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    ์ค‘์ฒฉ์„ธ๋Œ€ ๋™ํƒœ ์ผ๋ฐ˜๊ท ํ˜• ๋ชจํ˜•์„ ์ด์šฉํ•œ ๊ตญ๋ฏผ์—ฐ๊ธˆ ์ •์ฑ… ๊ฐœ์„  ๋ชจ์ƒ‰

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์ œํ•™๋ถ€, 2012. 8. ์ตœ๋ณ‘์„ .๋ณธ ๋…ผ๋ฌธ์€ ๊ธ‰์†ํ•œ ์ธ๊ตฌ๊ณ ๋ นํ™”๋กœ ์ธํ•ด ์šฐ๋ฆฌ๋‚˜๋ผ ๊ตญ๋ฏผ์—ฐ๊ธˆ๊ธฐ๊ธˆ์˜ ์žฌ์ •์ด ์•…ํ™”๋˜๋Š” ์ƒํ™ฉ ํ•˜์—์„œ, ์—ฐ๊ธˆ๊ธฐ๊ธˆ ์žฌ์ •์•ˆ์ •ํ™”๋ฅผ ์œ„ํ•œ ์—ฐ๊ธˆ์ œ๋„ ๊ฐœํ˜์•ˆ๋“ค์„ ์ œ์‹œํ•˜๊ณ , ๊ทธ ๊ฒฝ์ œ์  ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์ด ์ œ์‹œํ•œ ์—ฐ๊ธˆ์ œ๋„ ๊ฐœํ˜์•ˆ๋“ค์€ ํŠน์ • ์‹œ์ ์˜ ๊ตญ๋ฏผ์—ฐ๊ธˆ๊ธฐ๊ธˆ์„ ์ผ์ •๋Ÿ‰์œผ๋กœ ์œ ์ง€ํ•˜๋Š” ๊ฒƒ์„ ๊ณตํ†ต์˜ ์ •์ฑ… ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ •์ฑ… ๋ชฉํ‘œ ํ•˜์—์„œ ์†Œ๋“๋Œ€์ฒด์œจ์„ ํ˜„ํ–‰์˜ ์ œ๋„๋กœ ์œ ์ง€ํ•˜๋˜ ์†Œ๋น„์„ธ๋ฅผ ์—ฐ๊ธˆ๊ธฐ๊ธˆ์˜ ์žฌ์›์œผ๋กœ ๋„์ž…ํ•˜๊ณ  ์—ฐ๊ธˆ๋ณดํ—˜์š”์œจ์„ ์กฐ์ •ํ•˜๋Š” ๊ฐœํ˜์•ˆ์„ ๋ชจ์ƒ‰ํ•œ๋‹ค. ์ค‘์ฒฉ์„ธ๋Œ€ ๋™ํƒœ์ผ๋ฐ˜๊ท ํ˜•๋ชจํ˜•์„ ์ด์šฉํ•œ ๋ชจ์˜์‹คํ—˜ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด, ์—ฐ๊ธˆ๊ธฐ๊ธˆ์˜ ์žฌ์›์œผ๋กœ ์†Œ๋น„์„ธ์˜ ๋„์ž…ํ•˜์˜€์„ ๋•Œ ์†Œ๋น„์„ธ๋ฅผ ๋„์ž…ํ•˜์ง€ ์•Š์€ ๊ฒฝ์šฐ์— ๋น„ํ•ด ์—ฐ๊ธˆ๋ณดํ—˜์š”์œจ ์ธ์ƒํญ์ด ์ถ•์†Œ๋˜๋ฉฐ, ํ˜„์„ธ๋Œ€์˜ ํ›„์ƒ์€ ๋‹ค์†Œ ๊ฐ์†Œํ•˜์ง€๋งŒ ํ›„์„ธ๋Œ€์—๋Š” ๋” ํฐ ํ›„์ƒ์ฆ์ง„์˜ ํšจ๊ณผ๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ์†Œ๋น„์„ธ์˜ ๋„์ž…์œผ๋กœ ์ธํ•œ ์—ฐ๊ธˆ๋ณดํ—˜์š”์œจ ์ธ์ƒํญ์˜ ์ถ•์†Œ์ •๋„์™€ ํ˜„์„ธ๋Œ€ ๋ฐ ํ›„์„ธ๋Œ€์˜ ํ›„์ƒ๋ณ€ํ™”์ •๋„๋Š” ์†Œ๋น„์„ธ์˜ ๋„์ž… ๊ทœ๋ชจ์— ๋น„๋ก€ํ•œ๋‹ค. ํ•œํŽธ ์†Œ๋น„์„ธ์˜ ๋„์ž…์œผ๋กœ ์ธํ•œ ํ›„์ƒ๋ณ€ํ™”๋Š” ๋ชจ๋“  ์†Œ๋“๊ณ„์ธต์— ๋Œ€ํ•ด ๋™์ผํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚œ๋‹ค. ์ธ๊ตฌ๊ณ ๋ นํ™”์ •๋„๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ๋†’์ง€ ์•Š์€ ๊ณ ์œ„ ์ธ๊ตฌ๊ตฌ์กฐ ์‹œ๋‚˜๋ฆฌ์˜ค ํ•˜์—์„œ ์žฌ์ •์•ˆ์ •ํ™” ์ •์ฑ…๋ชฉํ‘œ ๋ฐ ์†Œ๋น„์„ธ ๋„์ž…๊ทœ๋ชจ๊ฐ€ ๋™์ผํ•œ ๊ฐœํ˜์•ˆ์„ ์ ์šฉํ–ˆ์„ ๋•Œ, ํ‘œ์ค€ ์ธ๊ตฌ๊ตฌ์กฐ ์‹œ๋‚˜๋ฆฌ์˜ค์˜ ๊ฒฝ์šฐ์— ๋น„ํ•ด ์—ฐ๊ธˆ๋ณดํ—˜์š”์œจ์˜ ์ธ์ƒํญ์€ ์ž‘๊ฒŒ ๋‚˜ํƒ€๋‚œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์†Œ๋น„์„ธ ๋„์ž…์œผ๋กœ ์ธํ•œ ํ›„์„ธ๋Œ€์˜ ํ›„์ƒ์ฆ์ง„ ํšจ๊ณผ๋Š” ํ‘œ์ค€ ์ธ๊ตฌ๊ตฌ์กฐ ์‹œ๋‚˜๋ฆฌ์˜ค์˜ ๊ฒฝ์šฐ์— ๋น„ํ•ด ์ค„์–ด๋“œ๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค.๋ชฉ ์ฐจ ์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 2 ์žฅ ์ธ๊ตฌ ๊ณ ๋ นํ™”์˜ ์ถ”์„ธ 1 ์ œ 3 ์žฅ ์ค‘์ฒฉ์„ธ๋Œ€ ์ผ๋ฐ˜๊ท ํ˜•๋ชจํ˜• 5 ์ œ 1 ์ ˆ ๋ชจํ˜•์˜ ๊ตฌ์กฐ 6 1. ์†Œ๋น„์ž ๋ถ€๋ฌธ 6 2. ๊ธฐ์—… ๋ถ€๋ฌธ 8 3. ์—ฐ๊ธˆ ๋ถ€๋ฌธ 8 4. ๊ฑฐ์‹œ๊ฒฝ์ œ์˜ ๋™ํƒœ์ผ๋ฐ˜๊ท ํ˜•์กฐ๊ฑด 9 ์ œ 2 ์ ˆ ๋ชจํ˜•์˜ ์„ค์ • 10 1. ์ธ๊ตฌ์ „๋ง๊ณผ ๊ฐ€์ • 10 2. ์†Œ๋“๊ณ„์ธต๋ณ„ ์ธ์ ์ž๋ณธ๊ณก์„ ์˜ ์„ค์ • 12 3. ์ฃผ์š” ๋ชจ์ˆ˜์˜ ์„ค์ • 13 4. ๊ตญ๋ฏผ ์—ฐ๊ธˆ ์ •์ฑ… ๋Œ€์•ˆ ์„ค์ • 13 5. ์‹œ๋ฎฌ๋ ˆ์ด์…˜์˜ ๊ฐœ์š” 16 ์ œ 4 ์žฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ 17 ์ œ 1 ์ ˆ ํ‘œ์ค€ ์ธ๊ตฌ๊ตฌ์กฐ ํ•˜์—์„œ์˜ ์ •์ฑ… ๋Œ€์•ˆ 17 1. ์—ฐ๊ธˆ๊ธฐ๊ธˆ์˜ ๋ณ€ํ™” 17 2. ์—ฐ๊ธˆ๋ณดํ—˜์š”์œจ์˜ ์กฐ์ • 18 3. ํ›„์ƒ๋ถ„์„ 19 ์ œ 2 ์ ˆ ๊ณ ์œ„ ์ธ๊ตฌ๊ตฌ์กฐ ํ•˜์—์„œ์˜ ์ •์ฑ… ๋Œ€์•ˆ 22 1. ์—ฐ๊ธˆ๊ธฐ๊ธˆ์˜ ๋ณ€ํ™” 22 2. ์—ฐ๊ธˆ๋ณดํ—˜์š”์œจ์˜ ์กฐ์ • 23 3. ํ›„์ƒ๋ถ„์„ 24 ์ œ 5 ์žฅ ๊ฒฐ๋ก  25 ์ฐธ๊ณ ๋ฌธํ—Œ 27 ๋ถ€๋ก1 28 ๋ถ€๋ก2 32 AbstractMaste

    ์ „ํ›„ ๋ถ„๋‹จ์ฒด์ œ์˜ ํ˜•์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์™ธ๊ตํ•™๊ณผ, 2021. 2. ์‹ ์šฑํฌ.This study aims to extend the temporal scope that stipulated the division of the Korean Peninsula as the โ€œ1953 Systemโ€ by looking at the case of the 1954 Geneva Conference and furthermore, to describe the Cold War of East Asia that was prevalent on the Korean Peninsula from various perspectives. The 1954 Geneva Conference was held for two months to follow up on the Military Armistice Agreement, which proposed the need for a conference to establish the peace on the Korean Peninsula. This conference was intended to replace the Military Armistice Agreement, which had suspended the combat between the China and North Korea and the U.N. forces, and ultimately strove to establish the peace on the Korean Peninsula in the political level. Although the conference discussed topics that had not been resolved through the Armistice Agreement, such as political issues like the withdrawal of the foreign militaries from the Peninsula and the arrangement of an of election, it failed to produce fruitful outcomes. This resulted in the unintended, yet natural transition of the military agreement into the division that we see today. Therefore, the roots of the Cold War on the Korean Peninsula grew deeper and more permanent. This study explains that the 1954 Geneva Conference failed to achieve the primary objective of the U.S. Eisenhower Administration, reaching unification through conditional neutralization of the