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    ์ƒˆ๋งŒ๊ธˆ ๊ฐ„์ฒ™์ง€์˜ ์—ผ์ƒ์‹๋ฌผ ๊ตฐ๋ฝ์ง€ ์กฐ์„ฑ ์š”์ธ๊ณผ ํ™˜๊ฒฝ์  ํšจ๊ณผ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์ƒ๋ช…๊ณตํ•™๋ถ€ ์‘์šฉ์ƒ๋ช…ํ™”ํ•™ ์ „๊ณต, 2013. 2. ๊น€๋ฏผ๊ท .The two objectives of the present dissertation are to clarify development of halophyte communities constructed in Saemangeum reclaimed land and to quantify the environmental effects of halophyte communities. Through halophyte community development using 11,652 kg of eight halophytic species in 2,866 ha of the land from 2006 to 2008, halophytic area confined to autogenous 689 ha in 2005 increased to 4,364 ha by 6.3 times. The number of halophytic species increased from 4 in 2006 to 25 in 2008 and both average and maximum population densities rapidly increased. Besides, the number of plant species increased in 79% of the common quadrats for three years and halophyte community growth classified into five classes (from Class 1 to Class 5 with smaller numbers indicating better growth) was improved as the proportion of quadrats belonging to Class 1 or Class 2 increased from 38% to 86.8%. In salt response and flooding experiments, halophytic species with ever-increasing emergence percentage as soil electrical conductivity (EC) dropped to 0.88 dS m-1 through four-time desaltings were Suaeda asparagoides, Suaeda japonica, Limonium tetragonum, and Atriplex gmelini. S. asparagoides and S. japonica had an advantage for growing in the intermittently-flooded condition. Annual halophytes of S. asparagoides, S. japonica, and S. europaea were categorized into the upper EC group. Halophyte communities always located above high water level during the growing seasons and did not emerge in the seawater-flooded areas. In 2006, magnesium, potassium and sodium as well as soil EC had a significantly negative relationship with halophyte community growth. The negative relationship between plant species and soil EC persisted until 2008. Plant height was found to have a significantly negative relationship (R2=0.95) with soil EC. The electrical conductivity of a saturated soil extract (ECe) according to halophyte community growth from seven ECe maps made by electromagnetic measurement system was 24 to 27(ยฑ9) dS m-1 for Class 1, 31 to 36(ยฑ8) dS m-1 for Class 2, 33 to 37(ยฑ10) dS m-1 for Class 3, and 29 to 33(ยฑ13) dS m-1 for Class 4 respectively. Sixty eight % of quadrats in rake-plowing area showed growth better than Class 4, whereas 53% of quadrats in non-plowing area did. In the experiments on wind reduction by halophyte communities, the wind was blocked at a height of 20 cm within a halophyte community beginning to be clearly reduced from the starting point of halophytes and stabilized through halophytes. The maximum wind reduction was 82 to 38% at a height of 20 cm within a halophyte community. Wind velocity was reduced through halophytes largely at heights of below 60 cm, which are lower than halophytes. Wind reduction percentage by a halophyte community was maximally 76% at a height of 20 cm. Total suspended particulate (TSP) decreased by 60.7% passing through a group of transplanted halophytes and the reduction effect continued almost the same to 50 m behind the community. Dust reduction behind halophyte communities amounted to 43.8 ug m-3 for TSP and 14.7 ug m-3 for PM10 (Particlulates <10 ฮผm). TSP collected within halophyte communities averaged 37.0 ug m-3 with differenences among four halophyte communities of S. asparagoides, Phragmites communis, Atriplex subulatus, and S. europaea. Nitrogen (total nitrogen), phosphorus (P2O5), and sodium (Na+) contents absorbed by halophytes in 2005 before halophyte community development were 2.60, 0.14, and 4.98 g m-2 respectively. Nitrogen, phosphorus, and sodium contents in S. asparagoides, S. japonica, and S. europaea were 1.24 to 2.20 g m-2, 0.07 to 0.13 g m-2, and 2.91 to 4.59 g m-2. However, in 2010 after halophyte community development, they were 0.45 g m-2, 0.04 g m-2, and 0.08 g m-2 respectively. On the basis of halophyte community area developed in 2006 (1,236 ha), the amount of total nitrogen (T-N), P2O5 and Na+ absorbed by only a major halophytic species of S. asparagoides amounted up to 439, 78, and 914 ton respectively. In conclusion, it was most critical to select halophytic species with superior qualities against the harsh environment of reclaimed land such as salinity and flooding for halophyte community development. The annual halophytes mainly used in sowing not only held high germination capacity and velocity