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    ์Šฌ๋ผ์ด๋”ฉ ์œˆ๋„์šฐ์ƒ์˜ ๋น ๋ฅธ ์ ์ง„์  ๋ฐ€๋„ ๊ธฐ๋ฐ˜ ํด๋Ÿฌ์Šคํ„ฐ๋ง

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2022. 8. ๋ฌธ๋ด‰๊ธฐ.Given the prevalence of mobile and IoT devices, continuous clustering against streaming data has become an essential tool of increasing importance for data analytics. Among many clustering approaches, density-based clustering has garnered much attention due to its unique advantage that it can detect clusters of an arbitrary shape when noise exists. However, when the clusters need to be updated continuously along with an evolving input dataset, a relatively high computational cost is required. Particularly, deleting data points from the clusters causes severe performance degradation. In this dissertation, the performance limits of the incremental density-based clustering over sliding windows are addressed. Ultimately, two algorithms, DISC and DenForest, are proposed. The first algorithm DISC is an incremental density-based clustering algorithm that efficiently produces the same clustering results as DBSCAN over sliding windows. It focuses on redundancy issues that occur when updating clusters. When multiple data points are inserted or deleted individually, surrounding data points are explored and retrieved redundantly. DISC addresses these issues and improves the performance by updating multiple points in a batch. It also presents several optimization techniques. The second algorithm DenForest is an incremental density-based clustering algorithm that primarily focuses on the deletion process. Unlike previous methods that manage clusters as a graph, DenForest manages clusters as a group of spanning trees, which contributes to very efficient deletion performance. Moreover, it provides a batch-optimized technique to improve the insertion performance. To prove the effectiveness of the two algorithms, extensive evaluations were conducted, and it is demonstrated that DISC and DenForest outperform the state-of-the-art density-based clustering algorithms significantly.๋ชจ๋ฐ”์ผ ๋ฐ IoT ์žฅ์น˜๊ฐ€ ๋„๋ฆฌ ๋ณด๊ธ‰๋จ์— ๋”ฐ๋ผ ์ŠคํŠธ๋ฆฌ๋ฐ ๋ฐ์ดํ„ฐ์ƒ์—์„œ ์ง€์†์ ์œผ๋กœ ํด๋Ÿฌ์Šคํ„ฐ๋ง ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒƒ์€ ๋ฐ์ดํ„ฐ ๋ถ„์„์—์„œ ์ ์  ๋” ์ค‘์š”ํ•ด์ง€๋Š” ํ•„์ˆ˜ ๋„๊ตฌ๊ฐ€ ๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๋งŽ์€ ํด๋Ÿฌ์Šคํ„ฐ๋ง ๋ฐฉ๋ฒ• ์ค‘์—์„œ ๋ฐ€๋„ ๊ธฐ๋ฐ˜ ํด๋Ÿฌ์Šคํ„ฐ๋ง์€ ๋…ธ์ด์ฆˆ๊ฐ€ ์กด์žฌํ•  ๋•Œ ์ž„์˜์˜ ๋ชจ์–‘์˜ ํด๋Ÿฌ์Šคํ„ฐ๋ฅผ ๊ฐ์ง€ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ณ ์œ ํ•œ ์žฅ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ ์ด์— ๋”ฐ๋ผ ๋งŽ์€ ๊ด€์‹ฌ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฐ€๋„ ๊ธฐ๋ฐ˜ ํด๋Ÿฌ์Šคํ„ฐ๋ง์€ ๋ณ€ํ™”ํ•˜๋Š” ์ž…๋ ฅ ๋ฐ์ดํ„ฐ ์…‹์— ๋”ฐ๋ผ ์ง€์†์ ์œผ๋กœ ํด๋Ÿฌ์Šคํ„ฐ๋ฅผ ์—…๋ฐ์ดํŠธํ•ด์•ผ ํ•˜๋Š” ๊ฒฝ์šฐ ๋น„๊ต์  ๋†’์€ ๊ณ„์‚ฐ ๋น„์šฉ์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. ํŠนํžˆ, ํด๋Ÿฌ์Šคํ„ฐ์—์„œ์˜ ๋ฐ์ดํ„ฐ ์ ๋“ค์˜ ์‚ญ์ œ๋Š” ์‹ฌ๊ฐํ•œ ์„ฑ๋Šฅ ์ €ํ•˜๋ฅผ ์ดˆ๋ž˜ํ•ฉ๋‹ˆ๋‹ค. ๋ณธ ๋ฐ•์‚ฌ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ์Šฌ๋ผ์ด๋”ฉ ์œˆ๋„์šฐ์ƒ์˜ ๋ฐ€๋„ ๊ธฐ๋ฐ˜ ํด๋Ÿฌ์Šคํ„ฐ๋ง์˜ ์„ฑ๋Šฅ ํ•œ๊ณ„๋ฅผ ๋‹ค๋ฃจ๋ฉฐ ๊ถ๊ทน์ ์œผ๋กœ ๋‘ ๊ฐ€์ง€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ธ DISC๋Š” ์Šฌ๋ผ์ด๋”ฉ ์œˆ๋„์šฐ์ƒ์—์„œ DBSCAN๊ณผ ๋™์ผํ•œ ํด๋Ÿฌ์Šคํ„ฐ๋ง ๊ฒฐ๊ณผ๋ฅผ ์ฐพ๋Š” ์ ์ง„์  ๋ฐ€๋„ ๊ธฐ๋ฐ˜ ํด๋Ÿฌ์Šคํ„ฐ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์ž…๋‹ˆ๋‹ค. ํ•ด๋‹น ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํด๋Ÿฌ์Šคํ„ฐ ์—…๋ฐ์ดํŠธ ์‹œ์— ๋ฐœ์ƒํ•˜๋Š” ์ค‘๋ณต ๋ฌธ์ œ๋“ค์— ์ดˆ์ ์„ ๋‘ก๋‹ˆ๋‹ค. ๋ฐ€๋„ ๊ธฐ๋ฐ˜ ํด๋Ÿฌ์Šคํ„ฐ๋ง์—์„œ๋Š” ์—ฌ๋Ÿฌ ๋ฐ์ดํ„ฐ ์ ๋“ค์„ ๊ฐœ๋ณ„์ ์œผ๋กœ ์‚ฝ์ž… ํ˜น์€ ์‚ญ์ œํ•  ๋•Œ ์ฃผ๋ณ€ ์ ๋“ค์„ ๋ถˆํ•„์š”ํ•˜๊ฒŒ ์ค‘๋ณต์ ์œผ๋กœ ํƒ์ƒ‰ํ•˜๊ณ  ํšŒ์ˆ˜ํ•ฉ๋‹ˆ๋‹ค. DISC ๋Š” ๋ฐฐ์น˜ ์—…๋ฐ์ดํŠธ๋กœ ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์—ฌ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๋ฉฐ ์—ฌ๋Ÿฌ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•๋“ค์„ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ธ DenForest ๋Š” ์‚ญ์ œ ๊ณผ์ •์— ์ดˆ์ ์„ ๋‘” ์ ์ง„์  ๋ฐ€๋„ ๊ธฐ๋ฐ˜ ํด๋Ÿฌ์Šคํ„ฐ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์ž…๋‹ˆ๋‹ค. ํด๋Ÿฌ์Šคํ„ฐ๋ฅผ ๊ทธ๋ž˜ํ”„๋กœ ๊ด€๋ฆฌํ•˜๋Š” ์ด์ „ ๋ฐฉ๋ฒ•๋“ค๊ณผ ๋‹ฌ๋ฆฌ DenForest ๋Š” ํด๋Ÿฌ์Šคํ„ฐ๋ฅผ ์‹ ์žฅ ํŠธ๋ฆฌ์˜ ๊ทธ๋ฃน์œผ๋กœ ๊ด€๋ฆฌํ•จ์œผ๋กœ์จ ํšจ์œจ์ ์ธ ์‚ญ์ œ ์„ฑ๋Šฅ์— ๊ธฐ์—ฌํ•ฉ๋‹ˆ๋‹ค. ๋‚˜์•„๊ฐ€ ๋ฐฐ์น˜ ์ตœ์ ํ™” ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ์‚ฝ์ž… ์„ฑ๋Šฅ ํ–ฅ์ƒ์—๋„ ๊ธฐ์—ฌํ•ฉ๋‹ˆ๋‹ค. ๋‘ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํšจ์œจ์„ฑ์„ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•ด ๊ด‘๋ฒ”์œ„ํ•œ ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€์œผ๋ฉฐ DISC ๋ฐ DenForest ๋Š” ์ตœ์‹ ์˜ ๋ฐ€๋„ ๊ธฐ๋ฐ˜ ํด๋Ÿฌ์Šคํ„ฐ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜๋“ค๋ณด๋‹ค ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ์Šต๋‹ˆ๋‹ค.1 Introduction 1 1.1 Overview of Dissertation 3 2 Related Works 7 2.1 Clustering 7 2.2 Density-Based Clustering for Static Datasets 8 2.2.1 Extension of DBSCAN 8 2.2.2 Approximation of Density-Based Clustering 9 2.2.3 Parallelization of Density-Based Clustering 10 2.3 Incremental Density-Based Clustering 10 2.3.1 Approximated Density-Based Clustering for Dynamic Datasets 11 2.4 Density-Based Clustering for Data Streams 11 2.4.1 Micro-clusters 12 2.4.2 Density-Based Clustering in Damped Window Model 12 2.4.3 Density-Based Clustering in Sliding Window Model 13 2.5 Non-Density-Based Clustering 14 2.5.1 Partitional Clustering and Hierarchical Clustering 14 2.5.2 Distribution-Based Clustering 15 2.5.3 High-Dimensional Data Clustering 15 2.5.4 Spectral Clustering 16 3 Background 17 3.1 DBSCAN 17 3.1.1 Reformulation of Density-Based Clustering 19 3.2 Incremental DBSCAN 20 3.3 Sliding Windows 22 3.3.1 Density-Based Clustering over Sliding Windows 23 3.3.2 Slow Deletion Problem 24 4 Avoiding Redundant Searches in Updating Clusters 26 4.1 The DISC Algorithm 27 4.1.1 Overview of DISC 27 4.1.2 COLLECT 29 4.1.3 CLUSTER 30 4.1.3.1 Splitting a Cluster 32 4.1.3.2 Merging Clusters 37 4.1.4 Horizontal Manner vs. Vertical Manner 38 4.2 Checking Reachability 39 4.2.1 Multi-Starter BFS 40 4.2.2 Epoch-Based Probing of R-tree Index 41 4.3 Updating Labels 43 5 Avoiding Graph Traversals in Updating Clusters 45 5.1 The DenForest Algorithm 46 5.1.1 Overview of DenForest 47 5.1.1.1 Supported Types of the Sliding Window Model 48 5.1.2 Nostalgic Core and Density-based Clusters 49 5.1.2.1 Cluster Membership of Border 51 5.1.3 DenTree 51 5.2 Operations of DenForest 54 5.2.1 Insertion 54 5.2.1.1 MST based on Link-Cut Tree 57 5.2.1.2 Time Complexity of Insert Operation 58 5.2.2 Deletion 59 5.2.2.1 Time Complexity of Delete Operation 61 5.2.3 Insertion/Deletion Examples 64 5.2.4 Cluster Membership 65 5.2.5 Batch-Optimized Update 65 5.3 Clustering Quality of DenForest 68 5.3.1 Clustering Quality for Static Data 68 5.3.2 Discussion 70 5.3.3 Replaceability 70 5.3.3.1 Nostalgic Cores and Density 71 5.3.3.2 Nostalgic Cores and Quality 72 5.3.4 1D Example 74 6 Evaluation 76 6.1 Real-World Datasets 76 6.2 Competing Methods 77 6.2.1 Exact Methods 77 6.2.2 Non-Exact Methods 77 6.3 Experimental Settings 78 6.4 Evaluation of DISC 78 6.4.1 Parameters 79 6.4.2 Baseline Evaluation 79 6.4.3 Drilled-Down Evaluation 82 6.4.3.1 Effects of Threshold Values 82 6.4.3.2 Insertions vs. Deletions 83 6.4.3.3 Range Searches 84 6.4.3.4 MS-BFS and Epoch-Based Probing 85 6.4.4 Comparison with Summarization/Approximation-Based Methods 86 6.5 Evaluation of DenForest 90 6.5.1 Parameters 90 6.5.2 Baseline Evaluation 91 6.5.3 Drilled-Down Evaluation 94 6.5.3.1 Varying Size of Window/Stride 94 6.5.3.2 Effect of Density and Distance Thresholds 95 6.5.3.3 Memory Usage 98 6.5.3.4 Clustering Quality over Sliding Windows 98 6.5.3.5 Clustering Quality under Various Density and Distance Thresholds 101 6.5.3.6 Relaxed Parameter Settings 102 6.5.4 Comparison with Summarization-Based Methods 102 7 Future Work: Extension to Varying/Relative Densities 105 8 Conclusion 107 Abstract (In Korean) 120๋ฐ•

