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    ํ•จ๋ฐฑ์‚ฐ ๋ถ„๋น„๋‚˜๋ฌด์˜ ์ง‘๋‹จ ๋‚ด ์œ ์ „๋ณ€์ด์™€ ์ข…์ž ํŠน์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ์‚ฐ๋ฆผ๊ณผํ•™๋ถ€(์‚ฐ๋ฆผํ™˜๊ฒฝํ•™์ „๊ณต), 2023. 2. ๊ฐ•๊ทœ์„.Abies nephrolepis (Trautv. ex Maxim.) Maxim. is a subalpine conifer species and its southernmost natural populations are distributed in South Korea. Conservation actions should be considered as population decline is expected due to climate change. For ex-situ conservation of A. nephrolepis, securing enough genetic diversity and quantity of seeds is important. There were several studies on the weak genetic variation among populations of A. nephrolepis in South Korea, but there were few studies on genetic variation within a population. This study aimed to provide a reference for ex-situ conservation of an A. nephrolepis population in South Korea. Genetic variations and seed characteristics were investigated in an A. nephrolepis population at Mt. Hambaeksan, Gangwon-do, South Korea. The population of A. nephorlepis in Mt. Hambaeksan had the observed heterozygosity of 0.809 and the expected heterozygosity of 0.820. Spatial autocorrelation analysis revealed that the individuals had a positive genetic relationship within a 30 m distance, which was interpreted as a minimal sampling distance. The bigger trees had a stronger spatial genetic structure than the smaller trees. The inferred number of clusters (K) within a population did not converge: STRUCTURE inferred K=1 while GENELAND inferred K=2, but principal coordinates analysis (PCoA) supported K=1. Sampling simulation found that at least 20 individuals need to be sampled to secure the genetic diversity in a population. The seeds of A. nephrolepis population in Mt. Hambaeksan had similar characteristics to other populations in South Korea, with a germination percentage of 32.2%. However, there was a lower purity, probably caused by the higher temperature and less precipitation in the sampling year. The correlation analysis showed that the seed weight could be the most effective indicator of seed quality. The mother trees that were genetically closer to overall individuals had poorer seed quality, but it was insignificant. This study provided some strategies that contribute to the ex-situ conservation of A. nephrolepis. Further studies on the mating system and gene dispersal are needed to improve the understanding of the genetic structure of an A. nephrolepis population.๋ถ„๋น„๋‚˜๋ฌด Abies nephrolepis (Trautv. ex Maxim.) Maxim.๋Š” ์•„๊ณ ์‚ฐ ์นจ์—ฝ์ˆ˜์ข…์˜ ํ•˜๋‚˜๋กœ ๋‚จ๋ฐฉํ•œ๊ณ„ ์ง‘๋‹จ์ด ๊ตญ๋‚ด์— ๋ถ„ํฌํ•ด ์žˆ๋‹ค. ํ˜„์žฌ ๋ถ„๋น„๋‚˜๋ฌด๋Š” ๊ธฐํ›„๋ณ€ํ™” ์ทจ์•ฝ์ข…์œผ๋กœ ์‡ ํ‡ด๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ์–ด ๋ณด์ „์˜ ์ค‘์š”์„ฑ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ํ˜„์ง€์™ธ ๋ณด์ „์„ ์œ„ํ•ด์„œ๋Š” ์œ ์ „๋‹ค์–‘์„ฑ๊ณผ ์ข…์ž์— ๋Œ€ํ•œ ์ดํ•ด๊ฐ€ ํ•„์ˆ˜์ ์ด๋‹ค. ๊ทธ ๋™์•ˆ ๋ถ„๋น„๋‚˜๋ฌด ์ง‘๋‹จ์— ๋Œ€ํ•ด์„œ๋Š” ์ง‘๋‹จ ๊ฐ„ ๋ณ€์ด๋ฅผ ํ™•์ธํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ์ฃผ๋กœ ์ง„ํ–‰๋˜์–ด ์™”์œผ๋ฉฐ, ์ง‘๋‹จ ๊ฐ„ ๋ถ„ํ™”๊ฐ€ ์ž‘์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ถ„๋น„๋‚˜๋ฌด์˜ ์ง‘๋‹จ ๋‚ด ๋ณ€์ด์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๊ฑฐ์˜ ์ง„ํ–‰๋œ ๋ฐ”๊ฐ€ ์—†๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ถ„๋น„๋‚˜๋ฌด์˜ ํ˜„์ง€์™ธ ๋ณด์ „ ์ „๋žต ์ˆ˜๋ฆฝ์— ํ•„์š”ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ํ•จ๋ฐฑ์‚ฐ ๋ถ„๋น„๋‚˜๋ฌด์˜ ์ง‘๋‹จ ๋‚ด ์œ ์ „๊ตฌ์กฐ์™€ ์ข…์ž ํŠน์„ฑ์„ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ํ•จ๋ฐฑ์‚ฐ ๋ถ„๋น„๋‚˜๋ฌด์˜ ์ดํ˜•์ ‘ํ•ฉ๋„ ๊ด€์ธก์น˜๋Š” 0.809, ์ดํ˜•์ ‘ํ•ฉ๋„ ๊ธฐ๋Œ€์น˜๋Š” 0.820๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ณต๊ฐ„์  ์ž๊ธฐ์ƒ๊ด€์„ฑ ๋ถ„์„ ๊ฒฐ๊ณผ, ๋ถ„๋น„๋‚˜๋ฌด ๊ฐœ์ฒด๋Š” 30 m ์ด๋‚ด์—์„œ ์„œ๋กœ ์œ ์ „์ ์œผ๋กœ ์—ฐ๊ด€๋˜์–ด ์žˆ์Œ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด๋Š” ๊ณง ์ƒ˜ํ”Œ๋ง์— ์žˆ์–ด ๊ฐœ์ฒด ๊ฐ„ ๊ฑฐ๋ฆฌ๊ฐ€ 30 m ์ด์ƒ ๊ฐ„๊ฒฉ์ด ์œ ์ง€๋˜์–ด์•ผ ํ•จ์„ ์˜๋ฏธํ•œ๋‹ค. ์ง๊ฒฝ๊ธ‰ ๊ฐ„ ๊ณต๊ฐ„์  ์œ ์ „๊ตฌ์กฐ์˜ ์ •๋„๋ฅผ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, ์ง๊ฒฝ์ด ํฐ ๋‚˜๋ฌด๋“ค์—์„œ ์ƒ๋Œ€์ ์œผ๋กœ ๊ฐ•ํ•œ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง€๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ์ง‘๋‹จ ๋‚ด ํ•˜๋ถ€ ์ง‘๋‹จ์˜ ์ˆ˜(K)๋ฅผ ๊ณ„์‚ฐํ•œ ๊ฒฐ๊ณผ, STRUCTURE์™€ GENELAND๋Š” ๊ฐ๊ฐ K=1, K=2๋กœ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ฃผ์ขŒํ‘œ ๋ถ„์„ ๊ฒฐ๊ณผ, ํŠน๋ณ„ํ•œ ํ•˜๋ถ€ ๊ตฌ์กฐ๊ฐ€ ๋ฐœ๊ฒฌ๋˜์ง€ ์•Š์•„ K=1์˜ ๊ฒฐ๊ณผ๊ฐ€ ์ง€์ง€๋˜์—ˆ๋‹ค. ์ƒ˜ํ”Œ๋ง ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ, ์ถฉ๋ถ„ํ•œ ์œ ์ „๋ณ€์ด๋ฅผ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ ์–ด๋„ 20 ๊ฐœ์ฒด ์ด์ƒ์ด ์ฑ„์ง‘๋˜์–ด์•ผ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ•จ๋ฐฑ์‚ฐ ๋ถ„๋น„๋‚˜๋ฌด์˜ ์ข…์ž๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ์˜ ๋‹ค๋ฅธ ์ง‘๋‹จ๊ณผ ์œ ์‚ฌํ•œ ํŠน์„ฑ์„ ๋ณด์˜€์œผ๋ฉฐ, 32.2%์˜ ๋ฐœ์•„์œจ์„ ๋ณด์˜€๋‹ค. ํ•˜์ง€๋งŒ ์ˆœ๋Ÿ‰์œจ์˜ ๊ฒฝ์šฐ ๋ถ„๋น„๋‚˜๋ฌด ์ž„๋ชฉ์ข…์žํ‘œ์ค€ํ’ˆ์งˆ์— ๋น„ํ•ด ๋‹ค์†Œ ๋‚ฎ์€ ์ˆ˜์น˜๋ฅผ ๋ณด์˜€๋‹ค. ์ด๋Š” ์ฑ„์ง‘ ์—ฐ๋„๊ฐ€ ํ‰๋…„์— ๋น„ํ•ด ๊ธฐ์˜จ์ด ๋” ๋†’์•˜๊ณ  ๊ฐ•์ˆ˜๋Ÿ‰์ด ๋” ์ ์—ˆ๋˜ ๊ฒƒ๊ณผ ๊ด€๋ จ๋˜์—ˆ์„ ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. ์ƒ๊ด€๋ถ„์„ ๊ฒฐ๊ณผ, ์ข…์ž ๋ฌด๊ฒŒ๊ฐ€ ์ข…์ž ํ’ˆ์งˆ์˜ ๊ฐ€์žฅ ํšจ๊ณผ์ ์ธ ์ง€ํ‘œ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๋ณด์˜€๋‹ค. ๋‹ค๋ฅธ ๊ฐœ์ฒด๋“ค๊ณผ ์œ ์ „์  ๊ฑฐ๋ฆฌ๊ฐ€ ๋น„๊ต์  ๊ฐ€๊นŒ์šด ๋ชจ์ˆ˜์—์„œ ์ข…์ž ํ’ˆ์งˆ์ด ๋–จ์–ด์ง€๋Š” ๊ฒฝํ–ฅ์ด ์กด์žฌํ•˜์˜€์œผ๋‚˜ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜์ง€๋Š” ์•Š์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ถ„๋น„๋‚˜๋ฌด์˜ ๊ณต๊ฐ„์  ์œ ์ „๊ตฌ์กฐ์™€ ์ข…์ž์ƒ์‚ฐ์„ ํ•จ๊ป˜ ๊ณ ๋ คํ•จ์œผ๋กœ์จ ํ˜„์ง€์™ธ ๋ณด์ „์— ์ ์šฉ๊ฐ€๋Šฅํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์˜€๋‹ค. ์ถ”ํ›„ ์œ ์ „์ž ๋ถ„์‚ฐ ๋ฐ ๊ต๋ฐฐ์–‘์‹์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋ถ„๋น„๋‚˜๋ฌด์˜ ์œ ์ „๊ตฌ์กฐ์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.1. Introduction 1 1.1. Study background 1 1.2. Objectives of this research 3 2. Literature Review 4 2.1. Abies nephrolepis 4 2.2. Spatial genetic structure within population 7 2.3. Ex-situ conservation 10 3. Materials and Methods 13 3.1. Study site 13 3.2. Within-population genetic variation 15 3.2.1. Sampling 15 3.2.2. Genetic diversity 16 3.2.3. Spatial genetic structure 18 3.2.4. Sampling simulation study 22 3.3. Seed characteristics and germination 23 3.3.1. Cone sampling and analysis 23 3.3.2. Germination test 24 3.3.3. Statistical analysis 27 4. Results 29 4.1. Within-population genetic variation 29 4.1.1. Selection of microsatellite markers 29 4.1.2. Genetic diversity 30 4.1.3. Spatial genetic structure 30 4.1.4. Sampling simulation study 39 4.2. Seed characteristics and germination 41 4.2.1. Seed characteristics 41 4.2.2. Germination test 45 4.2.3. Statistical analysis 46 5. Discussions 52 5.1. Spatial genetic structure 52 5.2. Seed production 56 5.3. Strategy for ex-situ conservation 59 6. Conclusions 61 References 63 ์ดˆ ๋ก 75์„

