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    ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ๊ณผ ์ง๋ฌดํŠน์„ฑ ๋ฐ ์กฐ์งํŠน์„ฑ์˜ ๊ด€๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์‚ฐ์—…๊ต์œก๊ณผ, 2012. 2. ์ด์ฐฌ.์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ๊ณผ ์ง๋ฌดํŠน์„ฑ ๋ฐ ์กฐ์งํŠน์„ฑ์˜ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š” ๋ฐ ์žˆ์—ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•œ ๊ตฌ์ฒด์ ์ธ ์—ฐ๊ตฌ ๋ชฉํ‘œ๋Š” ์ฒซ์งธ, ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ ์ˆ˜์ค€์„ ๊ตฌ๋ช…ํ•˜๊ณ , ๋‘˜์งธ, ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž๊ฐ€ ์ธ์‹ํ•œ ์ง๋ฌดํŠน์„ฑ๊ณผ ์กฐ์งํŠน์„ฑ ์ˆ˜์ค€์„ ๊ตฌ๋ช…ํ•˜๋ฉฐ, ์…‹์งธ, ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ๊ณผ ์ง๋ฌดํŠน์„ฑ์˜ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๊ณ , ๋„ท์งธ, ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ๊ณผ ์กฐ์งํŠน์„ฑ์˜ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๋ชจ์ง‘๋‹จ์€ ๊ตญ๋‚ด ๋Œ€๊ธฐ์—…์— ์ข…์‚ฌ ์ค‘์ธ ๋ชจ๋“  ๊ทผ๋กœ์ž๋“ค์ด๋ฉฐ, ์ฝ”์ฐธ๋น„์ฆˆ์—์„œ ์ œ๊ณตํ•˜๋Š” 1000๋Œ€ ๊ธฐ์—…์— ์ข…์‚ฌํ•˜๋Š” ๊ทผ๋กœ์ž๋ฅผ ๋ชฉํ‘œ๋ชจ์ง‘๋‹จ์œผ๋กœ ์„ค์ •ํ•˜์˜€๋‹ค. ํ‘œ๋ณธ์€ 1000๋Œ€ ๊ธฐ์—…์˜ ์—…์ข… ๋ถ„ํฌ๊ฐ€ ๋ชจ๋‘ ํฌํ•จ๋˜๋„๋ก 37๊ฐœ ๋Œ€๊ธฐ์—…์„ ์„ ์ •ํ•˜๊ณ , ์œ ์˜ํ‘œ์ง‘์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ์กฐ์‚ฌ๋„๊ตฌ๋Š” ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ, ์ง๋ฌดํŠน์„ฑ, ์กฐ์งํŠน์„ฑ(๋ณ€ํ˜์  ๋ฆฌ๋”์‹ญ, ์กฐ์งํ•™์Šต์ง€ํ–ฅ์„ฑ, ์กฐ์งํ›„์›) ๊ทธ๋ฆฌ๊ณ  ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ ๋“ฑ์˜ 6๊ฐ€์ง€ ์˜์—ญ ์ด 87๋ฌธํ•ญ์œผ๋กœ 5์  Likert ์ฒ™๋„๋กœ ๊ตฌ์„ฑ๋œ ์งˆ๋ฌธ์ง€๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ ์ธก์ •๋„๊ตฌ๋Š” Luthans, Youssef์™€ Avolio(2007)์˜ PCQ(Psychological Capital Questionnaire)๋ฅผ ์‚ฌ์šฉํ•˜์˜€๊ณ , ์ง๋ฌดํŠน์„ฑ์€ Hackman & Oldham(1980)์˜ JDS(Job Diagnostic Survey)๋ฅผ ์ด์ˆ˜ํ™”(2006)๊ฐ€ ๋ฒˆ์•ˆํ•œ ๋„๊ตฌ๋ฅผ, ์กฐ์งํŠน์„ฑ ๊ฐ€์šด๋ฐ ๋ณ€ํ˜์  ๋ฆฌ๋”์‹ญ์€ Bass์™€ Avolio (1995)์˜ MLQ(Multiโ€“factor Leadership Questionnaire) 5-45๋ฅผ ๊น€์ •๋‚จ(2009)์ด ๋ฒˆ์•ˆํ•œ ๋„๊ตฌ๋ฅผ, ์กฐ์งํ•™์Šต์ง€ํ–ฅ์„ฑ์€ ๊น€๊ฐ•ํ˜ธ(2008)์˜ ๋„๊ตฌ๋ฅผ, ์กฐ์งํ›„์›์€ Eisenberger et al(2001)์˜ ๋„๊ตฌ๋ฅผ ์ง์ ‘ ๋ฒˆ์•ˆํ•˜์—ฌ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์˜ˆ๋น„์กฐ์‚ฌ์™€ ๋ณธ์กฐ์‚ฌ๋ฅผ ํ†ตํ•˜์—ฌ ๋ชจ๋“  ์ธก์ •๋„๊ตฌ์˜ ์‹ ๋ขฐ๋„์™€ ํƒ€๋‹น๋„๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ž๋ฃŒ์ˆ˜์ง‘์€ 11์›” 8์ผ๋ถ€ํ„ฐ 11์›” 28์ผ๊นŒ์ง€ ์šฐํŽธ ๋ฐ ์ด๋ฉ”์ผ ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ, 37๊ฐœ ๊ธฐ์—…์— ์„ค๋ฌธ์ง€ 800๋ถ€๋ฅผ ๋ฐฐํฌํ•˜์—ฌ ์ด 468๋ถ€๊ฐ€ ํšŒ์ˆ˜๋˜์–ด ํšŒ์ˆ˜์œจ์€ 58.5%์˜€๋‹ค. ์ด ์ค‘์—์„œ ๋ถˆ์„ฑ์‹ค์‘๋‹ต, ์ค‘๋ณต์‘๋‹ต, ๋ฏธ์‘๋‹ต ์ž๋ฃŒ 41๋ถ€๋ฅผ ์ œ์™ธํ•œ 427๋ถ€๋ฅผ ์ตœ์ข… ๋ถ„์„์— ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ์ด์— ๋”ฐ๋ฅธ ์œ ํšจ์‘๋‹ต๋ฅ ์€ 53.3%์˜€๋‹ค. ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋Š” SPSS for Window 18.0 ํ”„๋กœ๊ทธ๋žจ์„ ํ™œ์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ํ†ต๊ณ„์  ์œ ์˜๋ฏธ์„ฑ์€ 0.05๋ฅผ ๊ธฐ์ค€์œผ๋กœ ํŒ๋‹จํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ๋กœ๋ถ€ํ„ฐ ์–ป์–ด์ง„ ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ตญ๋‚ด ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ ์ˆ˜์ค€์€ 5์  ํ™˜์‚ฐํ‰๊ท ์œผ๋กœ 3.65์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๊ณ , ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ ํ•˜์œ„์š”์ธ๋ณ„๋กœ๋Š” ์ž๊ธฐํšจ๋Šฅ๊ฐ์€ 3.70์ , ํฌ๋ง์€ 3.74์ , ๋‚™๊ด€์ฃผ์˜๋Š” 3.64์ , ํšŒ๋ณต๋ ฅ์€ 3.52์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ์€ ์„ฑ๋ณ„, ํ•™๋ ฅ, ๊ณ ์šฉํ˜•ํƒœ, ์ง๊ธ‰์— ๋”ฐ๋ผ์„œ๋Š” ์œ ์˜๋ฏธํ•œ ์ˆ˜์ค€ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚ฌ์œผ๋‚˜, ์—ฐ๋ น, ์ง๋ฌด์— ์žˆ์–ด์„œ๋Š” ์œ ์˜๋ฏธํ•œ ์ˆ˜์ค€ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค. ๋‘˜์งธ, ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž๊ฐ€ ์ธ์‹ํ•œ ์ง๋ฌดํŠน์„ฑ ์ˆ˜์ค€์€ 5์  ํ™˜์‚ฐํ‰๊ท ์œผ๋กœ ๊ธฐ์ˆ ๋‹ค์–‘์„ฑ์€ 3.51์ , ๊ณผ์—…์ •์ฒด์„ฑ์€ 3.51์ , ๊ณผ์—…์ค‘์š”์„ฑ์€ 3.59์ , ์ž์œจ์„ฑ์€ 3.21์ , ํ”ผ๋“œ๋ฐฑ์€ 3.51์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž๊ฐ€ ์ธ์‹ํ•œ ์กฐ์งํŠน์„ฑ ์ˆ˜์ค€์€ 5์  ํ™˜์‚ฐํ‰๊ท ์œผ๋กœ ๋ณ€ํ˜์  ๋ฆฌ๋”์‹ญ์€ 3.62์ , ์กฐ์งํ•™์Šต์ง€ํ–ฅ์„ฑ์€ 3.51์ , ์กฐ์งํ›„์›์€ 3.24์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ๊ณผ ์ง๋ฌดํŠน์„ฑ์€ ์œ ์˜๋ฏธํ•œ ์ •์  ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ€์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ์— ๋Œ€ํ•œ ์ง๋ฌดํŠน์„ฑ์˜ ์„ค๋ช…๋ ฅ์€ ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ๋ณ€์ธ(์„ฑ๋ณ„, ํ•™๋ ฅ, ๊ณ ์šฉํ˜•ํƒœ, ์ง๊ธ‰)์˜ ํšจ๊ณผ๋ฅผ ์ œ๊ฑฐํ•˜๊ณ  21.5%์˜ ์œ ์˜๋ฏธํ•œ ์„ค๋ช…๋ ฅ์ด ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ณผ์—…์ค‘์š”์„ฑ(ฮฒ=0.258, p<0.001), ํ”ผ๋“œ๋ฐฑ(ฮฒ=0.189, p<0.001), ์ž์œจ์„ฑ(ฮฒ=0.159, p<0.01), ๊ณผ์—…์ •์ฒด์„ฑ(ฮฒ=0.111, p<0.05)์€ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ์— ์ •์  ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋‚˜, ๊ธฐ์ˆ ๋‹ค์–‘์„ฑ์€ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋„ท์งธ, ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ๊ณผ ์กฐ์งํŠน์„ฑ์€ ์œ ์˜๋ฏธํ•œ ์ •์  ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ๋Œ€๊ธฐ์—… ๊ทผ๋กœ์ž์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ์— ๋Œ€ํ•œ ์กฐ์งํŠน์„ฑ์˜ ์„ค๋ช…๋ ฅ์€ ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ๋ณ€์ธ(์„ฑ๋ณ„, ํ•™๋ ฅ, ๊ณ ์šฉํ˜•ํƒœ, ์ง๊ธ‰)์˜ ํšจ๊ณผ๋ฅผ ์ œ๊ฑฐํ•˜๊ณ  19.5%์˜ ์œ ์˜๋ฏธํ•œ ์„ค๋ช…๋ ฅ์ด ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณ€ํ˜์  ๋ฆฌ๋”์‹ญ(ฮฒ=0.276, p<0.001)๊ณผ ์กฐ์งํ›„์›(ฮฒ=0.296, p<0.001)์€ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ์— ์ •์  ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋‚˜, ์กฐ์งํ•™์Šต์ง€ํ–ฅ์„ฑ์€ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด์™€ ๊ฐ™์€ ๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ โ‘  ์กฐ์ง์˜ ๋ฌธํ™”์  ํŠน์„ฑ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋ฌผ๋ฆฌ์  ํŠน์„ฑ๊นŒ์ง€ ํ•จ๊ป˜ ๊ณ ๋ คํ•˜์—ฌ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ๊ตฌ๋ช…ํ•˜๋Š” ์—ฐ๊ตฌ, โ‘ก ์ง๋ฌดํŠน์„ฑ๊ณผ ์กฐ์งํŠน์„ฑ ์ด์™ธ์˜ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ ์˜ํ–ฅ์š”์ธ์„ ๊ตฌ๋ช…ํ•˜๋Š” ์—ฐ๊ตฌ, โ‘ข ํ•™์Šต๋ชฐ์ž…, ํ•™์Šต๋™๊ธฐ, ํ•™์Šต์ „์ด์™€ ๊ฐ™์ด HRD ๋ถ„์•ผ์˜ ๊ด€์‹ฌ ๋ณ€์ธ๋“ค๊ณผ ๊ธ์ •์‹ฌ๋ฆฌ์ž๋ณธ์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ํ›„์†์—ฐ๊ตฌ๋กœ ์ œ์–ธํ•˜์˜€๋‹ค.The purpose of this study is to determine the relationship among PsyCap(Positive Psychological Capital), job characteristics and organizational characteristics of large-corporation employees. The specific objectives of this study are set as follows: First is to determine the PsyCap level of large-corporation employees. Second is to determine the job characteristics level and organizational characteristics level of large-corporation employees. Third is to determine the relationship between PsyCap and job characteristics of large- corporation employees. Last is to determine the relationship between PsyCap and organizational characteristics of large-corporation employees. The population of this study is composed mainly of all the employees in large corporations. However, in this study, employees in large corporations are defined as the employees who work for top 1000 companies listed by the Korea Chamber of Commerce and Industry. Using purposeful sampling technique, 800 corporate employees of 37 companies are taken as samples for the study. The Questionnaire is consist of a total of 87 questions divided into 