    ์„œ๋‚จ๊ทน ๋งˆ๋ฆฌ์•ˆ ์†Œ๋งŒ์—์„œ์˜ ๋ฏธ์„ธํ”Œ๋ผ์Šคํ‹ฑ ์ด์†ก

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2021.8. ํ™ฉ์ง„ํ™˜.Microplastics with a size of less than 5 mm have been discovered in the Antarctic Ocean known as a pristine sea. The origins of microplastics are largely estimated to be both outside and inside Antarctica. According to the survey, more than half of the scientific research stations residing in Antarctica do not have adequate sewage treatment systems, so it could be speculated that the wastewater has a regional effect on pollution in the Antarctic Ocean. Through the field survey, it was confirmed that microplastics were accumulated in Marian Cove where the King Sejong Station is located, and the concentration of microplastics in the wastewater was about 1000 times higher than that of the surrounding seawater. It is expected to be a major cause of microplastic pollution in seawater around the station. This study performed numerical modeling to elucidate the movement and accumulation mechanism of microplastics in the bay. When reproducing the flow around the station, the waves affecting the movement of microplastics were considered. The trajectories of the particles were then tracked according to waves, release time, and release location by using the Lagrangian Particle Tracking method that reflects the properties of microplastics. As a result of numerical simulation, the flow velocity of Marian Cove is slower than that of Maxwell Bay, and considering the wave effects, it has a significant effect on the surface flow, so it can affect the movement of particles floating on the surface layer. The lighter particles floated around the surface layer and could travel longer, so most of them could reach the shoreline, while the denser particles sank relatively quickly and accumulated on the seabed. In addition, the wave effect increases the traveling speed of particles twice comparing to the simulation cases without the wave. It is indicated that oceanographic processes such as waves are important factors in the transport of particles that float around the surface layer in the ocean. The present study then proposed a strategy for reducing the accumulation of particles in a specific location, which can cause more serious environmental and ecological problems. In order to reduce the concentration of microplastics in Marian Cove, it is most effective to release the wastewater before the low tide, but it was shown that the particles were accumulated near the Antarctic Specially Protected Area. Accordingly, when microplastics were released from the surface of seawater a little far from the shoreline, they were transported out to Maxwell Bay, and so no particles remain in Marian Cove or reach the Antarctic Specially Protected Area. Therefore, in determining the accumulation amount of microplastics contained in the wastewater discharged from the station, it is very critical to control the release location and release time at which the wastewater need to be released according to the tidal cycle.์ฒญ์ •ํ•ด์—ญ์œผ๋กœ ์•Œ๋ ค์ง„ ๋‚จ๊ทนํ•ด์—์„œ ๋ฐœ๊ฒฌ๋œ 5 mm ์ดํ•˜ ํฌ๊ธฐ์˜ ๋ฏธ์„ธํ”Œ๋ผ์Šคํ‹ฑ์˜ ๊ธฐ์›์€ ํฌ๊ฒŒ ๋‚จ๊ทนํ•ด ์™ธ๋ถ€์™€ ๋‚ด๋ถ€ ๋‘ ๊ฐ€์ง€๋กœ ์ถ”์ •๋œ๋‹ค. ํ•œ ์กฐ์‚ฌ์— ๋”ฐ๋ฅด๋ฉด, ๋‚จ๊ทน์— ์ƒ์ฃผํ•˜๋Š” ์—ฐ๊ตฌ ๊ณผํ•™ ๊ธฐ์ง€์˜ ๋ฐ˜ ์ด์ƒ์ด ์ ์ ˆํ•œ ํ•˜์ˆ˜์ฒ˜๋ฆฌ ์‹œ์Šคํ…œ์„ ๊ฐ–์ถ”๊ณ  ์žˆ์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ๊ธฐ์ง€์˜ ๋ฐฉ๋ฅ˜์ˆ˜๊ฐ€ ๋‚จ๊ทนํ•ด์— ์ง€์—ญ์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์›์ธ์œผ๋กœ ์ถ”์ธก๋  ์ˆ˜ ์žˆ๋‹ค. ํ˜„์žฅ ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ๋‚จ๊ทน ์„ธ์ข…๊ณผํ•™๊ธฐ์ง€๊ฐ€ ์œ„์น˜ํ•œ ๋งŒ์—์„œ ๋ฏธ์„ธํ”Œ๋ผ์Šคํ‹ฑ์ด ์ง‘์ ๋˜๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๊ณ , ๋ฐฉ๋ฅ˜์ˆ˜์—์„œ ๋ฐœ๊ฒฌ๋œ ๋ฏธ์„ธํ”Œ๋ผ์Šคํ‹ฑ์˜ ๋†๋„๊ฐ€ ์ฃผ๋ณ€ ํ•ด์ˆ˜์— ๋น„ํ•ด ์•ฝ 1000๋ฐฐ ์ด์ƒ ๋†’์€ ๊ฒƒ์œผ๋กœ ๋ณด์•„, ๋ฐฉ๋ฅ˜์ˆ˜์— ํฌํ•จ๋œ ๋ฏธ์„ธํ”Œ๋ผ์Šคํ‹ฑ์ด ๊ธฐ์ง€ ์ฃผ๋ณ€ ํ•ด์ˆ˜ ์˜ค์—ผ์˜ ์ฃผ์š” ์›์ธ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋งŒ ๋‚ด์—์„œ์˜ ๋ฏธ์„ธํ”Œ๋ผ์Šคํ‹ฑ ์ด๋™ ๋ฐ ์ง‘์  ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ์ˆ˜์น˜ ๋ชจ๋ธ์„ ํ†ตํ•ด ์„ธ์ข…๊ณผํ•™๊ธฐ์ง€ ์ฃผ๋ณ€์žฅ์˜ ํ๋ฆ„์„ ์žฌํ˜„ํ•˜๊ณ , ๋ฏธ์„ธํ”Œ๋ผ์Šคํ‹ฑ์˜ ํŠน์„ฑ์„ ๋ฐ˜์˜ํ•œ Lagrangian Particle Tracking ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด ์ž…์ž์˜ ๊ถค์ ์„ ํŒŒ๋ž‘ ํšจ๊ณผ, ๋ฐฉ์ถœ ์‹œ๊ฐ„, ๋ฐฉ์ถœ ์œ„์น˜์— ๋”ฐ๋ผ ์ถ”์ ํ•˜์˜€๋‹ค. ์ˆ˜์น˜ ๋ชจ์˜ ๊ฒฐ๊ณผ, ์„ธ์ข…๊ณผํ•™๊ธฐ์ง€๊ฐ€ ์œ„์น˜ํ•œ ๋งˆ๋ฆฌ์•ˆ ์†Œ๋งŒ์€ ๋ฉ•์Šค์›ฐ ๋งŒ์— ๋น„ํ•ด ์œ ์†์ด ๋Š๋ฆฌ๋ฉฐ, ํŒŒ๋ž‘์˜ ํšจ๊ณผ๋ฅผ ๊ณ ๋ คํ•˜๋ฉด ํ‘œ์ธต ํ๋ฆ„์— ์ƒ๋‹นํ•œ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฏ€๋กœ ํ‘œ์ธต์„ ๋ถ€์œ ํ•˜๋Š” ์ž…์ž์˜ ์›€์ง์ž„์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋‹ค. ํ•ด์ˆ˜์˜ ๋ฐ€๋„๋ณด๋‹ค ๊ฐ€๋ฒผ์šด ์ž…์ž๋“ค์€ ํ‘œ์ธต์„ ๋ถ€์œ ํ•˜๋ฉฐ ๋” ์˜ค๋ž˜ ์ด๋™ํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๋Œ€๋ถ€๋ถ„ ํ•ด์•ˆ์„ ์— ๋„๋‹ฌํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ๋ฌด๊ฑฐ์šด ์ž…์ž๋“ค์€ ์ƒ๋Œ€์ ์œผ๋กœ ๋น ๋ฅธ ์†๋„๋กœ ๊ฐ€๋ผ ์•‰์œผ๋ฉฐ ํ•ด์ €์— ์ง‘์ ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ํŒŒ๋ž‘์˜ ํšจ๊ณผ๋ฅผ ๊ณ ๋ คํ•˜๋ฉด ์ž…์ž์˜ ์ด๋™ ์†๋„๋ฅผ ๋‘ ๋ฐฐ๋กœ ์ฆ๊ฐ€์‹œ์ผฐ๋‹ค. ์ด๋Š” ํŒŒ๋ž‘๊ณผ ๊ฐ™์€ ๋ฌผ๋ฆฌํ•ด์–‘ํ•™์ ์ธ ํ”„๋กœ์„ธ์Šค๊ฐ€ ํ‘œ์ธต์„ ๋ถ€์œ ํ•˜๋Š” ์ž…์ž์˜ ์ด์†ก์— ์ค‘์š”ํ•œ ์š”์†Œ์ž„์„ ์˜๋ฏธํ•œ๋‹ค. ๋˜ํ•œ, ๋ฏธ์„ธํ”Œ๋ผ์Šคํ‹ฑ์ด ์‹ฌ๊ฐํ•œ ํ™˜๊ฒฝ ๋ฐ ์ƒํƒœ๊ณ„ ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋Š” ๊ณณ์— ์ง‘์ ๋˜๋Š” ๊ฒƒ์„ ์ค„์ด๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ–ˆ๋‹ค. ๋งˆ๋ฆฌ์•ˆ ์†Œ๋งŒ ๋‚ด๋ถ€์˜ ๋ฏธ์„ธํ”Œ๋ผ์Šคํ‹ฑ ๋†๋„๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด์„œ๋Š” ๊ฐ„์กฐ ์ „ ๋ฐฉ๋ฅ˜์ˆ˜๋ฅผ ๋ฐฉ์ถœํ•˜๋Š” ๊ฒƒ์ด ๊ฐ€์žฅ ํšจ๊ณผ์ ์ด๋‚˜, ์ž…์ž๋“ค์ด ๋‚จ๊ทนํŠน๋ณ„๋ณดํ˜ธ๊ตฌ์—ญ ๊ทผ์ฒ˜์— ์ง‘์ ๋จ์„ ๋ณด์˜€๋‹ค. ํ•ด์•ˆ์„ ์—์„œ ๋–จ์–ด์ง„ ํ•ด์ˆ˜์—์„œ ๊ฐ„์กฐ ์ „์— ์ž…์ž๋ฅผ ๋ฐฉ์ถœํ•˜๊ฒŒ ๋œ๋‹ค๋ฉด, ๋ชจ๋“  ์ž…์ž๋“ค์ด ๋ฐฉ์ถœ ์งํ›„ ํ•ด์•ˆ๊ฐ€์— ๋„๋‹ฌํ•˜์ง€ ์•Š๊ณ  ๋งˆ๋ฆฌ์•ˆ ์†Œ๋งŒ์„ ๋น ์ ธ๋‚˜๊ฐ€ ์™ธํ•ด๋กœ ์ด์†ก๋จ์„ ๋ณด์˜€๋‹ค. ๋”ฐ๋ผ์„œ, ๊ธฐ์ง€์—์„œ ๋ฐฉ์ถœ๋˜๋Š” ๋ฐฉ๋ฅ˜์ˆ˜์— ํฌํ•จ๋œ ๋ฏธ์„ธํ”Œ๋ผ์Šคํ‹ฑ์˜ ์ง‘์ ๋Ÿ‰ ๊ฒฐ์ •์— ์žˆ์–ด ์กฐ์„ ์ฃผ๊ธฐ์— ๋”ฐ๋ผ ๋ฐฉ๋ฅ˜์ˆ˜๋ฅผ ๋ฐฉ์ถœํ•ด์•ผ ํ•˜๋Š” ์žฅ์†Œ์™€ ์‹œ๊ธฐ๋ฅผ ์ œ์–ดํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค.CHAPTER 1 . INTRODUCTION 1 1.1 General introduction 1 1.2 Objectives 5 CHAPTER 2 . RESEARCH BACKGROUNDS 8 2.1 Properties of microplastics 8 2.2 Microplastics remaining in the Antarctic Ocean 10 2.2.1 Oceanographic process in Antarctica 10 2.2.2 Microplastics found in Antarctica 12 2.3 Research area 15 CHAPTER 3 . METHODOLOGY 19 3.1 Field survey 19 3.2 Numerical model descriptions 22 3.2.1 Hydrodynamic model 22 3.2.2 Microplastic tracking model 25 3.3 Numerical experiment setup 28 3.3.1 Computer performance 28 3.3.2 Hydrodynamic model setup 29 3.3.3 Microplastic tracking model setup 36 CHAPTER 4 . RESULTS AND DISCUSSION 39 4.1 Hydrodynamic characteristic 39 4.1.1 Water circulation in Marian Cove 39 4.1.2 HK angles for MPs 44 4.2 Impact of waves on the trajectory of MPs 47 4.3 MPsโ€™ trajectories depending on releasing time 51 4.4 MPsโ€™ trajectories depending on releasing location 58 CHAPTER 5 . SUMMARY AND SUGGESTION 61 REFERENCES 64 ๊ตญ๋ฌธ์ดˆ๋ก 68์„

    ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ™˜์ž์˜ ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฐ„ํ˜ธํ•™๊ณผ ๊ฐ„ํ˜ธํ•™ ์ „๊ณต, 2013. 2. ์†ก๋ฏธ์ˆœ.์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹์€ ์ „์ฒ˜์น˜๋กœ ๊ณ ์šฉ๋Ÿ‰์˜ ํ•ญ์•”์ œ๋ฅผ ํˆฌ์—ฌํ•˜๊ฑฐ๋‚˜ ์ „์‹  ๋ฐฉ์‚ฌ์„ ์กฐ์‚ฌ๋ฅผ ํ•œ ํ›„์—, ์ •์ƒ ์กฐํ˜ˆ๋ชจ์„ธํฌ๋ฅผ ์ด์‹ํ•˜์—ฌ ๊ณจ์ˆ˜ ๊ธฐ๋Šฅ์„ ํšŒ๋ณต์‹œํ‚ค๋Š” ์น˜๋ฃŒ๋ฒ•์œผ๋กœ(๋ฏผ์šฐ์„ฑ, 2001) ๋ฐฑํ˜ˆ๋ณ‘ ๋ฟ ๋งŒ ์•„๋‹ˆ๋ผ ์žฌ์ƒ๋ถˆ๋Ÿ‰์„ฑ ๋นˆํ˜ˆ, ๋‹ค๋ฐœ์„ฑ ๊ณจ์ˆ˜์ข… ๋“ฑ์œผ๋กœ ์ ์‘์ฆ์ด ํ™•๋Œ€๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ™˜์ž๋Š” ํ‡ด์› ํ›„ ๋ฉด์—ญ์ฒด๊ณ„๊ฐ€ ๋ถˆ์™„์ „ํ•œ ๊ธฐ๊ฐ„ ๋™์•ˆ ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰์„ ํ†ตํ•ด ํ•ฉ๋ณ‘์ฆ์„ ์ž˜ ๊ด€์ฐฐํ•˜๊ณ  ์˜ˆ๋ฐฉํ•ด์•ผ ํ•˜๋Š”๋ฐ, ์‹ค์ œ๋กœ ๋Œ€์ƒ์ž๋“ค์ด ์–ด๋–ป๊ฒŒ ์ž๊ธฐ๊ด€๋ฆฌ๋ฅผ ํ•˜๊ณ  ์žˆ๋Š”์ง€์— ๋Œ€ํ•ด ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š์•„ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ™˜์ž์˜ ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰ ์ •๋„ ๋ฐ ์ดํ–‰์˜ ๊ด€๋ จ์š”์ธ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋Œ€์ƒ์ž๋Š” ์„œ์šธ ์†Œ์žฌ ์ผ๊ฐœ ์ƒ๊ธ‰์ข…ํ•ฉ๋ณ‘์› ํ˜ˆ์•ก์•”์„ผํ„ฐ ์™ธ๋ž˜์— 2011๋…„ 12์›” 9์ผ๋ถ€ํ„ฐ 2012๋…„ 8์›” 3์ผ๊นŒ์ง€ ๋‚ด์›ํ•œ ํ™˜์ž ์ค‘ ๋Œ€์ƒ์ž ์„ ์ • ๊ธฐ์ค€์— ํ•ด๋‹นํ•˜๋Š” ์ด 112๋ช…์ด๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. 1)๋Œ€์ƒ์ž์˜ ํ‰๊ท  ์—ฐ๋ น์€ 45.1์„ธ์ด๊ณ  ๋™์ข… ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ™˜์ž๊ฐ€ 68.8%์ด์—ˆ๋‹ค. ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ›„ ๊ฒฝ๊ณผ๊ธฐ๊ฐ„์€ 1๋…„ ์ด๋‚ด์˜ ํ™˜์ž๊ฐ€ 81.2%์ด์—ˆ๋‹ค. 2) ์ž๊ธฐ๊ด€๋ฆฌ ํ•˜์œ„ ์˜์—ญ์€ 4์  ๋งŒ์ ์— ์ผ๋ฐ˜์  ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰ ํ‰๊ท  3.19์ , ํžˆํฌ๋งŒ ์นดํ…Œํ„ฐ ๊ด€๋ฆฌ ์ดํ–‰ 3.98์ , ์ฆ์ƒ๊ด€๋ฆฌ ์ดํ–‰ 3.20์ ์ด์—ˆ๋‹ค. ์ „์ฒด ์ž๊ธฐ๊ด€๋ฆฌ ๋ฌธํ•ญ ์ค‘ ๊ฐ€์žฅ ์ดํ–‰ ์ •๋„๊ฐ€ ๋‚ฎ์€ ๋ฌธํ•ญ์€ ์ž์กฐ์ง‘๋‹จ ํ™œ๋™ ์ดํ–‰์œผ๋กœ ํ‰๊ท  1.63์ ์ด์—ˆ๊ณ , ์ฆ์ƒ๊ด€๋ฆฌ ์ค‘์—๋Š” ์šฐ์šธ/๋ถˆ์•ˆ์˜ ๊ด€๋ฆฌ ์ดํ–‰์ด 2.28์ ์œผ๋กœ ๊ฐ€์žฅ ๋‚ฎ์•˜๋‹ค. 3) ์ผ๋ฐ˜์  ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰ ์ •๋„๋Š” ์—ฌ์„ฑ์ด ๋‚จ์„ฑ๋ณด๋‹ค(p=.01), ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ›„ 3๊ฐœ์›” ์ดํ•˜์ธ ํ™˜์ž๊ฐ€ 1๋…„ ์ดˆ๊ณผํ•œ ํ™˜์ž๋ณด๋‹ค(p=.00), ๋ฉด์—ญ์–ต์ œ์ œ๋ฅผ ๋ณต์šฉํ•˜๋Š” ํ™˜์ž๊ฐ€ ์•„๋‹Œ ํ™˜์ž๋ณด๋‹ค(p=.00), ํžˆํฌ๋งŒ ์นดํ…Œํ„ฐ๋ฅผ ์‚ฝ์ž…ํ•œ ํ™˜์ž๊ฐ€ ์•„๋‹Œ ํ™˜์ž๋ณด๋‹ค(p=.00), ์ ˆ๋Œ€ํ˜ธ์ค‘๊ตฌ์ˆ˜๊ฐ€ 1000/uL ์ดํ•˜, 1001โˆผ2000/uL, 2001/uL ์ด์ƒ์ธ ์ˆœ์œผ๋กœ(p=.00) ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰ ์ •๋„๊ฐ€ ๋†’์•˜๋‹ค. 4) ๋Œ€์ƒ์ž ํŠน์„ฑ์— ๋”ฐ๋ฅธ ํžˆํฌ๋งŒ ์นดํ…Œํ„ฐ ๊ด€๋ฆฌ ์ดํ–‰ ์ •๋„์—๋Š” ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. 5) ์ฆ์ƒ๊ด€๋ฆฌ ์ดํ–‰ ์ •๋„๋Š” ์—ฌ์„ฑ์ด ๋‚จ์„ฑ๋ณด๋‹ค(p=.00), ๋Œ€์กธ ์ด์ƒ ํ™˜์ž๊ฐ€ ์ค‘์กธ ์ดํ•˜ ํ™˜์ž๋ณด๋‹ค(p=.03), ์ง์—…์ด ์—†๋Š” ํ™˜์ž๊ฐ€ ์žˆ๋Š” ํ™˜์ž๋ณด๋‹ค(p=.04), ์ ˆ๋Œ€ํ˜ธ์ค‘๊ตฌ์ˆ˜๊ฐ€ 1001/uL ์ด์ƒ์ธ ํ™˜์ž๊ฐ€ 1000/uL ์ดํ•˜์ธ ํ™˜์ž๋ณด๋‹ค(p=.02) ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰์ •๋„๊ฐ€ ๋†’์•˜๋‹ค. 6) ๋Œ€์ƒ์ž์˜ ์ผ๋ฐ˜์  ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰์˜ ์œ ์˜ํ•œ ์˜ˆ์ธก ๋ณ€์ˆ˜๋Š” ์„ฑ๋ณ„, ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ›„ ๊ฒฝ๊ณผ๊ธฐ๊ฐ„์œผ๋กœ ์„ค๋ช…๋ ฅ์€ 22.1%์ด์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•˜๋ฉด ์ผ๋ฐ˜์  ์ž๊ธฐ๊ด€๋ฆฌ๋ฅผ ์ž˜ ํ•˜๋Š” ํ™˜์ž๋Š” ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ›„ ๊ฒฝ๊ณผ๊ธฐ๊ฐ„์ด ์งง๊ณ , ์—ฌ์„ฑ์ด๋ผ๋Š” ํŠน์„ฑ์„ ๊ฐ€์กŒ๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์˜๋ฃŒ์ง„์€ ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ›„ ํ‡ด์›ํ•œ ํ™˜์ž์˜ ์ง€์†์ ์ธ ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰์— ๋Œ€ํ•ด ๊ต์œก, ๊ฐ์‹œํ•˜์—ฌ ์ด์‹ ํ›„ ํ•ฉ๋ณ‘์ฆ ๋ฐ ๊ด€๋ จ ๋ถ€์ž‘์šฉ์„ ์˜ˆ๋ฐฉํ•˜๋„๋ก ํ•ด์•ผํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ๋‚˜ํƒ€๋‚œ ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰ ๊ด€๋ จ์š”์ธ ์œ„ํ—˜๊ตฐ์„ ๋Œ€์ƒ์œผ๋กœ ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰์„ ์ž˜ ํ•˜๋„๋ก ํ•˜๋Š” ์ค‘์žฌ๋ฅผ ๊ฐœ๋ฐœํ•˜๊ณ  ์ ์šฉํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ „์ฒด 12๊ฐ€์ง€์˜ ์ฆ์ƒ ์ค‘ ์šฐ์šธ/๋ถˆ์•ˆ, ์ŠคํŠธ๋ ˆ์Šค์˜ ์ฆ์ƒ๊ด€๋ฆฌ๊ฐ€ ๊ฐ€์žฅ ์ž˜ ์ดํ–‰๋˜์ง€ ์•Š์Œ์„ ๋ณผ ๋•Œ ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ™˜์ž์˜ ์‹ฌ๋ฆฌ์  ์ƒํƒœ๋ฅผ ์‚ฌ์ •ํ•˜๊ณ , ์ค‘์žฌํ•  ๊ฒƒ์„ ์ œ์–ธํ•œ๋‹ค. ์ถ”ํ›„ ๋Œ€์ƒ์ž ์ˆ˜๋ฅผ ํ™•๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์กฐํ˜ˆ๋ชจ์„ธํฌ์ด์‹ ํ™˜์ž์˜ ์ž๊ธฐ๊ด€๋ฆฌ ์ดํ–‰ ์ •๋„๋ฅผ ํ™•์ธํ•˜๋Š” ๋ฐ˜๋ณต ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค.I. ์„œ ๋ก  II. ๋ฌธํ—Œ๊ณ ์ฐฐ III. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• โ…ฃ. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ โ…ค. ๋…ผ์˜ โ…ฅ. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ ์ฐธ๊ณ ๋ฌธํ—Œ ๋ถ€๋ก AbstractMaste