    Healthcare spending and utilization of lung cancer patients using 2002-2012 health insurance claims data

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    Dept. of Public Health/๋ฐ•์‚ฌBackground: Over the past decades, lung cancer has one of the highest fatality rates, and is the leading cause of cancer-related mortality and disease burden not only in South Korea but also worldwide. Studies focused on lung cancer are well documented, however, the factors that are associated with survival probability of lung cancer patients and their healthcare spending and utilization using long periods of large dataset is less researched in this country. The purpose of this study was to investigate how different individual and hospital factors are associated with total, inpatient, outpatient spending and utilizations measured by length of stays and outpatient days among nationwide dead or 5 years follow-up lung cancer patients using 2002-2012 health insurance claims data.Materials and Methods: We used nationwide lung cancer patientsโ€™ health insurance claims during 2002-2012 which accounted for 1,417,380 (673,122 inpatients and 744,258 outpatients). We transposed the dataset into a retrospective cohort design study that the unit of analysis is information of each lung cancer patient. We included patients who newly diagnosed with lung cancer after 2005 and dead or follow-up of 60 months which eventually included patients diagnosed during 2005-2007. Furthermore, this study also excluded patients who had inpatient spending less than KRW 400,000 to minimize bias of real lung cancer patient selection. We then calculated various spending and utilization measures (total, inpatient, outpatient spending, length of stays and outpatient days). Survival time was also measure for each patient that the variable is measured by time from diagnosis to all-cause mortality or end of 60 months follow-up. Finally we obtained total population for analysis of 53,451 lung cancer patients and matched 916 hospitals. Hospital data included characteristics of the hospital, such as hospital type, teaching status, number of beds, specialists, and nurses. Cox-proportional hazard model was performed to investigate survival probability of lung cancer patients by using individual factors. In order to investigate individual and hospital factors associated with healthcare spending and utilization of lung cancer patients, multi-level linear mixed models that avoid problems created by possible nesting of patient level observations within hospital clusters and overestimation of significance were performed.Results: Our retrospective cohort design study using nationwide claim data of past decade showed that increase in new lung cancer cases during year 2005 to 2007 (16,654 in 2005, 18,149 in 2006, 18,648 in 2007 which are similar to actual number of patients reported by national cancer center), increased spending and utilization (total spending of KRW 22,883,645 to KRW 27,462,222; inpatient LOS of 51.4 days to 58.8 days; outpatient utilization of 25.4 days to 26.1 days for patient diagnosed in 2005 and 2007 respectively), and higher proportion of spending and utilization during very first periods after diagnosis and last periods before death or follow-up ends of lung cancer patients (about 70% over total), no significant improvements of 5 years survival rates by year of diagnosis (20.4%, 19.7%, and 20.3% for diagnosed in 2005, 2006, and 2007 respectively, P=0.462) and higher spending and utilization trend among dead population (5-years survivors: total spending of KRW 24,486,381, inpatient LOS of 39.2 days, outpatient utilization of 40.9 days; Dead population: total spending of KRW 15,936,865~54,945,330, inpatient LOS of 44.4~107.8 days, Outpatient utilization of 9.0~66.0 days). Results of Cox-proportional hazard model showed that indifferent hazard ratio by insurance type (health insurance vs. medical aids, HR=0.99, P=0.489), however, hazard ratio was increased for male (female HR=0.74, P<0.001), as older age of diagnosis after 40+ for lung cancer (HR range from 2.02 to 6.63, P<0.001). Using the multi-level linear mixed analysis models, we found evidences of differences in the use of healthcare resources among individual and hospital factors that individual with health insurance (2.9% higher in total spending, P<0.001; 23.8% higher in outpatient days, P<0.001), male (5.6% higher in total spending, P<0.001; 8.6% higher in outpatient days, P<0.001), 40-79 age group (28.0% to 61.0% higher in total spending, P<0.001; 24.8% to 34.0% in LOS, P<0.001; 38.9% to 65.8% higher in outpatient days, P<0.001) and hospital type with tertiary/large (27.6%, 12.7% higher in total spending), teaching (35.6% higher in total spending, P<0.001; 13.4% higher in LOS, P=0.001; 21.9% higher in outpatient days, P<0.001) had relatively higher spending and utilization among nationwide 5 year follow-up lung cancer patients. Some population groups showed that higher hazard ratios with higher healthcare spending and utilization.Discussion & Conclusion: This study might suggest that efficient manner of healthcare policy implementation for patientsโ€™ spending and utilization in order to maintain financial viability of national health insurance program that the allocation of limited health-care resources demands an agreed rational allocation principle, and consequently priority setting is considerably importance. In addition, healthcare spending and utilization considered to be targeted to under-served population groups that will ensure efficient locus of healthcare service delivery by accounting for survival probability of different sub-population groups. Results of this study might be useful to health policy makers not only in South Korea but also international readers that need to develop a national cancer management strategy that reduce differences in the use of healthcare resources and flexible healthcare benefits plan which might helpful to targeted sub population groups.ope