6 sections such as PsyCap, job characteristics, transformational leadership, learning orientation, perceived organizational support, and demographic characteristics. The tool for measuring PsyCap is adapted from Luthans, Youssef & Avolio (2007) PCQ (Psychological Capital Questionnaire), the job characteristics scale is adapted from Lee (2006) research which had translated Hackman & Oldham (1980) JDS (Job Diagnostic Survey), the transformational leadership scale is adapted from Kim (2009) research which had translated Bass & Avolio (1995) MLQ (Multiโ€“factor Leadership Questionnaire) 5-45. Moreover, the learning orientation scale is adapted from Kim (2008) research and the perceived organizational support scale is adapted from Eisenberger et al. (2001) research. The data are collected through mails and e-mails from the 8th of November to the 28th of November, 2011. A total of 468 out of 800 questionnaires are returned giving a return rate of 58.5%. 427 of which are used for analysis arriving on a valid data rate of 53.3% after data cleaning. The collected data is analyzed through SPSS 18.0 program for Windows. The significance level of statistics is set to 0.05. Based on the findings of the study, the conclusions are as follows: First, the average level of PsyCap of large-corporation employees is 3.65 in 5 points converted. Specifically, the average level of self-efficacy is 3.70, the average level of hope is 3.74, the average level of optimism is 3.64, and the average level of resilience is 3.52. The level of PsyCap of large-corporation employees by sex, education, employment type and position also varies significantly. Second, as for job characteristics level perceived by large-corporation employees in 5 point converted, the average level of skill variety is 3.51. The average level of task identity, task significance, autonomy and feedback are 3.51, 3.59, 3.21 and 3.51 respectively. As for organizational characteristics level perceived by large-corporation employees in 5 point converted, the average level of transformational leadership, learning orientation and organizational support are 3.62, 3.51 and 3.24 respectively. Third, results show that PsyCap of large-corporation employees is positively correlated with job characteristics. Job characteristics affect PsyCap of large-corporation employees and R2=0.215. Task significance(ฮฒ=0.258, p<0.001), feedback(ฮฒ=0.189, p<0.001), autonomy(ฮฒ=0.159, p<0.01), and task identity(ฮฒ =0.111, p<0.05) are prediction variables which explained PysCap significantly. Forth, the PsyCap of large-corporation employees is positively correlated with organizational characteristics. Organizational characteristics affect PsyCap of large-corporation employees and R2=0.195. Transformational leadership(ฮฒ=0.276, p<0.001) and perceived organizational support(ฮฒ=0.296, p<0.001) are prediction variables which explained PysCap significantly. Based on the conclusions aforementioned, some recommendations for future researches are suggested. First, further research needs to investigate on the relationship between PsyCap and organizational characteristics including objective organizational characteristics such as size and location. Second, more variables which affects PsyCap should be taken into consideration as well. Last, further research also needs to investigate on the relationship between PsyCap and variables often studied in HRD area such as learning flow, learning motivation, and learning transfer.Maste

    ํ˜ผํ•ฉ๋ฒ•์ด ์ž„์‹œ ์—ฐ์„ฑ ์ด์žฅ์žฌ์ธ Coe-Comfort์˜ ์ ํƒ„์„ฑ์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ.

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    Dept. of Dental science/์„์‚ฌ[ํ•œ๊ธ€] ์กฐ์ง ์–‘ํ™”์™€ ๊ธฐ๋Šฅ ์ธ์ƒ ๋ชฉ์ ์œผ๋กœ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” ์ž„์‹œ ์—ฐ์„ฑ ์ด์žฅ์žฌ๋Š” ํ˜ผํ•ฉ์‹œ์˜ ๋ถ„๋ง ์•ก์ฒด ๋น„์œจ ๋ฟ ์•„๋‹ˆ๋ผ ํ˜ผํ•ฉ ํ›„ ํ•จํฌ(ๅซๆณก) ์ •๋„์— ๋”ฐ๋ผ ๊ทธ ๋ฌผ์„ฑ์ด ๋‹ฌ๋ผ์ง์„ ์ž„์ƒ์—์„œ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋Š” ์žฌ๋ฃŒ์˜ ์ ํƒ„์„ฑ ๋ฟ ์•„๋‹ˆ๋ผ ์—ฐ์„ฑ์˜ ์œ ์ง€ ๊ธฐ๊ฐ„์—๋„ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ฒŒ ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ˆ˜๋™ ํ˜ผํ•ฉ, ์ธ์ƒ์žฌ ํ˜ผํ•ฉ๊ธฐ๋ฅผ ์ด์šฉํ•œ ๊ธฐ๊ณ„ ํ˜ผํ•ฉ, ์ง„๊ณต ์ฒ˜๋ฆฌ ํ˜ผํ•ฉ๊ณผ ๊ฐ™์ด ํ•จํฌ ์ •๋„์— ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ๋Š” ์„œ๋กœ ๋‹ค๋ฅธ ๋ฐฉ๋ฒ•์œผ๋กœ ์ž„์‹œ ์—ฐ์„ฑ ์ด์žฅ์žฌ์ธ Coe-ComfortR(Coe Lab., Illinois, USA)๋ฅผ ํ˜ผํ•ฉํ•˜์—ฌ ์‹œํŽธ์„ ์ œ์ž‘ํ•˜๊ณ , ์ด๋ฅผ ์ €์ž‘ ์ฃผ๊ธฐ๋ฅผ ๊ณ ๋ คํ•œ ๋™์  ํ•˜์ค‘์— ๋…ธ์ถœ ์‹œ์ผฐ์„ ๋•Œ ์‹œํŽธ ๋‘๊ป˜์˜ ๋ณ€์ด๋ฅผ ์ธก์ •, ๋น„๊ตํ•˜์—ฌ ํ˜ผํ•ฉ๋ฒ•์— ๋”ฐ๋ฅธ ๋™์  ํ•˜์ค‘์— ๋Œ€ํ•œ ๋ณ€ํ˜• ์–‘์ƒ์„ ๋น„๊ต ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค.๊ฐ๊ฐ์˜ ํ˜ผํ•ฉ๋ฒ• ๋‹น 10๊ฐœ์˜ ์‹œํŽธ์„ ์ค€๋น„ํ•˜์—ฌ ์ฆ๋ฅ˜์ˆ˜์— ๋ณด๊ด€ํ•˜๋ฉด์„œ, ํ˜ผํ•ฉ ํ›„ ์—ฐ์†์ ์œผ๋กœ 2์‹œ๊ฐ„, 12์‹œ๊ฐ„, 1์ผ, 2์ผ, 3์ผ ๋˜๋Š” ์‹œ์ ์— 2Hz๋กœ 1๋ถ„๊ฐ„ ๋™์  ํ•˜์ค‘์„ ๊ฐ€ํ•˜๋ฉฐ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋‘๊ป˜ ๋ณ€ํ™”๋ฅผ ์ธก์ •ํ•˜๊ณ , ๊ธฐ๋ก๋œ ์ดˆ๊ธฐ 30์ดˆ๊ฐ„์˜ ์ธก์ •๊ฐ’์„ ์„ ํ˜• ํšŒ๊ท€ ๋ถ„์„ ํ›„ ๊ทธ ๊ธฐ์šธ๊ธฐ๋ฅผ ํ˜ผํ•ฉ๋ฒ•๊ณผ ์ €์žฅ ์‹œ๊ฐ„์— ๋”ฐ๋ฅธ ์ด์› ๋ถ„์‚ฐ ๋ถ„์„๋ฒ•์œผ๋กœ ํ†ต๊ณ„ ์ฒ˜๋ฆฌํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ ๋ชจ๋“  ์ธก์ • ์‹œ์ ์—์„œ ํ˜ผํ•ฉ ๋ฐฉ๋ฒ•์— ๋”ฐ๋ฅธ ๋ณ€ํ˜• ์–‘์ƒ์˜ ์ฐจ์ด๋Š” ๊ด€์ฐฐ๋˜์ง€ ์•Š์•˜๊ณ (p>0.05) ์„ธ ๊ตฐ ๋ชจ๋‘์—์„œ ์ €์žฅ์‹œ๊ฐ„์˜ ์ฆ๊ฐ€์™€ ํ•˜์ค‘์— ๋Œ€ํ•œ ๋ณ€ํ˜• ์‚ฌ์ด์—๋Š” ์œ ์˜ํ•  ๋งŒํ•œ ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค(p>0.05). ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ €์žฅ ์šฉ์•ก์ด ์ฆ๋ฅ˜์ˆ˜์ธ ์ ๊ณผ ํ•˜์ค‘์— ๋…ธ์ถœ๋˜๋Š” ์‹œ๊ฐ„์ด ์งง์•˜๋˜ ์  ๋“ฑ์˜ ์ œํ•œ์„ ๊ฐ€์ง€๋‚˜, Coe-ComfortR์˜ ๋™์  ํ•˜์ค‘์— ๋Œ€ํ•œ ๋‘๊ป˜ ๋ณ€์ด ์ •๋„๋Š” ๋ถ„๋ง ์šฉ์•ก ๋น„๊ฐ€ ์ผ์ •ํ•˜๋‹ค๋ฉด ํ˜ผํ•ฉ๋ฒ•์— ์œ ์˜ํ•  ๋งŒํ•œ ์˜ํ–ฅ์„ ๋ฐ›์ง€ ์•Š์Œ์„ ์˜๋ฏธํ•˜๋ฉฐ, ํ–ฅํ›„ ํ•จํฌ ์ •๋„๋ฅผ ๋‹จ์ผ ์š”์†Œ๋กœ ํ•œ ํšจ๊ณผ ๋ถ„์„ ์‹คํ—˜๊ณผ ์ €์žฅ ์šฉ์•ก, ์ €์žฅ ์˜จ๋„, ํ•˜์ค‘ ๋ถ€์—ฌ ์ฃผ๊ธฐ์™€ ์‹œ๊ฐ„ ๋“ฑ์„ ์ž„์ƒ์กฐ๊ฑด๊ณผ ์œ ์‚ฌํ•˜๊ฒŒ ๊ณ ๋ คํ•˜๋Š” ์‹คํ—˜ ์„ค๊ณ„ ๋ฐ ๋‹ค์–‘ํ•œ ์—ฐ์„ฑ ์ด์žฅ์žฌ์— ๋Œ€ํ•œ ๋น„๊ต ์‹คํ—˜์ด ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค. [์˜๋ฌธ] Temporary soft lining materials used extensively for the purposes of tissue conditioning and functional impression would show different properties according to not only the powder to liquid ratio but also the degree of void entrapment, which would in turn affect on the viscoelasticity of the material and on the period of maintaining softness. The temporary soft lining material Coe-ComfortR(Coe Lab., Illinois, USA) was mixed to prepare the specimens used in the study using different methods of mixing that could affect on the degree of void entrapment such as hand mixing, machine mixing using an impression material mixer, and vacuum treated mixing. Then, the change in specimen thickness was measured and compared when the specimens were exposed to dynamic load considering the mastication cycle in order to compare the deformation pattern on dynamic load according to different mixing methods. Ten specimens were prepared with each mixing method and kept in distilled water. Dynamic load of 2Hz for 1 minute was applied time serially by 2 h, 12 h, 1 day, 2 day and 3 day after mixing and the change in thickness was measured in real time. After simple linear regression analysis was performed with the measured values recorded within initial 30 seconds, two-way ANOVA test was done on the steepness according to the mixing method and storage time. The results of analysis showed no difference in the pattern of deformation according to mixing methods (p>0.05) and no significant differences between increased storage time and deformation on load in all 3 groups (p>0.05). The limitations of the present study were storage solution being distilled water and short exposure time. The degree of change in the thickness of Coe-ComfortR according to dynamic load was not affected significantly by mixing methods provided that the powder to liquid ratio was constant. More studies are needed in the future on analyzing the effects using the degree of void entrapment as a single factor and on the comparison of test design and various soft lining materials considering similar clinical conditions such as storage solution, storage temperature, and cycle and time of applying load.ope