    Factors affecting drug expenditure in the elderly with multiple chronic conditions

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋ณด๊ฑด๋Œ€ํ•™์› : ๋ณด๊ฑดํ•™๊ณผ, 2014. 8. ๊ถŒ์ˆœ๋งŒ.๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ์˜๋ฃŒํŒจ๋„ 2011๋…„ ์—ฐ๊ฐ„๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ 65์„ธ ์ด์ƒ ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜ ๋…ธ์ธ์˜ ์•ฝ์ œ๋น„ ์ง€์ถœ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ์„ ์•Œ์•„๋ณด๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์˜๋ฃŒ๋ฅผ ์ด์šฉํ•˜๊ณ  ์˜์•ฝํ’ˆ์„ ๋ณต์šฉํ•œ ๋งŒ์„ฑ์งˆํ™˜์„ ๋ณด์œ ํ•œ ๋…ธ์ธ 2259๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ, ์ด ์ค‘ 79.68%๊ฐ€ ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์ž๊ตฐ์— ํ•ด๋‹นํ•˜์˜€๋‹ค. ๋น„๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์ž๊ตฐ๊ณผ ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์ž๊ตฐ์˜ ์•ฝ์ œ๋น„ ๋ณธ์ธ๋ถ€๋‹ด์•ก ๋ฐ ๊ฐ€๊ตฌ์†Œ๋“๋Œ€๋น„ ์•ฝ์ œ๋น„ ๋น„์ค‘์— ๋Œ€ํ•ด์„œ๋Š” ๋‹ค์ค‘ ํšŒ๊ท€๋ถ„์„์„, ์ฃผ๊ด€์  ์•ฝ์ œ๋น„ ๋ถ€๋‹ด์— ๋Œ€ํ•ด์„œ๋Š” ๋กœ์ง€์Šคํ‹ฑ ํšŒ๊ท€๋ถ„์„์„ ์‹ค์‹œํ•˜์˜€๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ, ๋น„๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์ž๊ตฐ๊ณผ ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์ž๊ตฐ ๋ชจ๋‘์—์„œ ์˜๋ฃŒ๋ณด์žฅํ˜•ํƒœ๊ฐ€ ์•ฝ์ œ๋น„ ๋ณธ์ธ๋ถ€๋‹ด๊ธˆ์— ๊ฒฐ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๊ณ , ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์ž๊ตฐ์—์„œ ๊ฐ€๊ตฌ์†Œ๋“์ด ๋‚ฎ๊ณ , ์žฅ์• ๊ฐ€ ์žˆ์œผ๋ฉฐ, ์ •ํ•ด์ง„ ๋ณ‘์˜์›์ด ์žˆ๋Š” ๊ฒฝ์šฐ ์•ฝ์ œ๋น„ ๋ณธ์ธ๋ถ€๋‹ด๊ธˆ์ด ๋‚ฎ์•˜๊ณ , ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ๊ฐ€ ๋‚˜์˜๋ฉด ์œ ์˜ํ•˜๊ฒŒ ์•ฝ์ œ๋น„ ๋ณธ์ธ๋ถ€๋‹ด๊ธˆ์ด ๋†’์•˜๋‹ค. ๊ฐ€๊ตฌ์†Œ๋“๋Œ€๋น„ ์•ฝ์ œ๋น„ ๋น„์ค‘์—๋Š” ๊ฐ€๊ตฌ์†Œ๋“, ์˜๋ฃŒ๋ณด์žฅํ˜•ํƒœ๊ฐ€ ๋‘ ๊ตฐ ๋ชจ๋‘์—์„œ ๊ฒฐ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์ณค๊ณ , ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์ž๊ตฐ์—์„œ ์—ฌ์„ฑ์ด๊ณ , ๊ฒฝ์ œํ™œ๋™์„ ํ•˜๋Š” ๊ฒฝ์šฐ ์•ฝ์ œ๋น„ ๋น„์ค‘์ด ๋‚ฎ์•˜๊ณ , ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ๊ฐ€ ๋‚˜์ ์ˆ˜๋ก ์•ฝ์ œ๋น„ ๋น„์ค‘์ด ์œ ์˜ํ•˜๊ฒŒ ๋†’์•„์กŒ๋‹ค. ์ฃผ๊ด€์  ์•ฝ์ œ๋น„ ๋ถ€๋‹ด์—๋Š” ๋‘ ๊ตฐ ๋ชจ๋‘์—์„œ ๊ฐ€๊ตฌ์†Œ๋“์ด ์œ ์˜ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์ณค์œผ๋ฉฐ, ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์ž๊ตฐ์—์„œ ๊ต์œก์ˆ˜์ค€์ด ๋‚ฎ๊ณ , ์ฃผ๊ด€์  ๊ฑด๊ฐ•์ƒํƒœ๊ฐ€ ๋‚˜์ ์ˆ˜๋ก ์ฃผ๊ด€์  ๋ถ€๋‹ด์ด ๋†’์•˜๊ณ , ์˜๋ฃŒ๊ธ‰์—ฌ ๋ฐ ํŠน๋ก€์ž์˜ ๊ฒฝ์šฐ ๊ฑด๊ฐ•๋ณดํ—˜ ๊ฐ€์ž…์ž๋ณด๋‹ค ์ฃผ๊ด€์  ๋ถ€๋‹ด์ด ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•˜๋‹ค. ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์„ ์ง€๋‹Œ ๋…ธ์ธ์€ ์•ฝ์ œ๋น„ ์ง€์ถœ์— ์žˆ์–ด ์‚ฌํšŒ๊ฒฝ์ œ์ ์š”์ธ๊ณผ ์งˆ๋ณ‘์š”์ธ์˜ ์˜ํ–ฅ์„ ํฌ๊ฒŒ ๋ฐ›์•„, ์ฒ˜๋ฐฉ์˜์•ฝํ’ˆ์— ๋Œ€ํ•œ ๋น„์šฉ ๋ถ€๋‹ด์ด ์ฆ๊ฐ€ํ•œ๋‹ค๋ฉด ์น˜๋ฃŒ์— ํ•„์š”ํ•œ ์˜์•ฝํ’ˆ์— ๋Œ€ํ•œ ์ ‘๊ทผ์„ฑ์ด ์ €ํ•ด๋˜์–ด ์ง€์†์ ์ธ ๋งŒ์„ฑ์งˆํ™˜ ๊ด€๋ฆฌ์— ์ œ์•ฝ์ด ๋”ฐ๋ฅผ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋กœ ์ธํ•œ ์งˆํ™˜์˜ ์•…ํ™”๋Š” ์žฅ๊ธฐ์  ๊ด€์ ์—์„œ ์•ฝ์ œ๋น„ ์ง€์ถœ์„ ๋”์šฑ ์ฆ๊ฐ€์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ด๋“ค์˜ ๋ถ€๋‹ด์„ ๊ฒฝ๊ฐ์‹œํ‚ค๊ณ  ๋ณตํ•ฉ์งˆํ™˜์˜ ๊ด€๋ฆฌ๋ฅผ ๋„์›€์œผ๋กœ์จ ๊ฑด๊ฐ•์ˆ˜์ค€์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๊ฐœ์ž…์˜ ํ•„์š”์„ฑ์„ ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ œ๊ณ ํ•˜๋Š” ๋ฐ”์ด๋ฉฐ, ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ํ–ฅํ›„ ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์˜ ์•ฝ์ œ๋น„ ๋ฐ ์˜๋ฃŒ๋น„ ๊ด€๋ จ ์ œ๋„์˜ ๋ฐฉํ–ฅ์„ค์ •์„ ์œ„ํ•œ ๊ธฐ์ดˆ๊ทผ๊ฑฐ๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๊ธฐ๋ฅผ ๊ธฐ๋Œ€ํ•ด ๋ณธ๋‹ค.The purpose of this study is to examine the factors that affect drug expenditure in the elderly over 65 years with multiple chronic conditions using 2011 Korea Health Panel data. The sample includes 2,259 elderly people who have chronic condition for which they have used outpatient service and have taken prescription drug. Multimorbid patient group accounted for 79.68%, and we compared multimorbid patient group with non-multimorbid patient group. The study analyzed out-of-pocket prescription drug spending and economic burden as the proportion of household income spent on prescription drugs by multiple regression models and subjective burden by logistic regression models. In multimorbid patient group, health insurance type, household income, disability, existence of regular hospital, and subjective health status significantly affected out-of-pocket prescription drug spending. The level of economic burden on household associated with prescription drugs was related to household income, health insurance type, sex, economic activity and subjective health status. With regard to subjective burden, household income, education level, subjective health status and health insurance type were influential. The elderly with multiple chronic conditions are vulnerable to economic and health problems. There are limitations of the access to drugs for treatment due to high prescription drug cost, which make the pre-existing diseases exacerbated and increase drug expenditure in the long term. This study implies that we need to intervene in relieving their burden and help them manage multiple chronic conditions appropriately to get higher health status.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  3 ์ œ 2 ์žฅ ์ด๋ก ์  ๊ณ ์ฐฐ 4 ์ œ 1 ์ ˆ ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜์˜ ์ •์˜ ๋ฐ ์ธก์ • 4 ์ œ 2 ์ ˆ ๋…ธ์ธ์˜ ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜ ํ˜„ํ™ฉ 6 ์ œ 3 ์ ˆ ๋ณตํ•ฉ๋งŒ์„ฑ์งˆํ™˜๊ณผ ์˜๋ฃŒ๋น„ ๋ฐ ์•ฝ์ œ๋น„ 7 ์ œ 4 ์ ˆ ์˜๋ฃŒ๋น„ ๋ฐ ์•ฝ์ œ๋น„ ์ง€์ถœ ์˜ํ–ฅ์š”์ธ ๊ด€๋ จ ์—ฐ๊ตฌ 9 ์ œ 3 ์žฅ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 10 ์ œ 1 ์ ˆ ์ž๋ฃŒ์› 10 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ 11 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ๋ชจํ˜• 12 ์ œ 4 ์ ˆ ๋ณ€์ˆ˜์ •์˜ 13 ์ œ 5 ์ ˆ ๋ถ„์„๋ฐฉ๋ฒ• 17 ์ œ 4 ์žฅ ์—ฐ๊ตฌ๊ฒฐ๊ณผ 18 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋Œ€์ƒ์ž์˜ ์ผ๋ฐ˜์  ํŠน์„ฑ 18 ์ œ 2 ์ ˆ ์•ฝ์ œ๋น„ ์ง€์ถœ ๋ถ„์„ 28 ์ œ 3 ์ ˆ ์•ฝ์ œ๋น„ ์ง€์ถœ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์š”์ธ 40 ์ œ 5 ์žฅ ๊ณ  ์ฐฐ 46 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๊ณ ์ฐฐ 46 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ๊ณ ์ฐฐ 53 ์ œ 6 ์žฅ ๊ฒฐ ๋ก  55 ์ฐธ๊ณ ๋ฌธํ—Œ 57 ๋ถ€ ๋ก 61 Abstract 65Maste