but also displayed broad salt-adaptability and tolerance to flooding. Sowing in the area at higher elevation than high level of seawater was important for halophyte communities to emerge. EC among soil properties had a significantly negative relationship with halophyte community until 2008. It was found that halophyte communities were capable of collecting significant dust as well as definitely reducing and stabilizing wind velocity. A great deal of nitrogen, phosphorus, and sodium absorbed into halophytic species in the reclaimed land can consequently contribute for lowering the levels of soil nitrogen and phosphorus leading to freshwater lake eutrophication or improving salt-affected soils.CONTENTS ABSTRACT ........................................................................................... i LITERATURE REVIEW .................................................................... 1 1. Halophyte community development ..................................................... 1 2. Factors affecting halophyte communities ............................................. 1 3. Soil EC measurement ............................................................................ 7 4. Analysis of halophyte community distribution ..................................... 8 5. Practical uses of halophytes .................................................................. 9 REFERENCES ................................................................................... 11 Chapter I. Development of Halophyte Communities ABSTRACT ........................................................................................ 24 INTRODUCTION .............................................................................. 26 MATERIALS AND METHODS ....................................................... 29 1. Germination experiment ..................................................................... 29 2. Salt response experiment .................................................................... 29 3. Flooding experiment ........................................................................... 30 4. Halophyte community development ................................................... 30 5. Halophyte community monitoring ...................................................... 46 6. Soil analysis ........................................................................................ 50 RESULTS ............................................................................................ 56 1. Methodology of halophyte community development ......................... 56 2. Change of halophyte communities ...................................................... 62 3. Development of halophyte communities ............................................ 78 DISCUSSION .................................................................................... 133 REFERENCES ................................................................................. 143 Chapter II. Environmental Effects of Halophyte Communities ABSTRACT ...................................................................................... 152 INTRODUCTION ............................................................................ 154 MATERIALS AND METHODS ..................................................... 156 1. Wind reduction experiments ............................................................. 156 2. Dust collection experiments .............................................................. 157 3. Absorbed nutrient analysis ................................................................ 161 RESULTS .......................................................................................... 164 1. Wind reduction .................................................................................. 164 2. Dust collection .................................................................................. 168 3. Nutrient absorption ........................................................................... 174 DISCUSSION .................................................................................... 182 REFERENCES ................................................................................. 187 CONCLUSION ................................................................................. 190 ABSTRACT IN KOREAN .............................................................. 192 ACKNOWLEDGEMENTS ............................................................. 196 APPENDIXES ................................................................................... 197Docto

