43 research outputs found

    Visual stimuli of food increase academic performance and create high beta and gamma EEG oscillations

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์ƒ๋ช…๊ณตํ•™๋ถ€, 2018. 2. ์ด๊ธฐ์›.Food and cooking have long been widely used as educational tools (McAfee, 1974)however, they have focused on cooking and pre-school children which causes too many limitations for wide use (Caraher, Michelle, & Seeley, 2010). There is too little scientific background and evidence to prove the efficacy of food in education (Caraher, Michelle, & Seeley, 2010). This study is designed to scientifically evaluate how food as concepts and visual stimuli are effective for learning science. In this study, 99 people were collected in total from four middle schools in Suwon city. Every participant was in the first grade of the middle school with normal visual activity, hearing ability and motor activity. Participants only visited the laboratory on one occasion. The participants with a BMI over 23 were excluded from the study and all food intake was prohibited for two hours before the experiment. First, participants were divided into three groups: a control group, an experimental group and an active control group. Grouping was conducted such that each group had a similar level of scientific attitude and knowledge of heat energy and temperature as determined by testing with Test of Science-Related Attitudes(TOSRA) and a pre-knowledge test. After grouping, brain waves were recorded by EEG methods while participants watched education videos. Film clips for experimental group had many pictures of food to explain heat energy and temperature in opposition to the two other groups. After learning, participants submitted a Course Interest Survey(CIS) and post-knowledge test. In conclusion, the experimental group showed a high frequency of high beta and gamma EEG oscillations, which are considered as complex mental activities. Those correlated with the results of CIS and the comparison of pre and post knowledge tests. The experimental group had especially improved their academic performance in difficult problem solving situations. In summary, visual stimuli and concepts of food can be effective tools in adolescents science education.Chapter 1. Introduction 1 1.1 Educational Effects of Food 1 1.2 My hypotheses 2 Chapter 2. Materials and Procedures 3 2.1 Participants 5 2.2 Grouping 6 2.3 Test of Science-Related Attitudes (TOSRA) 7 2.4 Film Clips 8 2.5 Course Interest Survey (CIS) 10 2.6 Pre-and-Post Knowledge Tests 11 2.7 Electrophysiological Recordings 12 Chapter 3. Results 15 3.1 Comparison of pre-and post-knowledge tests 16 3.2 Test of Science-Related Attitudes (TOSRA) Scores 20 3.3 Course Interest Survey (CIS) Scores 21 3.4 Relative High Beta Power Spectrum 22 3.5 Relative Gamma Power Spectrum 24 Chapter 4. Discussion 26 Reference 28Maste

    A study on revising the laws and regulations regarding the online training of government employees

    No full text
    ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ตœ๊ทผ ๊ณต๋ฌด์›๊ต์œกํ›ˆ๋ จ์˜ ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ธ‰์†ํžˆ ํ™•์‚ฐ๋˜๊ณ  ์žˆ๋Š” ์‚ฌ์ด๋ฒ„๊ต์œก์˜ ๋‚ด์‹ค ์žˆ๋Š” ์šด์˜์„ ์œ„ํ•˜์—ฌ ์„ ํ–‰ ์—ฐ๊ตฌ ๋ฐ ์œ ๊ด€ ๋ฒ•์ œ ๋ถ„์„์— ๋”ฐ๋ฅธ ์‹œ์‚ฌ์ ์„ ๋ฐ”ํƒ•์œผ๋กœ ํ˜„ํ–‰ ๋ฒ•์ œ๋„๊ฐ€ ์•ˆ๊ณ  ์žˆ๋Š” ๋ฌธ์ œ์ ์„ ๋ฐํžˆ๊ณ , ๋ฒ•์ œ๋„์ ์œผ๋กœ ๊ฐœ์„ ํ•ด์•ผ ํ•  ์‚ฌํ•ญ๋“ค์„ ์ œ์‹œํ•˜์˜€๋‹ค. ํ˜„ํ–‰ ๊ด€๋ จ ๋ฒ•์ œ๋„์˜ ๋ฌธ์ œ์ ์œผ๋กœ๋Š” ๊ด€๋ จ ๋ฒ•๋ น์ธ ๊ณต๋ฌด์›๊ต์œกํ›ˆ๋ จ๋ฒ•๊ณผ ์ง€๋ฐฉ๊ณต๋ฌด์›๊ต์œกํ›ˆ๋ จ๋ฒ• ๋ฐ ๋™๋ฒ• ์‹œํ–‰๋ น์— ์‚ฌ์ด๋ฒ„๊ต์œก์— ๊ด€ํ•œ ๊ทœ์ •์ด ๋ถ€์žฌํ•˜๋ฉฐ, ์‹ค์งˆ์ ์ธ ๊ณต๋ฌด์› ์‚ฌ์ด๋ฒ„๊ต์œก๊ณผ์ • ์šด์˜์˜ ์ง€์นจ์„œ ์—ญํ• ์„ ํ•˜๊ณ  ์ž‡๋Š” ๊ณต๋ฌด์›์‚ฌ์ด๋ฒ„๊ต์œก์ง€์นจ์˜ ๋‚ด์šฉ๋„ ๊ตฌ์ฒด์„ฑ์„ ๊ฒฐ์—ฌํ•˜๊ณ  ์žˆ์–ด์„œ ๊ต์œก์˜ ์งˆ์„ ํ™•๋ณดํ•˜๊ณ  ๋‹จ์œ„ ๊ต์œกํ›ˆ๋ จ๊ธฐ๊ด€์˜ ์‚ฌ์ด๋ฒ„๊ต์œก๊ณผ์ • ์šด์˜์„ ํšจ์œจ์ ์œผ๋กœ ํ•˜๋Š”๋ฐ ๋งค์šฐ ๋ฏธํกํ•˜๊ณ , ์‚ฌ์ด๋ฒ„ ๊ณต๋ฌด์›๊ต์œก์— ๊ด€ํ•œ ์ฒด๊ณ„์ ์ธ ํ‰๊ฐ€์‹œ์Šคํ…œ์ด ๋ถ€์žฌํ•˜๋ฉฐ, ํ•™์Šต๊ด€๋ฆฌ์‹œ์Šคํ…œ๊ณผ ์ฝ˜ํ…์ธ ์˜ ํ‘œ์ค€ํ™”๋ฅผ ์œ„ํ•œ ๊ตฌ์ฒด์ ์ธ ์ง€์นจ์ด ๋ช…๋ฌธํ™”๋˜์–ด ์žˆ์ง€ ์•Š๋‹ค๋Š” ์ ์„ ์ง€์ ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•œ ๋ฐฉ์•ˆ์œผ๋กœ๋Š” ๊ณต๋ฌด์›๊ต์œกํ›ˆ๋ จ๋ฒ• ๋ฐ ๋™๋ฒ• ์‹œํ–‰๋ น์—์„œ ๊ต์œก์˜ ๋ฐฉ๋ฒ•์„ ๊ทœ์ •ํ•œ ์กฐํ•ญ์— ์‚ฌ์ด๋ฒ„๊ต์œก์— ๊ด€ํ•œ ๊ทœ์ •์„ ๋ณด์™„ํ•˜๊ณ , ๊ต์œก๊ณผ์ • ์šด์˜๊ณ„ํš์— ์‚ฌ์ด๋ฒ„๊ต์œก์— ๊ด€ํ•œ ๊ณ„ํš์„ ํฌํ•จํ•˜๋„๋ก ํ•˜๋ฉฐ, ์‚ฌ์ด๋ฒ„๊ต์œก ์ „๋ฌธ๊ธฐ๊ด€์— ์˜ํ•œ ๊ฐ๊ด€์ ์ธ ์งˆ๊ด€๋ฆฌ ์žฅ์น˜๋ฅผ ๋ฒ•๋ น์— ํฌํ•จ์‹œํ‚ค๋„๋ก ํ•˜๋Š” ๋ฐฉ์•ˆ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ํ•œํŽธ, ๊ณต๋ฌด์›์‚ฌ์ด๋ฒ„๊ต์œก์ง€์นจ์˜ ๊ฐœ์„ ์„ ์œ„ํ•ด์„œ๋Š” ์ค‘์•™์ธ์‚ฌ์œ„์›ํšŒ์˜ ์žฅ์ด ์™ธ๋ถ€์˜ ์‚ฌ์ด๋ฒ„๊ต์œก ์ „๋ฌธ๊ธฐ๊ด€์„ ๊ตฌ์ฒด์ ์œผ๋กœ ์ง€์ •ํ•˜์—ฌ ์ฝ˜ํ…์ธ ์˜ ํ’ˆ์งˆ์ธ์ฆ ๋ฐ ์‚ฌ์ด๋ฒ„๊ต์œก ๊ณผ์ • ์šด์˜ ์ „๋ฐ˜์— ๊ด€ํ•œ ํ‰๊ฐ€์™€ ์ปจ์„คํŒ…์„ ํ•˜๋„๋ก ํ•˜๋ฉฐ, ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ ๋ฐ ํ•™์Šต๊ด€๋ฆฌ์‹œ์Šคํ…œ์˜ ํ‘œ์ค€ํ™”๋ฅผ ํ†ตํ•œ ์ฝ˜ํ…์ธ  ๊ณต๋™ ํ™œ์šฉ ์ด‰์ง„, ์šด์˜์š”์› ๋“ฑ ์ธ์ ์š”๊ฑด์˜ ๋ช…์‚ฌ๋ฅผ ํ†ตํ•œ ์ „๋ฌธ ์ธ๋ ฅ ํ™•๋ณด, ์ •๋ณด๋ณด์•ˆ ์ฒด์ œ์˜ ๊ตฌ์ถ•์„ ํ†ตํ•œ ์•ˆ์ •์ ์ธ ์‹œ์Šคํ…œ ์šด์˜ ๋“ฑ์˜ ๊ทœ์ •์„ ํฌํ•จํ•˜๋Š” ๋ฐฉ์•ˆ์„ ํ•จ๊ป˜ ์ œ์‹œํ•˜์˜€๋‹ค. ์•„์šธ๋Ÿฌ ๊ณต๋ฌด์› ๊ต์œกํ›ˆ๋ จ ์ข…ํ•ฉํ‰๊ฐ€์™€ ํ‰๊ฐ€ํ•ญ๋ชฉ์— ์‚ฌ์ด๋ฒ„๊ต์œก์— ๊ด€ํ•œ ํ•ญ๋ชฉ์„ ํฌํ•จ์‹œํ‚ค๋Š” ๋ฐฉ์•ˆ์„ ์ œ์•ˆํ•˜์˜€๋‹ค. The purpose of this study was to suggest revisions for improving the current laws and regulations pertaining to the online training of government employees. The previous research and literature related to the online training of government employees was reviewed, and some useful implications were drawn for a revision of the current laws and regulations. From these implications, possible improvements of the current laws and regulations were suggested. There are two major results of the study. First, it was found that the current law and regulations involved a variety of problems as a result of vague articles about regulating the online government employee training program in terms of quality of education. Second, revisions to the structure and content of the relevant laws and regulations (such as ใ€ŒGovernment Employee Training Actใ€ and ใ€ŒLocal Government Employee Training actใ€ and ใ€ŒOnline Government Employee Training guide linesใ€) were suggested and some ideas for new articles were proposed for the relevant regulations so that policy makers might use them as they revise the law. The suggestions included the evaluation and consulting of the online government employee training.๋ณธ ๋…ผ๋ฌธ์€ 2007๋…„ ํ•œ๊ตญ์™ธ๊ตญ์–ด๋Œ€ํ•™๊ต ๊ต๋‚ดํ•™์ˆ ์—ฐ๊ตฌ๋น„ ์ง€์›์— ์˜ํ•œ ๊ฒƒ์ž„

    ๋ถ„์—ด์„ฑ ํšจ๋ชจ Schizosaccharomyces pombe์˜ glutathione reductase์™€ superoxide dismutase ์œ ์ „์ž์˜ ๋ฐœํ˜„ ์กฐ์ ˆ

    No full text
    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋ฏธ์ƒ๋ฌผํ•™๊ณผ,1997.Docto

    Temperature Estimation of Winding and Permanent Magnet in PMSM Using Difference-Estimating Artificial Neural Network

    No full text
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2020. 8. ํ•˜์ •์ต.๋ณธ ๋…ผ๋ฌธ์€ ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ๊ถŒ์„ ๊ณผ ์˜๊ตฌ์ž์„์˜ ์˜จ๋„๋ฅผ ์ถ”์ •ํ•˜๋Š” ์ธ๊ณต ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ์˜จ๋„ ์ •๋ณด๋Š” ์ง€๋‚˜์น˜๊ฒŒ ๋†’์€ ์˜จ๋„์—์„œ์˜ ๊ตฌ๋™์„ ๋ฐฉ์ง€ํ•˜์—ฌ ์‚ฌ๊ณ ๋‚˜ ์˜๊ตฌ์ ์ธ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ๋ง‰๋Š”๋ฐ ํ•„์ˆ˜์ ์ด๋‹ค. ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ์˜จ๋„๋ฅผ ์ถ”์ •ํ•˜๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ๋กœ ๊ณ ์ •์ž ์ €ํ•ญ์ด๋‚˜ ์‡„๊ต์ž์† ๋“ฑ์˜ ์ „๊ธฐ์  ์ œ์ •์ˆ˜๋“ค์ด ์˜จ๋„์— ๋”ฐ๋ผ ๊ทธ ํฌ๊ธฐ๊ฐ€ ๋ณ€ํ•˜๋Š” ํŠน์ง•์„ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฐ๋ฐ ์ด ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ๊ถŒ์„ ์ด๋‚˜ ์˜๊ตฌ์ž์„์˜ ์˜จ๋„ ๋ถ„ํฌ ์ •๋ณด๋ฅผ ์–ป์„ ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์— ์ „๋™๊ธฐ ๋‚ด๋ถ€์˜ ์ตœ๊ณ  ์˜จ๋„๋ฅผ ์•Œ๊ธฐ ์–ด๋ ต๋‹ค. ์ด์™€ ๋‹ฌ๋ฆฌ ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ๊ตฌ์กฐ์™€ ์†์‹ค์„ ์—ด๋“ฑ๊ฐ€ํšŒ๋กœ๋กœ ํ‘œํ˜„ํ•˜์—ฌ ๊ด€์‹ฌ ๋ถ€์œ„๋“ค์˜ ์˜จ๋„๋ฅผ ์ถ”์ •ํ•˜๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์ด ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ด ๋ฐฉ๋ฒ•์—๋Š” ๊ฐ ๋ถ€์œ„๋กœ ์œ ์ž…๋˜๋Š” ๊ฐ ์ข…๋ฅ˜์˜ ์†์‹ค ์ •๋ณด๊ฐ€ ํ•„์š”ํ•œ๋ฐ ์ด๋ฅผ ์šด์ „ ์กฐ๊ฑด๊ณผ ์˜จ๋„์— ๋”ฐ๋ผ ์กฐ์‚ฌํ•˜๋Š” ๊ฒƒ์—๋Š” ์–ด๋ ค์›€์ด ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ธฐ์กด์˜ ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ ๊ด€๋ จ ๋ชจ๋ธ๋ง ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์ง€ ์•Š๊ณ , ๋ฐ์ดํ„ฐ ์ฃผ๋„์ ์ธ ์ ‘๊ทผ์„ ์ทจํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ๋ชจ๋ธ์€ ์šด์ „ ์กฐ๊ฑด์œผ๋กœ๋ถ€ํ„ฐ ๋ฐ”๋กœ ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ ๋‚ด ๊ด€์‹ฌ ๋ถ€์œ„๋“ค์˜ ์˜จ๋„๋ฅผ ์ถ”์ •ํ•˜๋Š” ๋ชจ๋ธ์ด๋‹ค. ์ œ์•ˆํ•˜๋Š” ์˜จ๋„ ์ถ”์ • ๋ชจ๋ธ์€ ์˜จ๋„์™€ ์šด์ „ ์กฐ๊ฑด์˜ ๊ณผ๊ฑฐ ์ •๋ณด๋“ค๋กœ๋ถ€ํ„ฐ ๋‹ค์Œ ์ƒ˜ํ”Œ๋ง์˜ ์˜จ๋„๋ฅผ ์ถ”์ •ํ•˜๋Š”๋ฐ, ์ž…์ถœ๋ ฅ ์‚ฌ์ด์˜ ๋น„์„ ํ˜• ๊ด€๊ณ„์˜ ๋ฌ˜์‚ฌ๋ฅผ ์œ„ํ•ด ์ธ๊ณต ์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ๊ธฐ๋ณธ์ ์ธ ๊ตฌ์กฐ์ธ FNN(ํ”ผ๋“œํฌ์›Œ๋“œ ์‹ ๊ฒฝ๋ง)๊ณผ ์‹œ๊ฐ„์— ๋”ฐ๋ฅธ ํŠน์ง•์„ ํฌ์ฐฉํ•˜๋Š” ๋ฐ ๊ฐ•์ ์„ ๊ฐ–๋Š” CNN(์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง), RNN(์ˆœํ™˜ ์‹ ๊ฒฝ๋ง)์„ ์‚ฌ์šฉํ•œ ์˜จ๋„ ์ถ”์ • ๋ชจ๋ธ๋“ค์„ ๊ตฌ์„ฑํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์„ค๋ช…ํ•˜๊ณ , ๊ฐ ๊ตฌ์กฐ์˜ ์ถ”์ • ์„ฑ๋Šฅ์„ ํ‘œํ˜„ ์ž์œ ๋„์— ๋”ฐ๋ผ ๋น„๊ตํ•ด ๋ณด์ธ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ธ๊ณต ์‹ ๊ฒฝ๋ง์˜ ์ถœ๋ ฅ์ด ์˜จ๋„๊ฐ€ ์•„๋‹ˆ๊ณ  ์˜จ๋„์˜ ๋ณ€ํ™”์ธ DFNN ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•˜๊ณ , DFNN์ด FNN์— ๋น„ํ•ด ํ•™์Šต ์†๋„์™€ ์ถ”์ • ์„ฑ๋Šฅ์— ์žˆ์–ด ๊ฐ•์ ์„ ๊ฐ€์ง์„ ์„ค๋ช…ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ , ์†์‹ค์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ƒ์„ฑ๋œ ๋น„์„ ํ˜• ๋ณ€์ˆ˜๋“ค๋กœ ์ž…๋ ฅ์„ ๋Œ€์ฒดํ•จ์œผ๋กœ์จ ์ธ๊ณต ์‹ ๊ฒฝ๋ง์˜ ๋ณต์žก๋„๋‚˜ ์ถ”์ • ์„ฑ๋Šฅ์— ๊ฐœ์„ ์„ ์ด๋ฃฐ ์ˆ˜ ์žˆ๋Š” ์ง€ ํ™•์ธํ•œ๋‹ค. ๋˜, ์—ฐ์†์ ์ธ(ํ๋ฃจํ”„) ์˜จ๋„ ์ถ”์ •์˜ ์˜ค์ฐจ๊ฐ€ ์ž‘์€ ๋ชจ๋ธ์„ ์–ป๊ธฐ ์œ„ํ•ด ์ธ๊ณต ์‹ ๊ฒฝ๋ง์ด ๊ฐ€์ ธ์•ผํ•  ํ‘œํ˜„ ์ž์œ ๋„๋ฅผ ์ •ํ•˜๋Š” ๊ณผ์ •์„ ์ œ์•ˆํ•œ๋‹ค. ์ธ๊ณต ์‹ ๊ฒฝ๋ง์€ ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ์˜ ์˜จ๋„๋ฅผ ๋ณ€ํ™”์‹œํ‚ค๋ฉฐ ์†๋„์™€ ํ† ํฌ์˜ ๋‹ค์–‘ํ•œ ์กฐํ•ฉ์„ ๋‹ด๊ณ  ์žˆ๋Š” ๋‘ ์ข…๋ฅ˜์˜ ํ”„๋กœํŒŒ์ผ๋กœ ํ›ˆ๋ จ๋˜๊ณ  ํ‰๊ฐ€๋˜์—ˆ๋‹ค. ์ „์ฒด ๋ฐ์ดํ„ฐ์…‹์— ๋Œ€ํ•ด ํ๋ฃจํ”„ ์ถ”์ • ์˜ค์ฐจ์˜ ์ตœ๋Œ€์น˜๊ฐ€ ๊ฐ€์žฅ ์ž‘์€ DFNN ๋ชจ๋ธ์€ ์ถ”์ • ์ค‘ 3.5 ยฐC์˜ ์ตœ๋Œ€ ์˜ค์ฐจ๋ฅผ ๋ณด์˜€๊ณ , ํ‰๊ท  ์ ˆ๋Œ“๊ฐ’ ์˜ค์ฐจ๋Š” 0.7 ยฐC์˜€๋‹ค. (๋น„๊ต๋ฅผ ์œ„ํ•ด ์ œ์ž‘๋œ ๋น„์„ ํ˜• ์„ค๋ช… ๋ณ€์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ํšŒ๊ท€ ๋ชจ๋ธ์€ ์ตœ๋Œ€ 14.2 ยฐC์˜ ์˜ค์ฐจ๋ฅผ ๋ณด์˜€๋‹ค.) ํ๋ฃจํ”„ ์ถ”์ •์—์„œ, ๋น„์„ ํ˜• ์„ค๋ช… ๋ณ€์ˆ˜๋ฅผ ํ™œ์šฉํ•˜๊ฑฐ๋‚˜ CNN, RNN ๊ตฌ์กฐ๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์— ๋น„ํ•ด ๋‹จ์ˆœํ•œ ์ž…๋ ฅ ๋ณ€์ˆ˜๋ฅผ ๊ฐ–๋Š” DFNN์ด ์•ˆ์ •์ ์ธ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‹๋ณ„๋œ ๋น„์„ ํ˜• ์˜จ๋„ ์ถ”์ • ๋ชจ๋ธ์˜ ์ ๊ทผ์  ์•ˆ์ •์„ฑ์„ ์กฐ์‚ฌํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ , ํ›ˆ๋ จ๋œ ๋ชจ๋ธ์˜ ์•ˆ์ •์„ฑ์„ ์ „์ฒด ์šด์ „ ์˜์—ญ์— ๊ฑธ์ณ ์กฐ์‚ฌํ•˜๋Š” ๊ณผ์ •์„ ํ•จ๊ป˜ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ์ธ๊ณต ์‹ ๊ฒฝ๋ง์˜ ๊ตฌ์กฐ ๋ฐ ์ƒ์„ฑ ๊ณผ์ •์˜ ์œ ํšจ์„ฑ์€ 2๊ฐœ ์‹คํ—˜๊ณผ 1๊ฐœ ์˜ˆ์‹œ ๋น„์„ ํ˜• ์‹œ์Šคํ…œ์„ ํ†ตํ•ด ๊ฒ€์ฆ๋˜์—ˆ๋‹ค. ์ œ์•ˆ๋œ DFNN ๊ธฐ๋ฐ˜ NARX ๋ชจ๋ธ์€ ์ถฉ๋ถ„ํžˆ ๋†’์€ ์ƒ˜ํ”Œ๋ง ์ฃผํŒŒ์ˆ˜๊ฐ€ ์‚ฌ์šฉ๋˜์—ˆ๊ฑฐ๋‚˜ ์ƒ˜ํ”Œ๋ง ๊ฐ„ ์ƒํƒœ์˜ ์ฐจ๋ถ„์ด ์ž‘๊ฒŒ ์ธก์ •๋œ ๋ฐ์ดํ„ฐ์…‹์— ๋Œ€ํ•ด ๋†’์€ ํ•™์Šต ๋ฐ ์ถ”์ • ์„ฑ๋Šฅ์„ ๊ฐ–๋Š”๋‹ค. ์ด์—, ์ „๋™๊ธฐ์˜ ์˜จ๋„ ์ถ”์ •์— ๊ตญํ•œ๋˜์ง€ ์•Š๊ณ , ์ œ์•ˆ๋œ ๋ชจ๋ธ์€ ์ž„์˜์˜ ๋น„์„ ํ˜• ์‹œ์Šคํ…œ์„ ํšจ๊ณผ์ ์œผ๋กœ ๋ฌ˜์‚ฌํ•˜๋Š”๋ฐ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋ผ ๊ธฐ๋Œ€๋œ๋‹ค.This dissertation proposes an artificial-neural-network(ANN)-based model for temperature estimation of winding and the magnet inside a permanent magnet synchronous machine (PMSM). The temperature information is essential to prevent the potential electrical fault or irreversible degradation of a PMSM. One type of existing studies on PMSM temperature estimation utilizes the characteristics of electrical parameters, which have varying values according to the temperature of the electric machine. However, as the temperature estimated from the electrical parameter does not have information about the distribution, this approach is not suitable for knowing the hottest point inside the electric machine. On the contrary, another type of forgoing researches describes a PMSM as a thermal circuit. It estimates the temperatures of multiple parts inside the machine with the loss-and-heat-flow point of view. The disadvantage of this method is that the loss flows into each part of the machine should be investigated for various operating conditions. Therefore, this dissertation proposes a model that estimates the temperature of multiple parts of a PMSM directly from the operating conditions. To know the temperature distribution of a PMSM, a model that describes the generation and flow of heat, as a conventional thermal-circuit-based method does, is proposed. The proposed model estimates the temperature at the next sampling from past information of temperature and operating conditions, and the nonlinear relationships between them are described by a neural network. This paper shows the structures of feedforward neural network(FNN), convolutional neural network(CNN), and recurrent neural network(RNN) for temperature estimation, where CNN and RNN are known to have the strength of detecting features of input data according to time. The estimation performances of the ANNs are compared by changing the DOF of expression (the number of layers and neurons). DFNN(difference-estimating FNN), which takes temperature variation as the output instead of temperature, is proposed, and the merits of DFNN on learning speed and estimation performance is explained. By changing the input variables of DFNN with nonlinear terms generated with understandings about losses, it is checked whether enhancement could be achieved on the complexity or estimation performance. A guide for deciding the structure of a DFNN is given that helps to obtain a model with a small continuous (closed-loop) estimation error. DFNNs were trained and tested with two types of profiles, which change the temperature of PMSM with various operating conditions. For whole datasets, the best DFNN model kept the closed-loop estimation errors of all channels under 3.5 ยฐC, and the average absolute error was 0.7 ยฐC. (A regression model with nonlinear explanatory variables showed 14.2 ยฐC of peak error.) In the closed-loop estimation, DFNN with simple input variables showed stable and good closed-loop estimation performance compared to CNN, RNN, or DFNN using nonlinear input variables. This paper proposes a method for checking the asymptotic stability of a nonlinear temperature estimation model, and the procedure of investigating the stability of the trained model over the whole operation range is shown. The proposed generation procedure of a DFNN-based temperature estimation model is verified with two real experiments with different PMSMs and with one example nonlinear system. The proposed DFNN-based NARX model is expected to show high learning and estimating performance, if the train dataset is acquired with sufficiently high sampling frequency, or if the difference of the state is measured in small magnitude. Not limited to a temperature estimation model of a PMSM, a DFNN-based NARX model is considered to be a suitable way for describing a nonlinear system.