    ์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์ƒํ™œ๊ณผํ•™๋Œ€ํ•™ ์•„๋™๊ฐ€์กฑํ•™๊ณผ, 2017. 8. ์ด์ˆœํ˜•.์ด ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ฒ˜๋ฆฌ ๋ฐ ์ˆ˜ํ–‰ ํŠน์„ฑ์„ ํ™•์ธํ•˜๊ณ , ๋‹ค๊ฐ๊ฐ ์ •๋ณด์ฒ˜๋ฆฌ์˜ ํšจ์šฉ์„ฑ์„ ๋ฐํžˆ๋ฉฐ, ์‹œ๊ฐ๊ณผ ์ฒญ๊ฐ ๊ณ ์œ ์˜ ์ฒ˜๋ฆฌ ํŠน์„ฑ์ด ์œ ์•„์˜ ์‹œโ€ง์ฒญ๊ฐ ๋‹ค์ค‘๊ณผ์ œ ์ฒ˜๋ฆฌ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ฐํžˆ๋Š” ๊ฒƒ์ด๋‹ค. ์ด์™€ ๊ฐ™์€ ์—ฐ๊ตฌ ๋ชฉ์ ์— ๋”ฐ๋ผ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์—ฐ๊ตฌ ๋ฌธ์ œ๋ฅผ ์„ค์ •ํ•˜์˜€๋‹ค. ใ€์—ฐ๊ตฌ๋ฌธ์ œ 1ใ€‘์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๋Š” ์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ์™€ ๊ณผ์ œ ๋‚œ์ด๋„, ์ž๊ทน์–‘์‹(๋™์ผ๊ฐ๊ฐ, ์ด์ค‘๊ฐ๊ฐ)์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด๋Š”๊ฐ€? ใ€์—ฐ๊ตฌ๋ฌธ์ œ 2ใ€‘์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๋Š” ๋™์ผ๊ฐ๊ฐ ์ž๊ทน์–‘์‹(์‹œ๊ฐ-์‹œ๊ฐ, ์ฒญ๊ฐ-์ฒญ๊ฐ)์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด๋Š”๊ฐ€? ใ€์—ฐ๊ตฌ๋ฌธ์ œ 3ใ€‘์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๋Š” ์ด์ค‘๊ฐ๊ฐ ์ž๊ทน์–‘์‹(์‹œ๊ฐ-์ฒญ๊ฐ, ์ฒญ๊ฐ-์‹œ๊ฐ)์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด๋Š”๊ฐ€? ์œ„์˜ ์—ฐ๊ตฌ๋ฌธ์ œ๋ฅผ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด, ์ด ์—ฐ๊ตฌ์—์„œ๋Š” E-prime software๋ฅผ ์ด์šฉํ•˜์—ฌ ๋‹ค์ค‘๊ณผ์ œ ๋„๊ตฌ๋ฅผ ์ œ์ž‘ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๋Œ€์ƒ์ž๋Š” ์„œ์šธ, ๊ฒฝ๊ธฐ, ์ถฉ์ฒญ, ์ „๋ผ ์ง€์—ญ์˜ ์–ด๋ฆฐ์ด์ง‘๊ณผ ์œ ์น˜์›์— ๋‹ค๋‹ˆ๋Š” ๋งŒ 5์„ธ ์ด์ƒ ์œ ์•„ 140๋ช…์ด์—ˆ๋‹ค. ์—ฐ๊ตฌ ์ฐธ์—ฌ ์œ ์•„๋“ค์€ ๋™์ผ๊ฐ๊ฐ ๊ณผ์ œ์ง‘๋‹จ๊ณผ ์ด์ค‘๊ฐ๊ฐ ๊ณผ์ œ์ง‘๋‹จ ์ค‘ ํ•˜๋‚˜์— ์ž„์˜๋กœ ๋ฐฐ์ •๋˜์—ˆ๊ณ (๊ฐ ์ง‘๋‹จ๋ณ„ 70๋ช…), ๊ฐ ์กฐ๊ฑด์— ๋งž๋Š” ๋‹ค์ค‘๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰ ์‹œ, ์œ ์•„์˜ ๋ฐ˜์‘์‹œ๊ฐ„์ด ์ธก์ •๋˜์—ˆ๋‹ค. ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋Š” SPSS 20 ํ”„๋กœ๊ทธ๋žจ์—์„œ ํ‰๊ท , ํ‘œ์ค€ํŽธ์ฐจ, ๋ฐ˜๋ณต์ธก์ • ๋ณ€๋Ÿ‰๋ถ„์„, ๋Œ€์‘ํ‘œ๋ณธ t-๊ฒ€์ฆ, ๋…๋ฆฝํ‘œ๋ณธ t-๊ฒ€์ฆ ๋“ฑ์„ ์ด์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ, ์œ ์•„์˜ 1์ฐจ ๊ณผ์ œ ๋ฐ˜์‘์‹œ๊ฐ„์ด ๊ธธ์–ด์ง€๊ณ , 2์ฐจ ๊ณผ์ œ ๋ฐ˜์‘์‹œ๊ฐ„๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๊ฐ€ ๊ฐ์†Œํ•˜์˜€๋‹ค. ๊ณผ์ œ ๋‚œ์ด๋„๊ฐ€ ๋‚ฎ์€ ์กฐ๊ฑด์— ๋น„ํ•ด ๋†’์€ ์กฐ๊ฑด์—์„œ, ์œ ์•„์˜ 1์ฐจ ๊ณผ์ œ ๋ฐ˜์‘์‹œ๊ฐ„๊ณผ 2์ฐจ ๊ณผ์ œ ๋ฐ˜์‘์‹œ๊ฐ„, ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๊ฐ€ ๋” ๊ธธ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ์™€ ๊ณผ์ œ ๋‚œ์ด๋„ ๊ฐ„์˜ ์œ ์˜ํ•œ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ๋„ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ์ž๊ทน์–‘์‹(๋™์ผ๊ฐ๊ฐ, ์ด์ค‘๊ฐ๊ฐ)์— ๋”ฐ๋ผ ์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๊ฐ€ ๋‹ฌ๋ผ์กŒ๋‹ค. ์ด์ค‘๊ฐ๊ฐ ๊ณผ์ œ์— ๋น„ํ•ด ๋™์ผ๊ฐ๊ฐ ๊ณผ์ œ์—์„œ, ์œ ์•„์˜ 2์ฐจ ๊ณผ์ œ ๋ฐ˜์‘์‹œ๊ฐ„๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๊ฐ€ ๋” ๊ธธ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. 1์ฐจ ๊ณผ์ œ ๋ฐ˜์‘์‹œ๊ฐ„์— ๋Œ€ํ•œ ์ž๊ทน์–‘์‹ ํšจ๊ณผ๋Š” ์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ๊ฐ€ ์งง์€ ์กฐ๊ฑด์—์„œ๋งŒ ์œ ์˜ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, ๋™์ผ๊ฐ๊ฐ ์ž๊ทน์–‘์‹(์‹œ๊ฐ-์‹œ๊ฐ, ์ฒญ๊ฐ-์ฒญ๊ฐ)์— ๋”ฐ๋ผ ์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๋Š” ๋‹ฌ๋ผ์ง€์ง€ ์•Š์•˜๋‹ค. ๊ทธ๋ ‡์ง€๋งŒ, ๊ณผ์ œ ๋‚œ์ด๋„๊ฐ€ ์ด ํšจ๊ณผ๋ฅผ ์กฐ์ ˆํ•˜์˜€๋‹ค. ๊ณผ์ œ์˜ ๋‚œ์ด๋„๊ฐ€ ๋†’์„ ๋•Œ, ์ฒญ๊ฐ-์ฒญ๊ฐ ๊ณผ์ œ์— ๋น„ํ•ด ์‹œ๊ฐ-์‹œ๊ฐ ๊ณผ์ œ์—์„œ, ์œ ์•„์˜ 1์ฐจ ๊ณผ์ œ ๋ฐ˜์‘์‹œ๊ฐ„๊ณผ 2์ฐจ ๊ณผ์ œ ๋ฐ˜์‘์‹œ๊ฐ„, ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๊ฐ€ ๋” ๊ธธ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ์ด์ค‘๊ฐ๊ฐ ์ž๊ทน์–‘์‹(์‹œ๊ฐ-์ฒญ๊ฐ, ์ฒญ๊ฐ-์‹œ๊ฐ)์— ๋”ฐ๋ผ ์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๊ฐ€ ๋‹ฌ๋ผ์กŒ๋‹ค. ์ฒญ๊ฐ-์‹œ๊ฐ ๊ณผ์ œ์— ๋น„ํ•ด ์‹œ๊ฐ-์ฒญ๊ฐ ๊ณผ์ œ์—์„œ, ์œ ์•„์˜ 1์ฐจ ๊ณผ์ œ ๋ฐ˜์‘์‹œ๊ฐ„๊ณผ 2์ฐจ ๊ณผ์ œ ๋ฐ˜์‘์‹œ๊ฐ„, ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ๊ฐ€ ๋” ๊ธธ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ๋•Œ, ์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ์™€ ๊ณผ์ œ ๋‚œ์ด๋„๋Š” ์ด ํšจ๊ณผ๋ฅผ ์กฐ์ ˆํ•˜์˜€๋‹ค. ์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ๊ฐ€ ๊ธธ ๋•Œ ๋˜๋Š” ๊ณผ์ œ ๋‚œ์ด๋„๊ฐ€ ๋‚ฎ์„ ๋•Œ์—๋งŒ ์ด์ค‘๊ฐ๊ฐ ์ž๊ทน์–‘์‹์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ์˜ ์ฐจ์ด๊ฐ€ ์œ ์˜ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ฒ˜๋ฆฌ์˜ ํ•ต์‹ฌ ๊ธฐ์ œ๋ฅผ ๋ฐํžˆ๊ณ , ์œ ์•„์˜ ๋‹ค๊ฐ๊ฐ ์ •๋ณด์ฒ˜๋ฆฌ์˜ ํšจ์šฉ์„ฑ๊ณผ ์‹œโ€ง์ฒญ๊ฐ ๋‹ค์ค‘์ •๋ณด์˜ ์ฒ˜๋ฆฌ ํŠน์„ฑ์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๋˜ํ•œ, ์œ ์•„์˜ ๋‹ค์ค‘์ •๋ณด ์ฒ˜๋ฆฌ์˜ ํšจ์œจ์„ฑ์„ ๋†’์ด๋Š” ๊ต์œกํ™˜๊ฒฝ์˜ ๊ตฌ์„ฑ ๋ฐ ๊ต์œก ์‹ค์ œ์— ๋Œ€ํ•˜์—ฌ ์‹œ์‚ฌ์ ์„ ์ œ์‹œํ•œ๋‹ค.โ… . ๋ฌธ์ œ ์ œ๊ธฐ 1 โ…ก. ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 8 1. ์œ ์•„์˜ ๋‹ค๊ฐ๊ฐ ์ •๋ณด์ฒ˜๋ฆฌ ๋ฐœ๋‹ฌ ๋ฐ ํŠน์„ฑ 8 1) ์œ ์•„์˜ ๋‹ค๊ฐ๊ฐ ์ •๋ณด์ฒ˜๋ฆฌ ๋ฐœ๋‹ฌ 8 2) ์œ ์•„์˜ ์‹œ๊ฐ ๋ฐ ์ฒญ๊ฐ ์ •๋ณด์ฒ˜๋ฆฌ ํŠน์„ฑ 10 2. ์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 13 1) ์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰ 13 2) ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 16 3) ์œ ์•„์˜ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ์˜ ๋ฐœ๋‹ฌ 19 3. ๊ณผ์ œ ํŠน์„ฑ์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 21 1) ์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 21 2) ๊ณผ์ œ ๋‚œ์ด๋„์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 22 3) ์ž๊ทน์–‘์‹์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 24 โ…ข. ์—ฐ๊ตฌ๋ฌธ์ œ ๋ฐ ์šฉ์–ด์˜ ์ •์˜ 28 1. ์—ฐ๊ตฌ๋ฌธ์ œ 28 2. ์šฉ์–ด์˜ ์ •์˜ 30 1) ๋‹ค์ค‘๊ณผ์ œ 30 2) ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰ 30 3) ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 31 4) ์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ 31 5) ๊ณผ์ œ ๋‚œ์ด๋„ 31 6) ์ž๊ทน์–‘์‹ 32 โ…ฃ. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ๋ฐ ์ ˆ์ฐจ 33 1. ์—ฐ๊ตฌ ๋Œ€์ƒ 33 2. ์—ฐ๊ตฌ ๋„๊ตฌ 34 3. ์—ฐ๊ตฌ ์ ˆ์ฐจ 48 4. ์ž๋ฃŒ์˜ ๋ถ„์„ 50 โ…ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋ฐ ํ•ด์„ 51 1. ์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ, ๊ณผ์ œ ๋‚œ์ด๋„, ์ž๊ทน์–‘์‹์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 51 1) ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ์˜ ์ „๋ฐ˜์ ์ธ ๊ฒฝํ–ฅ 51 2) ์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 53 3) ๊ณผ์ œ ๋‚œ์ด๋„์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 57 4) ์ž๊ทน์–‘์‹์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 63 2. ๋™์ผ๊ฐ๊ฐ ์ž๊ทน์–‘์‹์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 67 1) ๋™์ผ๊ฐ๊ฐ ์ž๊ทน์–‘์‹์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 67 2) ๋™์ผ๊ฐ๊ฐ ์ž๊ทน์–‘์‹๊ณผ ๊ณผ์ œ ์กฐ๊ฑด(์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ, ๊ณผ์ œ ๋‚œ์ด๋„) ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ 68 3. ์ด์ค‘๊ฐ๊ฐ ์ž๊ทน์–‘์‹์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 73 1) ์ด์ค‘๊ฐ๊ฐ ์ž๊ทน์–‘์‹์— ๋”ฐ๋ฅธ ๋‹ค์ค‘๊ณผ์ œ ์ˆ˜ํ–‰๊ณผ ์‹ฌ๋ฆฌ์  ๋ถˆ์‘๊ธฐ 73 2) ์ด์ค‘๊ฐ๊ฐ ์ž๊ทน์–‘์‹๊ณผ ๊ณผ์ œ ์กฐ๊ฑด(์ž๊ทน๊ฐ„ ์‹œ๊ฐ„์ฐจ, ๊ณผ์ œ ๋‚œ์ด๋„) ๊ฐ„์˜ ์ƒํ˜ธ์ž‘์šฉ ํšจ๊ณผ 76 โ…ฅ. ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 82 1) ๊ฒฐ๋ก  ๋ฐ ๋…ผ์˜ 82 2) ์˜์˜ ๋ฐ ์ œ์–ธ 87 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 93 ๋ถ€๋ก 105 ABSTRACT 109Docto