    The changing Meaning of Gardens in Jane Austen's Sense and Sensibility(1811) and Mansfield Park(1814)

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ™˜๊ฒฝ๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ™˜๊ฒฝ์กฐ๊ฒฝํ•™๊ณผ, 2016. 2. ์„ฑ์ข…์ƒ.๋ณธ ์—ฐ๊ตฌ๋Š” ์ •์›์˜ ์˜๋ฏธ๋ฅผ 18, 19์„ธ๊ธฐ ์˜๊ตญ์˜ ์ •์› ์–‘์‹์„ ๋ง๋ผํ•œ ํ’๊ฒฝ์‹ ์ •์›์„ ํ†ตํ•˜์—ฌ ๊ณ ์ฐฐํ•ด ๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ํ’๊ฒฝ์‹ ์ •์›์— ๋Œ€ํ•œ ์ฃผ์ฒด ๊ด€์ ์˜ ํ•ด์„์„ ๋‹น๋Œ€ ์ •์›๊ณผ ๊ฐ€๊นŒ์šด ๊ด€๊ณ„๋ฅผ ์œ ์ง€ํ•˜๋ฉฐ ์ง‘ํ•„๋œ ์ œ์ธ ์˜ค์Šคํ‹ด์˜ ์ผ๋ จ์˜ ์†Œ์„ค๋“ค, ใ€Ž์ด์„ฑ๊ณผ ๊ฐ์„ฑใ€, ใ€Ž๋งจ์Šคํ•„๋“œ ํŒŒํฌใ€๋ฅผ ํ†ตํ•ด ๋น„๊ตํ•ด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋‘ ์ž‘ํ’ˆ์€ ๊ฐ๊ฐ ๊ทธ๋…€๊ฐ€ ์ถœ๊ฐ„ํ•œ ์ž‘ํ’ˆ๋“ค ์ค‘ ์ฒซ์งธ์™€ ํ›„๊ธฐ ์„ธ ์ž‘ํ’ˆ ๊ฐ€์šด๋ฐ ์ฒซ ๋ฒˆ์งธ ์ž‘ํ’ˆ์œผ๋กœ, ๋‘ ์†Œ์„ค์ด ์ง‘ํ•„๋œ 18์„ธ๊ธฐ ๋ง๊ณผ 19์„ธ๊ธฐ ์ดˆ์˜ ์ •์›์— ๋Œ€ํ•œ ์ธ์‹์˜ ๋ณ€ํ™”๋ฅผ ๊ฐ๊ฐ ์‹œ๊ณต๊ฐ„์ ์œผ๋กœ ๋Œ€๋ณ€ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ˜•์‹์  ํ‹€๋กœ์„œ ๊ตฌ์ถ•๋œ ํ’๊ฒฝ์‹ ์ •์›์„ ํ•œ ์ž‘๊ฐ€์˜ ์ผ์ƒ์  ๊ธฐ๋ก์„ ํ†ตํ•˜์—ฌ ์žฌ-ํ•ด์ฒดํ•จ์œผ๋กœ์„œ ํ•œ ์‚ฌํšŒ์˜ ํ‘œํ˜„ ์–‘์‹์œผ๋กœ์„œ, ์ •์›์ด ์ง€๋‹Œ ์ž ์žฌ์„ฑ์„ ์ธก์ •ํ•ด๋ณด๊ณ  ๋‚˜์•„๊ฐ€ ์ •์› ์† ์ฃผ์ฒด์˜์‹ ๋ฐ ์ด๋ฐ์˜ฌ๋กœ๊ธฐ(ideology)์˜ ํŒฝํŒฝํ•œ ๊ธด์žฅ๊ฐ์ด ์ •์›์˜ ์š”์†Œ๋“ค์„ ํ†ตํ•˜์—ฌ ๋‹ค์–‘ํ•˜๊ณ  ๋ณตํ•ฉ์ ์œผ๋กœ ํ‘œํ˜„๋œ ์–‘์ƒ์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์˜ค์Šคํ‹ด์˜ ์†Œ์„ค ์† ์ •์›์€ ์ง‘, ๊ฐœ์ธ-์‚ฌํšŒ์˜ ๊ด€๊ณ„์˜ ์žฅ, ์ž์—ฐํ™” ๋ฐ ์‘์‹œ(gazing)์˜ ์žฅ์†Œ๋กœ์„œ ํŠนํžˆ ์ƒํƒœ์  ์ž์—ฐ์— ๋Œ€ํ•œ ์—ญ๋™์  ์‘์‹œ๋ฅผ ํ†ตํ•œ ์ƒˆ๋กœ์šด ์ž์•„์˜ ๋ฐœ๊ฒฌ ๋ฐ ๊ด€๊ณ„์„ฑ์˜ ํšŒ๋ณต์„ ๋„๋ชจํ•˜๋ฉฐ, ๊ท€๊ฒฐ๋˜๋Š” ์‚ถ์˜ ๊ณผ์ •์˜ ์ผ๋ถ€์˜€๋‹ค. ํ’๊ฒฝ์‹์ด๋ผ๋Š” ์ •ํƒœ์ ์ธ ์–‘์‹ ์•„๋ž˜ ๊ทธ๋…€์˜ ์ฃผ์ธ๊ณต๋“ค์˜ ๋‚ด๋ฉด์—๋Š” ํ›จ์”ฌ ๋™ํƒœ์ ์ด๊ณ  ์ƒํ˜ธ ์—ฐ๊ด€๋œ ๊ด€๊ณ„์„ฑ์ด ์ •๋ฆฝ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์‚ฌํšŒ-๋ฌธํ™”์  ๋งฅ๋ฝ ์†์—์„œ ์•„์ด๋ดํ‹ฐํ‹ฐ(identity)์˜ ๊ฐˆ๋“ฑ์ด ์‹ฌํ™”๋˜๊ฑฐ๋‚˜ ํ•ด์†Œ๋˜๋Š” ๊ณผ์ •์„ ํ”„๋กœํƒ€๊ณ ๋‹ˆ์ŠคํŠธ(protagonist)์˜ ์ •์›์„ ํ†ตํ•ด์„œ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์ด์—ˆ๋‹ค. ์˜ค์Šคํ‹ด์ด ๊ฑฐ์ฃผํ–ˆ๋˜ ์‹ค์ œ ์ •์›๋“ค์ด ์†Œ์„ค ์†์— ๋ฌ˜์‚ฌ๋œ ์˜๋ฏธ๋ฅผ ์œ ์ถ”ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์žฅ์†Œ(place), ํƒœ๋„(manner), ๊ฐ์ •(emotion), ์ธ๋ฌผ(character), ์ž์•„(self)์˜ ์˜๋ฏธ์†Œ๋“ค์„ ๊ธฐ์ €๋กœ ์„ค์ •ํ•˜๊ณ  ์†Œ์„ค ์† ์ •์› ๊ณต๊ฐ„์—์„œ ์ถœํ˜„ํ•œ ๋‹จ์–ด ๋นˆ๋„ํ‘œ๊ฐ€ ์ž‘์„ฑ๋˜์—ˆ๋‹ค. ์ดํ›„, ์ˆ˜์ง‘๋œ ์–ดํœ˜์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ด๋ถ„๋ฒ•์  ๊ตฌ๋„๋กœ ์˜๋ฏธ ๋‹จ์œ„๋ฅผ ์ž‘์„ฑํ•˜์˜€๋‹ค. ๋ถ„์„ ์ฃผ์ œ๋ฅผ ์„ค์ •ํ•˜์—ฌ ๋‘ ํ…์ŠคํŠธ๋ฅผ ๋น„๊ต ๋ถ„์„ํ•จ์œผ๋กœ์จ ์ •์›์˜ ์˜๋ฏธ๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ์ „์ž‘์ด ์กฐํ™”์™€ ๊ณผ์ •์œผ๋กœ์„œ์˜ ์ •์›, ๋‚ด๋ฉด๊ณผ ํ•˜๋‚˜ ๋˜๋Š” ์š”์†Œ๋กœ์„œ์˜ ํ’๊ฒฝ, ๊ด€์ƒ์  ํ™˜์›์ฃผ์˜, ์ฐฝ๋ฌธ ํ†ตํ•œ ์ด์ฐจ์  ์กฐ๋ง, ์ž์œ  ์˜์ง€ ๋“ฑ์œผ๋กœ ์ •์›์„ ์ธ์‹ํ•œ ๋ฐ˜๋ฉด ํ›„์ž‘์—์„œ๋Š” ๊ถŒ์œ„์™€ ๊ฒฐ๊ณผ๋กœ์„œ์˜ ์ •์›, ์ „์ฒด์  ๊ทธ๋ฆผ์œผ๋กœ์„œ์˜ ํ’๊ฒฝ, ํ–‰๋™์  ์ „์ฒด์ฃผ์˜, ์ง์ ‘์  ์ˆ˜์šฉ, ์ˆœ์‘ ์˜์‚ฌ ๋“ฑ์„ ํ†ตํ•˜์—ฌ ์ •์›์„ ๋ณด์•˜๋‹ค. ๋‚˜์•„๊ฐ€, ๋‘ ์†Œ์„ค์—์„œ ๋ณดํŽธ์ ์ธ ์ •์›์˜ ์˜๋ฏธ๋Š” ํšŒ์œ ์˜ ์ •์›, ๊ณต๊ฐ์˜ ์ •์›, ์ ˆ์ถฉ์˜ ์ •์›, ์น˜์œ ์˜ ์ •์›, ๋น„์œ ์˜ ์ •์›์ด์—ˆ๋‹ค. ๋‚ญ๋งŒ์ฃผ์˜์™€ ๊ณ ์ „์ฃผ์˜ ์‚ฌ์ด์—์„œ ์ œ์ธ ์˜ค์Šคํ‹ด์˜ ์†Œ์„ค ์† ์ •์›๋“ค์€ ์š”์†Œ๋“ค์ด ๋‘ ๊ฐ€์ง€ ๋งฅ๋ฝ์— ๋Œ€ํ•˜์—ฌ ๋ถ€๋ถ„์ ์œผ๋กœ ๋ณด์ถฉ๋˜๊ณ  ๊ฒฐํ•๋˜๋Š” ๊ธฐ์ž‘์„ ์žฅ์†Œ, ํƒœ๋„, ๊ฐ์ •, ์ธ๋ฌผ, ์ž์•„์˜ ์˜์‹์  ๋ณ€ํ™”๋ฅผ ํ†ตํ•˜์—ฌ ํ™˜๊ธฐํ•œ๋‹ค. ํ’๊ฒฝ์‹ ์ •์›์„ ์™„์ „ํžˆ ์ •ํ˜•์ ์ด์ง€๋„, ํ‘œํ˜„์ ์ด์ง€๋„ ์•Š์€ ๋‹จ์ง€ ๋งค๊ฐœ์  ์ƒํˆฌ๋ฌผ๋กœ ์ธ์‹ํ•œ ๊ทธ๋…€์—๊ฒŒ ์žˆ์–ด ์ •์›์˜ ์˜๋ฏธ์„ฑ์€ ๋‹ค์–‘ํ•œ ์ธ๋ฌผ๋“ค์˜ ์ฃผ์ฒด์  ์˜์‹์ด ์ •์›์˜ ์‚ฌํšŒ๋ฌธํ™”์  ๋งฅ๋ฝ ์•ˆ์—์„œ ์ˆœ๊ฐ„์ ์œผ๋กœ ์ œ๊ฐ๊ธฐ ๋ฐœํ™”๋˜๋Š” ์ˆœ๊ฐ„๋“ค์ด๋‹ค. ๊ทธ๋Ÿฌํ•œ ์ˆœ๊ฐ„๋“ค์ด ๊ทธ๋…€์˜ ์„œ์‚ฌ ๋‹จ์œ„๋กœ ๊ตฌ์„ฑ๋˜์–ด ์ •์›์—์„œ์˜ ๋ณด๋‹ค ์„ฑ์ˆ™๋œ ์ผ๋ฐ˜์  ์‚ฌ๊ณ  ๋ฐ ๋น„ํŒ ์˜์‹์œผ๋กœ ์ง„์ฒ™๋˜๋Š” ๋ฐœ๋‹ฌ ์–‘์‹์€ ๊ทธ๋…€๊ฐ€ ๋‹น์‹œ ํ™˜๊ฒฝ์„ ์ •์›์˜ˆ์ˆ ๋กœ์„œ ์ธ์‹ํ•œ ๊ฒฐ๊ณผ๋ฌผ๋กœ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์˜ค์Šคํ‹ด์ด ์‹ค์ œ ๊ฑฐ์ฃผํ•˜๋˜ ๋‹น์‹œ ์ •์›๋“ค์— ๋Œ€ํ•œ ๊ธฐ๋ก์— ๋Œ€ํ•œ ์„ธ๋ฐ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๋ถ€์กฑํ•˜๋ฉฐ, ํ–ฅํ›„ ์ด์šฉ์ž์˜ ์„ ํ˜ธ๋„ ๋ฐ ์–ดํœ˜์†Œ ์ˆ˜์ง‘์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋กœ ์ด์–ด์งˆ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค.์ œ1์žฅ ์„œ๋ก  01 1์ ˆ. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  01 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 01 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  07 2์ ˆ. ์ •์› ๊ฐœ๋… 08 1. ๊ณต๊ฐ„์˜ ํ•ด์„์  ๋ฒ”์œ„ 08 2. ๋‚ด์šฉ์˜ ํ•ด์„์  ๋ฒ”์œ„ 12 3์ ˆ. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 14 1. ์˜๋ฏธ์†Œ 15 2. ์˜๋ฏธ ๋‹จ์œ„ 23 3. ๋ถ„์„์˜ ์ฃผ์ œ 29 ์ œ2์žฅ ์ œ์ธ ์˜ค์Šคํ‹ด์˜ ์ •์› 42 1์ ˆ. ์ฝ”ํ‹ฐ์ง€ (1775-1801, 1809-1817) 42 1. ์ •์›์˜ ๋ฐฐ๊ฒฝ 42 2. ์†Œ์„ค ์† ๊ตฌํ˜„ ๋ฐฉ์‹ 43 2์ ˆ. ๋Œ€์ €ํƒ ์กฐ๊ฒฝ (1801-1806, 1806-1809) 49 1. ์ •์›์˜ ๋ฐฐ๊ฒฝ 49 2. ์†Œ์„ค ์† ๊ตฌํ˜„ ๋ฐฉ์‹ 50 3์ ˆ. ํํ—ˆ ์ •์› (1806-1809) 52 1. ์ •์›์˜ ๋ฐฐ๊ฒฝ 52 2. ์†Œ์„ค ์† ๊ตฌํ˜„ ๋ฐฉ์‹ 53 4์ ˆ. ์ œ์ธ ์˜ค์Šคํ‹ด์˜ ์†Œ์„ค ์† ์ •์›์˜ ์žฌ๊ตฌ์„ฑ 55 ์ œ3์žฅ ๋‘ ์†Œ์„ค ์† ์ •์›์˜ ์ธ์‹ ๋น„๊ต 58 1์ ˆ. ์žฅ์†Œ(place)์— ๋Œ€ํ•œ ์ธ์‹๊ณผ ์˜๋ฏธ 58 1. ์ด์„ฑ๊ณผ ๊ฐ์„ฑ 58 2. ๋ฉ˜์Šคํ•„๋“œ ํŒŒํฌ 65 2์ ˆ. ํƒœ๋„(manner)์— ๋Œ€ํ•œ ์ธ์‹๊ณผ ์˜๋ฏธ 71 1. ์ด์„ฑ๊ณผ ๊ฐ์„ฑ 71 2. ๋ฉ˜์Šคํ•„๋“œ ํŒŒํฌ 76 3์ ˆ. ๊ฐ์ •(emotion)์— ๋Œ€ํ•œ ์ธ์‹๊ณผ ์˜๋ฏธ 78 1. ์ด์„ฑ๊ณผ ๊ฐ์„ฑ 78 2. ๋ฉ˜์Šคํ•„๋“œ ํŒŒํฌ 80 4์ ˆ. ์ธ๋ฌผ(character)์— ๋Œ€ํ•œ ์ธ์‹๊ณผ ์˜๋ฏธ 82 1. ์ด์„ฑ๊ณผ ๊ฐ์„ฑ 82 2. ๋ฉ˜์Šคํ•„๋“œ ํŒŒํฌ 84 5์ ˆ. ์ž์•„(self)์— ๋Œ€ํ•œ ์ธ์‹๊ณผ ์˜๋ฏธ 84 1. ์ด์„ฑ๊ณผ ๊ฐ์„ฑ 84 2. ๋ฉ˜์Šคํ•„๋“œ ํŒŒํฌ 86 ์ œ4์žฅ ์ œ์ธ ์˜ค์Šคํ‹ด ์†Œ์„ค ์† ์ •์›์˜ ์˜๋ฏธ ๋ณ€ํ™” 88 1์ ˆ. ์žฅ์†Œ(place)์— ๋Œ€ํ•œ ์˜๋ฏธ ๋ณ€ํ™” 88 2์ ˆ. ํƒœ๋„(manner)์— ๋Œ€ํ•œ ์˜๋ฏธ ๋ณ€ํ™” 89 3์ ˆ. ๊ฐ์ •(emotion)์— ๋Œ€ํ•œ ์˜๋ฏธ ๋ณ€ํ™” 90 4์ ˆ. ์ธ๋ฌผ(character)์— ๋Œ€ํ•œ ์˜๋ฏธ ๋ณ€ํ™” 91 5์ ˆ. ์ž์•„(self)์— ๋Œ€ํ•œ ์˜๋ฏธ ๋ณ€ํ™” 92 ์ œ5์žฅ ๊ฒฐ๋ก  ๋ฐ ๊ณ ์ฐฐ 93 1์ ˆ. ์ œ์ธ ์˜ค์Šคํ‹ด์˜ ๋‘ ์†Œ์„ค ๊ฐ„ ์ •์› ์ธ์‹์˜ ๋ถ„ํ™” 93 2์ ˆ. ์ œ์ธ ์˜ค์Šคํ‹ด์˜ ์†Œ์„ค ์† ์ •์›์˜ ์˜๋ฏธ 95 1. ํšŒ์œ ์˜ ์ •์› 95 2. ๊ณต๊ฐ์˜ ์ •์› 95 3. ์ ˆ์ถฉ์˜ ์ •์› 96 4. ์น˜์œ ์˜ ์ •์› 96 5. ๋น„์œ ์˜ ์ •์› 97 3์ ˆ. ๊ฒฐ๋ก  ๋ฐ ๊ณ ์ฐฐ 97 ์ฐธ๊ณ ๋ฌธํ—Œ 99 ๋ถ€๋ก 103 Abstract 113Maste

    ์ผ๋ฐ˜ํ™”๋œ 4์ฐจ์› ๋™์ž‘ ํŠน์ง•์„ ์ด์šฉํ•œ ์‹œ์„ ๊ฐ์— ๋ฌด๊ด€ํ•œ ํ–‰๋™ ์ธ์‹

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2014. 8. ์ตœ์ง„์˜.๋ณธ ๋…ผ๋ฌธ์€ ์ผ๋ฐ˜ํ™”๋œ 4์ฐจ์› [x,y,z,t] ๋™์ž‘ ํŠน์ง•์„ ์ด์šฉํ•˜์—ฌ ์‹œ์„ ๊ฐ์— ๋ฌด๊ด€ํ•œ ํ–‰๋™ ๋ฐ ํ–‰๋™ ๋ฐฉํ–ฅ ์ธ์‹ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๊ธฐ์กด์˜ ํ–‰๋™ ์ธ์‹ ์—ฐ๊ตฌ๋Š” ์ฃผ๋กœ ์นด๋ฉ”๋ผ์˜ ์œ„์น˜๋Š” ๊ณ ์ •๋˜์–ด์žˆ๊ณ  ์‚ฌ๋žŒ๋“ค์€ ์นด๋ฉ”๋ผ๋ฅผ ๋ฐ”๋ผ๋ณด๊ณ  ์„œ์žˆ๋Š” ์ƒํ™ฉ์„ ๊ฐ€์ •ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ ๋น„๋””์˜ค๋‚˜ ๊ฐ์‹œ์นด๋ฉ”๋ผ์— ๋“ฑ์žฅํ•˜๋Š” ์‚ฌ๋žŒ๋“ค์€ ์นด๋ฉ”๋ผ๋ฅผ ์˜์‹ํ•˜์ง€ ์•Š๊ณ  ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ํ–‰๋™ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ œํ•œ๋œ ์กฐ๊ฑด, ํ™˜๊ฒฝ์—์„œ ํ–‰๋™์„ ์ธ์‹ํ•˜๋Š” ๊ฒƒ๊ณผ ๋‹ฌ๋ฆฌ, ์นด๋ฉ”๋ผ์˜ ์œ„์น˜์™€ ์‚ฌ๋žŒ์˜ ๋ฐฉํ–ฅ์— ๋”ฐ๋ผ์„œ ๋‹ค์–‘ํ•œ ์‹œ์„ ๊ฐ์—์„œ ์˜์ƒ์ด ์ดฌ์˜๋  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์‹ค์ œ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์— ์ ์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋ฌด์ž‘์œ„์˜ ์‹œ์„ ๊ฐ์—์„œ ์˜์ƒ์ด ๋“ค์–ด์™”์„ ๋•Œ ํ–‰๋™ ์ธ์‹์„ ํ•˜๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ด๋ฉฐ, ์–ด๋–ค ๋ฐฉํ–ฅ์œผ๋กœ ํ–‰๋™ํ•˜๋Š” ์ง€ ์•Œ ์ˆ˜ ์žˆ๋‹ค๋ฉด ๋ˆ„๊ตฌ์™€ ์ƒํ˜ธ์ž‘์šฉ์„ ํ•˜๋Š” ์ง€ ์•„๋Š”๋ฐ ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ช‡ ๊ฐœ์˜ ๋‹ค๋ฅธ ์‹œ์„ ๊ฐ์—์„œ ์ฐํžŒ ์˜์ƒ์„ ์ด์šฉํ•˜์—ฌ 3์ฐจ์› [x,y,z] ์ž…์ฒด๋ฅผ ๋ณต์›ํ•˜๊ณ , ์—ฐ์†๋œ 3์ฐจ์› ์ž…์ฒด์—์„œ 4์ฐจ์› ์‹œ๊ณต๊ฐ„ ํŠน์ง•์ ์„ ๊ตฌํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์—ฌ ์‹œ์„ ๊ฐ์— ๋ฌด๊ด€ํ•œ ํ–‰๋™ ๋ฐ ํ–‰๋™ ๋ฐฉํ–ฅ ์ธ์‹์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. 3์ฐจ์› ์ž…์ฒด ๋ฐ ์—ฐ์†๋œ 3์ฐจ์› ์ž…์ฒด์—์„œ ๊ตฌํ•œ 4์ฐจ์› ์‹œ๊ณต๊ฐ„ ํŠน์ง•์ ์€ ๋ชจ๋“  ์‹œ์„ ๊ฐ์—์„œ์˜ ์ •๋ณด๋ฅผ ๊ฐ–๊ณ  ์žˆ์œผ๋ฏ€๋กœ, ์›ํ•˜๋Š” ์‹œ์„ ๊ฐ์œผ๋กœ ์‚ฌ์˜์„ ํ•˜์—ฌ ๊ฐ ์‹œ์„ ๊ฐ์—์„œ์˜ ํŠน์ง•์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ์‚ฌ์˜๋œ ์‹ค๋ฃจ์—ฃ๊ณผ 4์ฐจ์› ์‹œ๊ณต๊ฐ„ ํŠน์ง•์ ์˜ ์œ„์น˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐ๊ฐ ์›€์ง์ด๋Š” ๋ถ€๋ถ„๊ณผ ์›€์ง์ด์ง€ ์•Š๋Š” ๋ถ€๋ถ„์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ํฌํ•จํ•˜๋Š” motion history images (MHIs)์™€ non motion history images (NMHIs) ๋ฅผ ๋งŒ๋“ค์–ด ํ–‰๋™ ์ธ์‹์„ ์œ„ํ•œ ํŠน์ง•์œผ๋กœ ์‚ฌ์šฉ์„ ํ•˜์˜€๋‹ค. MHIs๋งŒ์œผ๋กœ๋Š” ํ–‰๋™ ์‹œ ์›€์ง์ด๋Š” ๋ถ€๋ถ„์ด ๋น„์Šทํ•œ ํŒจํ„ด์„ ๋ณด์ผ ๋•Œ ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์žฅํ•  ์ˆ˜ ์—†๊ณ  ๋”ฐ๋ผ์„œ ํ–‰๋™ ์‹œ ์›€์ง์ด์ง€ ์•Š๋Š” ๋ถ€๋ถ„์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ค„ ์ˆ˜ ์žˆ๋Š” NMHIs๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ํ–‰๋™ ์ธ์‹์„ ์œ„ํ•œ ํ•™์Šต ๋‹จ๊ณ„์—์„œ MHIs์™€ NMHIs๋Š” ํด๋ž˜์Šค๋ฅผ ๊ณ ๋ คํ•œ ์ฐจ์› ์ถ•์†Œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ธ class-augmented principal component analysis (CA-PCA)๋ฅผ ํ†ตํ•ด์„œ ์ฐจ์›์ด ์ถ•์†Œ๋˜๋ฉฐ, ์ด ๋•Œ ํ–‰๋™ ๋ผ๋ฒจ์„ ์ด์šฉํ•˜์—ฌ ์ฐจ์›์„ ์ถ•์†Œํ•˜๋ฏ€๋กœ ๊ฐ ํ–‰๋™์ด ์ž˜ ๋ถ„๋ฆฌ๊ฐ€ ๋˜๋„๋กํ•˜๋Š” principal axis๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ๋‹ค. ์ฐจ์›์ด ์ถ•์†Œ๋œ MHIs์™€ NMHIs๋Š” support vector data description (SVDD) ๋ฐฉ๋ฒ•์œผ๋กœ ํ•™์Šต๋˜๊ณ , support vector domain density description (SVDDD)๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธ์‹๋œ๋‹ค. ํ–‰๋™ ๋ฐฉํ–ฅ์„ ํ•™์Šตํ• ๋•Œ์—๋Š” ๊ฐ ํ–‰๋™์— ๋Œ€ํ•ด ๋ฐฉํ–ฅ ๋ผ๋ฒจ์„ ์‚ฌ์šฉํ•˜์—ฌ principal axis๋ฅผ ๊ตฌํ•˜๋ฉฐ, ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ SVDD๋กœ ํ•™์Šต์„ ํ•˜๊ณ  SVDDD๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธ์‹๋œ๋‹ค. ์ œ์•ˆ๋œ 4์ฐจ์› ์‹œ๊ณต๊ฐ„ ํŠน์ง•์ ์€ ์‹œ์„ ๊ฐ์— ๋ฌด๊ด€ํ•œ ํ–‰๋™ ๋ฐ ํ–‰๋™ ๋ฐฉํ–ฅ ์ธ์‹์— ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์‹คํ—˜์„ ํ†ตํ•ด 4์ฐจ์› ์‹œ๊ณต๊ฐ„ ํŠน์ง•์ ์ด ๊ฐ ํ–‰๋™์˜ ํŠน์ง•์„ ์••์ถ•์ ์œผ๋กœ ์ž˜ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ์Œ์„ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ์‹ค์ œ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์—์„œ์ฒ˜๋Ÿผ ๋ฌด์ž‘์œ„์˜ ์‹œ์„ ๊ฐ์—์„œ ์˜์ƒ์ด ๋“ค์–ด์™”์„ ๊ฒฝ์šฐ๋ฅผ ๊ฐ€์ •ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํ•™์Šต ๋ฐ์ดํ„ฐ์…‹๊ณผ ์ „ํ˜€ ๋‹ค๋ฅธ ์ƒˆ๋กœ์šด ์ธ์‹ ๋ฐ์ดํ„ฐ์…‹์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ๊ธฐ์กด์˜ ์—ฌ๋Ÿฌ ์‹œ์„ ๊ฐ์—์„œ ์ดฌ์˜ ๋œ IXMAS ํ–‰๋™ ์ธ์‹ ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉํ•˜์—ฌ ํ•™์Šต์„ ํ•˜๊ณ , ํ•™์Šต ๋ฐ์ดํ„ฐ์…‹๊ณผ ๋‹ค๋ฅธ ์‹œ์„ ๊ฐ์—์„œ ์ดฌ์˜ํ•œ SNU ๋ฐ์ดํ„ฐ์…‹์—์„œ ์ธ์‹ ์‹คํ—˜์„ ํ•˜์—ฌ ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์€ ํ•™์Šต์„ ์œ„ํ•ด ์ดฌ์˜ํ•œ ์˜์ƒ์— ํฌํ•จ๋˜์ง€ ์•Š๋Š” ์‹œ์„ ๊ฐ์—์„œ ํ…Œ์ŠคํŠธ ์˜์ƒ์ด ๋“ค์–ด์™”์„ ๊ฒฝ์šฐ์—๋„ ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ 5๊ฐœ์˜ ๋ฐฉํ–ฅ์œผ๋กœ ์ดฌ์˜๋œ SNU ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉํ•˜์—ฌ ํ–‰๋™ ๋ฐฉํ–ฅ ์ธ์‹ ์‹คํ—˜์„ ํ•˜์˜€์œผ๋ฉฐ, ์ข‹์€ ๋ฐฉํ–ฅ ์ธ์‹๋ฅ ์„ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ–‰๋™ ๋ฐฉํ–ฅ ์ธ์‹์„ ํ†ตํ•ด์„œ ์˜์ƒ ๋‚ด์—์„œ ์—ฌ๋Ÿฌ ์‚ฌ๋žŒ์ด ๋“ฑ์žฅํ•  ๋•Œ ๋‹ค๋ฅธ์‚ฌ๋žŒ๋“ค๊ณผ ์–ด๋–ป๊ฒŒ ์ƒํ˜ธ ์ž‘์šฉ์„ ํ•˜๋Š”์ง€ ์ •๋ณด๋ฅผ ์•Œ ์ˆ˜ ์žˆ๊ณ , ์ด๋Š” ์˜์ƒ์„ ํ•ด์„ํ•˜๋Š”๋ฐ ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค.In this thesis, we propose a method to recognize human action and their orientation independently of viewpoints using generalized 4D [x,y,z,t] motion features. The conventional action recognition methods assume that the camera view is fixed and people are standing towards the cameras. However, in real life scenarios, the cameras are