    ๋ฒผ ๋„์—ด๋ณ‘๊ท ์˜ Lignin Peroxidase ์œ ์ „์ž์— ๋Œ€ํ•œ ํŠน์„ฑ ๊ทœ๋ช…

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์ƒ๋ช…๊ณตํ•™๋ถ€, 2017. 2. ์ด์šฉํ™˜.Magnaporthe oryzae is a causal agent of the rice blast which is one of the most devastating diseases of rice worldwide. The rice blast is considered as an important model for studying plant-fungal pathogen interactions. Plants induce a wide range of defense responses to cope with pathogen attacks. Lignification is one of the induced defense responses and involved in not only fortifying the plant cell wall but also inactivating fungal membranes and secretions. In addition, fungal hyphae may lose plasticity necessary for growth due to the lignification of a hyphal tip. In order to overcome this defense reactions, pathogens secrete lignin-degrading enzymes such as laccases and lignin peroxidases. Two of bZIP transcription factors of the rice blast fungus were found to regulate genes related to pathogenicity, including lignin-degrading enzyme genes. Among these lignin-degrading enzyme genes, the expression levels of MoLIP1 and MoLIP3 were the most notably affected. Therefore, these two genes were chosen for in-depth analysis using gene deletion strategy. There were no significant differences in mycelial growth, conidiation, conidial germination, appressorium formation and resistance of oxidative stress between the wild-type and ฮ”Molip1 and ฮ”Molip3 mutants. These results indicated that MoLIP genes were not directly involved in mycelial growth, infection-related morphogenesis and detoxification of reactive oxygen species. However, pathogenicity of the mutants decreased and the invasive growth of the mutants was delayed when the mutants were inoculated on rice leaves and in sheath, respectively. These results suggest that lignin peroxidase is required for early infection stage of M. oryzae.I. INTRODUCTION 1 II. MATERIALS AND METHODS 4 1. Selection of lignin peroxidase gene 4 2. Fungal strains, culture conditions and conditions of specialized treatments 4 3. Generation targeted disruption of the MoLIP1 and MoLIP3 5 4. Nucleic acid isolation and manipulation 5 5. Developmental phenotype assays - Mycelial growth, conidiation, germination and appressorium formation 6 6. Pathogenicity assessment 7 7. Oxidative stress resistance assessment 8 III. RESULTS 10 1. Selection of genes encoding lignin-degrading enzymes in M. oryzae 10 2. Domain architecture of MoLIP1 and MoLIP3 in M. oryzae 11 3. Phylogenetic analysis of MoLIP1 and MoLIP3 in M. oryzae 12 4. Targeted genes replacement of MoLIP1 and MoLIP3 in M. oryzae 13 5. Development phenotypes and resistance of oxidative stress of ฮ”Molip1, ฮ”Molip3 mutants 15 6. Pathogenicity of ฮ”Molip1 and ฮ”Molip3 mutants 19 7. Retardation of invasive growth 21 IV. DISCUSSION 23 LITERATURES CITED 26 ABSTRACT IN KOREAN 30Maste

    The association between e-Health literacy and health behaviors in elderly people

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฐ„ํ˜ธ๋Œ€ํ•™ ๊ฐ„ํ˜ธํ•™๊ณผ, 2019. 2. ์žฅ์„ ์ฃผ.Abstract The association between e-Health literacy and health behaviors in elderly people Hyunju Ryu Department of Nursing The Graduate school Seoul National University Directed by Professor Chang Sunju, PhD, RN Korea's internet penetration rate is the highest among OECD countries. As of July 2017, 99.5% of all households in Korea have access to the internet. Moreover, the proportion of internet users among the elderly population aged โ‰ฅ65 years is increasing annually. A plethora of health-related information is available on the internet and thus, it becomes important to evaluate the quality of such information from the perspective of ease of understanding and applicability. Hence, it is necessary to investigate the ability of the elderly population to understand health information available on the internet. This research was conducted as a basic data to promote the health behaviors of the elderly. This research was a descriptive correlation study. The aim of this research is to investigate the correlation between e-Health literacy and health behaviors in elderly people. The samples population for this research comprised individuals who had enrolled in a senior welfare center in Seoul and were โ‰ฅ65 years. The research was aimed at elderly individuals who searched the internet