    ๋ถ€์ฑ„ ๋ถ€๋‹ด์ด ๊ฐ€๊ณ„์†Œ๋น„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ๊ฒฝ์ œํ•™๋ถ€, 2017. 8. ๊น€์†Œ์˜.This paper analyzes whether household debt burden affects household consumptionand if so, whether its impact differed before and after the financial crisis of 2008. These questions are addressed by means of regression analyses that include debt burden as an independent variable, to examine the extent to which debt burden affects consumption. The models replace debt burden with debt-to-income ratio (DTI) or change in debt. To check the robustness of the regressions, alternative regressions with interaction terms, and with log variables are also examined. The results show that debt burden negatively affects household consumption, and such influence was especially pronounced in absolute value immediately after the 2008 financial crisis.1. Introduction 1 2. Previous Literature 3 3. Data 5 4. Model 6 4.1 Baseline models 7 4.1.1 Models with debt-to-income ratio (DTI) 7 4.1.2 Models with change in debt 9 4.2 Robustness checks 10 4.2.1 Models with interaction terms 10 4.2.2 Models with log variables 12 5 Results 13 5.1 Result of regression with debt-to-income ratio (DTI) 13 5.2 Result of regression with change in debt 18 5.3 Result of regression with interaction terms 21 5.4 Result of regression with log varaiables 22 6. Conclusion 27 7.Reference 28Maste

    ๋‹ค๊ฐ€์˜ ๊ธˆ์†์ด์˜จ์„ ์ด์šฉํ•œ ๊ธฐํฌ์˜ ์ „์œ„์กฐ์ ˆ๊ณผ ์ด๋ฅผ ์ด์šฉํ•œ ์กฐ๋ฅ˜์ œ๊ฑฐ ๊ธฐ์ˆ 

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2013. 2. ํ•œ๋ฌด์˜.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ”Œ๋กํ˜•์„ฑ๊ณต์ •์„ ์ ์šฉํ•˜๊ธฐ ์–ด๋ ค์šด ์ž์—ฐ์ˆ˜๊ณ„์˜ ์กฐ๋ฅ˜ ์ œ๊ฑฐ๋ฅผ ์œ„ํ•ด ๋‹ค๊ฐ€์˜ ๊ธˆ์†์ด์˜จ(Al3+, Fe3+)์„ ์ด์šฉํ•œ ๊ธฐํฌ์˜ ์ „์œ„์กฐ์ ˆ๊ณผ ์ด๋ฅผ ์ด์šฉํ•œ ์ˆ˜์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ œ๊ฑฐํ•˜๊ณ ์ž ํ•˜๋Š” ์กฐ๋ฅ˜์— ๋Œ€ํ•œ ํŠน์„ฑ์„ ์กฐ์‚ฌ๋ฅผ ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋‹ค๊ฐ€์˜ ๊ธˆ์†์ด์˜จ์˜ ์ข…๋ฅ˜์™€ ์ฃผ์ž…ํ˜•ํƒœ๊ฐ€ ๊ธฐํฌ์˜ ์ „์œ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ณ  ์–‘์˜ ๊ธฐํฌ ์ƒ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์–‘์˜ ๊ธฐํฌ๋ฅผ ๋ฐœ์ƒ์‹œํ‚ค๋Š” ์‹œ์Šคํ…œ์˜ ์ž์—ฐ์ˆ˜๊ณ„๋‚ด ์กฐ๋ฅ˜์ œ๊ฑฐ์— ๋Œ€ํ•œ ์ ์šฉ๊ฐ€๋Šฅ์„ฑ์„ ํ‰๊ฐ€ํ•˜๊ณ  ์ œ๊ฑฐํšจ์œจ์„ ์•Œ์•„๋ณด์•˜๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๊ธฐํฌ์ „์œ„๋ฅผ ํ†ตํ•œ ์กฐ๋ฅ˜ ์ œ๊ฑฐ์‹œ์Šคํ…œ์˜ ์ ์šฉ์„ฑ์„ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด ์ œ๊ฑฐ์‹คํ—˜์— ์ด์šฉํ•œ ์กฐ๋ฅ˜๋Š” Microcystis sp.๋กœ ๊ฐ•์‚ฐ์„ฑ์˜์—ญ์—์„œ ์–‘์˜ ์ „์œ„๋ฅผ ๋„๋‚˜, ๋Œ€๋ถ€๋ถ„์˜ pH ์˜์—ญ์—์„œ ์Œ์˜ ์ „ํ•˜๋ฅผ ๋ˆ๋‹ค. ์ž…์ž์™€ ๊ธฐํฌ์˜ ์ถฉ๋Œํšจ์œจ์„ ๊ณ ๋ คํ•  ๋•Œ, ๋†’์€ ์ œ๊ฑฐํšจ์œจ์„ ๊ฐ€์ง€๊ธฐ ์œ„ํ•ด์„œ ๊ธฐํฌ๊ฐ€ ์–‘์˜ ์ „ํ•˜๋กœ ๋Œ€์ „๋  ํ•„์š”์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ž์—ฐ์ˆ˜๊ณ„์˜ ์กฐ๊ฑด์—์„œ๋„ ์–‘์˜ ๊ธฐํฌ ๋ฐœ์ƒ์ด ๊ฐ€๋Šฅํ•œ 3๊ฐ€์˜ ๊ธˆ์†์ด์˜จ(Al3+, Fe3+)์ด ๊ธฐํฌ์˜ ์ „์œ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ, ์ฃผ์ž…ํ˜•ํƒœ(์•Œ๋ฃจ๋ฏธ๋Š„ ์ „๊ธฐ๋ถ„ํ•ด, Alum, PAHCs)์— ๋”ฐ๋ฅธ ์˜ํ–ฅ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์•Œ๋ฃจ๋ฏธ๋Š„๊ณผ ์ฒ  ์ด์˜จ์˜ ์ฃผ์ž…์— ๋”ฐ๋ผ ์ „์œ„๊ฐ€ ์–‘์œผ๋กœ ๋Œ€์ „๋˜๋ฉฐ ์ผ์ •๋†๋„ ์ด์ƒ์˜ ์ฃผ์ž…์—์„œ๋Š” ์ „์œ„๊ฐ€ ๋” ์ด์ƒ ์ฆ๊ฐ€ํ•˜์ง€ ์•Š์•˜๋‹ค. ๋˜ํ•œ ์•Œ๋ฃจ๋ฏธ๋Š„์˜ ๊ฐ€์ˆ˜๋ถ„ํ•ด์ข… ๋ถ„์„์„ ํ†ตํ•ด ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, ๋Œ€๋ถ€๋ถ„ ๋ชจ๋…ธ๋ชจ์„ฑ ๊ฐ€์ˆ˜๋ถ„ํ•ด์ข…์„ ๊ฐ€์ง€๋Š” Alum๊ณผ ๋‹ฌ๋ฆฌ PAHCs์™€ ์•Œ๋ฃจ๋ฏธ๋Š„ ์ „๊ธฐ๋ถ„ํ•ด ์šฉ์ถœ์•ก์ด ๊ฐ๊ฐ ์ตœ๋Œ€ 88%, 91%์˜ ๋†’์€ ํด๋ฆฌ๋จธ์„ฑ ์•Œ๋ฃจ๋ฏธ๋Š„ ๊ฐ€์ˆ˜๋ถ„ํ•ด์ข…์„ ๊ฐ€์ง์„ ๋ณด์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด PAHCs์™€ ์•Œ๋ฃจ๋ฏธ๋Š„ ์ „๊ธฐ๋ถ„ํ•ด ์šฉ์ถœ์•ก์„ ์ด์šฉํ•œ ์•Œ๋ฃจ๋ฏธ๋Š„ ์ด์˜จ ์ฃผ์ž…์ด ์–‘์˜ ๊ธฐํฌ ์ƒ์„ฑ์— ๊ธ์ •์ •์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์œผ๋กœ ์—ฌ๊ฒจ์ง„๋‹ค. ์–‘์˜ ๊ธฐํฌ๋ฅผ ์ด์šฉํ•œ ๋ถ€์ƒ๊ณต์ •์˜ ์ ์šฉ์„ฑ ํ‰๊ฐ€๋ฅผ ์œ„ํ•ด ๊ธฐ์กด์˜ ๊ณต์ •๋“ค๊ณผ ์กฐ๋ฅ˜์ œ๊ฑฐํšจ์œจ์„ ๋น„๊ต๋ถ„์„ํ•˜์˜€๋‹ค. ์ œ๊ฑฐํšจ์œจ์€ DAF > (+) bubble > Flocculation & Sedimentation > (-) bubble ์ˆœ์œผ๋กœ ๋†’์•˜๋‹ค. ์–‘์˜ ๊ธฐํฌ๋ฅผ ์ด์šฉํ•œ ๊ณต์ •์€ DAF๊ณต์ •๋ณด๋‹ค๋Š” ๋‚ฎ์•˜์ง€๋งŒ ๊ฐ€์••์ˆ˜์˜ ๋ถ„์‚ฌ์‹œ ๋ฐœ์ƒํ•˜๋Š” ์ˆ˜๋ฆฌํ•™์  ์—๋„ˆ์ง€๋งŒ์œผ๋กœ๋„ ๊ต๋ฐ˜๊ณต์ •์—†์ด ๋†’์€ ํšจ์œจ์„ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ์ด๋Š” ๊ต๋ฐ˜ ๋ฐ ์‘์ง‘๊ณต์ • ์ˆ˜ํ–‰์ด ์–ด๋ ค์šด ์ž์—ฐ์ˆ˜๊ณ„์˜ ์กฐ๋ฅ˜ ์ฒ˜๋ฆฌ๊ณต์ •์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์•Œ๋ฃจ๋ฏธ๋Š„ ์ด์˜จ(Alum, PAHCs, ์•Œ๋ฃจ๋ฏธ๋Š„ ์ „๊ธฐ๋ถ„ํ•ด)๊ณผ ์ฒ  ์ด์˜จ(์—ผํ™”์ œ1์ฒ )์„ 0 โˆผ 30 mg/L์˜ ๋†๋„๋กœ Saturator์— ์ฃผ์ž…ํ•ด ๊ฐ€์••์ˆ˜๋ฅผ ๋ฐœ์ƒ์‹œํ‚ค๊ณ  ์ด๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฐ˜์‘์กฐ ์•ˆ์˜ ์กฐ๋ฅ˜์ž…์ž๋ฅผ ๋ถ€์ƒ์‹œํ‚จ ํ›„ ์ œ๊ฑฐํšจ์œจ์„ ๋น„๊ตํ•˜์—ฌ ์ตœ์ ์˜ ์กฐ๊ฑด์„ ๋„์ถœํ•˜์˜€๋‹ค. ์ „์œ„๊ฐ€ ์กฐ์ ˆ๋œ ๊ธฐํฌ๋กœ ์ฒ˜๋ฆฌํ•œ ์กฐ๋ฅ˜์›์ˆ˜์˜ ์ œ๊ฑฐ ์ „ํ›„์˜ ์ œ๊ฑฐํšจ์œจ ๋ถ„์„๊ฒฐ๊ณผ, ์•Œ๋ฃจ๋ฏธ๋Š„ ์ „๊ธฐ๋ถ„ํ•ด> PAHCs> ์—ผํ™”์ œ1์ฒ > Alum ์ˆœ์œผ๋กœ ๋†’์€ ํšจ์œจ์„ ๋ณด์˜€๋‹ค. ์•Œ๋ฃจ๋ฏธ๋Š„ ์ „๊ธฐ๋ถ„ํ•ด๋Š” 15 mg-Al/L๋†๋„์—์„œ 90.89%๋กœ ์ตœ์ ์˜ ํšจ์œจ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์•Œ๋ฃจ๋ฏธ๋Š„์ด๋‚˜ ์ฒ ์˜ ์ฃผ์ž…๋†๋„๋‚˜ ํ˜•ํƒœ์— ๊ด€๊ณ„์—†์ด ์Œ์˜ ๊ธฐํฌ๋ฅผ ์ด์šฉํ•œ ์ œ๊ฑฐํšจ์œจ์ธ 25.12%๋ณด๋‹ค ์›”๋“ฑํžˆ ๋†’์€ ํšจ์œจ์„ ๋ณด์˜€๋‹ค. ์ด๋Š” ์–‘์˜ ๊ธฐํฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ํ”Œ๋กํ˜•์„ฑ ๊ณต์ • ์—†์ด๋„ ์ž์—ฐ์ˆ˜๊ณ„์—์„œ์˜ ์กฐ๋ฅ˜์ฒ˜๋ฆฌ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋˜ํ•œ ์ „๊ธฐ๋ถ„ํ•ด๋ฅผ ์ด์šฉํ•œ ์–‘์˜ ๊ธฐํฌ๋ฅผ ๋ฐœ์ƒ๋ฐฉ๋ฒ•์€ ์ˆ˜๊ณ„์— 2์ฐจ ์˜ค์—ผ์„ ์œ ๋ฐœํ•˜์ง€ ์•Š์œผ๋ฉฐ ์šด์ „์กฐ๊ฑด์˜ ์ตœ์ ํ™”๋ฅผ ํ†ตํ•˜์—ฌ ์ œ๊ฑฐํšจ์œจ์˜ ํ–ฅ์ƒ๋„ ๊ฐ€๋Šฅํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์€ ๊ธฐํฌ์˜ ์ „์œ„์กฐ์ ˆ์„ ํ†ตํ•ด ์ž์—ฐ์ˆ˜๊ณ„ ๋‚ด ์กฐ๋ฅ˜์ œ๊ฑฐ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์‹ ์†ํ•˜๊ฒŒ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค.1. ์„œ๋ก  1 1.1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  4 2. ๋ฌธํ—Œ๊ณ ์ฐฐ ๋ฐ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 5 2.1 ๋ถ€์ƒ๊ณต์ •์—์„œ ๊ธฐํฌ์™€ ์ž…์ž์˜ ๊ด€๊ณ„ 5 2.2 ๊ธฐํฌ ์ „์œ„์˜ ์ •์˜ ๋ฐ ์ œ์–ด๊ธฐ๋ฒ• 7 2.2.1 ํ‘œ๋ฉด์ „์œ„์™€ ๊ทธ ์ค‘์š”์„ฑ(Hunter, 1981) 7 2.2.2. ์ œํƒ€์ „์œ„์˜ ์ •์˜(Hunter, 1981) 10 2.2.3. ๊ธฐํฌ ์ œํƒ€์ „์œ„ ์ œ์–ด์˜ ํ•„์š”์„ฑ 11 2.2.4. ๊ธฐํฌ์˜ ์ œํƒ€์ „์œ„ ์ œ์–ด ๋ฉ”์ปค๋‹ˆ์ฆ˜ 14 2.3 ๋งž์ถคํ˜• ๊ธฐํฌ๋ฐœ์ƒ์žฅ์น˜IIBG)์˜ ๊ฐœ๋ฐœ ๋ฐ ์ˆ˜์ฒ˜๋ฆฌ๊ณต์ •์˜ ์ ์šฉ 19 3. ์‹คํ—˜ ์กฐ๊ฑด ๋ฐ ๋ฐฉ๋ฒ• 22 3.1 ์กฐ๋ฅ˜์ค€๋น„ ๋ฐ ํŠน์„ฑ์‹คํ—˜ 22 3.2 ๊ธฐํฌ์˜ ์ œํƒ€์ „์œ„ ์ธก์ • 24 3.2.1 ์ธก์ •์žฅ์น˜์˜ ๊ตฌ์„ฑ 25 3.2.2 ์ธก์ •๊ณผ์ • 26 3.2.3 ์–‘์˜ ๊ธฐํฌ ๋ฐœ์ƒ๋ฐฉ๋ฒ•๋“ค์˜ ํ‰๊ฐ€ 28 3.2.4 ๊ฐ€์ˆ˜๋ถ„ํ•ด์ข… ๋ถ„์„ 32 3.3 Batch test 34 3.3.1 ์–‘์˜ ๊ธฐํฌ์˜ ์ ์šฉ์„ฑ ํ‰๊ฐ€ 34 3.3.2 ์กฐ๋ฅ˜์ œ๊ฑฐ ํšจ์œจํ‰๊ฐ€ 35 4. ์‹คํ—˜๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 38 4.1 ์กฐ๋ฅ˜์˜ ํŠน์„ฑ์กฐ์‚ฌ 38 4.2 ๊ธฐํฌ์˜ ์ œํƒ€์ „์œ„ ์ธก์ • ๋ฐ ์กฐ์ ˆ 41 4.2.1 ์ฃผ์ž…๋œ ๊ธˆ์†์ด์˜จ(Al3+, Fe3+)์˜ ์˜ํ–ฅ 42 4.2.2 ์ฃผ์ž…๋œ ์‘์ง‘์ œ ํ˜•ํƒœ์˜ ์˜ํ–ฅ 47 4.3 ์–‘(+)์˜ ๊ธฐํฌ ๋ถ€์ƒ๊ณต์ •์˜ ์ ์šฉ์„ฑ ํ‰๊ฐ€ 52 4.4 ์กฐ๋ฅ˜์ œ๊ฑฐ๋ฅผ ์œ„ํ•œ ์ ์šฉ์„ฑ ํ‰๊ฐ€ 54 4.4.1 ์•Œ๋ฃจ๋ฏธ๋Š„ ์ด์˜จ์— ์˜ํ•œ ์˜ํ–ฅ 54 4.4.2 ์ฒ  ์ด์˜จ์˜ ์˜ํ–ฅ 57 5. ๊ฒฐ ๋ก  60 6. ์ฐธ๊ณ ๋ฌธํ—Œ 64Maste