    ๊ฑด์„ค๊ธฐ๊ณ„ ์—”์ง„๋งˆ์šดํŠธ ์ตœ์ ์„ค๊ณ„์— ๊ด€ํ•œ ์‹ค์šฉ์  ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2014. 2. ๊ฐ•์—ฐ์ค€.์—”์ง„๋งˆ์šดํŠธ ์ตœ์ ์„ค๊ณ„์— ๊ด€ํ•œ ๊ธฐ์กด์˜ ์—ฐ๊ตฌ๋Š” ๋งˆ์šดํŠธ ๊ฐ•์„ฑ์„ ๊ณ ์ •๋œ ๊ฐ’์œผ๋กœ ๊ฐ€์ •ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ๋งˆ์šดํŠธ ๊ฐ•์„ฑ์€ ํƒ‘์žฌ์กฐ๊ฑด(์ฃผํŒŒ์ˆ˜, ๋ณ€์œ„, ์ดˆ๊ธฐํ•˜์ค‘)์— ๋”ฐ๋ผ์„œ ๋‹ฌ๋ผ์ง€๋ฉฐ ์™ธ๋ถ€ํ™˜๊ฒฝ ์กฐ๊ฑด(์˜จ๋„)์— ๋”ฐ๋ผ์„œ๋„ ๋‹ค๋ฅธ ๊ฐ’์„ ๊ฐ€์ง„๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์กฐ๊ฑด์— ๋”ฐ๋ผ ๋‹ฌ๋ผ์ง€๋Š” ๋งˆ์šดํŠธ ๊ฐ•์„ฑ์„ ํ‰๊ท ๊ณผ ํ‘œ์ค€ํŽธ์ฐจ๋ฅผ ๊ฐ€์ง€๋Š” ์ •๊ทœํ™•๋ฅ ๋ถ„ํฌ๋กœ ํ‘œํ˜„ํ•˜์—ฌ ์ตœ์ ์„ค๊ณ„๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ  ์ด๋ฅผ ์‹ ๋ขฐ์„ฑ๊ธฐ๋ฐ˜ ์ตœ์ ์„ค๊ณ„ (Reliability Based Design Optimization)๋ผ๊ณ  ํ•œ๋‹ค. ์ตœ์ ์„ค๊ณ„๋Š” ๋ชฉ์ ํ•จ์ˆ˜, ์ œํ•œ์กฐ๊ฑด, ์„ค๊ณ„๋ณ€์ˆ˜๋กœ ์ด๋ฃจ์–ด์ง€๋ฉฐ ๋ชฉ์ ํ•จ์ˆ˜๋Š” ๊ฐ€์ค‘์น˜๊ฐ€ ๋ถ€์—ฌ๋œ ์ „๋‹ฌ๋ ฅ์˜ ํ•ฉ์œผ๋กœ ํ‘œํ˜„๋˜๊ณ  ์ œํ•œ์กฐ๊ฑด์€ ์ฃผ์š” ๊ฐ€์ง„๋ชจ๋“œ์˜ ์ฃผํŒŒ์ˆ˜๋น„์ด๋ฉฐ ์„ค๊ณ„๋ณ€์ˆ˜๋Š” ๋งˆ์šดํŠธ ๊ฐ•์„ฑ๊ณผ ๋งˆ์šดํŠธ ์œ„์น˜์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” 5๊ธฐํ†ต ์—”์ง„์— ๋Œ€ํ•œ ์‹ ๋ขฐ์„ฑ๊ธฐ๋ฐ˜ ์ตœ์ ์„ค๊ณ„๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ ์‹ ๋ขฐ๋„์— ๋”ฐ๋ฅธ ์ตœ์ ์˜ ์„ค๊ณ„๋ณ€์ˆ˜๊ฐ’์„ ์ฐพ์•„๋‚ด์—ˆ๋‹ค.์ดˆ ๋ก โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ i ๋ชฉ ์ฐจ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ ii LIST OF TABLES โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ iii LIST OF FIGURES โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ iv 1. ์„œ ๋ก  โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 1 2. ์—”์ง„๋งˆ์šดํŠธ ๊ฐ•์„ฑ์˜ ํŠน์„ฑ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 2 2.1 ์šด์ „์กฐ๊ฑด(์ฃผํŒŒ์ˆ˜, ๋ณ€์œ„, ์ดˆ๊ธฐํ•˜์ค‘) โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 2 2.2 ์˜จ๋„์กฐ๊ฑด โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 4 2.3 ๊ธฐํƒ€ ๊ณ ๋ ค์‚ฌํ•ญ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 5 3. ์ „๋‹ฌ๋ ฅ ๋ฐ ์—”์ง„๊ฐ€์ง„๋ ฅ ํ•ด์„ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 6 3.1 ์ „๋‹ฌ๋ ฅ ํ•ด์„ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 6 3.2 ์—”์ง„๊ฐ€์ง„๋ ฅ ํ•ด์„ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 7 3.3 ์ „๋‹ฌ๋ ฅ ์‹œํ—˜ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 9 4. ์ตœ์ ์„ค๊ณ„ ์ˆ˜ํ–‰ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 11 4.1 ์‹ ๋ขฐ์„ฑ๊ธฐ๋ฐ˜ ์ตœ์ ์„ค๊ณ„(RBDO) โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 11 4.2 ๋ชฉ์ ํ•จ์ˆ˜ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 12 4.3 ์„ค๊ณ„๋ณ€์ˆ˜ ๋ฐ ์ œํ•œ์กฐ๊ฑด โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 13 4.4 ์ตœ์ ์„ค๊ณ„ ์˜ˆ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 14 5. ๊ฒฐ ๋ก  โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 31 ์ฐธ๊ณ  ๋ฌธํ—Œ โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 32 ABSTRACT โ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆโ€ฆ 34Maste