Peninsula, due to the adversary game with Peopleโ€™s Republic of China and alliance game with the Republic of Korea. Pre-existing research has not simultaneously examined these two variables, so this research takes a closer look at some important features of the international relations of the Korean Peninsula utilizing the primary data from the United States, China and Korea. Firstly, the armistice system, which is the core structure on the Korean Peninsula, is the product of the Cold War in East Asia. Even through the Cold War may have originated from the competition between the United States and Soviet Union, it evolved into a major competition between the United States and China in East Asia after the Korean War. In order to manage stability in the Peninsula, the United States implemented The New Look Policy to change the state of the Peninsula through the conditional neutralization that were correspondent to the national interest. On the other hand, China preferred to maintain the status quo. As a consequence, the two different perspectives on how to manage stability in the Peninsula resulted to the systematization of the division. Secondly, not only is the competition for national interest in the field of international politics shown through hostile relationships, but in allied relationships as well. The formation of the alliance between the Republic of Korea and the United States covered the time period from the Mutual Defense Treaty to the exchange of the ratification instrument, which overlapped with the 1954 Geneva Conference. Both states played a round of tug-of-war during the ratification process in the midst of conflicts on multiple issues. For instance, South Korea President Syngman Rhee attempted to use the Geneva Conference as an opportunity to establish regime security and expedite nation-building while the United States attempted to deploy its East Asia strategy with Japan as the core pillar. The Korean Peninsula still exists in an unstable state, even 70 years after the outbreak of the Korean War. This study confirms that the dynamic structure of the international relations on the Korean Peninsula, which has prolonged the state of division, is formed by the complex application of the characteristics of the international politics that are trilaterally intertwined through the adversary, alliance and the domestic politics.๋ณธ ์—ฐ๊ตฌ๋Š” 1954๋…„ ์ œ๋„ค๋ฐ” ์ •์น˜ํšŒ๋‹ด์„ ๋‹จ์ผ์‚ฌ๋ก€๋กœ ์‚ดํŽด๋ด„์œผ๋กœ์จ ํ•œ๋ฐ˜๋„ ๋ถ„๋‹จ์„ 1953๋…„ ์ฒด์ œ๋กœ ๊ทœ์ •ํ•˜๋˜ ์‹œ๊ฐ„์  ๋ฒ”์œ„๋ฅผ ํ™•์žฅํ•˜๊ณ  ๋” ๋‚˜์•„๊ฐ€ ํ•œ๋ฐ˜๋„ ์•ˆ์—์„œ ๋‚˜ํƒ€๋‚œ ๋™์•„์‹œ์•„ ๋ƒ‰์ „์˜ ๋ชจ์Šต์„ ์ž…์ฒด์ ์œผ๋กœ ๊ทธ๋ฆฌ๋ ค๋Š” ์‹œ๋„๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์•ฝ ๋‘ ๋‹ฌ๊ฐ„ ์ œ๋„ค๋ฐ”์—์„œ ๊ฐœ์ตœ๋œ ํ•œ๋ฐ˜๋„ ์ •์น˜ํšŒ๋‹ด์€ ํ•œ๋ฐ˜๋„ ํ‰ํ™” ์ •์ฐฉ์„ ์œ„ํ•œ ํ˜‘์˜์˜ ํ•„์š”์„ฑ์„ ์ œ๊ธฐํ•œ ์ •์ „ํ˜‘์ •์˜ ๊ทœ์ •์— ๋”ฐ๋ผ ์ด๋ฃจ์–ด์กŒ๋‹ค. ๋ณธ ํšŒ๋‹ด์€ ์–‘์ธก์˜ ์ „ํˆฌํ–‰์œ„๋ฅผ ์ค‘๋‹จํ•˜๋Š” ๊ตฐ์‚ฌ์  ํ•ฉ์˜์ธ ์ •์ „ํ˜‘์ •์„ ๋Œ€์ฒดํ•˜๋Š” ๊ฒƒ์œผ๋กœ, ์ •์น˜์  ์ฐจ์›์˜ ํ•œ๋ฐ˜๋„ ํ‰ํ™” ์ •์ฐฉ์„ ์œ„ํ•œ ํ˜‘์ •์„ ์˜๋ฏธํ–ˆ๋‹ค. ์ •์ „ํ˜‘์ •์—์„œ๋Š” ํ•ด๊ฒฐ๋˜์ง€ ์•Š์€ ์ „ํ›„ ๊ณผ์ œ, ์ฆ‰ ์™ธ๊ตญ๊ตฐ ์ฒ ์ˆ˜์™€ ์„ ๊ฑฐ๋ฐฉ์‹ ๋“ฑ ์ •์น˜์  ์‚ฌ์•ˆ์„ ๋…ผ์˜ํ–ˆ์ง€๋งŒ ๋ณธ ํšŒ๋‹ด์€ ์‹คํŒจ๋กœ ๋๋‚ฌ์œผ๋ฉฐ, ์ผ์‹œ์ ์ธ ํ•ฉ์˜์— ๋ถˆ๊ณผํ–ˆ๋˜ ์ •์ „ํ˜‘์ •์ด ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ๋ถ„๋‹จ์ฒด์ œ๋กœ ์ด์–ด์ ธ ํ•œ๋ฐ˜๋„ ๋ƒ‰์ „ ์งˆ์„œ๋Š” ๊ณ ์ฐฉํ™”๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ 1954๋…„ ์ œ๋„ค๋ฐ” ์ •์น˜ํšŒ๋‹ด์—์„œ ๋ฏธ๊ตญ ์•„์ด์  ํ•˜์›Œ ํ–‰์ •๋ถ€์˜ ์กฐ๊ฑด๋ถ€ ์ค‘๋ฆฝํ™” ํ†ต์ผ์ด๋ผ๋Š” ํ•œ๋ฐ˜๋„์˜ ํ˜„์ƒ๋ณ€๊ฒฝ ๋ชฉํ‘œ๊ฐ€ ์ค‘๊ตญ๊ณผ์˜ ์ ์ˆ˜๊ฒŒ์ž„ ๋ฐ ํ•œ๊ตญ๊ณผ์˜ ๋™๋งน๊ฒŒ์ž„์„ ๊ฑฐ์น˜๋ฉฐ ์‹คํ˜„๋˜์ง€ ๋ชปํ•˜๊ณ  ๊ฒฐ๊ตญ ํ˜„์ƒ์œ ์ง€์ „๋žต, ์ฆ‰ ๋ถ„๋‹จ์˜ ์ œ๋„ํ™”๋กœ ๊ท€๊ฒฐ๋˜์—ˆ์Œ์„ ๋“œ๋Ÿฌ๋ƒˆ๋‹ค. ๊ธฐ์กด์˜ ์„ ํ–‰์—ฐ๊ตฌ๋“ค์ด ์ด ๋‘ ๊ฐ€์ง€์˜ ๋ณ€์ˆ˜๋ฅผ ๋™์‹œ์— ๋‹ค๋ฃจ์ง€ ๋ชปํ–ˆ์œผ๋‚˜, ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฏธ๊ตญ, ์ค‘๊ตญ, ํ•œ๊ตญ์˜ 1์ฐจ ์ž๋ฃŒ๋“ค์„ ํ™œ์šฉํ•˜์—ฌ ํ•œ๋ฐ˜๋„ ๊ตญ์ œ๊ด€๊ณ„์—์„œ ๋‚˜ํƒ€๋‚œ ์ค‘์š”ํ•œ ํŠน์ง•๋“ค์— ์ฃผ๋ชฉํ•˜์˜€๋‹ค. ์ฒซ์งธ, ํ•œ๋ฐ˜๋„๋ฅผ ๊ทœ์ •ํ•˜๋Š” ํ•ต์‹ฌ์ ์ธ ๊ตฌ์กฐ์ธ ์ •์ „์ฒด์ œ๋Š” ๋™์•„์‹œ์•„ ๋ƒ‰์ „ ์งˆ์„œ๋ผ๋Š” ๊ตฌ์กฐ์  ๋ณ€์ˆ˜๊ฐ€ ์ž‘์šฉํ•œ ๊ฒฐ๊ณผ๋‹ค. ๋ƒ‰์ „์˜ ๊ธฐ์›์ด ๋ฏธ๊ตญ๊ณผ ์†Œ๋ จ์˜ ๋Œ€๊ฒฐ์ด์—ˆ๋‹ค๊ณ  ํ• ์ง€๋ผ๋„ ํ•œ๊ตญ์ „์Ÿ ์ดํ›„ ๋™์•„์‹œ์•„๋ผ๋Š” ์ง€์—ญ์  ๊ณต๊ฐ„์—์„œ๋Š” ๋ฏธ๊ตญ๊ณผ ์ค‘๊ตญ์˜ ๋Œ€๊ฒฐ๋กœ ์ž๋ฆฌ๋ฅผ ์žก๊ฒŒ ๋˜์—ˆ๋‹ค. ๋ฏธ๊ตญ์€ ํ•œ๋ฐ˜๋„๋ฅผ ์•ˆ์ •์ ์œผ๋กœ ๊ด€๋ฆฌํ•˜๊ธฐ ์œ„ํ•ด ๋‰ด๋ฃฉ์ •์ฑ…์ด๋ผ๋Š” ์„ธ๊ณ„์ „๋žต ํ‹€ ์•ˆ์—์„œ ๋ฏธ๊ตญ์˜ ์ด์ต์— ๋ถ€ํ•ฉํ•˜๋Š” ํ•œ๋ฐ˜๋„ ์ค‘๋ฆฝํ™” ํ†ต์ผ๋ฐฉ์•ˆ์„ ํ†ตํ•œ ํ˜„์ƒ๋ณ€๊ฒฝ์„ ์‹œ๋„ํ–ˆ๊ณ , ๋ฐ˜๋Œ€๋กœ ์ค‘๊ตญ์€ ๋Œ€๋‚ด์™ธ์  ์•ˆ์ •์„ ์ถ”๊ตฌํ•˜๊ธฐ ์œ„ํ•œ ํ˜„์ƒ์œ ์ง€ ์ •์ฑ…์„ ์„ ํ˜ธํ–ˆ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ํ•œ๋ฐ˜๋„๋ฅผ ์•ˆ์ •์ ์œผ๋กœ ๊ด€๋ฆฌํ•˜๋ ค๋Š” ์–‘ ๊ตญ๊ฐ€์˜ ์ž…์žฅ์€ ํ˜„์ƒ์œ ์ง€๋กœ ๊ท€๊ฒฐ๋˜๋ฉฐ ๋ถ„๋‹จ์ด ์ œ๋„ํ™”๋˜์—ˆ๋‹ค. ๋‘˜์งธ, ๊ตญ์ œ์ •์น˜์—์„œ ๊ตญ๊ฐ€์ด์ต์„ ๋‘˜๋Ÿฌ์‹ผ ํˆฌ์Ÿ์ด ์ ๋Œ€์  ๊ด€๊ณ„๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋™๋งน๊ตญ๋ผ๋ฆฌ๋„ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ํ•œ๋ฏธ๋™๋งน์˜ ํ˜•์„ฑ๊ธฐ๋Š” ํ•œ๋ฏธ์ƒํ˜ธ๋ฐฉ์œ„์กฐ์•ฝ ์ฒด๊ฒฐ๋ถ€ํ„ฐ ๋น„์ค€์„œ ๊ตํ™˜๊นŒ์ง€๋กœ ๋ณผ ์ˆ˜ ์žˆ๋Š”๋ฐ, ์ด๋Š” ์ œ๋„ค๋ฐ” ์ •์น˜ํšŒ๋‹ด๊ณผ ์‹œ๊ธฐ์ ์œผ๋กœ ์ค‘์ฒฉ๋˜์–ด ์žˆ์œผ๋ฉฐ, ์—ฌ๋Ÿฌ ๋ฐฉ๋ฉด์—์„œ ๊ฐˆ๋“ฑ์„ ๋ณด์˜€๋˜ ํ•œ๋ฏธ ์–‘๊ตญ์€ ์ด ํšŒ๋‹ด์—์„œ ์น˜์—ดํ•œ ๋น„์ค€ ๊ฒŒ์ž„์„ ํŽผ์ณ๋‚˜๊ฐ”๋‹ค. ๋น„์ค€ ๊ฒŒ์ž„์˜ ์ด๋ฉด์—๋Š” ์ •๊ถŒ ์•ˆ๋ณด์™€ ๊ตญ๊ฐ€ ๊ฑด์„ค์ด๋ผ๋Š” ํ•ต์‹ฌ ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐํšŒ์˜ ์žฅ์œผ๋กœ ์ œ๋„ค๋ฐ” ํšŒ๋‹ด์„ ํ™œ์šฉํ•˜๋ ค๋Š” ์ด์Šน๋งŒ ๋Œ€ํ†ต๋ น์˜ ์ „๋žต๊ณผ ์ผ๋ณธ์„ ํ•ต์‹ฌ์ถ•์œผ๋กœ ๋™์•„์‹œ์•„ ์ „๋žต์„ ํŽผ์น˜๋ ค๋Š” ๋ฏธ๊ตญ์˜ ์ „๋žต์ด ์ถฉ๋Œํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ํ•œ๊ตญ์ „์Ÿ์ด ๋ฐœ๋ฐœํ•œ ์ง€ 70๋…„์ด ์ง€๋‚œ ์ง€๊ธˆ๋„ ์—ฌ์ „ํžˆ ํ•œ๋ฐ˜๋„๋Š” ๋ถˆ์•ˆ์ •ํ•œ ์ƒํ™ฉ์— ๋†“์—ฌ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒํ™ฉ ์†์—์„œ ์ œ๋„ค๋ฐ” ์ •์น˜ํšŒ๋‹ด์„ ๋‹ค๋ฃจ๋Š” ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ถ„๋‹จ ์ƒํ™ฉ์„ ์ง€์†์‹œํ‚ค๋Š” ํ•œ๋ฐ˜๋„ ๊ตญ์ œ๊ด€๊ณ„์˜ ์—ญํ•™๊ตฌ์กฐ๊ฐ€ ์ ์ˆ˜, ๋™๋งน, ๋” ๋‚˜์•„๊ฐ€ ๊ตญ๋‚ด์ •์น˜๋ผ๋Š” ์‚ผ์ค‘๊ฒŒ์ž„์˜ ๊ตญ์ œ์ •์น˜์  ํŠน์„ฑ์ด ๋ณตํ•ฉ์ ์œผ๋กœ ์ž‘์šฉํ•œ๋‹ค๋Š” ์‚ฌ์‹ค์„ ํ™•์ธํ•œ๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ๊ฐ€์ง„๋‹ค.์ œ1์žฅ ์„œ๋ก  1 ์ œ1์ ˆ ๋ฌธ์ œ ์ œ๊ธฐ 1 ์ œ2์ ˆ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  5 ์ œ3์ ˆ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ๋ฐ ์ž๋ฃŒ 7 ์ œ4์ ˆ ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 11 ์ œ2์žฅ ์ œ๋„ค๋ฐ” ์ •์น˜ํšŒ๋‹ด์˜ ์„ฑ๋ฆฝ๋ฐฐ๊ฒฝ 12 ์ œ1์ ˆ ์ •์ „ํ˜‘์ • 4์กฐ 60ํ•ญ์˜ ์ •์น˜์  ์˜์˜ 12 ์ œ2์ ˆ ํšŒ๋‹ด ๊ตฌ์„ฑ ๋…ผ์˜: ํŒ๋ฌธ์  ์˜ˆ๋น„ํšŒ๋‹ด๊ณผ ๋ฒ ๋ฅผ๋ฆฐ ์™ธ์ƒํšŒ๋‹ด 15 ์ œ3์žฅ ์ œ๋„ค๋ฐ” ์ •์น˜ํšŒ๋‹ด๊ณผ ๋ฏธ์ค‘ ์ ์ˆ˜๊ฒŒ์ž„ 20 ์ œ1์ ˆ ๋ฏธ๊ตญ์˜ ๋™์•„์‹œ์•„ ์ „ํ›„์งˆ์„œ ๊ตฌ์ƒ๊ณผ ๋Œ€์ค‘(ๅฐไธญ)์ „๋žต 20 ์ œ2์ ˆ ์ •์น˜ํšŒ๋‹ด์—์„œ ๋‚˜ํƒ€๋‚œ ๋ฏธ์ค‘ ๋Œ€๊ฒฐ 24 ์ œ3์ ˆ ๋ฏธ๊ตญ๊ณผ ์˜์—ฐ๋ฐฉ์˜ ์ด๊ฒฌ 33 ์ œ4์žฅ ์ œ๋„ค๋ฐ” ์ •์น˜ํšŒ๋‹ด๊ณผ ํ•œ๋ฏธ ๋™๋งน๊ฒŒ์ž„ 38 ์ œ1์ ˆ ๋ฏธ๊ตญ์˜ ํ•œ๋ฐ˜๋„ ์ •์ฑ… ๋…ผ์Ÿ: ์ค‘๋ฆฝํ™”๋ก  38 ์ œ2์ ˆ ์ •์น˜ํšŒ๋‹ด ์ฐธ๊ฐ€์™€ ํ•œ๋ฏธ๋™๋งน์˜ ์œ ์˜ˆ 46 ์ œ3์ ˆ ์ •์น˜ํšŒ๋‹ด์˜ ๋Œ€์‘์ „๋žต๊ณผ ํ•œ๋ฏธ๊ฐˆ๋“ฑ 53 ์ œ5์žฅ ๊ฒฐ๋ก  63 ์ฐธ๊ณ ๋ฌธํ—Œ 69Maste

    ์™ธ์ƒํ›„์šธ๋ถ„์žฅ์• (PTED) ์ž๊ฐ€์ธก์ •๋„๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ(๋ณด๊ฑด์ •์ฑ…๊ด€๋ฆฌํ•™์ „๊ณต),2020. 2. ์œ ๋ช…์ˆœ.As a result of exposure to humidifier disinfectants sold from 1994 to 2011, 1,520 have died and 6,718 people have claimed damages(Korea Environmental Industry & Technology Institute, 2020). Humidifier disinfectant damage can be largely attributed to physical damage caused by direct exposure to toxic chemicals, and collateral mental damage due to our societys damage response. However, so far, surveys and studies have focused too much on physical diseases, with only four studies covering mental health, requiring a public health - level approach. Against this backdrop, this study will examine the mental health of the victims of humidifier disinfectants by applying the concept of Embitterment and using the scale of Post-traumatic Embitterment Disorder(PTED). Embitterment is an emotion that develops after the experience of a negative life event and is perceived as unfair and unjust(Alexander, 1960; Linden, 2003). Preliminary studies have included severity of disease (Lee et al., 2019), household income, experiences of negative life events (Seoul National University Center for Happiness Studies, 2018), fairness, overcontrol (Michailidis et al., 2017; 2018), social support, communication about change(Sensky et al., 2015) as the influencing factors of embitterment. The analysis of media reports (Ju & You, 2019) indicates that the victims of the disaster and their families are the most embittered groups, and the main causes of embitterment are the deprivation of basic needs and rights, defamation or insult, and misuse of power. How these social factors affect the mental health of the victims can be seen through Kleinman and Das(1997)'s concept of "social suffering" and the amplification of social damage in disasters(Hong & Seo,2019). This study looked into the factors affecting the embitterment of humidifier disinfectants victims