    3, 4, 5์„ธ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•„๋™๊ฐ€์กฑํ•™๊ณผ, 2013. 2. ์ด์ˆœํ˜•.์ด ์—ฐ๊ตฌ๋Š” ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์ด ์œ ์•„์˜ ์—ฐ๋ น๊ณผ ์„ฑ๋ณ„์— ๋”ฐ๋ผ ์–ด๋– ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ๋Š”์ง€, ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์„ ํ†ตํ•œ ๊ณผ์ œ๋ณ„ ๋‚œ์ด๋„๋Š” ์–ด๋– ํ•œ์ง€ ๋ฐํžˆ๊ณ , ๊ณผ์ œ ์ œ์‹œ ๋งค์ฒด์— ๋”ฐ๋ผ ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์ด ์–ด๋– ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด๋Š”์ง€ ๋ฐํžˆ๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ ๋ชฉ์ ์— ๋”ฐ๋ผ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์—ฐ๊ตฌ ๋ฌธ์ œ๋ฅผ ์„ค์ •ํ•˜์˜€๋‹ค. ใ€์—ฐ๊ตฌ๋ฌธ์ œ 1ใ€‘ ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ(๋ฐฉํ–ฅ, ํšŒ์ „, ๋Œ€์นญ, ์ ‘ํ•ฉ, ๋ถ€๋ถ„/์ „์ฒด) ์ˆ˜ํ–‰์€ ์—ฐ๋ น ๋ฐ ์„ฑ๋ณ„์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ๋Š”๊ฐ€? ใ€์—ฐ๊ตฌ๋ฌธ์ œ 2ใ€‘ ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์„ ํ†ตํ•œ ๊ณผ์ œ๋ณ„ ๋‚œ์ด๋„์˜ ์ˆœ์„œ๋Š” ์–ด๋– ํ•œ๊ฐ€? ใ€์—ฐ๊ตฌ๋ฌธ์ œ 3ใ€‘ ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์€ ๊ณผ์ œ ์ œ์‹œ ๋งค์ฒด(์ข…์ด, ํƒœ๋ธ”๋ฆฟPC)์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ๋Š”๊ฐ€? ์œ„์˜ ์—ฐ๊ตฌ๋ฌธ์ œ๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ๊ฒฝ๊ธฐ๋„์— ์œ„์น˜ํ•œ ์œ ์น˜์›์— ๋‹ค๋‹ˆ๋Š” ๋งŒ 3, 4, 5์„ธ ์œ ์•„ ๊ฐ 20๋ช…์”ฉ ์ด 60๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ๋ฐฉํ–ฅ, ํšŒ์ „, ๋Œ€์นญ, ์ ‘ํ•ฉ, ๋ถ€๋ถ„/์ „์ฒด ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋„๋ก ํ–ˆ๋‹ค. ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋Š” SPSS 20 ํ”„๋กœ๊ทธ๋žจ์—์„œ ํ‰๊ท , ํ‘œ์ค€ํŽธ์ฐจ, ์ด์›๋ถ„์‚ฐ๋ถ„์„, ๋ฐ˜๋ณต์ธก์ • ๋ณ€๋Ÿ‰๋ถ„์„(repeated measured ANOVA), ๋Œ€์‘ํ‘œ๋ณธ t-๊ฒ€์ฆ์„ ์ด์šฉํ•˜์—ฌ ํ†ต๊ณ„๋ถ„์„ ๋˜์—ˆ๋‹ค. ๋˜ํ•œ Kruskal-Wallis ๊ฒ€์ฆ, Mann-Whitney U ๊ฒ€์ฆ, Wilcoxon signed rank ๊ฒ€์ฆ์„ ํ†ตํ•œ ๋น„๋ชจ์ˆ˜ ๋ถ„์„์ด ๋ณ„๋„๋กœ ์ด๋ฃจ์–ด์กŒ๋‹ค. ์ด ์—ฐ๊ตฌ์˜ ์ฃผ์š” ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์€ ์œ ์•„์˜ ์—ฐ๋ น์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋จผ์ € ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ •๋‹ต๋ฅ ์—์„œ๋Š” ๋ฐฉํ–ฅ, ๋ถ€๋ถ„/์ „์ฒด ๊ณผ์ œ์—์„œ 5์„ธ ์œ ์•„์˜ ์ •๋‹ต๋ฅ ์ด 3์„ธ ์œ ์•„๋ณด๋‹ค ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜์œผ๋ฉฐ, ํšŒ์ „, ๋Œ€์นญ, ์ ‘ํ•ฉ ๊ณผ์ œ์—์„œ 4, 5์„ธ ์œ ์•„์˜ ์ •๋‹ต๋ฅ ์ด 3์„ธ๋ณด๋‹ค ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜๋‹ค. ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์‘๋‹ต์‹œ๊ฐ„์—์„œ๋Š” ํšŒ์ „ ๊ณผ์ œ์—์„œ 5์„ธ ์œ ์•„์˜ ์‘๋‹ต์‹œ๊ฐ„์ด 3์„ธ ์œ ์•„๋ณด๋‹ค ์œ ์˜ํ•˜๊ฒŒ ์งง์•˜์œผ๋ฉฐ, ์ ‘ํ•ฉ ๊ณผ์ œ์—์„œ 5์„ธ ์œ ์•„์˜ ์‘๋‹ต์‹œ๊ฐ„์ด 3, 4์„ธ ์œ ์•„๋ณด๋‹ค ์œ ์˜ํ•˜๊ฒŒ ์งง์•˜๋‹ค. ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์€ ์„ฑ๋ณ„์— ๋”ฐ๋ผ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค. ๋‘˜์งธ, ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์˜ ์ •๋‹ต๋ฅ ์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ณผ์ œ๋ณ„ ๋‚œ์ด๋„๋ฅผ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ, 3์„ธ ์œ ์•„์˜ ๊ฒฝ์šฐ ๋ถ€๋ถ„/์ „์ฒด ๋ฐ ์ ‘ํ•ฉ ๊ณผ์ œ๊ฐ€ ๊ฐ€์žฅ ๋‚ฎ์€ ๋‚œ์ด๋„๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ๋‹ค์Œ์œผ๋กœ ๋ฐฉํ–ฅ ๊ณผ์ œ๊ฐ€ ๋” ๋†’์€ ๋‚œ์ด๋„๋ฅผ ๋ณด์˜€๊ณ , ๋Œ€์นญ ๋ฐ ํšŒ์ „ ๊ณผ์ œ๊ฐ€ ๊ฐ€์žฅ ๋†’์€ ๋‚œ์ด๋„๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. 4์„ธ ์œ ์•„์˜ ๊ฒฝ์šฐ์—๋Š” ์ ‘ํ•ฉ ๋ฐ ๋ถ€๋ถ„/์ „์ฒด ๊ณผ์ œ๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ๋‚ฎ์€ ๋‚œ์ด๋„๋ฅผ ๋ณด์˜€๊ณ , ๋ฐฉํ–ฅ, ๋Œ€์นญ, ํšŒ์ „ ๊ณผ์ œ๊ฐ€ ๊ฐ™์€ ์ˆ˜์ค€์˜ ์ƒ๋Œ€์ ์œผ๋กœ ๋†’์€ ๋‚œ์ด๋„๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. 5์„ธ ์œ ์•„์˜ ๊ฒฝ์šฐ์—๋Š” ๋ถ€๋ถ„/์ „์ฒด ๋ฐ ์ ‘ํ•ฉ ๊ณผ์ œ๊ฐ€ ๊ฐ€์žฅ ๋‚ฎ์€ ๋‚œ์ด๋„๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ๋‹ค์Œ์œผ๋กœ ํšŒ์ „ ๊ณผ์ œ๊ฐ€ ๋” ๋†’์€ ๋‚œ์ด๋„๋ฅผ ๋ณด์˜€๊ณ , ๋Œ€์นญ ๋ฐ ๋ฐฉํ–ฅ ๊ณผ์ œ๊ฐ€ ๊ฐ€์žฅ ๋†’์€ ๋‚œ์ด๋„๋ฅผ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์…‹์งธ, ๊ณผ์ œ ์ œ์‹œ ๋งค์ฒด์— ๋”ฐ๋ผ ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์€ ๋ถ€๋ถ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ขŒ์šฐ๋ฐฉํ–ฅ, X์ถ•๋Œ€์นญ ๊ณผ์ œ์—์„œ๋Š” ์œ ์•„๊ฐ€ ํƒœ๋ธ”๋ฆฟPC๋กœ ์ œ์‹œ๋œ ๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ–ˆ์„ ๋•Œ, ์ข…์ด๋กœ ์ œ์‹œ๋œ ๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ–ˆ์„ ๋•Œ๋ณด๋‹ค ์ •๋‹ต๋ฅ ์ด ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜์œผ๋ฉฐ, ์šฐ๋กœํšŒ์ „, ์ขŒ์šฐ์ ‘ํ•ฉ ๊ณผ์ œ์—์„œ๋Š” ์ด์™€ ๋ฐ˜๋Œ€๋กœ ์œ ์•„๊ฐ€ ํƒœ๋ธ”๋ฆฟPC๋กœ ์ œ์‹œ๋œ ๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ–ˆ์„ ๋•Œ, ์ข…์ด๋กœ ์ œ์‹œ๋œ ๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ–ˆ์„ ๋•Œ๋ณด๋‹ค ์ •๋‹ต๋ฅ ์ด ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•˜๋‹ค. ๋˜ํ•œ ๋ฐฉํ–ฅ, ํšŒ์ „, ๋Œ€์นญ, ์ ‘ํ•ฉ, ๋ถ€๋ถ„/์ „์ฒด ๊ณผ์ œ ๋ชจ๋‘์—์„œ ํƒœ๋ธ”๋ฆฟ์„ ํ†ตํ•œ ์ˆ˜ํ–‰์˜ ์‘๋‹ต์‹œ๊ฐ„์€ ์ข…์ด๋ฅผ ํ†ตํ•œ ์ˆ˜ํ–‰์˜ ์‘๋‹ต์‹œ๊ฐ„๋ณด๋‹ค ์œ ์˜ํ•˜๊ฒŒ ๋” ์งง์•˜๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” 3, 4, 5์„ธ ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์ด ์—ฐ๋ น์— ๋”ฐ๋ผ ์ฐจ์ด๊ฐ€ ์žˆ์Œ์„ ๋ฐํ˜”์œผ๋ฉฐ, ๊ณผ์ œ๋ณ„ ๋‚œ์ด๋„ ์ˆœ์„œ๋ฅผ ๋ฐํ˜€๋‚ด์—ˆ๋‹ค. ๋˜ํ•œ ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์ด ๊ณผ์ œ ์ œ์‹œ ๋งค์ฒด์— ๋”ฐ๋ผ ๊ณผ์ œ๋ณ„๋กœ ๋ถ€๋ถ„์ ์œผ๋กœ ์ฐจ์ด๊ฐ€ ์žˆ์Œ์„ ๋ฐํ˜”๋‹ค.๊ตญ๋ฌธ์ดˆ๋ก โ… . ๋ฌธ์ œ ์ œ๊ธฐ โ…ก. ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 1. ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€ ๋ฐœ๋‹ฌ 1) ๊ณต๊ฐ„ ์ธ์ง€ ๋ฐœ๋‹ฌ 2) ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€ ๋ฐœ๋‹ฌ 3) ๊ณ ์ „์  ์ธ์ง€ ๋ฐœ๋‹ฌ ์ด๋ก  2. ์œ ์•„์˜ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ์˜ ์ˆ˜ํ–‰ 1) ๋ฐฉํ–ฅ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 2) ํšŒ์ „ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 3) ๋Œ€์นญ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 4) ์ ‘ํ•ฉ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 5) ๋ถ€๋ถ„/์ „์ฒด ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 3. ์ธ์ง€๊ณผ์ œ ์ œ์‹œ ๋งค์ฒด์™€ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ โ…ข. ์—ฐ๊ตฌ๋ฌธ์ œ ๋ฐ ์šฉ์–ด์˜ ์ •์˜ 1. ์—ฐ๊ตฌ๋ฌธ์ œ 2. ์šฉ์–ด์˜ ์ •์˜ 1) ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€ โ…ฃ. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ๋ฐ ์ ˆ์ฐจ 1. ์—ฐ๊ตฌ ๋Œ€์ƒ 2. ์—ฐ๊ตฌ ๋„๊ตฌ 3. ์—ฐ๊ตฌ ์ ˆ์ฐจ 1) 1์ฐจ ๋ณธ์กฐ์‚ฌ 2) 2์ฐจ ๋ณธ์กฐ์‚ฌ 4. ์ž๋ฃŒ์˜ ๋ถ„์„ โ…ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ ๋ฐ ํ•ด์„ 1. ์œ ์•„์˜ ์—ฐ๋ น๊ณผ ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 1) ์ธ์ง€๊ณผ์ œ ์ •๋‹ต๋ฅ , ์‘๋‹ต์‹œ๊ฐ„์˜ ์ „๋ฐ˜์ ์ธ ๊ฒฝํ–ฅ 2) ๋ฐฉํ–ฅ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 3) ํšŒ์ „ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 4) ๋Œ€์นญ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 5) ์ ‘ํ•ฉ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 6) ๋ถ€๋ถ„/์ „์ฒด ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ 2. ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ๋ณ„ ๋‚œ์ด๋„์— ๋”ฐ๋ฅธ ๊ณผ์ œ ์ˆ˜ํ–‰ 1) ๊ณผ์ œ ๋‚œ์ด๋„์— ๋”ฐ๋ฅธ ์ˆ˜ํ–‰์˜ ์ „๋ฐ˜์ ์ธ ๊ฒฝํ–ฅ 2) ๊ณผ์ œ์˜ ๋‚œ์ด๋„์— ๋”ฐ๋ฅธ ์ˆ˜ํ–‰ ์ฐจ์ด (1) ๊ณต๊ฐ„๊ธฐํ•˜ ์ธ์ง€๊ณผ์ œ๋ณ„ ๋‚œ์ด๋„ (2) ๋ฐฉํ–ฅ ์ธ์ง€๊ณผ์ œ ๋‚œ์ด๋„ (3) ํšŒ์ „ ์ธ์ง€๊ณผ์ œ ๋‚œ์ด๋„ (4) ๋Œ€์นญ ์ธ์ง€๊ณผ์ œ ๋‚œ์ด๋„ (5) ์ ‘ํ•ฉ ์ธ์ง€๊ณผ์ œ ๋‚œ์ด๋„ (6) ๋ถ€๋ถ„/์ „์ฒด ์ธ์ง€๊ณผ์ œ ๋‚œ์ด๋„ 3. ๊ณผ์ œ ์ œ์‹œ ๋งค์ฒด์— ๋”ฐ๋ฅธ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰ ์ฐจ์ด 1) ๋ฐฉํ–ฅ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์˜ ์ฐจ์ด 2) ํšŒ์ „ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์˜ ์ฐจ์ด 3) ๋Œ€์นญ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์˜ ์ฐจ์ด 4) ์ ‘ํ•ฉ ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์˜ ์ฐจ์ด 5) ๋ถ€๋ถ„/์ „์ฒด ์ธ์ง€๊ณผ์ œ ์ˆ˜ํ–‰์˜ ์ฐจ์ด โ…ฅ. ๊ฒฐ๋ก  ๋ฐ ๋…ผ์˜ ์ฐธ ๊ณ  ๋ฌธ ํ—Œ ๋ถ€๋ก ABSTRACTMaste