for health information within the past 1 month. In July 2018, a total of 99 elderly people were recruited for this research. The collected data were analyzed using SPSS 23.0 statistical program. The demographic characteristics of the subjects were expressed as mean, standard deviation, frequency, and percentage. The normality test was performed using the Shapiro-Wilk test, and the differences between the variables according to general characteristics were analyzed using independent t-test and ANOVA. Pearson's correlation coefficient was used to examine the correlation between variables. Post-test analysis was conducted using Scheffe test, and the reliability of the instrument was calculated using Cronbach's alpha value. We conducted a hierarchical regression analysis to examine the effects of demographic characteristics, e-Health literacy, computer anxiety, subjective health status, and internet-based health information reliability on health behaviors. Based on the theoretical framework of this research, a regulated regression analysis was conducted to determine whether the reliability of internet-based health information regulates the effect of e-Health literacy on health behavior. The results are as described below. First, the ability of the elderly to understand internet-based health information was 29.99 points from a total of 40 points. Among 10 items, the highest average score was 3.92 points for finding health information on the internet and the lowest average score was 3.52 points for evaluating the quality of internet-based health information. Second, difference in the understanding ability of internet-based health information with respect to age group, sex, and education level, which encompass demographic characteristics of our subjects, was not statistically significant. Third, e-Health literacy differed according to the experience of internet users'. The group of subjects who maximally used the internet, i.e., prolonged and more frequent use of the internet per day, had higher e-Health literacy than the groups who minimally used the Internet. Fourth, e-Health literacy positively correlated with the health status, attitude toward internet-based health information, and health behaviors of subjects. Fifth, the effect of e-Health literacy on health behavior was greater depending on subjective health status and the understanding of internet-based health information of each subjectfurthermore, women more frequently engaged in health behaviors than men. However, subjective health status and internet-based health information did not significantly influence the effects on health behaviors. In this research, it was found that e-Health literacy of the subjects had a positive correlation with their health behavior as well as statistically significant effect on health behavior. The results of this research will serve as the basic data, which can be used while planning an intervention program to improve the understanding of internet-based health information keeping the characteristics of elderly people in mind. Keywords : e-Health literacy, health behavior, elderly people, internet-based health information, association Student number : 2017-22935์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์ธํ„ฐ๋„ท ๋ณด๊ธ‰๋ฅ ์€ OECD ๊ตญ๊ฐ€ ์ค‘์—์„œ ๊ฐ€์žฅ ๋†’์€ ์ˆœ์œ„๋ฅผ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, 2017๋…„ 7์›”์„ ๊ธฐ์ค€์œผ๋กœ ์šฐ๋ฆฌ๋‚˜๋ผ ์ „์ฒด ๊ฐ€๊ตฌ์˜ 99.5%๊ฐ€ ์ธํ„ฐ๋„ท ์ ‘์†์ด ๊ฐ€๋Šฅํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ๋งŒ 65์„ธ ์ด์ƒ ๋…ธ์ธ ์ค‘ ์ธํ„ฐ๋„ท ์ด์šฉ์ž์˜ ๋น„์œจ์€ 45.7%๋กœ ํ•ด๋งˆ๋‹ค ์ฆ๊ฐ€ํ•˜๋Š” ์ถ”์„ธ๋ฅผ ๋ณด์ด๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ธํ„ฐ๋„ท์ƒ์—๋Š” ์ˆ˜๋งŽ์€ ๊ฑด๊ฐ• ๊ด€๋ จ ์ •๋ณด๊ฐ€ ์žˆ์œผ๋ฉฐ ๊ทธ ์ค‘์—์„œ ์›ํ•˜๋Š” ์ •๋ณด๋ฅผ ์ฐพ์•„๋‚ด๊ณ  ๊ทธ ์ •๋ณด์˜ ํ’ˆ์งˆ์— ๋Œ€ํ•ด ๋น„ํŒ์ ์œผ๋กœ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์€ ๊ฐœ์ธ์˜ ์ •๋ณด ์ดํ•ด ๋Šฅ๋ ฅ์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ๋‹ค. ๊ฑด๊ฐ• ์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์ด ๋…ธ์ธ ์—ฐ๋ น์ธต์—์„œ ์ทจ์•ฝํ•˜๋‹ค๋Š” ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋…ธ์ธ์˜ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ• ์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์— ๋Œ€ํ•œ ์กฐ์‚ฌ๊ฐ€ ํ•„์š”ํ•œ ์‹œ์ ์ด๋ฉฐ ๊ฑด๊ฐ•ํ–‰์œ„์™€์˜ ์ƒ๊ด€์„ฑ์„ ํŒŒ์•…ํ•˜์—ฌ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ์ธํ„ฐ๋„ท ํ™˜๊ฒฝ์„ ์ž˜ ํ™œ์šฉํ•˜์—ฌ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ• ์ •๋ณด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋…ธ์ธ์˜ ๊ฑด๊ฐ• ํ–‰์œ„๋ฅผ ์ฆ์ง„์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๊ธฐ์ดˆ ์ž๋ฃŒ๋กœ์„œ ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ์‹œํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋งŒ 65์„ธ ์ด์ƒ ๋…ธ์ธ์˜ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ๊ณผ ๊ฑด๊ฐ• ํ–‰์œ„์— ๋Œ€ํ•œ ์กฐ์‚ฌ๋ฅผ ์‹œํ–‰ํ•˜์—ฌ ๋…ธ์ธ์˜ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์ •๋„์™€ ๊ฑด๊ฐ• ํ–‰์œ„์™€์˜ ์ƒ๊ด€์„ฑ์„ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜๋Š” ์„œ์ˆ ์  ์ƒ๊ด€๊ด€๊ณ„ ์—ฐ๊ตฌ์ด๋‹ค. 2018๋…„ 7์›”, ์„œ์šธํŠน๋ณ„์‹œ ์†Œ์žฌ์˜ S ๋…ธ์ธ ์ข…ํ•ฉ ๋ณต์ง€๊ด€์—์„œ ์—ฐ๊ตฌ ๋Œ€์ƒ์ž 99๋ช…์—๊ฒŒ ์„œ๋ฉด ๋™์˜๋ฅผ ๋ฐ›๊ณ  ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€๋‹ค. ์ˆ˜์ง‘ํ•œ ์ž๋ฃŒ๋Š” SPSS ํ†ต๊ณ„ ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ๋Œ€์ƒ์ž์˜ ์ธ๊ตฌ์‚ฌํšŒํ•™์  ํŠน์„ฑ์€ ํ‰๊ท ๊ณผ ํ‘œ์ค€ ํŽธ์ฐจ, ๋นˆ๋„ ๋ฐ ๋ฐฑ๋ถ„์œจ๋กœ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์ •๊ทœ์„ฑ ๊ฒ€์ •์€ Shapiro Wilk test๋ฅผ ์ด์šฉํ•˜์˜€๊ณ , ์ผ๋ฐ˜์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ๋ณ€์ˆ˜๊ฐ„์˜ ์ฐจ์ด๋Š” independent t-test, ANOVA๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณ€์ˆ˜๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด Pearsons correlation coefficient๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ์‚ฌํ›„๊ฒ€์ •์€ Scheff test๋กœ ๋ถ„์„ํ•˜์˜€๊ณ , ๋„๊ตฌ์˜ ์‹ ๋ขฐ๋„๋Š” Cronbachs alpha ๊ฐ’์œผ๋กœ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ํ†ต๊ณ„์˜ ์œ ์˜์ˆ˜์ค€์€ ๋กœ ํ•˜์˜€๋‹ค. ์ธ๊ตฌํ•™์  ํŠน์„ฑ, ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ, ์ปดํ“จํ„ฐ ๋ถˆ์•ˆ, ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ, ์ธํ„ฐ๋„ท ๊ฑด๊ฐ• ์ •๋ณด ์‹ ๋ขฐ๋„๊ฐ€ ๊ฑด๊ฐ• ํ–‰์œ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ์œ„๊ณ„์  ํšŒ๊ท€ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๊ณ  ๋ณธ ์—ฐ๊ตฌ์˜ ์ด๋ก ์  ๊ธฐํ‹€์— ๊ทผ๊ฑฐํ•˜์—ฌ ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ, ์ธํ„ฐ๋„ท ๊ฑด๊ฐ• ์ •๋ณด ์‹ ๋ขฐ๋„๊ฐ€ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ• ์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์ด ๊ฑด๊ฐ•ํ–‰์œ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์ ˆํ•˜๋Š”์ง€ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ์กฐ์ ˆ ํšŒ๊ท€ ๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์„œ์šธ ์‹œ๋‚ด์˜ ์ผ๊ฐœ ๋…ธ์ธ ์ข…ํ•ฉ ๋ณต์ง€๊ด€์˜ ๋งŒ 65์„ธ ๋…ธ์ธ์˜ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์€ ์ด์  40์  ๋งŒ์ ์— 29.99์ , 5์  ์ฒ™๋„ ๋งŒ์ ์— ํ‰๊ท  3.75์ ์ด์—ˆ๋‹ค. ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด๋ฅผ ์ฐพ๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด์„œ ๊ฐ€์žฅ ๋†’์€ ํ‰๊ท ์ ์ˆ˜ 3.92์ ์ด ๋‚˜์™”์œผ๋ฉฐ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด์˜ ์งˆ์ด ๋‚ฎ์Œ๊ณผ ๋†’์Œ์„ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€์žฅ ๋‚ฎ์€ ํ‰๊ท  ์ ์ˆ˜์ธ 3.52์ ์œผ๋กœ ์‚ฐ์ถœ๋˜์—ˆ๋‹ค. ๋‘˜์งธ, ๋Œ€์ƒ์ž์˜ ์ธ๊ตฌํ•™์ ์ธ ํŠน์„ฑ์ธ ์—ฐ๋ น ๊ทธ๋ฃน๋ณ„, ์„ฑ๋ณ„, ๊ต์œก ์ˆ˜์ค€์— ๋”ฐ๋ฅธ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์˜ ์ฐจ์ด๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜์ง€ ์•Š์•˜๋‹ค. ์…‹์งธ, ๋Œ€์ƒ์ž์˜ ์ธํ„ฐ๋„ท ์‚ฌ์šฉ ๊ฒฝํ—˜์˜ ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์˜ ์ฐจ์ด๋Š” ์ธํ„ฐ๋„ท์˜ ์‚ฌ์šฉ ๊ธฐ๊ฐ„์ด ๊ธด ์ง‘๋‹จ์ด(=.002), ํ•˜๋ฃจ ํ‰๊ท  ์ธํ„ฐ๋„ท์˜ ์‚ฌ์šฉ ์‹œ๊ฐ„์ด ๊ธด ์ง‘๋‹จ์ด(=.046), ์ผ์ฃผ์ผ ๊ธฐ์ค€์œผ๋กœ ์ธํ„ฐ๋„ท์„ ์‚ฌ์šฉํ•˜๋Š” ์ผ์ˆ˜๊ฐ€ ๋งŽ์€ ์ง‘๋‹จ์ด(=.042) ์ ์€ ์ง‘๋‹จ์— ๋น„ํ•ด ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์ด ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜, ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค(<.05). ๋„ท์งธ, ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์€ ์ฃผ๊ด€์ ์ธ ๊ฑด๊ฐ•์ƒํƒœ(r=.412, =.000), ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด์— ๋Œ€ํ•œ ํƒœ๋„(r=.621, =.000), ๊ฑด๊ฐ•ํ–‰์œ„(r=.337, =.001)์™€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์–‘์ ์ธ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค. ๋‹ค์„ฏ์งธ, ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์ด ๊ฑด๊ฐ•ํ–‰์œ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•˜์—ฌ ์ฃผ๊ด€์ ์ธ ๊ฑด๊ฐ•์ƒํƒœ(=.082, =.003)๊ฐ€ ์ข‹์„์ˆ˜๋ก, ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ(=.118, =.044)์ด ๋†’์„์ˆ˜๋ก, ์„ฑ๋ณ„_์—ฌ์ž(=.245, =.006)๋Š” ์—ฌ์„ฑ์ด ๋‚จ์„ฑ์— ๋น„ํ•ด ๊ฑด๊ฐ•ํ–‰์œ„๋ฅผ ๋งŽ์ด ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•˜์ง€๋งŒ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์ด ๊ฑด๊ฐ•ํ–‰์œ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์กฐ์ ˆํ•˜๋Š” ์กฐ์ ˆ๋ณ€์ˆ˜๋กœ ์ฃผ๊ด€์ ์ธ ๊ฑด๊ฐ•์ƒํƒœ์™€ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋…ธ์ธ์˜ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ์€ ๊ฑด๊ฐ•ํ–‰์œ„์™€ ์–‘์ ์ธ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ฐ€์ง€๋ฉฐ ๊ฑด๊ฐ•ํ–‰์œ„์— ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ถ”ํ›„ ๋…ธ์ธ ๋Œ€์ƒ์ž์˜ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ• ์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ ํ–ฅ์ƒ์„ ์œ„ํ•œ ์ค‘์žฌ ํ”„๋กœ๊ทธ๋žจ์„ ๊ณ„ํšํ•  ๋•Œ ์ฐธ๊ณ ํ•  ๊ธฐ์ดˆ์ž๋ฃŒ๊ฐ€ ๋  ๊ฒƒ์œผ๋กœ ๋ณธ๋‹ค. ์ฃผ์š”์–ด: ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ, ๊ฑด๊ฐ•ํ–‰์œ„, ๋…ธ์ธ, ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด, ์ƒ๊ด€์„ฑ ํ•™๋ฒˆ: 2017-22935๊ตญ๋ฌธ์ดˆ๋ก โ…ฐ ๋ชฉ์ฐจ โ…ณ โ… .