by applying the concept of embitterment, given that the concept and affecting factors of embitterment can help analyze not only individual characteristics but also the effects of social factors on the mental health of victims. In addition, this study verifies the effect of the influencing factors on the quality of life and the mediating effect of the embitterment in their relationship. The analysis used 129 adult survey data from "2018 Household survey on health and social damage among the victims of humidifier disinfectants" of Special Investigation Commission on Humidifier Disinfectants and 416 Sewol Ferry Disaster. Predicting variables of embitterment include damage(individual damage level, household damage stage, changes in economic status after damage), health(physical discomfort, depression), government's response to the damage (time taken until victims received assessment results to notification of damages, number of services given, whether victims rights were violated during the treatment, rehabilitation, damage relief and other response processes, acceptance of evidence and explanation of the assessment results, and the adequacy of the assessment standards), social support (number of neighbors victims can turn to), information experience (satisfaction of information on humidifier disinfectants), and Just world belief. This study adopted stepwise regression analysis and multiple logistic regression analysis to identify the factors that affect the embitterment of the humidifier disinfectant victims. The mediating effect of the embitterment was analyzed through hierarchical multiple regression analysis. The study found that the average PTED score of all subjects was 2.0, and 33.3% of the study subjects had a PTED score of 3.0 on average, indicating that they are in a state of clinically significant intensity of reactive embitterment. The PTED score varied depending on age, whether to go through change in economic status, whether to suffer depression, whether their rights were violated, and the level of supporting services. The result of the stepwise regression analysis aiming to determine the influence factors of the embitterment was that the PTED score was higher when the victims live in families with severe damage levels, feel relatively severe physical discomfort, waited longer time for getting the damage assessment results and suffered depression. In addition, when the study subjects were divided into two groups, which are more severe PTED group(PTEDโ‰ง2.5) and non severe PTED groups(PTED<2.5), households with severe damage levels, high physical discomfort, depression, and longer waiting time for the damage assessment increased statistically significant amounts of the odds to belong to the PTED group. Finally, Embitterment partly mediated the quality of life and depression. This study is the first in Korea to analyze the effects of personal characteristics, experiences and perceptions in social damage response on mental health of humidifier disinfectant victims by applying the concept of embitterment. Aimed at expanding the concept of the damage of humidifier disinfectants from existing physical disease-oriented descriptions into a broader one, this study emphasizes the need to recognize a wider range of damage in the event of social disasters and to establish of victim-oriented support measures.1994๋…„๋ถ€ํ„ฐ 2011๋…„๊นŒ์ง€ ์‹œํŒ๋œ ๊ฐ€์Šต๊ธฐ์‚ด๊ท ์ œ์˜ ๋…ธ์ถœ๋กœ ์ธํ•œ ํ”ผํ•ด์‹ ์ฒญ์ž๋Š” ํ˜„์žฌ๊นŒ์ง€ 6,718๋ช…, ์‚ฌ๋ง์ž๋Š” 1,520๋ช…์— ์ด๋ฅธ๋‹ค(ํ•œ๊ตญํ™˜๊ฒฝ์‚ฐ์—…๊ธฐ์ˆ ์›, 2020). ์ด๋Ÿฌํ•œ ๊ฐ€์Šต๊ธฐ์‚ด๊ท ์ œ ํ”ผํ•ด์˜ ์‹ค์ œ๋Š” ์œ ๋… ํ™”ํ•™๋ฌผ์งˆ ๋…ธ์ถœ์— ๊ธฐ์ธํ•˜๋Š” ์ธ๊ณผ์ ์ธ ์‹ ์ฒด์  ํ”ผํ•ด ์™ธ์—, ์‚ฌํšŒ์ ์ธ ํ”ผํ•ด ๋Œ€์‘ ๋ฐฉ์‹์œผ๋กœ ์ธํ•œ ์ •์‹ ์  ํ”ผํ•ด๊ฐ€ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ˜„์กดํ•˜๋Š” ์กฐ์‚ฌ ๋ฐ ์—ฐ๊ตฌ๋Š” ์‹ ์ฒด์  ์งˆํ™˜ ์œ„์ฃผ๋กœ ํŽธํ–ฅ๋˜์—ˆ์œผ๋ฉฐ, ์ •์‹ ์‹ฌ๋ฆฌ์  ๊ด€์ ์˜ ํ”ผํ•ด ์—ฐ๊ตฌ๋Š” ๋„ค ๊ฑด์— ๊ทธ์น˜๊ณ  ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ์™ธ์ƒํ›„์šธ๋ถ„์žฅ์• (Post-traumatic Embitterment Disorder, PTED) ์ž๊ฐ€์ธก์ •๋„๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ฐ€์Šต๊ธฐ์‚ด๊ท ์ œ ์‚ฌ๊ฑด์ด ํ”ผํ•ด์ž์˜ ์ •์‹ ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ™•์ธํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์šธ๋ถ„์€ ์ผ์ƒ์ƒํ™œ์—์„œ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ๋ถ€์ •์ ์ธ ์ƒ์• ์‚ฌ๊ฑด์„ ๊ฒช๊ณ , ํ•ด๋‹น ๊ฒฝํ—˜์ด ๋ถˆ๊ณต์ •ํ•˜๊ณ  ๋ถ€๋‹นํ•˜๋‹ค๊ณ  ๋Š๋ผ๋ฉฐ ์ƒ๊ธฐ๋Š” ๊ฐ์ •(Alexander, 1960; Linden, 2003)์ด๋‹ค. ์„ ํ–‰์—ฐ๊ตฌ๋“ค์€ ์šธ๋ถ„์˜ ์˜ํ–ฅ์š”์ธ์œผ๋กœ ์งˆ๋ณ‘์˜ ์ค‘์ฆ๋„(์ด๊ฒฝ์ˆ˜ ์™ธ, 2019), ๊ฐ€๊ตฌ ์†Œ๋“, ๋ถ€์ •์  ์ƒ์• ์‚ฌ๊ฑด ๊ฒฝํ—˜(์„œ์šธ๋Œ€ ํ–‰๋ณต์—ฐ๊ตฌ์„ผํ„ฐ, 2018), ๊ณต์ •์„ฑ๊ณผ ๊ณผ์ž‰ ํ†ต์ œ(Michailidis ์™ธ, 2017; 2018), ์‚ฌํšŒ์  ์ง€์ง€, ๋ณ€ํ™”์— ๋Œ€ํ•œ ์˜์‚ฌ์†Œํ†ต(Sensky ์™ธ, 2015)์„ ๋“ค์—ˆ๋‹ค. ์–ธ๋ก ๋ณด๋„๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ(Ju & You, 2019)์—์„œ๋Š” ์žฌ๋‚œ ์‚ฌ๊ฑด, ์‚ฌ๊ณ ์˜ ํ”ผํ•ด์ž์™€ ๊ทธ ๊ฐ€์กฑ์ด ๊ฐ€์žฅ ์šธ๋ถ„ํ•˜๋Š” ์ง‘๋‹จ์ด๋ฉฐ, ์ฃผ์š”ํ•œ ์šธ๋ถ„์˜ ์›์ธ์œผ๋กœ๋Š” ์‚ถ์˜ ๊ธฐ๋ณธ์  ํ•„์š”์™€ ๊ถŒ๋ฆฌ ๋ฐ•ํƒˆ, ๋ช…์˜ˆํ›ผ์†์ด๋‚˜ ๋ชจ์š•, ๊ทธ๋ฆฌ๊ณ  ๊ถŒ๋ ฅ์˜ ์˜ค๋‚จ์šฉ์ด ์ง€์ ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์‚ฌํšŒ์ ์ธ ์š”์ธ์ด ํ”ผํ•ด์ž์˜ ์ •์‹ ๊ฑด๊ฐ•์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์€ Kleinman๊ณผ Das(1997)์˜ ์‚ฌํšŒ์  ๊ณ ํ†ต ๊ฐœ๋…๊ณผ ์žฌ๋‚œ์—์„œ์˜ ์‚ฌํšŒ์  ํ”ผํ•ด ์ฆํญ(ํ™์„ฑ๋งŒ, ์„œ์ธ์„, 2019) ๋“ฑ์˜ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ํ™•์ธํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ์šธ๋ถ„์˜ ๊ฐœ๋…๊ณผ ์˜ํ–ฅ์š”์ธ์€ ๊ฐœ์ธ์  ํŠน์„ฑ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์‚ฌํšŒ์ ์œผ๋กœ ์ž˜๋ชป๋œ ๋Œ€์‘์ด ํ”ผํ•ด์ž์˜ ์ •์‹ ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๋ฉด์—์„œ ํ•ด๋‹น ๊ฐœ๋…์„ ์ ์šฉํ•˜์—ฌ, ๊ฐ€์Šต๊ธฐ์‚ด๊ท ์ œ ํ”ผํ•ด์ž๋“ค์˜ ์šธ๋ถ„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ํƒ์ƒ‰์ ์œผ๋กœ ํ™•์ธํ•˜๊ณ , ์ถ”๊ฐ€๋กœ ์šธ๋ถ„์˜ ์˜ํ–ฅ ์š”์ธ๋“ค์ด ์‚ถ์˜ ์งˆ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๊ทธ ๊ด€๊ณ„์— ์žˆ์–ด์„œ ์šธ๋ถ„์˜ ๋งค๊ฐœํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ถ„์„์—๋Š” ๊ฐ€์Šต๊ธฐ์‚ด๊ท ์ œ์‚ฌ๊ฑด๊ณผ 4ยท16์„ธ์›”ํ˜ธ์ฐธ์‚ฌ ํŠน๋ณ„์กฐ์‚ฌ์œ„์›ํšŒ์˜ 2018๋…„ ์˜ ์„ฑ์ธ ์„ค๋ฌธ ๋ฐ์ดํ„ฐ 129๊ฑด์ด ํ™œ์šฉ๋˜์—ˆ๋‹ค. ์šธ๋ถ„์˜ ์ธก์ •์€ ๊ธฐ์กด ๊ฐœ๋ฐœ๋œ ์™ธ์ƒํ›„์šธ๋ถ„์žฅ์•  ์ž๊ฐ€์ธก์ •๋„๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ์‹ค์‹œํ•˜์˜€๋‹ค. ์šธ๋ถ„์˜ ์˜ˆ์ธก๋ณ€์ˆ˜๋กœ๋Š” ํ”ผํ•ด(๊ฐœ์ธํ”ผํ•ด ํŒ์ •๊ฒฐ๊ณผ, ๊ฐ€๊ตฌํ”ผํ•ด๋‹จ๊ณ„, ํ”ผํ•ด ์ดํ›„ ๊ฒฝ์ œ์  ์ƒํƒœ ๋ณ€ํ™”), ๊ฑด๊ฐ•(์‹ ์ฒด์  ๋ถˆํŽธ๋„, ์šฐ์šธ), ์ •๋ถ€์˜ ํ”ผํ•ด๋Œ€์‘ ๊ฒฝํ—˜๊ณผ ์ธ์‹(ํ”ผํ•ดํŒ์ •๊ฒฐ๊ณผ ํ†ต๋ณด๊นŒ์ง€ ๊ฑธ๋ฆฐ ์‹œ๊ฐ„, ์ง€์›๋ฐ›์€ ์„œ๋น„์Šค์˜ ๊ฐœ์ˆ˜, ์น˜๋ฃŒ์žฌํ™œํ”ผํ•ด๊ตฌ์ œ๊ธฐํƒ€ ๋Œ€์‘ ๊ณผ์ •์—์„œ ๊ถŒ๋ฆฌ๋ฅผ ์นจํ•ด๋‹นํ–ˆ๋Š”์ง€์˜ ์—ฌ๋ถ€, ํŒ์ •๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๊ทผ๊ฑฐ์™€ ์„ค๋ช… ์ˆ˜์šฉ, ํ”ผํ•ด ์ธ์ • ๊ธฐ์ค€์—์„œ ํ”ผํ•ด๊ตฌ์ œ์งˆํ™˜๋ฒ”์œ„์˜ ์ ์ ˆ์„ฑ), ์‚ฌํšŒ์  ์ง€์ง€(๋ถ€ํƒํ•  ์ˆ˜ ์žˆ๋Š” ์ด์›ƒ์˜ ์ˆ˜), ์ •๋ณด๊ฒฝํ—˜(๊ฐ€์Šต๊ธฐ์‚ด๊ท ์ œ ๊ด€๋ จ ์ •๋ณด์˜ ๋งŒ์กฑ๋„), ๊ทธ๋ฆฌ๊ณ  ๊ณต์ •์„ธ๊ณ„์‹ ๋…์„ ์„ค์ •ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ€์Šต๊ธฐ์‚ด๊ท ์ œ ํ”ผํ•ด์ž์˜ ์šธ๋ถ„์˜ ์˜ํ–ฅ ์š”์ธ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ๋‹จ๊ณ„์  ํšŒ๊ท€๋ถ„์„, ๋‹ค์ค‘๋กœ์ง€์Šคํ‹ฑํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€์œผ๋ฉฐ, ์ถ”๊ฐ€์ ์œผ๋กœ ์šธ๋ถ„์˜ ๋งค๊ฐœํšจ๊ณผ ๊ฒ€์ฆ์€ ์œ„๊ณ„์  ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•ด ์‹ค์‹œํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ์—ฐ๊ตฌ๋Œ€์ƒ์ž ์ „์ฒด์˜ PTED ์ ์ˆ˜ ํ‰๊ท ์€ 2.0์ ์ด๋ฉฐ ์—ฐ๊ตฌ๋Œ€์ƒ์ž์˜ 33.3%๋Š” PTED ์ ์ˆ˜๊ฐ€ ํ‰๊ท  3.0์ ์œผ๋กœ ์ค‘์ฆ๋„ ์ด์ƒ์˜ ์šธ๋ถ„ ์ƒํƒœ์ž„์ด ๋“œ๋Ÿฌ๋‚ฌ๋‹ค. PTED ์ ์ˆ˜๋Š” ์—ฐ๋ น๊ณผ ๊ฒฝ์ œ์  ์ƒํƒœ๋ณ€ํ™”, ์šฐ์šธ, ๊ถŒ๋ฆฌ ์นจํ•ด ์—ฌ๋ถ€์™€ ์ง€์›์„œ๋น„์Šค์˜ ์ถฉ๋ถ„๋„์— ๋”ฐ๋ผ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๋‹ค. ์šธ๋ถ„์˜ ์˜ํ–ฅ์š”์ธ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•œ ๋‹จ๊ณ„์  ํšŒ๊ท€๋ถ„์„ ๊ฒฐ๊ณผ, ๊ฐ€๊ตฌํ”ผํ•ด๋‹จ๊ณ„๊ฐ€ ์ค‘์ฆ์ผ์ˆ˜๋ก, ์‹ ์ฒด์ ์œผ๋กœ ๋ถˆํŽธํ•˜๋‹ค๊ณ  ์‘๋‹ตํ• ์ˆ˜๋ก, ํŒ์ •๊นŒ์ง€ ๊ฑธ๋ฆฐ ์‹œ๊ฐ„์ด ๊ธธ์–ด์งˆ์ˆ˜๋ก, ๊ทธ๋ฆฌ๊ณ  ์šฐ์šธํ•˜๋‹ค๊ณ  ์‘๋‹ตํ• ์ˆ˜๋ก PTED์˜ ์ ์ˆ˜๊ฐ€ ๋” ๋†’์•„์กŒ๋‹ค. ์ถ”๊ฐ€๋กœ ์‘๋‹ต์ž๋ฅผ ์ค‘์ฆ๋„ ์ด์ƒ์˜ PTED ์ง‘๋‹จ๊ณผ ์•„๋‹Œ ์ง‘๋‹จ์œผ๋กœ ๋‚˜๋ˆ„์—ˆ์„ ๋•Œ, ํ”ผํ•ด ๋‹จ๊ณ„๊ฐ€ ์ค‘์ฆ์ธ ๊ฐ€๊ตฌ๋Š” 1.708๋ฐฐ, ๋†’์€ ์‹ ์ฒด์  ๋ถˆํŽธ๋„๋Š” 2.423๋ฐฐ, ์ง€์—ฐ๋œ ํŒ์ • ํ†ต๋ณด๋Š” 3.072๋ฐฐ๋กœ PTED ์ง‘๋‹จ์— ์†ํ•  ์˜ค์ฆˆ๋ฅผ ์ฆ๊ฐ€์‹œ์ผฐ์œผ๋ฉฐ, ์šฐ์šธ ์œ ๋ฌด์˜ ๊ฒฝ์šฐ ์šฐ์šธ์ด ์—†๋Š” ์‚ฌ๋žŒ์ด ์žˆ๋Š” ์‚ฌ๋žŒ๋ณด๋‹ค PTED ์ง‘๋‹จ์— ์†ํ•  ์˜ค์ฆˆ๊ฐ€ 12.820๋ฐฐ๋กœ ๋‚ฎ์•„์กŒ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์šธ๋ถ„์€ ์šฐ์šธ๊ณผ ์‚ถ์˜ ์งˆ์˜ ๊ด€๊ณ„๋ฅผ ๋งค๊ฐœํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ฐ€์Šต๊ธฐ์‚ด๊ท ์ œ ํ”ผํ•ด์ž์˜ ๊ฐœ์ธ์  ํŠน์„ฑ๊ณผ ์‚ฌํšŒ์ ์ธ ํ”ผํ•ด ๋Œ€์‘์— ๋Œ€ํ•œ ๊ฒฝํ—˜, ์ธ์‹์ด ์ •์‹ ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์šธ๋ถ„์„ ํ†ตํ•ด ๋ถ„์„ํ•œ ๊ตญ๋‚ด ์ตœ์ดˆ์˜ ์—ฐ๊ตฌ์ด๋‹ค. ์•ž์œผ๋กœ ์‚ฌํšŒ์  ์žฌ๋‚œ ๋ฐœ์ƒ ์‹œ์— ๋ณด๋‹ค ํ”ผํ•ด์ž ์ค‘์‹ฌ์ ์ธ ์ง€์› ๋Œ€์ฑ… ์ˆ˜๋ฆฝ์„ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ํ™œ์šฉํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ ๋ชฉ์  5 ์ œ 2 ์žฅ ๋ฌธํ—Œ๊ณ ์ฐฐ 6 ์ œ 1 ์ ˆ ์šธ๋ถ„(Embitterment) 6 1. ์šธ๋ถ„์˜ ๊ฐœ๋… 6 