installed at various positions for their purposes and the orientation of people are chosen arbitrarily. Therefore, the images can be taken with various views according to the position of camera and the orientation of people. To recognize human action and their orientation under this difficult scenario, we focus on the view invariant action recognition method which can recognize the test videos from any arbitrary view. For this purpose, we propose a method to recognize human action and their orientation independently of viewpoints by developing 4D space-time interest points (4D-STIPs, [x,y,z,t]) using 3D space (3D-S, [x,y,z]) volumes reconstructed from images of a finite number of different views. Since the 3D-S volumes and the 4D-STIPs are constructed using volumetric information, the features for arbitrary 2D space (2D-S, [x,y]) viewpoint can be generated by projecting the 3D-S volumes and 4D-STIPs on corresponding test image planes. With these projected features, we construct motion history images (MHIs) and non-motion history images (NMHIs) which encode the moving and non-moving parts of an action respectively. Since MHIs cannot guarantee a good performance when moving parts of an object show similar patterns, we propose NMHIs and combine it with MHIs to add the information from stationary parts of an object in the description of the particular action class. To reduce the dimension of MHIs and NMHIs, we apply class-augmented principal component analysis (CA-PCA) which uses class information for dimension reduction. Since we use the action label for reducing the dimension of features, we obtain the principal axis which can separate each action well. After reducing the feature dimension, the final features are trained by support vector data description method (SVDD) and tested by support vector domain density description (SVDDD). As for the recognition of action orientation, the features are reduced the dimension using orientation label. Similarly, the reduced features are trained by SVDD and tested by SVDDD. The proposed 4D-STIPs can be applied to view invariant recognition of action and their orientation, and we verify that they represent the properties of each action compactly in experiments. To assume arbitrary test view as in real applications, we develop a new testing dataset which is totally different from the training dataset. We verify our algorithm by training action models using the multi-view IXMAS dataset and testing using SNU dataset. Experimental results show that the proposed method is more generalized and outperforms the state-of-the-art methods, especially when training the classifier with the information insufficient about the test views. As for the recognition of action orientation, we experiment with SNU dataset taken from 5 different orientations to verify recognition performance. The recognition of action orientation can be helpful in analyzing the video by providing the information about interactions of people.1 Introduction 1 1.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contents of the research . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2.1 Generalized 4D motion features . . . . . . . . . . . . . . . . 10 1.2.2 View invariant action recognition . . . . . . . . . . . . . . . 11 1.2.3 Recognition of action orientation . . . . . . . . . . . . . . . 12 2 Generalized 4D Motion Features 14 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.1 Harris corner detector . . . . . . . . . . . . . . . . . . . . . 18 2.2.2 3D space-time interest points . . . . . . . . . . . . . . . . . 21 2.2.3 3D reconstruction . . . . . . . . . . . . . . . . . . . . . . . 23 2.3 Proposed method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.1 Modified 3D space-time interest points . . . . . . . . . . . . 27 2.3.2 4D space-time interest points . . . . . . . . . . . . . . . . . 30 2.4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.5 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3 View Invariant Action Recognition 40 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2.1 Motion history images . . . . . . . . . . . . . . . . . . . . . 45 3.2.2 Class-augmented principal component analysis . . . . . . . 47 3.2.3 Support vector data description . . . . . . . . . . . . . . . . 53 3.2.4 Support vector domain density description . . . . . . . . . . 56 3.3 Proposed method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 3.3.1 Silhouettes . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.3.2 Space-time interest points . . . . . . . . . . . . . . . . . . . 67 3.3.3 Motion history images and Non-motion history images . . . 69 3.3.4 Training and Testing . . . . . . . . . . . . . . . . . . . . . . 72 3.4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 73 3.5 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4 Recognition of Action Orientation 87 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 4.2 Proposed method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.2.1 Training and Testing . . . . . . . . . . . . . . . . . . . . . . 93 4.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 95 4.4 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5 Conclusions 100 Bibliography 103 Abstract in Korean 113Docto