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋ชฉ์  9 1.3 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 10 ์ œ 2 ์žฅ ์ „๋™๊ธฐ์˜ ์†์‹ค๊ณผ ๊ธฐ์กด ์˜จ๋„ ์ถ”์ • ๊ธฐ๋ฒ• 13 2.1 ์—ดํšŒ๋กœ์™€ ์ „๋™๊ธฐ์˜ ์†์‹ค 13 2.1.1 ์—ดํšŒ๋กœ 13 2.1.2 ์ „๋™๊ธฐ์˜ ์†์‹ค 15 2.2 ๊ธฐ์กด ์˜๊ตฌ์ž์„ ์ „๋™๊ธฐ ์˜จ๋„ ์ถ”์ • ๊ธฐ๋ฒ• 24 2.2.1 ์ „๊ธฐ์  ์ œ์ •์ˆ˜ ๊ธฐ๋ฐ˜ ์˜จ๋„ ์ถ”์ • ๊ธฐ๋ฒ• 25 2.2.2 ์ „๊ธฐ์  ์ œ์ •์ˆ˜ ๊ธฐ๋ฐ˜ ์˜จ๋„ ์ถ”์ • ๊ธฐ๋ฒ•์˜ ์ ์šฉ 33 2.2.3 ์—ด๋“ฑ๊ฐ€ํšŒ๋กœ ๊ธฐ๋ฐ˜ ์˜จ๋„ ์ถ”์ • ๊ธฐ๋ฒ• 42 2.3 NARX ๋ชจ๋ธ๊ณผ ๋‹จ๊ณ„์  ํšŒ๊ท€ 45 ์ œ 3 ์žฅ ์ „๋™๊ธฐ ์˜จ๋„ ๋ณ€ํ™” ๋ฐ์ดํ„ฐ ์ทจ๋“ 56 3.1 ์‹คํ—˜ A 57 3.1.1 ์‹คํ—˜ A์˜ ํ™˜๊ฒฝ 57 3.1.2 ์‹คํ—˜ A์˜ ํ›ˆ๋ จ ๋ฐ ํ‰๊ฐ€ ๋ฐ์ดํ„ฐ์…‹ 63 3.2 ์‹คํ—˜ B 74 3.2.1 ์‹คํ—˜ B์˜ ํ™˜๊ฒฝ 74 3.2.2 ์‹คํ—˜ B์˜ ํ›ˆ๋ จ ๋ฐ ํ‰๊ฐ€ ๋ฐ์ดํ„ฐ์…‹ 79 ์ œ 4 ์žฅ ์ธ๊ณต ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ ์˜จ๋„ ์ถ”์ • ๋ชจ๋ธ 84 4.1 ์ธ๊ณต ์‹ ๊ฒฝ๋ง์˜ ๊ตฌ์กฐ์™€ ์—ฐ์‚ฐ 86 4.1.1 FNN(ํ”ผ๋“œํฌ์›Œ๋“œ ์‹ ๊ฒฝ๋ง)์˜ ๊ตฌ์กฐ์™€ ์—ฐ์‚ฐ 86 4.1.2 CNN(์ปจ๋ณผ๋ฃจ์…˜ ์‹ ๊ฒฝ๋ง)์˜ ๊ตฌ์กฐ์™€ ์—ฐ์‚ฐ 94 4.1.3 RNN(์ˆœํ™˜ ์‹ ๊ฒฝ๋ง)์˜ ๊ตฌ์กฐ์™€ ์—ฐ์‚ฐ 99 4.2 ์ธ๊ณต ์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•œ NARX ๋ชจ๋ธ ๊ตฌํ˜„ 103 4.2.1 FNN๊ณผ DFNN์„ ์ด์šฉํ•œ NARX ๋ชจ๋ธ ๊ตฌํ˜„ โ€“ ์ถœ๋ ฅ ๋ณ€์ˆ˜ ๋ณ€ํ™˜์„ ํ†ตํ•œ ํ•™์Šต ์„ฑ๋Šฅ ๊ฐœ์„  103 4.2.2 CNN์„ ์ด์šฉํ•œ NARX ๋ชจ๋ธ ๊ตฌํ˜„ 123 4.2.3 RNN์„ ์ด์šฉํ•œ NARX ๋ชจ๋ธ ๊ตฌํ˜„ 126 4.3 ์ธ๊ณต ์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•œ ์˜จ๋„ ์ถ”์ • ๊ฒฐ๊ณผ โ€“ ์‹คํ—˜ A 128 4.3.1 FNN ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์˜ ์ถ”์ • ๊ฒฐ๊ณผ 128 4.3.2 DFNN ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์˜ ์˜จ๋„ ์ถ”์ • ๊ฒฐ๊ณผ 137 4.3.3 ๋‹จ๊ณ„์  ํšŒ๊ท€ ๊ฒฐ๊ณผ๋ฅผ ํ™œ์šฉํ•˜๋Š” DFNN ๋ชจ๋ธ 159 4.3.4 CNN ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์˜ ์˜จ๋„ ์ถ”์ • ๊ฒฐ๊ณผ 175 4.3.5 RNN ๊ธฐ๋ฐ˜ ๋ชจ๋ธ์˜ ์˜จ๋„ ์ถ”์ • ๊ฒฐ๊ณผ 180 4.4 ์ธ๊ณต ์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•œ ์˜จ๋„ ์ถ”์ • ๊ฒฐ๊ณผ โ€“ ์‹คํ—˜ B 188 4.5 ์ธ๊ณต ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ NARX ๋ชจ๋ธ์˜ ์•ˆ์ •์„ฑ 196 4.6 ์ธ๊ณต ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ NARX ๋ชจ๋ธ์˜ ํ™œ์šฉ์„ ์œ„ํ•œ ์ถ”๊ฐ€ ๊ณ ๋ ค ์‚ฌํ•ญ 203 ์ œ 5 ์žฅ ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ 212 5.1 ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 212 5.2 ํ–ฅํ›„ ์—ฐ๊ตฌ 215 ์ฐธ๊ณ ๋ฌธํ—Œ 217Docto

    ๋””์ ค ์—”์ง„ EGR ์ฟจ๋Ÿฌ์˜ ํŒŒ์šธ๋ง ํŠน์„ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2014. 2. ๋ฏผ๊ฒฝ๋•.In recent years, HSDI (high speed direct injection) diesel engines have been largely applied to RV, SUV and passenger cars due to good fuel economy, high thermal efficiency and very low carbon dioxide emissions. However, worldwide environmental issues such as global warming have led to ever tightening exhaust emission legislations for passenger diesel vehicles, especially on NOx and PM emissions. For example, Euro-6 emission legislation enforced since 2014 mandates that NOx emissions should be reduced by 56% more than the current Euro-5. An exhaust gas recirculation (EGR) system has been widely applied to diesel engines to reduce NOx emissions without a large modification on the engine body. The principle of EGR is to reduce the temperatures of flame and the oxygen concentration of working fluid in a combustion chamberthereby, reducing NOx emissions. The existing EGR system uses only EGR valve to reduce NOx emissions, resulting in increase in PM emissions. Therefore, modification on the system is required to avoid the PM penalty. Recently, numerous researches have been actively conducted to reduce NOx emissions as well as to overcome the disadvantages of deteriorating PM and fuel consumption through a new EGR cooler system. In order to satisfy the strict Euro-6 NOx emission standard, the use of a high EGR rate becomes more crucial. If a high EGR rate is applied, PM emissions will be inevitably increased. Practically, high concentration of PM in the exhaust gas is relatively easy to be deposited on the surface of an EGR cooler. This is so-called fouling which deteriorates the thermal effectiveness of an EGR cooler and increases the pressure drop through it. As a result, the NOx reduction rate and the performance