์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  4 3. ์šฉ์–ด ์ •์˜ 5 โ…ก.๋ฌธํ—Œ ๊ณ ์ฐฐ 8 1. ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ 8 2. ๋…ธ์ธ์˜ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ๊ณผ ๊ฑด๊ฐ•ํ–‰์œ„ 10 3. ๋…ธ์ธ์˜ ์ธํ„ฐ๋„ท ๊ฑด๊ฐ•์ •๋ณด ์ดํ•ด๋Šฅ๋ ฅ๊ณผ ๊ด€๋ จ์š”์ธ 12 โ…ข.์ด๋ก ์  ๊ธฐํ‹€ 14 โ…ฃ.์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 18 โ…ค.์—ฐ๊ตฌ ๊ฒฐ๊ณผ 25 โ…ฅ๋…ผ์˜ 40 โ…ฆ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 50 ์ฐธ๊ณ ๋ฌธํ—Œ 54 ๋ถ€๋ก 65 Abstract 83Maste

    ๋‹จ์ผ๊ด‘์ž๋‹จ์ธต์ดฌ์˜์žฅ์น˜์˜ ๊ณต๊ฐ„ ํ•ด์ƒ๋„ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋‹ค์ค‘ ๋ฐ”๋Š˜๊ตฌ๋ฉ ์กฐ์ค€๊ธฐ ์„ค๊ณ„ ๋ฐ ์ „์ž„์ƒ์  ์ ์šฉ๊ฐ€๋Šฅ์„ฑ ํ‰๊ฐ€

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    Dept. of Radiological Science/์„์‚ฌA multi-pinhole (MPH) collimator was designed for a pre-clinical SPECT system for small animal imaging to provide maximum detection efficiency and highest image quality given a targeted system spatial resolution and other system constraints. The performance of the collimator was evaluated through simulation and experimental studies. The optimum number of pinhole was calculated based on the geometry of the small animal SPECT system for 24 mm common volume-of-view (CVOV) with the target system resolution of 1 mm. The optimized MPH collimator design consisted of 15 pinholes with 0.56 mm effective pinhole diameter and were placed 22.0 mm from the CVOV. In addition, the MPH collimator-detector response (CDR) function was incorporated in the 3D MPH maximum-likelihood expectation-maximization (ML-EL) image reconstruction algorithm. With CDR modeling, even the smallest rods can be differentiated. The reconstructed images of the phantom showed that the MPH SPECT system gives a fine resolution for small animal imaging.ope

    ๊นŠ์ด ์ •๋ณด๋ฅผ ๋ถ€์—ฌํ•˜๊ธฐ ์œ„ํ•œ ์ด๋ฏธ์ง€ ๋ถ„๋ฅ˜

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2016. 2. ๊น€ํƒœ์ •.Due to development of 3D display technology, industries related 3D have been grown. For this reason, the demand of 3D contents increases, but there is a short sup- ply of 3D contents. Consequently, research on 2D-to-3D conversion is underway. In 2D-to-3D conversion, the depth information of scene is obtained through an analysis of several depth cues on input sequence and the depth map corresponding to a scene can be generated by combining several depth cues and assigning an appropriate depth level. Scene classification for depth assignment is needed in this process. This paper classifies a scene into landscape, linear perspective, and normal type automatically. The proposed method analyzes landscape type and found there is a relation between image pattern and distribution of color and edge, and suggest the criteria for clas- sification. Moreover, the other criteria for linear perspective classification based on vanishing point detection is proposed. To verify performance, the proposed features are fed into a linear SVM classifier, and 651 images are used. Experiment results show that the algorithm has an advantages in performance by about 13%.Chapter 1 Introduction 1 Chapter 2 Scene Classification for depth assignment 3 Chapter 3 Feature Extraction 8 3.1 Features for landscape classification 8 3.1.1 Image partition 8 3.1.2 Color-related features 9 3.1.3 Edge-related features 11 3.2 Features for linear perspective classification 14 3.2.1 Criterion for classification of linear perspective type scene 14 3.2.2 Vanishing point detection 14 3.2.3 Features based on vanishing point detection 17 Chapter 4 Experiment results 20 4.1 Performance of classification on each step 20 4.2 Performance of scene classification result for depth assignment 24 Chapter 5 Conclusion 27 Bibliography 28 ๊ตญ๋ฌธ ์ดˆ๋ก 31Maste
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