2. ์šธ๋ถ„์˜ ์˜ํ–ฅ์š”์ธ 10 ์ œ 3 ์žฅ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 17 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ์„ค๊ณ„ 17 1. ์—ฐ๊ตฌ ๋ชจํ˜• 17 2. ์—ฐ๊ตฌ ์ž๋ฃŒ ๋ฐ ์—ฐ๊ตฌ ๋Œ€์ƒ 19 3. ์—ฐ๊ตฌ ์งˆ๋ฌธ 21 4. ์—ฐ๊ตฌ ์œค๋ฆฌ 22 ์ œ 2 ์ ˆ ๋ถ„์„ ๋ฐฉ๋ฒ• 23 1. ๋ณ€์ˆ˜ ๊ตฌ์„ฑ 23 2. ํ†ต๊ณ„ ๋ถ„์„ 28 ์ œ 4 ์žฅ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 29 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ์ฐธ์—ฌ์ž์˜ ํŠน์„ฑ 29 1. ์ผ๋ฐ˜์  ํŠน์„ฑ 29 2. ์šธ๋ถ„์˜ ์ˆ˜์ค€๊ณผ ์šธ๋ถ„ ์ง‘๋‹จ์˜ ๋นˆ๋„ 33 ์ œ 2 ์ ˆ ๋ณ€์ˆ˜ ๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„ 37 ์ œ 3 ์ ˆ ์šธ๋ถ„์˜ ์˜ํ–ฅ์š”์ธ 39 1. ์šธ๋ถ„์˜ ์˜ํ–ฅ์š”์ธ์— ๋Œ€ํ•œ ๋‹จ๊ณ„์  ํšŒ๊ท€๋ถ„์„ 39 2. ์šธ๋ถ„์˜ ์˜ํ–ฅ์š”์ธ์— ๋Œ€ํ•œ ๋‹ค์ค‘๋กœ์ง€์Šคํ‹ฑํšŒ๊ท€๋ถ„์„ 42 ์ œ 4 ์ ˆ ์šธ๋ถ„์˜ ๋งค๊ฐœํšจ๊ณผ ๊ฒ€์ฆ 44 ์ œ 5 ์žฅ ๊ณ ์ฐฐ ๋ฐ ๋…ผ์˜ 48 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์— ๋Œ€ํ•œ ๊ณ ์ฐฐ 48 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์™€ ์˜์˜ 54 ์ œ 6 ์žฅ ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 56 ์ฐธ๊ณ ๋ฌธํ—Œ 59 Abstract 66Maste

    An Enhanced Approach to Place Recognition using Hierarchical Object Model

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2014. 2. ์ด๋ฒ”ํฌ.๋ณธ ๋…ผ๋ฌธ์€ ์žฅ์†Œ ์ธ์‹ ๋ฌธ์ œ๋ฅผ ํ’€๊ธฐ ์œ„ํ•˜์—ฌ 3๋‹จ๊ณ„์˜ ๊ฐ์ฒด ๊ณ„์ธต ๋ชจ๋ธ์„ ์ด์šฉํ•œ ์žฅ์†Œ ์ธ์‹ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ฐ์ฒด ๊ธฐ๋ฐ˜ ์žฅ์†Œ ์ธ์‹ ์—ฐ๊ตฌ์˜ ์—ฐ์žฅ์„ ์ƒ์—์„œ ๊ฐ์ฒด๋“ค์˜ ์ข…๋ฅ˜์™€ ๊ฐœ์ˆ˜, ๊ทธ๋ฆฌ๊ณ  ์œ„์น˜ ๊ด€๊ณ„๋ฅผ ํ™œ์šฉํ•œ ์žฅ์†Œ ์ธ์‹์œผ๋กœ ํ–ฅ์ƒ๋œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์˜€๋˜ ๊ฐ์ฒด ์ถœํ˜„ ๋ชจ๋ธ์€ ๊ฐ์ฒด์˜ ์กด์žฌ ์—ฌ๋ถ€๋กœ ์žฅ์†Œ๋ฅผ ํŒ๋‹จํ•˜์˜€์œผ๋‚˜ ๊ฐ์ฒด์˜ ์ข…๋ฅ˜๊ฐ€ ์ ๊ฑฐ๋‚˜ ๊ฐœ์ˆ˜๊ฐ€ ๋‹ค๋ฅธ ๊ฒฝ์šฐ์— ์žฅ์†Œ๋ฅผ ๊ตฌ๋ถ„ํ•˜๊ธฐ ์–ด๋ ค์šด ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ƒˆ๋กœ์šด ์žฅ์†Œ ์ธ์‹๋ฐฉ๋ฒ•์œผ๋กœ ๊ฐ์ฒด ๊ฐœ์ˆ˜ ๋ชจ๋ธ๊ณผ ๊ฐ์ฒด ์œ„์น˜ ๊ด€๊ณ„ ๋ชจ๋ธ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ฐ์ฒด ๊ฐœ์ˆ˜ ๋ชจ๋ธ์€ ๋ผํ”Œ๋ผ์Šค์˜ ์—ฐ์† ๋ฒ•์น™์„ ์ผ๋ฐ˜ํ™”ํ•œ ๊ณต์‹์„ ์ด์šฉํ•˜์—ฌ ๊ฐ์ฒด์˜ ๊ฐœ์ˆ˜๋ฅผ ๊ณ ๋ คํ•œ ์žฅ์†Œ ์ธ์‹์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์—ฌ, ๊ฐ์ฒด ์ถœํ˜„ ๋ชจ๋ธ์—์„œ ๊ตฌ๋ถ„ํ•˜์ง€ ๋ชปํ–ˆ๋˜ ์žฅ์†Œ๋ฅผ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด ๋ชจ๋ธ์—์„œ๋Š” ์žฅ์†Œ์˜ ์œ ์‚ฌ๋„๋ฅผ ๋น„๊ตํ•˜๊ธฐ ์œ„ํ•ด ๋‚˜์ด๋ธŒ ๋ฒ ์ด์ฆˆ ๋ถ„๋ฅ˜ ์ด๋ก ๊ณผ ํ™•๋ฅ  ์ด๋ก ์„ ์ด์šฉํ•˜์˜€๋‹ค. ๊ฐ์ฒด ์œ„์น˜ ๊ด€๊ณ„ ๋ชจ๋ธ์€ ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜์˜€๋˜ ๊ฐ์ฒด ๊ฐ„ ์œ ํด๋ฆฌ๋“œ ๊ฑฐ๋ฆฌ ์ •๋ณด๋ฅผ ์ด์šฉํ•œ ์žฅ์†Œ ์ธ์‹ ๋ฐฉ๋ฒ•์—์„œ ๋ฒ—์–ด๋‚˜ ์žฅ์†Œ์— ์กด์žฌํ•˜๋Š” ๊ฐ์ฒด๋“ค์˜ ๊ธฐํ•˜ํ•™์ ์ธ ์œ„์น˜ ๊ด€๊ณ„๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์„œ๋กœ ๊ฐ„์˜ ๊ฑฐ๋ฆฌ์™€ ๊ฐ๋„ ๋“ฑ์„ ์ด์šฉํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๊ทธ๋ž˜ํ”„ ์ด๋ก ์„ ์ด์šฉํ•˜์—ฌ ๊ทธ๋ž˜ํ”„๊ฐ„์˜ ์œ ์‚ฌ๋„๋กœ ์žฅ์†Œ์˜ ์œ ์‚ฌ๋„๋ฅผ ๊ณ„์‚ฐํ•˜์˜€์œผ๋ฉฐ ํ—๊ฐ€๋ฆฌ์•ˆ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ๋Œ€์‘ ๊ด€๊ณ„๋ฅผ ์•Œ ์ˆ˜ ์—†๋Š” ๋ถ„ํฌ ๊ฐ„์˜ ๋ณ€ํ™˜ ํ–‰๋ ฌ์„ ๊ตฌํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์žฅ์†Œ ์ธ์‹์˜ 3๋‹จ๊ณ„ ๋ชจ๋ธ์„ ํ†ตํ•ด์„œ ๋‚ฎ์€ ๋‹จ๊ณ„์—์„œ ๋†’์€ ๋‹จ๊ณ„๋กœ ๊ฐˆ์ˆ˜๋ก ๊ธฐ์กด์— ๊ตฌ๋ถ„ํ•˜์ง€ ๋ชปํ–ˆ๋˜ ์žฅ์†Œ๋ฅผ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ์œผ๋ฉฐ ๋กœ๋ด‡์ด ๋ฐ›์•„๋“ค์ด๋Š” ์ •๋ณด๊ฐ€ ์ผ๋ถ€๋ถ„ ๋ˆ„๋ฝ๋˜์–ด๋„ ๊ฒฌ๊ณ ํ•œ ์žฅ์†Œ ์ธ์‹์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๋˜ํ•œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ์‹คํ—˜์„ ํ†ตํ•ด ์žฅ์†Œ ์ธ์‹ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋˜์—ˆ์Œ์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ๋™๊ธฐ 1 ์ œ 2 ์ ˆ ๊ด€๋ จ ์—ฐ๊ตฌ 3 ์ œ 3 ์ ˆ ๊ธฐ์—ฌ๋„ 6 ์ œ 4 ์ ˆ ๋…ผ๋ฌธ ๊ตฌ์„ฑ 7 ์ œ 2 ์žฅ ๊ฐ์ฒด ๊ธฐ๋ฐ˜ ์žฅ์†Œ ์ธ์‹ ๋ฌธ์ œ 9 ์ œ 1 ์ ˆ ๊ฐ€์ • ๋ฐ ํ‘œ๊ธฐ๋ฒ• 9 ์ œ 2 ์ ˆ ๋ฌธ์ œ ์ •์˜ 13 ์ œ 3 ์ ˆ ๊ฐ์ฒด ๊ธฐ๋ฐ˜ ์žฅ์†Œ ์ธ์‹์˜ ๋‹จ๊ณ„๋ณ„ ์ ‘๊ทผ 15 ์ œ 3 ์žฅ ์ผ๋ฐ˜์ ์ธ ๊ฒฝ์šฐ๋ฅผ ๊ณ ๋ คํ•œ ํ™•์žฅ๋œ ๊ฐ์ฒด ๊ฐœ์ˆ˜ ๋ชจ๋ธ 19 ์ œ 1 ์ ˆ ๋‚˜์ด๋ธŒ ๋ฒ ์ด์ฆˆ ๋ถ„๋ฅ˜๊ธฐ 19 ์ œ 2 ์ ˆ ๊ธฐ์กด์˜ ๊ฐ์ฒด ์ถœํ˜„ ๋ชจ๋ธ๊ณผ ํ•œ๊ณ„์  23 ์ œ 3 ์ ˆ ์ผ๋ฐ˜์ ์ธ ๊ฐ์ฒด ๊ฐœ์ˆ˜๋ฅผ ๊ณ ๋ คํ•œ ํ™•์žฅ๋œ ๋ชจ๋ธ 30 ์ œ 4 ์žฅ ๊ทธ๋ž˜ํ”„ ์œ ์‚ฌ๋„๋ฅผ ์ด์šฉํ•œ ํ–ฅ์ƒ๋œ ๊ฐ์ฒด ์œ„์น˜ ๊ด€๊ณ„ ๋ชจ๋ธ 34 ์ œ 1 ์ ˆ ๊ธฐ์กด์˜ ๊ฐ์ฒด ๊ฐ„ ๊ฑฐ๋ฆฌ๋ฅผ ์ด์šฉํ•œ ๊ด€๊ณ„ ๋ชจ๋ธ 34 ์ œ 2 ์ ˆ ๊ฐ์ฒด ๋งต์˜ ๋ณ€ํ™˜ ๋ฐ ๋ณ€ํ™˜ ํ–‰๋ ฌ์˜ ์ถ”์ • 41 ์ œ 3 ์ ˆ ๊ทธ๋ž˜ํ”„๋ฅผ ์ด์šฉํ•œ ์œ ์‚ฌ๋„ ๊ณ„์‚ฐ 66 ์ œ 4 ์ ˆ ๊ทธ๋ž˜ํ”„ ์œ ์‚ฌ๋„๋ฅผ ์ด์šฉํ•œ ํ–ฅ์ƒ๋œ ์žฅ์†Œ ์ธ์‹ ๋ชจ๋ธ 75 ์ œ 5 ์žฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ์‹คํ—˜ ๊ฒฐ๊ณผ 77 ์ œ 1 ์ ˆ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ 77 ์ œ 2 ์ ˆ ์‹คํ—˜ ๊ฒฐ๊ณผ 84 ์ œ 6 ์žฅ ๊ฒฐ๋ก  100 ์ œ 7 ์žฅ ์ฐธ๊ณ  ๋ฌธํ—Œ 101Maste