    Serum Calcitonin-Negative Medullary Thyroid Carcinoma: A Case Series of 19 Patients in a Single Center

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    Introduction: Medullary thyroid carcinoma (MTC) is a rare cancer that accounts for 5% of thyroid cancers. Serum calcitonin is a good biomarker for MTC, which is used for diagnosis, prognosis, and monitoring of recurrence. Calcitonin-negative MTC (CNMTC) is rare but confounds diagnostic and prognostic directions. This study introduces 19 cases of CNMTC in a single center. Method: From 2002 March to 2020 July, more than 76,500 patients had undergone thyroid surgery due to thyroid cancer at the Severance Hospital, and a total of 320 patients were diagnosed with MTC (0.4%). Serum calcitonin levels were obtained from every patient who was suspected with MTC. These patients had undergone either bilateral total thyroidectomy or unilateral thyroidectomy with central compartment lymph node dissection, and additional modified radical lymph node dissection if lateral lymph node metastasis was positive. Postoperative monitoring and out-patient clinic follow-up were performed with obtaining the serum calcitonin levels. Result: Nineteen patients tested negative for calcitonin preoperatively (6%). The mean preoperative calcitonin level was 5.1pg/mL if undetectable level is regarded as 0pg/mL. Only two patients were males, and the female bias was significant (p = 0.017). No one except two patients with modified radical neck dissection showed central compartment lymph node metastasis. Every patient's postoperative calcitonin level remained low. The median follow-up period was 71 months. There was no recurrence and only one fatality, and the overall survival rate was 95%. Conclusion: Since incidence of CNMTC is not negligible, MTC should not be ruled out in the diagnostic phase even if serum calcitonin is negative in preoperative examination. We presented 19 cases of CNMTC whose prognosis in general were favorable. Markers of serum and immunohistochemical samples other than calcitonin should be actively examined.ope

    Effect of Nutritional Intervention by the Nutrition Support Team on Postnatal Growth in Preterm Infants

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    "Purpose: Nutritional intervention by an interdisciplinary nutrition support team (NST) can potentially improve postnatal growth outcomes in preterm infants. This study aimed to measure the growth impact of a nutritional intervention package performed by an NST in a quality improvement effort in a neonatal intensive care unit (NICU). Methods: Fifty-two infants born below 2,000 g and admitted to NICU participated in the Quality Improvement (QI) program between March 2016 and February 2017. The nutritional intervention was applied according to newly established nutritional guidelines on parenteral and enteral nutrition, and an NST performed a weekly nutritional assessment. The Z-scores of weight, height, and head circumference were calculated according to the gestational age and sex. The clinical impact on postnatal growth was compared between the QI and pre-QI groups. The pre-QI group included 69 infants admitted in the same NICU between 2014 and 2015. Results: The time to the initiation of enteral nutrition decreased significantly (P๏ผœ0.001). Changes in weight (P=0.027), head circumference (P=0.003), Z-scores between birth, and 40 weeks postconceptional age (PCA) were significantly larger in the QI than the pre-QI group. The percentage of infants weighing below the 10th percentile at one month after birth and at 40 weeks PCA was higher in the pre-QI than the QI group. Conclusion: The implementation of evidence-based best practices for preterm nutrition resulted in significant improvements in the growth outcomes in preterm infants."22othe

    Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells.

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    BACKGROUND: In the postgenome era, a prediction of response to treatment could lead to better dose selection for patients in radiotherapy. To identify a radiosensitive gene signature and elucidate related signaling pathways, four different microarray experiments were reanalyzed before radiotherapy. RESULTS: Radiosensitivity profiling data using clonogenic assay and gene expression profiling data from four published microarray platforms applied to NCI-60 cancer cell panel were used. The survival fraction at 2โ€‰Gy (SF2, range from 0 to 1) was calculated as a measure of radiosensitivity and a linear regression model was applied to identify genes or a gene set with a correlation between expression and radiosensitivity (SF2). Radiosensitivity signature genes were identified using significant analysis of microarrays (SAM) and gene set analysis was performed using a global test using linear regression model. Using the radiation-related signaling pathway and identified genes, a genetic network was generated. According to SAM, 31 genes were identified as common to all the microarray platforms and therefore a common radiosensitivity signature. In gene set analysis, functions in the cell cycle, DNA replication, and cell junction, including adherence and gap junctions were related to radiosensitivity. The integrin, VEGF, MAPK, p53, JAK-STAT and Wnt signaling pathways were overrepresented in radiosensitivity. Significant genes including ACTN1, CCND1, HCLS1, ITGB5, PFN2, PTPRC, RAB13, and WAS, which are adhesion-related molecules that were identified by both SAM and gene set analysis, and showed interaction in the genetic network with the integrin signaling pathway. CONCLUSIONS: Integration of four different microarray experiments and gene selection using gene set analysis discovered possible target genes and pathways relevant to radiosensitivity. Our results suggested that the identified genes are candidates for radiosensitivity biomarkers and that integrin signaling via adhesion molecules could be a target for radiosensitization.ope

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    Weavable asymmetric carbon nanotube yarn supercapacitor for electronic textiles

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    Asymmetric supercapacitors are receiving much research interests due to their wide operating potential window and high energy density. In this study, we report the fabrication of asymmetrically configured yarn based supercapacitor by using liquid-state biscrolling technology. High loading amounts of reduced graphene oxide anode guest (90.1 wt%) and MnO2 cathode guest (70 wt%) materials were successfully embedded into carbon nanotube yarn host electrodes. The resulting asymmetric yarn supercapacitor coated by gel based organic electrolyte (PVDF-HFP-TEABF(4)) exhibited wider potential window (up to 3.5 V) and resulting high energy density (43 W h cm(-2)). Moreover, the yarn electrodes were mechanically strong enough to be woven into commercial textiles. The textile supercapacitor exhibited stable electrochemical energy storage performances during dynamically applied deformations.This work was supported by the Creative Research Initiative Center for Self-powered Actuation in Korea, Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1A6A3A04004987), and DGIST R&amp;amp;D Program of Ministry of Science, ICT and Future Planning of Korea (17-NT-02). Support at the University of Texas at Dallas was provided by Air Force Office of Scientific Research grants AOARD-FA2386-13-1-4119 and FA9550-15-1-0089 and Robert A. Welch Foundation grant AT-0029. Additional support was from the Australian Research Council (DP110101073)

    Harvesting electrical energy from torsional thermal actuation driven by natural convection

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    The development of practical, cost-effective systems for the conversion of low-grade waste heat to electrical energy is an important area of renewable energy research. We here demonstrate a thermal energy harvester that is driven by the small temperature fluctuations provided by natural convection. This harvester uses coiled yarn artificial muscles, comprising well-aligned shape memory polyurethane (SMPU) microfibers, to convert thermal energy to torsional mechanical energy, which is then electromagnetically converted to electrical energy. Temperature fluctuations in a yarn muscle, having a maximum hot-to-cold temperature difference of about 13 degrees C, were used to spin a magnetic rotor to a peak torsional rotation speed of 3,000 rpm. The electromagnetic energy generator converted the torsional energy to electrical energy, thereby producing an oscillating output voltage of up to 0.81 V and peak power of 4 W/kg, based on SMPU mass.This work was supported by the Creative Research Initiative Center for Self-Powered Actuation of the National Research Foundation and the Ministry of Science, ICT &amp;amp; Future Planning (MSIP) in Korea. Support in Australia was from Centre of Excellence funding from the Australian Research Council. Support in the USA was from Air Force Grant AOARD-FA2386-13-4119, Air Force Office of Scientific Research grant FA9550-15-1-0089, and Robert A. Welch Foundation grant AT-0029
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