of an engine will be decreased. On the other hand, recent combustion technologies of HSDI diesel engine such as LTC (low temperature combustion) diesel engine and ultra-high pressure fuel injection system lead to severe fouling phenomena because smaller sized particles deposit on the inner surface of an EGR cooler. Accordingly, many studies have been conducted in the field of EGR cooler fouling. However, there are few researches concerning the cross correlation on the fouling phenomena between a test rig and a real engine. In this study, the design features of existing and modified EGR coolers were reviewed to maintain desired performance against fouling resistance. The behavior of fluid such as velocity vector, temperature and pressure contour of exhaust gases in EGR cooler was analyzed through 3-D CFD (Computational Fluid Dynamics). The results which consist of outlet temperatures, heat transfer effectiveness and pressure drops and so on were obtained by using 2 kinds of EGR coolers in production and another 2 kinds of modified EGR coolers. Then, the characteristics of the above EGR coolers on a test rig were investigated at clean and fouled conditions. During this step, fouling resistances which are directly related to the heat transfer effectiveness were verified with gas velocities. In addition, the characteristics of EGR coolers on engine dynamometers were evaluated at clean and fouled conditions. The fouling test mode was made on the basis of ESC 13 mode and proposal from an EGR cooler manufacturer. The characteristics of heat transfer effectiveness and pressure drop were examined with EGR mass flow rates. For the further analyses on the emission level of NOx and PM were performed according to EGR ratio. This work has the advantages to identify the change rate of NOx and PM for the purpose of evaluation of EGR coolers. The fouling characteristics of each step, such as design review, rig test and engine test were evaluated through the aforementioned studies. On the basis of these results, the improved design specification was proposed. The effectiveness of improved EGR cooler was confirmed through CFD analysis. From these, it is possible to suggest proper methodology to obtain good characteristics against fouling of EGR cooler.Chapter 1. Introduction 1.1 Background and Technical review 1.1.1 Emission legislations 1.1.2 Previous researches on fouling phenomena 1.2 Objectives Chapter 2. Theoretical basis 2.1 Fouling mechanism 2.1.1 Thermophoresis by temperature gradient 2.1.2 Condensation and diffusion 2.2 Principle of heat exchanger 2.2.1 Overall heat transfer coefficient 2.2.2 The effectiveness of EGR cooler Chapter 3. Design review for EGR coolers 3.1 CFD analysis 3.1.1 3-D Modeling 3.1.2 CFD analysis condition 3.2 The results of analysis Chapter 4. Experimental test on a test rig 4.1 Experimental setup 4.1.1 Experimental apparatus 4.1.2 Experimental conditions 4.2 Experimental results 4.2.1 The effects of EGR mass flow 4.2.2 The effects of PM feeding 4.3 Fouling mechanics of EGR cooler 4.3.1 The equations for fouling resistance 4.3.2 The results of fouling resistance Chapter 5. Experimental test on an engine 5.1Experim ental setup 5.1.1 Experimental apparatus 5.1.2 Experimental conditions 5.2 Experimental results 5.2.1 Effectiveness characteristics of I-flow EGR coolers 5.2.2 NOx/PM characteristics of I-flow EGR coolers 5.2.3 Effectiveness characteristics of U-flow EGR coolers 5.2.4 NOx/PM characteristics of U-flow EGR coolers 5.2.5 PM characteristics of U-flow type at equivalent NOx 5.2.6 Correlation of effectiveness between rig and engine Chapter 6. Virtual modification of Fin geometry 6.1Virtual modification of fin geometry 6.2 Results of CFD analysis 6.3Validation process for the performance development of EGR cooler 6.4 NOx trade-off as per improvement of effectiveness Chapter 7. Conclusions Bibliography ์ดˆ ๋กDocto

    A Study on the Stress Relief Effects of Adolescent Physical Activity in City Parks

    No full text
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์กฐ๊ฒฝํ•™, 2017. 2. ๊น€์„ฑ๊ท .ํ•œ๊ตญ ์ฒญ์†Œ๋…„์˜ ์ฃผ๊ด€์  ํ–‰๋ณต์ง€์ˆ˜๋Š” OECD ํšŒ์›๊ตญ 22๊ฐœ๊ตญ ์ค‘ ๊ฐ€์žฅ ๋‚ฎ๊ณ  2009๋…„ ์ดํ›„ 2016๋…„๊นŒ์ง€ 2015๋…„(23๊ฐœ๊ตญ ์ค‘ 19์œ„)์„ ์ œ์™ธํ•˜๋ฉด ๋งค๋…„ ์ตœํ•˜์œ„๋กœ ๊ธฐ๋กํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฐ ํ˜„์‹ค ์†์— ์‚ด์•„๊ฐ€๋Š” ํ•œ๊ตญ ์ฒญ์†Œ๋…„๋“ค์„ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์—์„œ ๋ณด๋‹ค ์‹ค์ฒœ์ ์ธ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋ฉฐ, ํŠนํžˆ ์ฒญ์†Œ๋…„์˜ ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ์™„ํ™”์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์•ˆ์— ๋Œ€ํ•œ ๊ตฌ์ฒด์ ์ธ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ด์œ ๋กœ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ ๋ฐ ์˜์˜๋ฅผ ๊ฐ€์ง„๋‹ค. ์ฒซ์งธ, ์ฒญ์†Œ๋…„ ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™”๋ฅผ ์œ„ํ•ด ์—ฌ๊ฐ€์‹œ๊ฐ„์„ ์ด์šฉํ•œ ์‹ ์ฒดํ™œ๋™์ด ๋งค์šฐ ํ•„์š”ํ•˜๋ฏ€๋กœ ๊ทธ๋Ÿฌํ•œ ์‹ ์ฒดํ™œ๋™์„ ์œ„ํ•œ ๋ฐฉ์•ˆ ์ค‘์— ํ•˜๋‚˜๋กœ ๋„์‹œ๊ณต์›์˜ ์ฒญ์†Œ๋…„ ํ™œ์šฉ ๋ฐฉ์•ˆ์— ๋Œ€ํ•œ ๊ทผ๊ฑฐ๋ฅผ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋‘˜์งธ, ์ฒญ์†Œ๋…„ ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™”๋ฅผ ์œ„ํ•ด ์‰ฝ๊ฒŒ ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๊ณ  ์ž์ฃผ ๋ฐฉ๋ฌธ๊ฐ€๋Šฅํ•˜๋ฉฐ ๋‹ค์–‘ํ•œ ํ™œ๋™์ด ์šฉ์ดํ•œ ๋ณด๋‹ค ํ˜„์‹ค์ ์ธ ์‹ค์ฒœ๋ฐฉ์•ˆ์ด ํ•„์š”ํ•˜๋ฏ€๋กœ ์กฐ๊ฒฝ๋ถ„์•ผ์—์„œ ์ฒญ์†Œ๋…„ ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™” ๋ฐฉ์•ˆ ๋ชจ์ƒ‰์˜ ์ผํ™˜์œผ๋กœ ๊ธฐ์กด์—ฐ๊ตฌ์˜ ๋Œ€์ƒ์ง€์ธ ์ˆฒ์œผ๋กœ ๋Œ€ํ‘œ๋˜๋Š” ์ž์—ฐํ™˜๊ฒฝ์ด๋‚˜ ์ฒด์œก์‹œ์„ค ๋ฐ ์˜คํ”ˆ์ŠคํŽ˜์ด์Šค๊ฐ€ ์•„๋‹Œ ๋„์‹œ๊ณต์›์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ์…‹์งธ, ์ฒญ์†Œ๋…„ ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™”ํšจ๊ณผ ๊ฒ€์ฆ์— ๊ด€ํ•œ ์—ฐ๊ตฌ์— ์žˆ์–ด์„œ ํ™•์‹คํ•œ ์‹ ๋ขฐ์„ฑ ํ™•๋ณด๊ฐ€ ํ•„์š”ํ•˜๋ฏ€๋กœ, ์ด๋ฅผ ์œ„ํ•ด ์‹ฌ๋ฆฌ์‹คํ—˜๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ํŠนํžˆ ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ์‹คํ—˜ ์—ฌ๊ฑด, ํ”ผํ—˜์ž๊ตฌ์„ฑ์˜ ์–ด๋ ค์›€, ์ ˆ์ฐจ์ƒ์˜ ๋ณต์žก์„ฑ ๋“ฑ์œผ๋กœ ์ธํ•ด ์ฒญ์†Œ๋…„ ๋Œ€์ƒ์œผ๋กœ ๋งŽ์€ ์—ฐ๊ตฌ์—์„œ ๋„์ž…ํ•˜์ง€ ๋ชปํ–ˆ๋˜ ์ƒ๋ฆฌ์‹คํ—˜๋„ ์ง„ํ–‰ํ•ด์„œ ๋ณด๋‹ค ๊ตฌ์ฒด์ ์ด๊ณ  ๊ณผํ•™์ ์ธ ๊ฒ€์ฆ์„ ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๋ชฉ์ ์„ ๊ฐ€์ง€๊ณ  ์ง„ํ–‰๋˜์—ˆ๋‹ค. ์„œ์šธ ๊ฐ•๋‚จ๊ถŒ ๊ฑฐ์ฃผ ์ค‘๊ณ ๋“ฑํ•™์ƒ์ธ ์ฒญ์†Œ๋…„์„ ๋Œ€์ƒ์œผ๋กœ ์ŠคํŠธ๋ ˆ์Šค ๋Œ€์ฒ˜ ๋ฐฉ๋ฒ•์—์„œ ๋„์‹œ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™” ํšจ๊ณผ์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์ด๋‹ค๋ผ๋Š” ๊ฐ€์„ค์„ ์„ค์ •ํ•˜๊ณ , ๋„์‹œ๊ณต์› ์ฒดํ—˜ํ™œ๋™์„ ๊ฐ•๋‚จ๊ถŒ ์ฒญ์†Œ๋…„์—๊ฒŒ ์ ์šฉํ•จ์œผ๋กœ์จ ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™” ํšจ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‹ฌ๋ฆฌ ๋ฐ ์ƒ๋ฆฌ์ ์œผ๋กœ ์ฒด๊ณ„์ ์ด๊ณ  ๋ช…ํ™•ํ•˜๊ฒŒ ๊ฒ€์ฆํ•˜์—ฌ, ์ฒญ์†Œ๋…„ ์ŠคํŠธ๋ ˆ์Šค ๋Œ€์ฒ˜ ๋ฐฉ์•ˆ์œผ๋กœ ๋„์‹œ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ ๊ทน ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋„๋ก ํ•˜๋Š” ํ† ๋Œ€๋ฅผ ๋งˆ๋ จํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ ๋ชฉ์ ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ์—ฐ๊ตฌ์˜ ๊ณผ์ •์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ตœ๊ทผ ๋™ํ–ฅ๋ถ„์„์œผ๋กœ ๋ฌธ์ œ์ ์„ ํŒŒ์•…ํ•˜๊ณ  ์„ ํ–‰์—ฐ๊ตฌ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์—ฐ๊ตฌ์˜ ์ค‘์š”์„ฑ์„ ์ธ์ง€ํ•ด์„œ ์—ฐ๊ตฌ๋ฐฉํ–ฅ ๋ฐ ๋ชฉ์ ์„ ์„ค์ •ํ•˜์˜€๋‹ค. ๋‘˜์งธ, ๊ตฌ์ฒด์ ์œผ๋กœ ์ฒญ์†Œ๋…„ ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™”ํšจ๊ณผ ๊ฒ€์ฆ์„ ์œ„ํ•ด ๊ธฐ์กด ์—ฐ๊ตฌ ๋ฐ ๋ฐฉ๋ฒ•์„ ๊ฒ€ํ† ํ•œ ํ›„ ์ด๋ฒˆ ์‹คํ—˜ ๋Œ€์ƒ์ง€์ธ ์„œ์šธ์ˆฒ๊ณต์›์— ์ ํ•ฉํ•˜๊ณ  ํšจ๊ณผ์ ์ธ ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๊ณผ ์ธก์ •์ง€ํ‘œ๋“ค์„ ์„ ๋ณ„ํ•˜์˜€๋‹ค. ์…‹์งธ, ์ด์„ ํ† ๋Œ€๋กœ ๊ตฌ์ฒด์ ์ธ ์‹คํ—˜ ์„ค๊ณ„๋ฅผ ๊ณ ์•ˆํ•˜์˜€๋‹ค. ๋„ท์งธ, ์‹คํ—˜์„ค๊ณ„๋Œ€๋กœ ์˜ˆ๋น„์‹คํ—˜ ํ›„ ๋ณธ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ ์ธก์ •๋œ ์‹คํ—˜๋ฐ์ดํ„ฐ๋ฅผ ๊ฐ ๋ถ„์•ผ ์ „๋ฌธ๊ฐ€์™€ ์˜๋ฃŒ ๋ฐ ์—ฐ๊ตฌ๊ธฐ๊ด€๊ณผ ํ˜‘์กฐํ•˜์—ฌ ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ๋‹ค์„ฏ์งธ, ๋„์ถœ๋œ ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ํ†ต๊ณ„๊ด€๋ จ ํ”„๋กœ๊ทธ๋žจ๋“ค์„ ํ™œ์šฉํ•ด์„œ ๋ถ„์„ํ•˜์˜€๊ณ  ๋ถ„์„๋œ ๋‚ด์šฉ์˜ ํ‰๊ฐ€ ๋ฐ ๊ณ ์ฐฐ์„ ํ†ตํ•ด ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์— ๋งž๋Š” ๊ฒฐ๋ก ์„ ๋„์ถœํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๊ณผ์ •์œผ๋กœ ์ง„ํ–‰๋œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋„์‹œ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ฒญ์†Œ๋…„์—๊ฒŒ ์‹ฌ๋ฆฌ ๋ฐ ์ƒ๋ฆฌ์ ์œผ๋กœ ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ์ฃผ๋Š”์ง€๋ฅผ ๋ช…ํ™•ํ•˜๊ฒŒ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด 2014๋…„ 3์›”์ดˆ์— ๊ฐ•๋‚จ๊ถŒ ์ฒญ์†Œ๋…„ 10๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์„œ์šธ์ˆฒ๊ณต์›์—์„œ ์˜ˆ๋น„์‹คํ—˜์„ ์‹ค์‹œํ•˜์˜€๊ณ  ์ด๋ฅผ ํ† ๋Œ€๋กœ ์ฒด๊ณ„์ ์ธ ์‹คํ—˜์„ค๊ณ„๋ฅผ ๊ณ ์•ˆํ•˜์˜€๋‹ค. ๊ตฌ์ฒด์ ์ธ ๋‚ด์šฉ์„ ์‚ดํŽด๋ณด๋ฉด ๊ฐ™์€ ํ•ด 10์›”์ดˆ์— ๊ฐ•๋‚จ์— ๊ฑฐ์ฃผํ•˜๋Š” ์ค‘ํ•™๊ต 2ํ•™๋…„์—์„œ ๊ณ ๋“ฑํ•™๊ต 1ํ•™๋…„์ธ ์ฒญ์†Œ๋…„ 25๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์„œ์šธ์ˆฒ๊ณต์› Park 1์—์„œ ์‹คํ—˜์„ ์ง„ํ–‰ํ•œ๋‹ค. ๊ณต์› ์ฒดํ—˜ํ™œ๋™ ์ „์— ์ƒ๋ฆฌ์ธก์ •์ธ ์ฝ”ํ‹ฐ์†”, ์‹ฌ๋ฐ•์ˆ˜, ์‹ฌ๋ฐ•๋ณ€์ด๋„์™€ ์‹ฌ๋ฆฌ๊ฒ€์‚ฌ์ธ ์ž์•„์กด์ค‘๊ฐ, ์ƒํƒœ๋ถˆ์•ˆ๊ฐ, ๊ธฐ๋ถ„์ƒํƒœ๊ฒ€์‚ฌ๋ฅผ ์‹ค์‹œํ•œ ํ›„, ๊ณต์› ํƒ๋ฐฉ์ฒดํ—˜ ๋ฐ ์„ ํ˜ธ์žฅ์†Œ ์‹ ์ฒดํ™œ๋™์„ 1์‹œ๊ฐ„๋™์•ˆ ์ง„ํ–‰ํ•˜๊ณ  ๊ทธ ํ›„์— ์ฒดํ—˜ ์ „์— ์‹ค์‹œํ–ˆ๋˜ ๊ฐ™์€ ์ƒ๋ฆฌ ๋ฐ ์‹ฌ๋ฆฌ๊ฒ€์‚ฌ๋ฅผ ์ง„ํ–‰ํ•œ๋‹ค. ์—ฌ๊ธฐ์„œ ๋‚˜์˜จ ์ƒ๋ฆฌ์‹คํ—˜ ๋ฐ์ดํ„ฐ๋Š” ์˜๋ฃŒ๊ธฐ๊ด€์— ์˜๋ขฐํ•˜๊ณ  ์‹ฌ๋ฆฌ์‹คํ—˜ ๋ฐ์ดํ„ฐ๋Š” ์ „๋ฌธ๊ฐ€์™€ ํ˜‘์กฐํ•˜์—ฌ ๋ถ„์„ํ•œ๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ๋น„๊ต๋ถ„์„ํ•œ ํ›„ ๊ฒฐ๊ณผ ๊ณ ์ฐฐ์„ ํ†ตํ•ด ๋ณธ ์—ฐ๊ตฌ ๋ชฉ์ ์— ๋งž๋Š” ๊ฒฐ๋ก ์„ ๋„์ถœํ•œ๋‹ค. ์‹คํ—˜์„ค๊ณ„๋Œ€๋กœ ์ง„ํ–‰ํ•œ ์‹คํ—˜ ์ค‘ ์ƒ๋ฆฌ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ •๋ฆฌํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋ณธ ์—ฐ๊ตฌ์˜ ํƒ€์•ก ์ค‘ ์ฝ”ํ‹ฐ์†”์˜ ๋†๋„์— ๋Œ€ํ•ด ์‚ดํŽด๋ณด๋ฉด ๊ณต์› ์ฒดํ—˜ํ™œ๋™ ์ „์— 0.35 ยตg/dl์—์„œ ์ฒดํ—˜ํ™œ๋™ ํ›„์— 0.28 ยตg/dl๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•„์กŒ๋Š”๋ฐ(p=0.024), ์ด๋Š” ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ŠคํŠธ๋ ˆ์Šค ์ƒํƒœ์— ์žˆ์„ ๋•Œ ๋ถ„๋น„๋˜๋Š” ์ฝ”ํ‹ฐ์†” ํ˜ธ๋ฅด๋ชฌ ์ˆ˜์น˜๋ฅผ ๋‚ฎ์ถฐ์ค˜์„œ ์ŠคํŠธ๋ ˆ์Šค ํ•ด์†Œ์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ์—ˆ๋‹ค๊ณ  ํŒ๋‹จ๋œ๋‹ค. ๋‘˜์งธ, ๋ณธ ์—ฐ๊ตฌ์˜ ์‹ฌ์žฅ๋ฐ•๋™์ˆ˜๋ฅผ ์‚ดํŽด๋ณด๋ฉด ๊ณต์› ์ฒดํ—˜ํ™œ๋™ ์ „์— 81.85ํšŒ/๋ถ„์—์„œ ์ฒดํ—˜ํ™œ๋™ ํ›„์— 75.18ํšŒ/๋ถ„๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•„์กŒ๋Š”๋ฐ(p=0.024), ์ด๋Š” ์‹ ์ฒด์˜ ๊ธด์žฅ์„ ๋‚ฎ์ถฐ์ฃผ๊ณ  ๋ชธ๊ณผ ๋งˆ์Œ์ด ์•ˆ์ •ํ™”๋˜์—ˆ์Œ์„ ๋ณด์—ฌ์ค€ ๊ฒƒ์ด๋‹ค. ์…‹์งธ, ๋ณธ ์—ฐ๊ตฌ์˜ ์‹ฌ๋ฐ•๋ณ€์ด๋„ ์ธก์ •์‹คํ—˜์„ ํ†ตํ•ด HF์„ฑ๋ถ„์„ ์‚ดํŽด๋ณด๋ฉด ๊ณต์› ์ฒดํ—˜ํ™œ๋™ ์ „์— 521.64(msec2)์—์„œ ํ›„์— 767.97(msec2)๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋†’์•„์กŒ๋Š”๋ฐ(p=0.039), ์ด๋Š” ์•ˆ์ •๋  ๋•Œ์— ํ™œ์„ฑํ™”๋˜๋Š” ๋ถ€๊ต๊ฐ์‹ ๊ฒฝํ™œ๋™์ด ํ™œ์„ฑํ™”๋˜์—ˆ์Œ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๋„ท์งธ, LF/HF์„ฑ๋ถ„์—์„œ๋„ ๊ณต์› ์ฒดํ—˜ํ™œ๋™ ์ „์— 1.81์—์„œ ์ฒดํ—˜ํ™œ๋™ ํ›„์— 1.21๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•„์กŒ๋Š”๋ฐ(p=0.002), ์ด๋Š” ์ŠคํŠธ๋ ˆ์Šค ์ƒํƒœ์—์„œ ํ™œ์„ฑํ™”๋˜๋Š” ๊ต๊ฐ์‹ ๊ฒฝํ™œ๋™์ด ์–ต์ œ๋จ๊ณผ ๋™์‹œ์— ๋ถ€๊ต๊ฐ์‹ ๊ฒฝํ™œ๋™์ด ํ™œ์„ฑํ™”๋˜์—ˆ์Œ์„ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ์‹คํ—˜์„ค๊ณ„๋Œ€๋กœ ์ง„ํ–‰ํ•œ ์‹คํ—˜ ์ค‘ ์‹ฌ๋ฆฌ์‹คํ—˜ ๊ฒฐ๊ณผ๋ฅผ ์ •๋ฆฌํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋ณธ ์—ฐ๊ตฌ์˜ ์ž์•„์กด์ค‘๊ฐ ๊ฒ€์‚ฌ๊ฒฐ๊ณผ๋ฅผ ์‚ดํŽด๋ณด๋ฉด 69.22์—์„œ ๊ณต์› ์ฒดํ—˜ํ™œ๋™ ํ›„์— 74.96์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋†’์•„์กŒ๋Š”๋ฐ(p=0.032), ์ด๋Š” ํ”ผํ—˜์ž์˜ ์ž์‹ ๊ฐ๊ณผ ์ž๊ธ์‹ฌ์„ ํšŒ๋ณต์‹œ์ผœ ์ž์•„์กด์ค‘๊ฐ ํ–ฅ์ƒ์— ๋„์›€์„ ์ฃผ์—ˆ์Œ์„ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ๋‘˜์งธ, ๋ณธ ์—ฐ๊ตฌ์˜ ์ƒํƒœ๋ถˆ์•ˆ๊ฐ ๊ฒ€์‚ฌ๊ฒฐ๊ณผ๋ฅผ ์‚ดํŽด๋ณด๋ฉด 44.13์ ์—์„œ ๊ณต์› ์ฒดํ—˜ํ™œ๋™ ํ›„์— 38.57์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๊ฐ์†Œํ•˜์˜€๋Š”๋ฐ(p=0.013), ์ด๋Š” ํ”ผํ—˜์ž์˜ ๋ถˆ์•ˆ์ƒํƒœ๋ฅผ ๊ฐ์†Œ์‹œํ‚ค๊ณ  ์ •์„œ์•ˆ์ •์— ๊ธฐ์—ฌํ•˜๊ณ  ์žˆ์Œ์„ ๋‚˜ํƒ€๋‚ด๊ณ  ์žˆ๋‹ค. ์…‹์งธ, ๋ณธ ์—ฐ๊ตฌ์˜ ๊ธฐ๋ถ„์ƒํƒœ๊ฒ€์‚ฌ(POMS)์˜ ๊ฒ€์‚ฌ๊ฒฐ๊ณผ๋ฅผ ์‚ดํŽด๋ณด๋ฉด ๊ณต์› ์ฒดํ—˜ํ™œ๋™ ํ›„์— ๊ธด์žฅ-๋ถˆ์•ˆ(T-A)์€ 6.04์—์„œ 2.83์œผ๋กœ(p=0.013), ์šฐ์šธ(D)์€ 4.83์—์„œ 1.96์œผ๋กœ(p=0.003), ํ”ผ๋กœ(F)๋Š” 6.91์—์„œ 4.35๋กœ(p=0.009), ์ข…ํ•ฉ์ •์„œ์žฅ์• (TMD)๋Š” 16.87์—์„œ 3.57๋กœ(p=0.004) ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•„์กŒ๊ณ , ๋ถ„๋…ธ(A-H)๋Š” 3.39์—์„œ 0.04๋กœ, ํ˜ผ๋ž€(C)์€ 6.96์—์„œ 5.48๋กœ ๋‚ฎ์•„์กŒ์œผ๋ฉฐ ํ™œ๊ธฐ(V)๋Š” 11.26์—์„œ 13.09๋กœ ๋†’์•„์กŒ๋Š”๋ฐ, ์ด๋Š” ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์‹ฌ๋ฆฌ์ƒํƒœ๋ฅผ ์•ˆ์ •์‹œ์ผœ ์ฃผ๊ณ  ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ์คฌ์Œ์„ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ํ† ๋Œ€๋กœ ๊ฒฐ๋ก ์„ ๋‚ด๋ฆฌ๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ŠคํŠธ๋ ˆ์Šค์˜ ์ƒ๋ฆฌ์  ์ง€ํ‘œ์ธ ํƒ€์•ก ์ค‘ ์ฝ”ํ‹ฐ์†”์˜ ๋†๋„(Salivary Cortisol)์™€ ์‹ฌ๋ฐ•์ˆ˜(HR)์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ฐธ์—ฌ ์ „๋ณด๋‹ค ์ฐธ์—ฌ ํ›„ ์ฝ”ํ‹ฐ์†” ๋†๋„์™€ ์‹ฌ๋ฐ•์ˆ˜๊ฐ€ ๊ฐ์†Œํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๊ฐ•๋‚จ๊ถŒ ์ฒญ์†Œ๋…„์„ ๋Œ€์ƒ์œผ๋กœ ๋„์‹œ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™”์— ํšจ๊ณผ๊ฐ€ ์žˆ์—ˆ์Œ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ํŠนํžˆ ์ฒดํ—˜ ์ „ ๋†’์€ ์ฝ”ํ‹ฐ์†” ๋†๋„์™€ ์‹ฌ๋ฐ•์ˆ˜๋ฅผ ํ†ตํ•ด ๊ฐ•๋‚จ๊ถŒ ์ฒญ์†Œ๋…„๋“ค์ด ํ‰์†Œ์— ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ๋” ๋งŽ์ด ๋ฐ›๊ณ  ์žˆ๋‹ค๋ผ๋Š” ๊ฐ€์ •์„ ์ž…์ฆํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‘˜์งธ, ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์‹ฌ๋ฐ•๋ณ€์ด๋„(HRV)์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ฐธ์—ฌ ์ „๋ณด๋‹ค ์ฐธ์—ฌ ํ›„ ์•ˆ์ •๋  ๋•Œ์— ํ™œ์„ฑํ™”๋˜๋Š” ๋ถ€๊ต๊ฐ์‹ ๊ฒฝํ™œ๋™์ด ํ™œ์„ฑํ™”๋˜๊ณ , ์ŠคํŠธ๋ ˆ์Šค ์ƒํƒœ์—์„œ ํ™œ์„ฑํ™”๋˜๋Š” ๊ต๊ฐ์‹ ๊ฒฝํ™œ๋™์ด ์–ต์ œ๋œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ฒญ์†Œ๋…„์˜ ์ƒ๋ฆฌ์ ์œผ๋กœ ์•ˆ์ •์‹œ์ผœ ์ค€ ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ํŠนํžˆ ๊ธฐ์กด ์—ฐ๊ตฌ์— ๋น„ํ•ด ์œ ์˜์„ฑ ์žˆ๋Š” ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜์˜ฌ ์ˆ˜ ์žˆ์—ˆ๋˜ ๊ฒƒ์€ ์ˆฒ ์ฒดํ—˜, ์ž์—ฐ๊ฒฝ๊ด€ ๊ฐ์ƒ, ์šด๋™ ๋“ฑ์˜ ์‹ ์ฒดํ™œ๋™ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‹ค์–‘ํ•œ ์‹ ์ฒดํ™œ๋™์ด ๊ณต์›์„ ํ†ตํ•ด ๋ณตํ•ฉ์ ์œผ๋กœ ์ด๋ฃจ์–ด์ง€๋ฉด์„œ ์‹œ๋„ˆ์ง€ ํšจ๊ณผ๊ฐ€ ๋‚œ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋˜์—ˆ๋‹ค. ์…‹์งธ, ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ฒญ์†Œ๋…„์˜ ์ž์•„์กด์ค‘๊ฐ(SEI)๊ณผ ์ƒํƒœ๋ถˆ์•ˆ๊ฐ(STAI-X1)์— ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ฐธ์—ฌ ์ „๊ณผ ํ›„์˜ ๋ณ€ํ™”๋ฅผ ์‚ดํŽด๋ณด๋ฉด, ์ž์•„์กด์ค‘๊ฐ์€ ์œ ์˜ํ•˜๊ฒŒ ํ–ฅ์ƒ๋˜์—ˆ๊ณ  ์ƒํƒœ๋ถˆ์•ˆ๊ฐ์€ ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•„์ง„ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ ์ด๋ฅผ ํ†ตํ•ด ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ฒญ์†Œ๋…„์˜ ๋ถˆ์•ˆ๊ฐ์„ ์•ˆ์ •ํ™”์‹œ์ผœ์ฃผ๊ณ  ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ์–ด ์ž์‹ ์ด ๊ฐ€์น˜๊ฐ€ ์žˆ๊ณ  ์ค‘์š”ํ•˜๋‹ค๊ณ  ์ƒ๊ฐํ•˜๋Š” ์ž์•„์กด์ค‘๊ฐ์„ ํ–ฅ์ƒ์‹œ์ผœ ์ค€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋„ท์งธ, ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ฒญ์†Œ๋…„์˜ ์ •์‹ ๊ฑด๊ฐ•์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ์‹ค์‹œํ•œ ๊ธฐ๋ถ„์ƒํƒœ๊ฒ€์‚ฌ(POMS)๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ฐธ์—ฌ ์ „๊ณผ ํ›„์˜ ๋ณ€ํ™”๋ฅผ ์‚ดํŽด๋ณด๋ฉด, ๊ธด์žฅ-๋ถˆ์•ˆ, ์šฐ์šธ, ํ”ผ๋กœ, ํ˜ผ๋ž€, ํ™œ๊ธฐ, ๋ถ„๋…ธ, ์ข…ํ•ฉ์ •์„œ์žฅ์• ์—์„œ ๋ชจ๋‘ ๊ธ์ •์ ์ธ ๊ฒฐ๊ณผ๊ฐ€ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ ํŠนํžˆ ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ํ‰๊ฐ€์— ์ ํ•ฉํ•œ ํ•˜์œ„์š”์†Œ์ธ ๊ธด์žฅ-๋ถˆ์•ˆ, ์šฐ์šธ, ํ”ผ๋กœ, ์ข…ํ•ฉ์ •์„œ์žฅ์• ์—์„œ ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•„์ง„ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ณต์› ์ฒดํ—˜ํ™œ๋™์ด ์ƒํƒœ๋ถˆ์•ˆ๊ฐ๊ณผ ๋”๋ถˆ์–ด ์‹ฌ๋ฆฌ์ ์œผ๋กœ ๊ธด์žฅ๊ณผ ๋ถˆ์•ˆํ•œ ๊ฐ์ •์„ ์•ˆ์ •์‹œ์ผœ์ฃผ๊ณ  ํ”ผ๋กœ๋ฅผ ํ’€์–ด์ฃผ๋Š” ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ์—ˆ์œผ๋ฉฐ ์ด๋กœ ์ธํ•ด ์šฐ์šธ๊ฐ์ด ํšŒ๋ณต๋˜์–ด ์ „์ฒด์ ์œผ๋กœ ๊ธฐ๋ถ„์ƒํƒœ๋ฅผ ๊ฐœ์„ ์‹œ์ผœ์คŒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ œํ•œ ๋ฐ ์‹œ์‚ฌ์ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฒญ์†Œ๋…„ 25๋ช…(์‹คํ—˜๊ฒฐ๊ณผ๋ฐ˜์˜ ์ฐธ๊ฐ€์ž 23๋ช…)์„ ๋Œ€์ƒ์œผ๋กœ ๊ณต์› ์ฒดํ—˜ํ™œ๋™์„ ์‹ค์‹œํ•œ ๊ฒฐ๊ณผ๋กœ ์ง€์—ญ์ , ํ‘œ๋ณธ์  ์ œํ•œ์ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์ฒญ์†Œ๋…„ ๋ชจ๋‘์—๊ฒŒ ์ผ๋ฐ˜ํ™”ํ•˜๋Š” ๊ฒƒ์€ ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค. ์ด์— ๋ณด๋‹ค ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ๊ฒฐ๋ก ์„ ๋„์ถœํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ฒ€์‚ฌ์žฅ๋น„ ๋ฐ ์ถฉ๋ถ„ํ•œ ์ธ๋ ฅ ํ™•๋ณด๋ฅผ ํ†ตํ•ด ๋” ๋งŽ์€ ํ”ผํ—˜์ž๊ฐ€ ์ฐธ์—ฌํ•˜๋Š” ์‹คํ—˜์„ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋‘˜์งธ, ๋ณธ ์—ฐ๊ตฌ๋Š” ๋น„๊ต์ง‘๋‹จ์„ ์„ ์ •ํ•˜์ง€ ์•Š๊ณ  ์‹คํ—˜์ง‘๋‹จ๋งŒ์„ ๋Œ€์ƒ์œผ๋กœ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฏ€๋กœ ์ถ”๊ฐ€ ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„๊ต์ง‘๋‹จ์ด ์„ค์ •๋œ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ๋„์‹œ๊ณต์›๊ณผ ์ˆฒ๊ณผ ์ฒด์œก์‹œ์„ค ๊ณต๊ฐ„ ๋“ฑ ๋‹ค๋ฅธ ์œ ํ˜•์˜ ๋Œ€์ƒ์ง€๋‚˜ ๋‹ค๋ฅธ ๊ณต์›์„ ๋Œ€์ƒ์œผ๋กœ ์ฒดํ—˜ํ™œ๋™์„ ํ†ตํ•œ ๋น„๊ต ๊ฒ€์ฆ ๋“ฑ์˜ ํ›„์† ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ์…‹์งธ, ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ์ฒดํ—˜ํ™œ๋™ ์ด์™ธ์— ๋‹ค์–‘ํ•œ ํ”„๋กœ๊ทธ๋žจ ์ ์šฉ์„ ํ†ตํ•œ ๋น„๊ต ๊ฒ€์ฆ ๋“ฑ์˜ ํ›„์† ์—ฐ๊ตฌ ๋ฐ ๋ณด๋‹ค ํšจ๊ณผ์ ์ธ ๊ณต์› ์ฒดํ—˜ํ™œ๋™ ํ”„๋กœ๊ทธ๋žจ ๊ฐœ๋ฐœ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ฒญ์†Œ๋…„์˜ ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™” ํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๋Š”๋ฐ ๋ชฉ์ ์ด ์žˆ์—ˆ์œผ๋ฏ€๋กœ ์ถ”ํ›„์—๋Š” ํšจ๊ณผ์˜ ์ง€์†์„ฑ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ์„œ์šธ์ˆฒ๊ณต์› ๋“ฑ ๋„์‹œ๊ณต์›์— ์ธ์ ‘ํ•œ ์ค‘๊ณ ๋“ฑํ•™๊ต ํ•™์ƒ๋“ค์˜ ๋ฐฉ๊ณผ ํ›„ ์ˆ˜์—…๊ณผ ์—ฐ๊ณ„ํ•˜์—ฌ 1๋…„ ๋™์•ˆ ์ง€์†์ ์œผ๋กœ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๋ฉด์„œ ์—ฌ๋Ÿฌ ๋ฒˆ์˜ ์ •๊ธฐ์ ์ธ ์ธก์ •์„ ํ†ตํ•ด ๊ณต์›ํ”„๋กœ๊ทธ๋žจ์— ๋”ฐ๋ฅธ ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™” ํšจ๊ณผ์˜ ์ง€์†์„ฑ์„ ์ธก์ •ํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋˜ํ•œ ๊ฐ์ข… ์ฒดํ—˜ํ™œ๋™์— ๋”ฐ๋ฅธ ํšจ๊ณผ์˜ ์ง€์†์„ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ ๋“ฑ๋„ ์ œ์•ˆํ•˜๊ณ ์ž ํ•œ๋‹ค.I. ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 1.2 ์—ฐ๊ตฌ์˜ ๋ชฉ์  8 II. ์ด๋ก ์  ๋ฐฐ๊ฒฝ 10 2.1 ์„ ํ–‰ ์—ฐ๊ตฌ ๊ณ ์ฐฐ 10 2.1.1 ์ฒญ์†Œ๋…„์˜ ์—ฌ๊ฐ€ํ™œ๋™ 10 2.1.2 ๋„์‹œ๊ณต์› ์ฒดํ—˜ํ™œ๋™ 12 2.1.3 ์ŠคํŠธ๋ ˆ์Šค ์™„ํ™”ํšจ๊ณผ 14 2.2 ์ƒ๋ฆฌ์‹คํ—˜ ์ธก์ •์ง€ํ‘œ 16 2.2.1 ์ฝ”ํ‹ฐ์†” ๋†๋„(Cortisol) 16 2.2.2 ์‹ฌ์žฅ๋ฐ•๋™์ˆ˜(Heart Rate) 17 2.2.3 ์‹ฌ๋ฐ•๋ณ€์ด๋„(HRV) 18 2.3 ์‹ ๋ขฐ๋„ ๊ณ„์ˆ˜(Cronbachs) 23 2.4 ์‹ฌ๋ฆฌ์‹คํ—˜ ์ธก์ •์ง€ํ‘œ 24 2.4.1 ์ž์•„์กด์ค‘๊ฐ(SES) 25 2.4.2 ์ƒํƒœํŠน์„ฑ๋ถˆ์•ˆ์ฒ™๋„(STAI) 32 2.4.3 ๊ธฐ๋ถ„์ƒํƒœ๊ฒ€์‚ฌ(POMS) 35 2.5 ํ†ต๊ณ„์  ๊ฒ€์ •๋ ฅ ๋ถ„์„ 38 III. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• ๋ฐ ๋‚ด์šฉ 42 3.1 ์—ฐ๊ตฌ์˜ ๊ณผ์ • 42 3.2 ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ 43 3.2.1 ํ‘œ๋ณธ ํฌ๊ธฐ 43 3.2.2 ํ”ผํ—˜์ž 46 3.2.3 ๋Œ€์ƒ์ง€ 47 3.3 ์‹คํ—˜ ์„ค๊ณ„ 50 3.4 ๊ณต์› ์ฒดํ—˜ํ™œ๋™ ์ ์šฉ 53 3.4.1 ๊ณต์›ํƒ๋ฐฉ ์ฒดํ—˜ํ™œ๋™ 57 3.4.2 ์„ ํ˜ธ์žฅ์†Œ ์‹ ์ฒดํ™œ๋™ 58 3.5 ํ‰๊ฐ€ ๋„๊ตฌ 60 3.5.1 ์ƒ๋ฆฌ์‹คํ—˜ 60 3.5.2 ์‹ฌ๋ฆฌ์‹คํ—˜ 63 3.6 ์ž๋ฃŒ ๋ถ„์„ 64 IV. ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 65 4.1 ๊ณต์›์ฒดํ—˜ ์ƒ๋ฆฌํ‰๊ฐ€ 65 4.1.1 ํƒ€์•ก ์ค‘ ์ฝ”ํ‹ฐ์†” ๋†๋„(Salivary Cortisol) 65 4.1.2 ์‹ฌ์žฅ๋ฐ•๋™์ˆ˜(HR) 68 4.1.3 ์‹ฌ๋ฐ•๋ณ€์ด๋„(HRV) : HF์„ฑ๋ถ„๊ณผ LF/HF์„ฑ๋ถ„ 69 4.1.4 ์†Œ ๊ฒฐ 72 4.2 ๊ณต์›์ฒดํ—˜ ์‹ฌ๋ฆฌํ‰๊ฐ€ 74 4.2.1 ์ž์•„์กด์ค‘๊ฐ(SEI) 75 4.2.2 ์ƒํƒœ๋ถˆ์•ˆ๊ฐ(STAI-X1) 76 4.2.3 ๊ธฐ๋ถ„์ƒํƒœ๊ฒ€์‚ฌ(POMS) 77 4.2.4 ์†Œ ๊ฒฐ 85 4.3 ์‹คํ—˜๊ฒฐ๊ณผ์— ๊ด€ํ•œ ํ”ผํ—˜์ž๋ณ„ ๊ณ ์ฐฐ 87 V. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 91 ์ธ์šฉ๋ฌธํ—Œ 94 ๊ตญ๋‚ด๋ฌธํ—Œ 94 ๊ตญ์™ธ๋ฌธํ—Œ 102 ๋ถ€๋ก 110 Abstract 113Docto
    corecore