    ๋ณ€ํ™”ํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ ๋”ฅ๋Ÿฌ๋‹ ํŠน์ง•์„ ์ด์šฉํ•œ ์‹œํ€€์Šค ๊ธฐ๋ฐ˜์˜ ์žฅ์†Œ ์ธ์‹ ๋ฐฉ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2018. 2. ์ด๋ฒ”ํฌ.๋น„์ „ ๊ธฐ๋ฐ˜์˜ ์žฅ์†Œ ์ธ์‹ ๊ธฐ์ˆ ์€ ์นด๋ฉ”๋ผ๋ฅผ ํ†ตํ•ด ํš๋“ํ•œ ์ด๋ฏธ์ง€๋กœ๋ถ€ํ„ฐ, ๋กœ๋ด‡์ด ์ž์‹ ์ด ๋ฐฉ๋ฌธํ–ˆ๋˜ ์žฅ์†Œ๋ฅผ ์ธ์‹ํ•˜๋Š” ๊ธฐ์ˆ ์ด๋‹ค. ์ด๋ฏธ์ง€๋Š” ์ฃผ๋ณ€ ํ™˜๊ฒฝ์— ๋Œ€ํ•œ ํ’๋ถ€ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์ง€๋งŒ ๊ณ„์ ˆ์ด๋‚˜ ๋‚ ์”จ ๋“ฑ์˜ ์˜ํ–ฅ์„ ๋งŽ์ด ๋ฐ›๋Š” ํŠน์„ฑ์ด ์žˆ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์€ ์ด ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋ณ€ํ™”ํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ ์ด๋ฏธ์ง€ ๊ธฐ๋ฐ˜์œผ๋กœ ์žฅ์†Œ๋ฅผ ์ธ์‹ํ•˜๋Š” ๋ฌธ์ œ์— ๋Œ€ํ•˜์—ฌ ๋‹ค๋ฃฌ๋‹ค. ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€ ์ ‘๊ทผ๋ฒ•์„ ์ œ์‹œํ•˜๋Š”๋ฐ ์ฒซ ๋ฒˆ์งธ๋Š” ๋”ฅ๋Ÿฌ๋‹์„ ํ™œ์šฉํ•˜์—ฌ ์ด๋ฏธ์ง€๋กœ๋ถ€ํ„ฐ ํ™˜๊ฒฝ ๋ณ€ํ™”์— ๊ฐ•์ธํ•œ ํŠน์ง•์„ ์ถ”์ถœํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๊ณ  ๋‘ ๋ฒˆ์งธ๋Š” ๋กœ๋ด‡์˜ ์œ„์น˜ ๋ฐ ์†๋„ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ด๋ฏธ์ง€ ๊ฐ„ ๊ด€๊ณ„๋ฅผ ์ด์šฉํ•˜๋Š” ์‹œํ€€์Šค ๊ธฐ๋ฐ˜์˜ ์žฅ์†Œ ์ธ์‹ ๋ฐฉ๋ฒ•์ด๋‹ค. ์˜คํ† ์ธ์ฝ”๋”(Auto-encoder)๋Š” ์ธ์ฝ”๋”ฉ ๊ณผ์ •๊ณผ ๋””์ฝ”๋”ฉ ๊ณผ์ •์œผ๋กœ ์ด๋ฃจ์–ด์ง„ ๋”ฅ๋Ÿฌ๋‹ ๊ตฌ์กฐ๋กœ, ์ด๋ฏธ์ง€ ์ „์ฒด์˜ ์ •๋ณด๋ฅผ ์••์ถ•ํ•˜๋Š” ํŠน์ง•์„ ์ถ”์ถœํ•  ์ˆ˜ ์žˆ๋‹ค. ํ™˜๊ฒฝ ์ •๋ณด๋ฅผ ํฌํ•จํ•˜๋Š” ์ƒˆ๋กœ์šด ์˜คํ† ์ธ์ฝ”๋” ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•˜์—ฌ ํ™˜๊ฒฝ์˜ ๋ณ€ํ™”๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๊ตฌ์กฐ๋Š” ๋‹ค์–‘ํ•œ ํ™˜๊ฒฝ์˜ ์ด๋ฏธ์ง€๋ฅผ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ํ™˜๊ฒฝ์˜ ์˜ํ–ฅ์„ ๋ฐ›์ง€ ์•Š๋Š” ์ด๋ฏธ์ง€๋ฅผ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ์–ด์„œ ๋ณ€ํ™”์— ๊ฐ•์ธํ•œ ํŠน์ง•์„ ์ถ”์ถœํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ Variational ์˜คํ† ์ธ์ฝ”๋” ๊ตฌ์กฐ๋ฅผ ๋ณ€ํ˜•ํ•œ ์ƒˆ๋กœ์šด ํŠน์ง• ์ถ”์ถœ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์žฅ์†Œ๋Š” ํ™˜๊ฒฝ ๋ณ€ํ™”์— ๋ณ€ํ•˜์ง€ ์•Š๋Š” ๋ถ€๋ถ„๊ณผ ๋ณ€ํ™”์— ๋ฏผ๊ฐํ•œ ๋ถ€๋ถ„์œผ๋กœ ๋‚˜๋ˆ„์–ด ์žˆ๋‹ค๊ณ  ๊ฐ€์ •ํ•˜๊ณ  Variational Bayes ์ด๋ก ์„ ์ „๊ฐœํ•˜์—ฌ ํ•™์Šต์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ๋ณ€ํ™”์— ๊ฐ•์ธํ•œ ๋ถ€๋ถ„์„ ์ด๋ฏธ์ง€ ํŠน์ง•์œผ๋กœ ํ™œ์šฉํ•˜์—ฌ ๊ณ„์ ˆ์ด๋‚˜ ๋‚ ์”จ ๋ณ€ํ™”์—๋„ ์žฅ์†Œ๋ฅผ ์ •ํ™•ํ•˜๊ฒŒ ์ธ์‹ํ•  ์ˆ˜ ์žˆ๋‹ค. ์‹œํ€€์Šค ๊ธฐ๋ฐ˜์˜ ์žฅ์†Œ ์ธ์‹ ๋ฐฉ๋ฒ•์€ ๋ณ€ํ™”๊ฐ€ ํฐ ํ™˜๊ฒฝ์—์„œ ์ด๋™ ๋กœ๋ด‡์˜ ์œ„์น˜์™€ ์†๋„๋กœ๋ถ€ํ„ฐ ์ด๋™ ๋ฒ”์œ„๋ฅผ ๊ณ„์‚ฐํ•˜์—ฌ ์‹œํ€€์Šค ์ •๋ณด๋กœ ์žฅ์†Œ๋ฅผ ์ธ์‹ํ•˜๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” Dynamic programming์„ ์ด์šฉํ•˜์—ฌ ๋กœ๋ด‡์˜ ์†๋„๊ฐ€ ๋ณ€ํ™”ํ•˜๊ฑฐ๋‚˜ ์—ญ์ฃผํ–‰ํ•˜๋Š” ํ™˜๊ฒฝ์—์„œ๋„ ์ •ํ™•ํžˆ ๊ฒฝ๋กœ๋ฅผ ์ถ”์ •ํ•˜์—ฌ ์žฅ์†Œ๋ฅผ ์ธ์‹ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋˜ํ•œ Glocal alignment ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ์ผ๋ถ€๋ถ„์˜ ๊ฒฝ๋กœ๋ถ€ํ„ฐ ๋จผ์ € ์ฐพ์€ ํ›„ ์ „์—ญ์ ์ธ ๊ด€์ ์—์„œ ๋กœ๋ด‡์˜ ๊ฒฝ๋กœ๋ฅผ ์ถ”์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ์ค‘๊ฐ„์— ์‹œํ€€์Šค๊ฐ€ ๋Š๊ธฐ๊ฑฐ๋‚˜ ์ž˜๋ชป๋œ ๋ฐฉํ–ฅ์œผ๋กœ ์ถ”์ •ํ•˜๋Š” ์˜ค๋ฅ˜๋ฅผ ์ตœ์†Œํ™”ํ•˜์—ฌ ์žฅ์†Œ ์ธ์‹์˜ ์„ฑ๋Šฅ์„ ๋†’์ผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์˜ ํƒ€๋‹น์„ฑ์„ ์ฆ๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ๋‚ ์”จ, ๊ณ„์ ˆ, ์‹œ๊ฐ„์˜ ๋ณ€ํ™”์™€ ๊ฐ™์€ ํ™˜๊ฒฝ ๋ณ€ํ™”์™€ ๋กœ๋ด‡์˜ ๊ฐ€์†, ๊ฐ์†, ์ •์ง€ ์ƒํƒœ์™€ ๊ฐ™์€ ์†๋„ ๋ณ€ํ™” ํ•˜์—์„œ ์ˆ˜์ง‘ํ•œ ๊ณต์ธ๋œ ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉํ•˜์—ฌ ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ํ™˜๊ฒฝ ๋ณ€ํ™”์— ๊ฐ•์ธํ•œ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ํŠน์ง• ์ถ”์ถœ ๋ฐฉ๋ฒ•๊ณผ ์†๋„ ๋ณ€ํ™”์— ๊ฐ•์ธํ•œ ์‹œํ€€์Šค ๊ธฐ๋ฐ˜ ์žฅ์†Œ ์ธ์‹ ๋ฐฉ๋ฒ•์˜ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜์—ฌ ๋ณธ ๋…ผ๋ฌธ์˜ ํƒ€๋‹น์„ฑ์„ ์ฆ๋ช…ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.Visual place recognition is one of the most fundamental requirements for autonomous navigation, simultaneous localization and mapping (SLAM) for mobile robots, as it can increase the precision of a pose estimate and global localization results by correcting the error accumulation problem. As cameras have become more compact and accurate while providing rich qualitative information about the environment, they have been successfully used as the primary sensor for learning and recognizing places. However, visual information is ineffective when dealing with extreme perceptual changes such as weather or seasonal variations. This dissertation presents visual place recognition methods for robots operating in changing environments. To solve the problem, condition-invariant feature extraction