ƒ๊ต์œก์˜ ์˜๋ฌดํ™” ์ถ”์ง„ 53 2. ๋ณด์„ธ์‚ฌ ๋“ฑ๊ธ‰์ œ๋„ ๋„์ž…์„ ํ†ตํ•œ ๋ณ€๋ณ„๋ ฅ ํ™•๋ณด 54 ์ œ3์ ˆ ๋ณด์„ธ์‚ฌ ์ž๊ฒฉ์˜ ์‚ฌํšŒ์  ์œ„์ƒ ์ œ๊ณ  55 1. ๋ณด์„ธ์‚ฌ์˜ ์ง๋ฌด ๊ถŒํ•œ ํ™•๋Œ€๋ฅผ ํ†ตํ•œ ์„ธ๊ด€ํ–‰์ • ์ ๊ทน ํ™œ์šฉ 55 2. ๋ณด์„ธ์‚ฌ ํ˜‘ํšŒ ๊ฒฐ์„ฑ ๋ฐ ๋ฒ•๊ทœ์ผ์›ํ™” ์ถ”์ง„ 58 ์ œ4์ ˆ ๋ณด์„ธ์‚ฌ์˜ ๋…๋ฆฝ์„ฑ ํ™•๋ณด๋ฅผ ์œ„ํ•œ ๊ณ„๊ธฐ ์ œ๊ณต 60 1. ๋ณด์„ธ์‚ฌ ์˜๋ฆฌ๋ฒ•์ธ ์„ค๋ฆฝโ€ค์šด์˜ 60 2. ๋ณด์„ธ์‚ฌ์˜ ๋ถˆ๋ฒ•ํ–‰์œ„์— ๋Œ€ํ•œ ์ฒ˜๋ฒŒ ๊ฐ•ํ™” 61 ์ œ 5 ์žฅ ๊ฒฐ๋ก  64 ์ œ1์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 64 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์  ๋ฐ ํ–ฅํ›„ ๊ณผ์ œ 67 ์ฐธ๊ณ ๋ฌธํ—Œ 68 ์„ค๋ฌธ์ง€ 6

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    Thesis (master`s)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ™”ํ•™๊ณตํ•™๊ณผ,1998.Maste

    ์ตœ๊ทผ ๋ผ์ง€์—ด๋ณ‘ ๊ตญ๋‚ด ๋ถ„๋ฆฌ์ฃผ์˜ Recombinant E2 protein์„ ์ด์šฉํ•˜์—ฌ ๊ฐœ์„ ๋œ Sero-monitoring ๊ธฐ๋ฒ•

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    Thesis(masters) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ˆ˜์˜ํ•™๊ณผ(์ˆ˜์˜๋ฏธ์ƒ๋ฌผํ•™์ „๊ณต),2010.2.Maste

    Experience of patients with lung cancer, participating in a self-help group

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    ์ข…์–‘๊ฐ„ํ˜ธ์ „๊ณต/์„์‚ฌ[ํ•œ๊ธ€]๋ณธ ์—ฐ๊ตฌ๋Š” ์ž์กฐ๋ชจ์ž„์— ์ฐธ์—ฌํ•œ ํ์•”ํ™˜์ž๋“ค์˜ ๊ฒฝํ—˜์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•œ ํƒ์ƒ‰์  ์—ฐ๊ตฌ์ด๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ์ž๋Š” ํ์•”ํ™˜์ž ์ž์กฐ๋ชจ์ž„์— 6๊ฐœ์›” ์ด์ƒ ์ฐธ์—ฌํ•ด ์˜จ 7๋ช…์˜ ํ์•”ํ™˜์ž๋“ค์ด๋‹ค. ์ž๋ฃŒ ์ˆ˜์ง‘ ๊ธฐ๊ฐ„์€ 2006๋…„ 4์›” 25์ผ๋ถ€ํ„ฐ 6์›” 30์ผ๊นŒ์ง€๋กœ, ์ž๋ฃŒ ์ˆ˜์ง‘์€ ๊ฐœ๋ณ„ ์‹ฌ์ธต๋ฉด๋‹ด์„ ์ด์šฉํ•˜์˜€๊ณ , ์ž๋ฃŒ ๋ถ„์„์€ ๋…น์ทจํ•œ ๋ฉด๋‹ด ๊ธฐ๋ก์˜ ๋‚ด์šฉ๋ถ„์„์„ ํ†ตํ•ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ž์กฐ๋ชจ์ž„์— ์ฐธ์—ฌํ•œ ํ์•”ํ™˜์ž์˜ ๊ฒฝํ—˜์— ๊ด€ํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค.1. ์ž์กฐ๋ชจ์ž„ ์ฐธ์—ฌ ์ „ ๊ฒฝํ—˜:ใ€”8๋ฒ”์ฃผ, 24๊ฐœ๋…ใ€•์ถฉ๊ฒฉ, ๋‘๋ ค์›€, ๋ถˆ์•ˆ, ์ขŒ์ ˆ ๋ฐ ํฌ๊ธฐํ•˜๊ณ  ์‹ถ์Œ, ๋Œ€์ธ๊ด€๊ณ„์˜ ์œ„์ถ•, ๋ถ€๋‹ด๊ฐ๊ณผ ๋ฌด๋ ฅ๊ฐ, ์‚ถ์— ๋Œ€ํ•œ ํฌ๋ง, ์ ˆ๋Œ€์ž์™€์˜ ํƒ€ํ˜‘2. ์ž์กฐ๋ชจ์ž„ ์ฐธ์—ฌ ํ›„ ๊ฒฝํ—˜:ใ€”7๋ฒ”์ฃผ, 18๊ฐœ๋…ใ€•๋‚˜๋ˆ”๊ณผ ์ˆ˜์šฉ, ์ •๋ณด ํš๋“, ๋™๋ฃŒ ๋ฐ ํƒ€์ธ๊ณผ ์–ด์šธ๋ฆผ, ์ž์‹  ๋ฐ ์งˆ๋ณ‘์˜ ์ธ์‹ ๋ฐ ํƒ์ƒ‰, ์‚ถ์˜ ์˜๋ฏธ์™€ ๊ฐ€์น˜ ์žฌ๋ฐœ๊ฒฌ, ๊ธฐ๋ถ„์ „ํ™˜, ํฌ๋ง๊ณผ ๋„์ „๊ฒฐ๋ก ์ ์œผ๋กœ, ํ์•”ํ™˜์ž๋Š” ์ž์กฐ๋ชจ์ž„ ์ฐธ์—ฌ๋ฅผ ํ†ตํ•ด ์‹ฌ๋ฆฌ์‚ฌํšŒ์ ์œผ๋กœ ๊ธ์ •์ ์ธ ๊ฒฝํ—˜์„ ํ•˜๋Š” ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ์•”ํ™˜์ž๋ฅผ ์œ„ํ•œ ์‹ฌ๋ฆฌ์‚ฌํšŒ์  ์ค‘์žฌ๋ฐฉ๋ฒ•์œผ๋กœ ์ž์กฐ๋ชจ์ž„์„ ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ๊ธ์ •์ ์ด๋ผ ์ƒ๊ฐ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ž์กฐ๋ชจ์ž„์ด ํšจ๊ณผ์ ์ธ ์ค‘์žฌ๋ฐฉ๋ฒ•์ด ๋˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ตฌ์กฐํ™”๋œ ์ค‘์žฌ ํ”„๋กœ๊ทธ๋žจ์˜ ๊ฐœ๋ฐœ์ด ํ•„์š”ํ•˜๋ฉฐ ์ด ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ง€์ง€์ ์ด๊ณ  ์ˆ˜์šฉ์ ์ธ ๋ชจ์ž„์ด ํ˜•์„ฑ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๊ทธ ์•ˆ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋‹ค์–‘ํ•œ ๊ทธ๋ฃน์—ญ๋™์„ ์ถฉ๋ถ„ํžˆ ํ™œ์šฉํ•˜์—ฌ ์ด๋ฅผ ๊ธ์ •์ ์ธ ๋ฐฉํ–ฅ์œผ๋กœ ์ด๋Œ๊ณ  ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํšจ๊ณผ์ ์ธ ์ „๋žต์ด ์š”๊ตฌ๋œ๋‹ค ํ•˜๊ฒ ๋‹ค. [์˜๋ฌธ]The purpose of this study was to investigate the experience of patients with lung cancer in a self-help group.The participants in this study were 7 patients with lung cancer who were taking part in a self-help group. They had participated in the self-help group for more than 6 months. Data collection was done using in-depth interviews, that were recorded on audio tape. The data were collected from April 25 to June 30, 2006. Transcribed data was analyzed through content analysis.The results of this study are as follows.1. Experience before the self-help group:ใ€”8 categories, 24 conceptsใ€• impact, fear, anxiety, discouragement and abandonment, shriveling of interpersonal relations, burden and powerlessness, hope of life, compromise with the absolute being2. Experience after the self-help group:ใ€”7 categories, 18 conceptsใ€•sharing and accommodation, obtaining information, mingle with peers and others, self awareness & exploration and awareness of one's disease & exploration, a reawakening to the meaning of one's life & values, refreshed, hope and challengeIn conclusion, patients with lung cancer who participated in a self-help group experienced affirmative changes psychosocially. Therefore, a selp-help group as psychosocial intervention for lung cancer patients is highly recommended. However, in order to make the more efficacious psychosocial intervention for patients with lung cancer, it is necessary to develope structured programs. In addition, the make up of the group so as to be supportive and receptive is important. It is necessary to consider the group dynamics.ope