    Optimal Tuning of a Digital PID Controller Based on the PPGA and Model

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    In this paper, a methodology for estimating the parameters of discrete-time systems and designing a digital PID controller is presented. To deal with optimization problems occurring regarding parameter estimation and controller design, a pseudo parallel genetic algorithm(PPGA) is developed. The PPGA is characterized by several serial GAs, the migration model and the ring topology . The parameters of a discrete-time system are estimated using both the model technique and a PPGA. The digital PID controller is described by the pulse transfer function and its parameters are tuned based on both the model reference technique and another PPGA. A set of experimental works on two processes are carried out to illustrate the performance of the proposed method.์ œ1์žฅ ์„œ๋ก  1 ์ œ2์žฅ ์ตœ์ ํ™” ๋„๊ตฌ๋กœ์„œ์˜ ๋ณ‘๋ ฌ ์œ ์ „์•Œ๊ณ ๋ฆฌ์ฆ˜ 4 2.1 ์ง๋ ฌ ์œ ์ „์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๊ตฌ์กฐ 4 2.1.1 ์—ผ์ƒ‰์ฒด์˜ ํ‘œํ˜„ ๋ฐ ์ง‘๋‹จ์˜ ์ดˆ๊ธฐํ™” 4 2.1.2 ์ ํ•ฉ๋„ ํ‰๊ฐ€ 5 2.1.3 ์œ ์ „ ์—ฐ์‚ฐ์ž 6 2.1.4 ๊ธฐํƒ€ ์ „๋žต 8 2.1.5 ์ •์ง€์กฐ๊ฑด 9 2.2 ์œ ์ „์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋ณ‘๋ ฌํ™” 10 2.2.1 ์ด์ฃผ๋ชจ๋ธ ๊ตฌ์กฐ์˜ ๋ณ‘๋ ฌํ™” 11 2.2.2 ์ด์ฃผ๋ฐฉ๋ฒ• 11 2.3 PPGA์˜ ๊ตฌํ˜„ 13 ์ œ3์žฅ ์ด์‚ฐ์‹œ๊ฐ„ ์‹œ์Šคํ…œ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ • 16 3.1 ์ œ์–ด๋Œ€์ƒ ์‹œ์Šคํ…œ์˜ ํ‘œํ˜„ 16 3.2 ์ œ์–ด๋Œ€์ƒ ์‹œ์Šคํ…œ์˜ ๋ชจ๋ธ 18 3.3 ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์ถ”์ • 19 ์ œ4์žฅ ๋””์ง€ํ„ธ PID ์ œ์–ด๊ธฐ์˜ ์ตœ์ ๋™์กฐ 23 4.1 ๋””์ง€ํ„ธ PID ์ œ์–ด๊ธฐ 23 4.2 PPGA์™€ ๊ธฐ์ค€๋ชจ๋ธ ์ œ์–ด๊ธฐ๋ฒ•์„ ์ด์šฉํ•œ ์ตœ์ ๋™์กฐ 27 4.2.1 ๊ธฐ์ค€๋ชจ๋ธ 27 4.2.2 PPGA๋ฅผ ์ด์šฉํ•œ PID ์ œ์–ด๊ธฐ์˜ ์ตœ์ ๋™์กฐ 29 ์ œ5์žฅ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐ ๊ฒฐ๊ณผ๊ฒ€ํ†  31 5.1 ์ด์‚ฐ์‹œ๊ฐ„ ๋ชจ๋ธ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ ์ถ”์ • 31 5.1.1 ์‹œ์Šคํ…œ I 31 5.1.2 ์‹œ์Šคํ…œ II 36 5.2 ๋””์ง€ํ„ธ PID ์ œ์–ด๊ธฐ์˜ ์ตœ์ ๋™์กฐ 40 5.2.1 ์‹œ์Šคํ…œ I 40 5.2.2 ์‹œ์Šคํ…œ II 44 ์ œ6์žฅ ๊ฒฐ๋ก  48 ์ฐธ๊ณ ๋ฌธํ—Œ 4