methods using deep architectures and sequence-based place recognition algorithms that are robust to both environmental changes and vehicle speed variation are presented. An auto-encoder, a deep learning structure which consists of the encoder and the decoder networks, is employed to predict environmental changes by containing the image and context information. This structure can reconstruct images of various environments, and generate condition-invariant images for place recognition in changing environments. Another method is variational Bayesian approach to robust feature extraction. Under the assumption that a latent representation of the variational auto-encoder can be divided into condition-invariant and condition-sensitive features, a new structure of the variational auto-encoder is proposed and a variational lower bound is derived to train the model. After training the model, condition-invariant features are extracted from test images to calculate the similarity between them, and the places can be recognized even in a severe environmental changes. Sequence-based place recognition approaches are back-end methods to overcome perceptual changes. A new dynamic programming method is proposed to align sequences of image features. As this method considers not only environmental variations, but also the motion constraint of the mobile robot, places from changing environment can be successfully recognized by finding the most likely path sequence. Moreover, a glocal sequence alignment which combines the global and local approaches is proposed. The proposed method detects local fragments from the similarity matrix and calculates the optimal global path by chaining them. As the chained fragments provide reliable clues to find the global path, false matchings on featureless structures or partial failures during the alignment could be recovered. Experiments were performed to show the effectiveness of the proposed methods. Datasets from different environmental changes such as weather, day/night, seasons were used to compare proposed methods with existing place recognition algorithms. Experimental results were analyzed by precision-recall curves, and the proposed techniques showed improved results than existing methods.1 Introduction 1 1.1 Background 2 1.2 Visual Place Recognition Problem 3 1.3 Scope 4 1.4 Contributions 5 1.5 Dissertation Outline 7 2 Literature Review 9 2.1 Overview of Visual Place Recognition System 9 2.2 Feature Extraction 10 2.2.1 Local Feature Descriptors 11 2.2.2 Global Descriptors 12 2.2.3 Deep Architectures 13 2.3 Belief Generation 14 2.4 Evaluation 16 3 Appearance Change Prediction Using Deep Auto-encoder 19 3.1 Introduction 20 3.2 Related Work 22 3.3 Appearance Change Prediction Using Convolutional Auto-encoder 24 3.3.1 Preliminaries 24 3.3.2 CAE Learning for Appearance Change Prediction 27 3.3.3 Condition-invariant Image Generation 29 3.3.4 Feature Extraction and Comparison 30 3.4 Experimental Results 31 3.4.1 Experimental Setup 31 3.4.2 Reconstruction Test 33 3.4.3 Effect of the Training Set 39 3.4.4 Precision-Recall Analysis 41 3.4.5 Improving SeqSLAM 45 3.5 Summary 45 4 Variational Bayesian Approach to Condition-invariant Feature Extraction 49 4.1 Introduction 50 4.2 Variational Autoencoder for Feature Extraction 51 4.2.1 Basic Variational Autoencoder 51 4.2.2 VAE using Context Information 53 4.2.3 VAE using Context and Position Information 55 4.2.4 Condition-invarinat Feature Extraction 57 4.3 Experimental Results 58 4.3.1 Experimental Setup 58 4.3.2 Visualization Results 59 4.3.3 Precision-recall Analysis 65 4.4 Summary 66 5 Dynamic Programming Approach to Visual Place Recognition in Changing Environment 70 5.1 Introduction 70 5.2 Constructing Similarity Matrix Using Convolutional Auto-encoder 72 5.3 Sequence Matching Based on Dynamic Programming 74 5.3.1 Smith-Waterman Algorithm 74 5.3.2 Proposed Method 75 5.4 Simulation Results 78 5.4.1 Acceleration and Deceleration 78 5.4.2 Stop Motion 79 5.4.3 Reverse Moving 79 5.4.4 Combined General Situation 80 5.5 Experimental Results 82 5.6 Summary 85 6 Glocal Alignment for Vision-based Place Recognition under Severe Appearance Changes 87 6.1 Introduction 87 6.2 Problem Definition 90 6.3 Finding Local Fragments from Similarity Matrix 91 6.4 Rectangle Chaining Algorithm 91 6.5 Global Alignment Using Local Anchors 94 6.6 Experimental Results 95 6.7 Summary 97 7 Conclusion 101 Abstract (In Korean) 123Docto
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