    ๊ณ ํ•ด์ƒ๋„ ์ž…์ž์˜์ƒ์œ ์†๊ณ„๋ฅผ ์œ„ํ•œ ๊ด‘๋ฅ˜์˜ ์‘์šฉ ๋ฐ ์†๋„์žฅ ๊ฐœ์„  ๋ฐฉ๋ฒ• ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์—๋„ˆ์ง€์‹œ์Šคํ…œ๊ณตํ•™๋ถ€, 2016. 8. ๊น€์‘์ˆ˜.์ž…์ž์˜์ƒ์œ ์†๊ณ„(Particle image velocimetry: PIV)๋Š” ๊ณต๊ฐ„์ƒ์˜ ์œ ๋™์„ ๊ฐ€์‹œํ™”ํ•˜๊ธฐ ์œ„ํ•œ ๋ชฉ์ ์œผ๋กœ ์›์ž๋ ฅ ์—ด์ˆ˜๋ ฅ ๊ธฐ์ดˆ์—ฐ๊ตฌ์— ๋‹ค์–‘ํ•˜๊ฒŒ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์ตœ๊ทผ์—๋Š” ์›์ž๋กœ ๋‚ด ์ข์€ ๊ณต๊ฐ„์—์„œ์˜ ๋ณต์žกํ•œ ์œ ๋™ํ˜„์ƒ (์˜ˆ: ๊ฒฝ์ˆ˜๋กœ ํ•ต์—ฐ๋ฃŒ๋ด‰ ๋ฐ ์ง€์ง€๊ฒฉ์ž ์‚ฌ์ด์˜ ์œ ๋™, ์†Œ๋“๊ณ ์†๋กœ ์ฑ„๋„ ๋ฐ ์™€์ด์–ด ์ฃผ๋ณ€ ์œ ๋™, ๋ฏธ์„ธ์œ ๋กœ ์—ด๊ตํ™˜๊ธฐ ๋‚ด ์œ ๋™ ๋“ฑ)์„ ๋‹ค๋ฃจ๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์•„ ๋ณด๋‹ค ๋†’์€ ๊ณต๊ฐ„ ํ•ด์ƒ๋„๋ฅผ ํ•„์š”๋กœ ํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋Š˜์–ด๋‚˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด์˜ ์ƒ๊ด€๋ถ„์„ ๊ธฐ๋ฐ˜์˜ ์ž…์ž์˜์ƒ์œ ์†๊ณ„์˜ ํ•ด์ƒ๋„๋ฅผ ๋†’์ด๊ธฐ ์œ„ํ•œ ๋ฐฉ์•ˆ์œผ๋กœ ์˜์ƒ์ฒ˜๋ฆฌ (Computer vision) ๋ถ„์•ผ์—์„œ ๋„๋ฆฌ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋Š” ๊ด‘๋ฅ˜ (Optical flow) ํ•ด์„์„ ๋„์ž…ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ๊ฐ€์ง€ ๊ด‘๋ฅ˜ ํ•ด์„ ๋ฐฉ์‹ ์ค‘, ์ž…์ž์ด๋ฏธ์ง€๋ฅผ ํ•ด์„ํ•˜๊ธฐ ๋” ์šฉ์ดํ•œ ์ „์—ญ์  ๊ด‘๋ฅ˜ ํ•ด์„ ๋ฐฉ๋ฒ•์„ ๋ฐ”ํƒ•์œผ๋กœ ์ž…์ž์˜์ƒ์œผ๋กœ๋ถ€ํ„ฐ ์†๋„์žฅ์„ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋ฐ ์ฝ”๋“œ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœํ•œ ์ฝ”๋“œ(O-Flow PIV)๋Š” ๋ฐ๊ธฐ ํ•ญ์ƒ์„ฑ(Brightness constancy), ์ˆœํƒ„์„ฑ(Smoothness) ๋ฐ ๋ฐ๊ธฐ๊ตฌ๋ฐฐํ•ญ์ƒ์„ฑ(Gradient constancy) ๊ฐ€์ •์— ๊ธฐ๋ฐ˜ํ•œ ๊ด‘๋ฅ˜ ๋ฐฉ์ •์‹๊ณผ ์ด๋ฏธ์ง€ ํ”ผ๋ผ๋ฏธ๋“œ (Image pyramids) ๋ฐ ์ด๋ฏธ์ง€ ์™€ํ•‘ (Image warping), ๊ทธ๋ฆฌ๊ณ  ์„ ํ˜• ๋ฐฉ์ •์‹์˜ ํ’€์ด๋กœ์„œ ์ผค๋ ˆ ๊ตฌ๋ฐฐ๋ฒ• (Conjugate gradient method) ๋“ฑ์„ ์ด์šฉํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœํ•œ ์ฝ”๋“œ์˜ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด, ์ฐธ ์†๋„์žฅ์˜ ๊ฐ’์„ ์•Œ๊ณ  ์žˆ๋Š” ํ•ฉ์„ฑ ์ž…์ž ์ด๋ฏธ์ง€ ์Œ(์„ ํ˜•, ๋“ฑ ๋Œ€๊ฐ, ํšŒ์ „, ์™€๋ฅ˜์˜ ์œ ๋™)์— ์ ์šฉํ•˜์—ฌ ์†๋„์žฅ์„ ๋ถ„์„ ํ•˜์˜€์œผ๋ฉฐ, ๊ทธ ๊ฒฐ๊ณผ ์ž‘์€ ์˜์—ญ์—์„œ ๋ณ€ํ™”๊ฐ€ ํฐ ์œ ๋™(์™€๋ฅ˜)์— ๋Œ€ํ•ด ๊ด‘๋ฅ˜์˜ ๋ถ„์„๋Šฅ๋ ฅ์ด ๊ธฐ์กด์˜ ์ƒ๊ด€๋ถ„์„ ๊ธฐ๋ฐ˜์˜ ๋ฐฉ๋ฒ•๋ณด๋‹ค ๋” ์šฐ์ˆ˜ํ•œ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ƒ์šฉํ™”๋œ ์ƒ๊ด€๋ถ„์„ ๊ธฐ๋ฐ˜์˜ ์ž…์ž์˜์ƒ์œ ์†๊ณ„์˜ ์†๋„๋ฅผ ๋ณด์กดํ•จ๊ณผ ๋™์‹œ์—, ๊ด‘๋ฅ˜๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ธฐ์กด ๋ฐฉ๋ฒ•์˜ ํ•ด์ƒ๋„๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ์†๋„์žฅ ๊ฐœ์„  ๋ฐฉ๋ฒ•๋„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์†๋„์žฅ ๊ฐœ์„  ์ฝ”๋“œ์˜ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด, ์ง์ ‘ ์ˆ˜์น˜ ๋ชจ์‚ฌ(Direct numerical simulation)๋กœ ์–ป์–ด๋‚ธ ์†๋„์žฅ์„ ๋ฐ”ํƒ•์œผ๋กœ ๋งŒ๋“  ํ•ฉ์„ฑ ์ž…์ž ์ด๋ฏธ์ง€ ์Œ(์ฑ„๋„ ์œ ๋™)์„ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ๊ทธ ๊ฒฐ๊ณผ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ํ†ตํ•˜์—ฌ ์†๋„์žฅ ๋ถ„์„์˜ ๊ณต๊ฐ„ํ•ด์ƒ๋„๋ฅผ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๊ฐœ๋ฐœ๋œ ์ฝ”๋“œ์˜ ์‹ค์ œ ์ ์šฉ์„ฑ์„ ๊ฒ€์ฆ ๋ฐ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์—ฐ์†๋ ˆ์ด์ €(Continuous wave laser)์™€ ๊ณ ์†์นด๋ฉ”๋ผ(High speed camera)๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธก์ •ํ•œ ์ œํŠธ์œ ๋™์‹คํ—˜์˜ ์˜์ƒ์„ ๋ถ„์„ํ•ด ๋ณด์•˜๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ฐœ๋ฐœํ•œ ์ฝ”๋“œ๊ฐ€ ์‹ค์ œ ์‹คํ—˜ ์ž…์ž์ด๋ฏธ์ง€์—๋„ ์ž˜ ์ ์šฉ๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.For several decades, detecting fluid flow from a sequence of images has been a primary problem in the field of experimental fluid mechanics. Quantitative measurement of a flow velocity field is fundamentally important for understanding the physical nature of flow. To that end, particle image velocimetry (PIV) has been widely used as one of the flow motion estimators in many laboratories. PIV is a well-established optical measurement technique that widely used for visualization of multi-dimensional velocity field in the nuclear thermal-hydraulic researches. The demand for extraction of high resolution velocity fields is increasing, as the recent studies frequently require flow measurement in narrow and complicated spaces in nuclear power plants (i.e. spacer grid and mixing vane in light water reactor (LWR) fuel bundle, sodium fast reactor (SFR) wire wrapped fuel bundle, compact heat exchanger and etc.). Based on this motivation, this study attempts to improve spatial resolution of the conventional correlation based PIV using an optical flow analysis. In this study, an optical flow PIV algorithm and code are developed based on a global method due to its better adequacy to particle image treatment over the local approach. The optical flow PIV code (O-Flow PIV) is developed based on the assumptions and constraints of brightness constancy, smoothness, and gradient constancy. The code also adopted several concepts including