    A Study of the Classification of 4-Year University Students by Job Preference and the Length of Job Searching Period

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    ๋ณธ ์—ฐ๊ตฌ๋Š” 250๋ช…์˜ 4๋…„์ œ ๋Œ€ํ•™์ƒ์„ ๋Œ€์ƒ์œผ๋กœ ์ทจ์—…์„ ํ˜ธ๋„์— ๋”ฐ๋ฅธ ์ž ์žฌ์ง‘๋‹จ๋ถ„์„๊ณผ ๊ฐ ์ž ์žฌ์ง‘๋‹จ๋ณ„ ์ทจ์—…์†Œ์š”๊ธฐ๊ฐ„ ๋ถ„์„ ๋ฐ ์˜ํ–ฅ์š”์ธ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์กธ์—… ์‹œ์ ์˜ ์ทจ์—…์„ ํ˜ธ๋„์— ๋”ฐ๋ฅธ ์ž ์žฌ์ง‘๋‹จ๋ถ„์„ ๊ฒฐ๊ณผ, ์ด์ƒ์ถ”๊ตฌํ˜•, ์ง์—…ํƒ์ƒ‰ํ˜•, ์ž์•„์‹คํ˜„ํ˜•, ์‚ฌ๋ฌด์ง์„ ํ˜ธํ˜•, ๋น„์‚ฌ๋ฌด์ง์„ ํ˜ธํ˜•์˜ 5๊ฐœ ์ง‘๋‹จ์ด ๋„์ถœ๋˜์—ˆ๋‹ค. ๊ฐ ์ทจ์—…์†Œ์š”๊ธฐ๊ฐ„์— ๋Œ€ํ•œ ์ƒ์กด๋ถ„์„๊ฒฐ๊ณผ, ์ž ์žฌ์ง‘๋‹จ ๋ณ„๋กœ ์ƒ์ดํ•œ ์ทจ์—…์†Œ์š”๊ธฐ๊ฐ„๊ณผ ๋ฏธ์ทจ์—…๋ฅ ์„ ๋ณด์˜€๋‹ค. ์ทจ์—…์†Œ์š”๊ธฐ๊ฐ„์—๋Š” ์ธ์ ์ž๋ณธ ์š”์ธ์˜ ์˜์–ด์ ์ˆ˜์™€ ์‚ฌํšŒ๊ฒฝ์ œ์  ์š”์ธ์˜ ์–ด๋จธ๋‹ˆ ์›”ํ‰๊ท  ์†Œ๋“์ด ์œ ์˜ํ•œ ์˜ํ•ญ์„ ์ฃผ๋Š” ๋ณ€์ˆ˜๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํ† ์ต ์ ์ˆ˜๊ฐ€ ์—†์„ ๊ฒฝ์šฐ์— ๋น„ํ•ด 800์  ์ด์ƒ์ผ ๊ฒฝ์šฐ, ์–ด๋จธ๋‹ˆ ์›”ํ‰๊ท  ์†Œ๋“์ด ์—†์„ ๊ฒฝ์šฐ์— ๋น„ํ•ด 500๋งŒ์› ๋ฏธ๋งŒ 1,000๋งŒ์› ๋ฏธ๋งŒ์ผ ๊ฒฝ์šฐ ์ทจ์—…์†Œ์š”๊ธฐ๊ฐ„์ด ๋‹จ์ถ•๋  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ๊ณ ์šฉ์˜ ์งˆ์— ๋Œ€ํ•œ ๋ถˆ๊ท ํ˜•์„ ํ•ด์†Œํ•˜๊ณ , ์ฒญ๋…„ ๋ฏธ์ทจ์—…์ž๋“ค์ด ์ž์‹ ์˜ ์ง์—…๊ฐ€์น˜์— ๋ถ€ํ•ฉํ•˜๋Š” ๋‹ค์–‘ํ•œ ์ผ์ž๋ฆฌ๋ฅผ ํƒ์ƒ‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ง€์›ํ•˜๋Š” ํšจ๊ณผ์ ์ธ ๋ฐฉํ–ฅ์„ฑ์ด ์ œ์‹œ๋˜๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•œ๋‹ค.The present study applied Latent Class Analysis and Survival Analysis to classify college students by their job preferences and investigate the length of job searching period. According to the results of identifying latent classes by job preference, five classes were discerned for 250 college students. Each class showed distinctive length of job searching period and the unemployment rate. Also, the results of Survival Analysis showed that English test score (TOEIC) and motherโ€™s average monthly income were significant determinants of length of job searching period. Based on the findings from this study, it is suggested that insightful measures to enhance imbalance of employment quality should be considered