    Leiomyosarcoma of the middle ear and temporal bone

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    Leiomyosarcoma is a malignant tumor of smooth muscle cells that is exceedingly rare in the middle ear and temporal bone. Wide surgical resection is treatment of choice and adjuvant treatment has not proven to be of benefit. This is a report on a patient with otorrhea and rapidly growing mass on postauricualr area. A tumor that was mainly located in the middle ear and temporal bone was surgically removed and proved to be a leiomyosarcoma. The optimal surgical technique and other treatment strategy are discussed.ope

    A Case of Inflammatory Pseudotumor in Temporal Bone Treated with Methotraxate

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    Inflammatory pseudotumor is a pathologically benign condition, but it demonstrates a wide range of clinical features ranging from silent small sized tumors to aggressive features mimicking malignancy. Pseudotumors most commonly occur in the orbital area, and the involvement of the middle ear cavity is extremely rare. Several modalities are known for the treatment of pseudotumors, including complete surgical excision, oral steroid therapy, and radiation therapy. We describe a 35-year-old woman with inflammatory pseudotumor involving the middle ear cavity. The patient was treated with canal wall up tympanomastoidectomy and additional treatments with steroid and radiation therapy. However, she showed side effects to high dose steroid treatment and no response to radiation therapy. Therefore, we decided to use methotrexate with low dose steroid. After treatment, symptoms were completely resolved and there was no evidence of recurrence 1 year after maintaining immunosuppressant treatment.ope
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