image pyramid, image warping, and conjugate gradient linear solver. Validation of the code performance is conducted using particle image pairs synthetized from known velocity fieldlinear translation, rotation, and vortex. By comparisons of the code analysis with the known velocity, it is found that the optical flow PIV performs better than the cross-correlation PIV especially for high rotations in a small area (large vorticity). Based on the optical flow algorithm, a method to refine cross-correlation PIV velocity field is also developed. Validation of the code is conducted using synthetic particle image pairs obtained from direct numerical simulation (DNS) results. As a result, it is found that the refined velocity has higher spatial resolution than the original one. Finally, validity of the developed optical flow PIV algorithm and code is evaluated using real experimental images of jet flow. The reference image pairs are obtained from a jet flow experiment in a pool using a continuous laser and a high speed camera. As a result of the validation, it is concluded that the developed algorithm and code are well applicable to the real particle image pairs with higher spatial resolution.Chapter 1. Introduction 1 1.1 Background 1 1.2 Motivation 3 1.3 Objectives 4 Chapter 2. Previous Studies 10 2.1 Conventional PIV Method 10 2.2 Optical Flow Method 11 2.2.1 Local Method 12 2.2.2 Global Method 13 2.3 Optical Flow PIV 14 Chapter 3. Development of Optical Flow PIV Code: O-Flow PIV 20 3.1 Basic Theory 20 3.1.1 Optical Flow Equations 20 3.1.2 Image Derivatives 24 3.1.3 Linear Equation Solver 25 3.1.4 Image Pyramids 26 3.1.5 Image Warping 27 3.2 Code Algorithm 28 3.3 Validation of O-Flow PIV 28 3.3.1 PIVlab setting 28 3.3.2 O-Flow PIV setting 28 3.3.3 Synthetic data description 29 3.3.4 Results 29 Chapter 4. Development of the Velocity Refinement Method using Optical Flow 64 4.1 Basic Idea 64 4.2 Algorithm 65 4.3 Validation of the Method 65 Chapter 5. Application of O-Flow PIV for PIV Measurements 74 Chapter 6. Conclusion and Recommendations 79 6.1 Conclusion 79 6.2 Recommendations 80 References 81 Appendix 87 Appendix A. Gradient Constancy Assumption Verification 87 Appendix B. Gradient Constancy Assumption Performance 93 Appendix C. Image Intensity Normalization 98 ๊ตญ๋ฌธ ์ดˆ๋ก 102Maste
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