    Hereditary Property for Joseon Royal Family During the Japanese Colonial Period

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    ๆฑบๆ˜Ž(Cassia tora), ์‘ฅ(Artemisia asiatica), ๆœจๆํƒ€๋ฅด ๆŠฝๅ‡บ็‰ฉ์— ์˜ํ•œ ๋ฐญๅœŸๅฃคไธญ ๏ฆฝ็ด ๅˆ†่งฃ์™€ ็ช’้…ธๅŒ–ไฝœ็”จ ๆŠ‘ๅˆถๆ•ˆๆžœ

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    Thesis (master`s)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋†ํ™”ํ•™๊ณผ,1997.Maste

    ์‚ฌํšŒ๊ณ„์ธต๊ฐ„ ์ž๋…€์˜ ํ•™์—…์„ฑ์ทจ๋„ ๊ฒฉ์ฐจ์™€ ์–‘์œก๋ฌธํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ธ๋ฅ˜ํ•™๊ณผ ์ธ๋ฅ˜ํ•™์ „๊ณต, 2004.Maste

    Why do the children from affluent families show better academic performance?: -The academic performance gap between social classes and parenting practices-

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์‚ฌํšŒ๋ณต์ง€ํ•™๊ณผ,2010.2.Docto

    Generalized Ligamentous Laxity Is an Independent Predictor of Poor Outcomes After the Modified Brostr?m Procedure for Chronic Lateral Ankle Instability

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    BACKGROUND: The modified Brostr?m procedure for chronic lateral ankle instability (CLAI) has presented outstanding clinical results. However, after the procedure, some patients with generalized ligamentous laxity have experienced a recurrence of ankle instability. PURPOSE: To understand the effect of generalized ligamentous laxity on prognosis and risk of recurrence in a cohort of patients with CLAI after the modified Brostr?m procedure. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: A total of 199 ankles from 188 patients underwent the modified Brostr?m procedure for CLAI with a mean follow-up of 60.1 months (range, 48-108 months). Generalized ligamentous laxity was assessed in all patients. The Karlsson-Peterson ankle score (Karlsson score), talar tilt angle, and anterior displacement of the talus were used to evaluate clinical and radiological outcomes. Risk factors associated with clinical outcomes were evaluated using bivariate analysis and logistic regression analysis. Survival outcomes were compared using Kaplan-Meier analysis. RESULTS: Generalized ligamentous laxity was evident in 42 cases (21.1%). The average Karlsson score improved from 54.6 ยฑ 7.1 preoperatively to 87.9 ยฑ 7.2 at last follow-up (P 15ยฐ), and high preoperative anterior displacement of the talus (>10 mm) were significantly associated with clinical failure. Multivariate logistic regression analysis revealed that generalized ligamentous laxity was the most important independent predictor of clinical failure after the modified Brostr?m procedure. The cumulative success rates for the nonlaxity group were significantly superior to those for the laxity group in Kaplan-Meier curves (P < .001). CONCLUSION: Generalized ligamentous laxity is an independent predictor of poor outcomes and a risk factor of recurrent instability following the modified Brostr?m procedure for CLAI.restrictio
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