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    A Study on the Container Terminal Operator Restructuring in Busan Port for enhancing Global Competitiveness

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    This paper is highly motivated to restructure terminal operator mix in Busan port in order to make it more competitive and sustainable as global hub port amid of fierce global competition. Among selective measurements worth of substantial consideration in strengthening global competitiveness of Busan port as T/S port, this paper clearly spells out the necessities to restructure current mix of terminal operator to which governmentโ€™s non-strategic approaches yet are attributed in carrying out port administration policies. In general, 5 types of entities are engaged in operation of container terminal: Port Authority(PAO), Global Terminal Operator(GTO), Regional operator(RO), Global Carrier(CO, Hybrid Operator) and Financial Operator(FO). Whereas, the Governments and Port Authorities are importantly demanded in all circumstances to appoint the most suitable parties among 5 aforementioned entities as operator or developer of terminal ahead of commencing port development in order to ensure the operational efficiencies and sustainability of ports. It is imperative, therefore, that systematic and strategic policy making processes in drawing the picture of port development and operation should be implemented from the phase of planning, however, broadly speaking, most of the Port Authorities much rather tend to put the โ€˜situational decisionโ€™ than to practise those critical โ€˜strategic processesโ€™ to say nothing of Busan port to very large extent. From the analysis, therefore, that Busan Port, as the 3rd busiest transshipment port in the world, does not structure optimal mix of terminal operators, this paper aims to suggest optimal operators mix in Busan port most importantly in order to ensure sustainable status as the Northeast Asia gate port. In the comparison with foreign leading ports such as Shanghai, Singapore, Hong Kong, Long Beach and Hamburg who are having similarities in the scale of port and type of handling cargo, Busan Port is found to have several striking differences from them which appears to lower the competitiveness of Busan port in spite of its potential strengths. To explore the most competitive mix of operator in Busan port, four inherently discriminating features of Busan port, Geographical superiority, High proportion of T/S cargo, Two port System by North Port and New Port, Low level of terminal handling charges are pointed out, then, most competitive terminal operator mix is proposed thereto in the end. Through the research, it is proved that Busan port needs some corrective measurements in restructuring the terminal operator mix for the improvement of operational efficiency and these actions proposed herewith could be, at the same time, the conclusion of this paper. Firstly, Global carriers should be placed at the key position in operating terminal while considering the type of handling cargo in Busan port. Secondly, excessively multiple operators should be reduced and minimized through consolidation among operators in the efficient direction. Thirdly, National carrier should secure own terminal independently or by allying with Global Terminal Operator or with leading Global carriers. Fourthly, Financial Operator should sell their shares to Global carrier operators, who, then, may position as the key terminal operators in stead. Finally, Busan Port Authority needs to execute higher influential leadership in the management of entire port of Busan by expanding shares in the operating corporates or by the regulatory backups from the government. Once above measurements are to be implemented, feasible effects, whereupon, comprising T/S cargo volume increase, enhancement of operational efficiencies, national carriersโ€™competitiveness regaining and Port Authorityโ€™s augmented leadership can be expected. As methodology of this research, survey with experts in the industries comprising shipping companies, entire terminal operators in Busan and Port Authority is conducted. To draw more objective results, whereas, additional survey with highly experienced researchers in the national research institute is conducted as supplementation. In the mean time, this paper may deserve high value and originality from the point of first approach in making efficient terminal operator mix, which, therefore, likely to provide policy maker in the government with insight how to ensure Busan port to have competitiveness and sustainability simply by refixing the type of operators. Furthermore, the Authorities in the foreign countries who are planning to develop ports may take insightful references from this paper in selecting the operator and developer of the terminal in their countries.Abstract โ…ฐ ์ œ1์žฅ ์„œ ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ํ•„์š”์„ฑ 1 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋ชฉ์  5 ์ œ3์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ•๊ณผ ๋ฒ”์œ„ 6 ์ œ2์žฅ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ์œ ํ˜•๊ณผ ์šด์˜ ํ˜„ํ™ฉ 10 ์ œ1์ ˆ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ์œ ํ˜• ๋ฐ ์žฅ๋‹จ์  ๋ถ„์„ 10 1. ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ์œ ํ˜• 10 2. ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ์œ ํ˜•๋ณ„ ์žฅ๋‹จ์  ๋ถ„์„ 19 ์ œ2์ ˆ ๋ถ€์‚ฐํ•ญ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ํ˜„ํ™ฉ๊ณผ ์‹œ์‚ฌ์  21 1. ๋ถํ•ญ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ํ˜„ํ™ฉ 22 2. ์‹ ํ•ญ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ํ˜„ํ™ฉ 30 3. ๋ถ€์‚ฐํ•ญ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ๊ตฌ์„ฑ๊ณผ ์‹œ์‚ฌ์  39 ์ œ3์ ˆ ํ•ด์™ธ ์ฃผ์š” ํ•ญ๋งŒ์˜ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ํ˜„ํ™ฉ๊ณผ ์‹œ์‚ฌ์  42 1. ์ƒํ•ด(PA ์ค‘์‹ฌํ•ญ) 42 2. ์‹ฑ๊ฐ€ํฌ๋ฅด(GTO ์ค‘์‹ฌํ•ญ) 44 3. ํ™์ฝฉ(GTO ์ค‘์‹ฌํ•ญ) 47 4. ๋กฑ๋น„์น˜(๊ธ€๋กœ๋ฒŒ์„ ์‚ฌ ์ค‘์‹ฌํ•ญ) 48 5 ํ•จ๋ถ€๋ฅดํฌ(GTOโ€งRO ์ค‘์‹ฌํ•ญ) 50 6. ์„ธ๊ณ„ ์ฃผ์š” ํ•ญ๋งŒ์˜ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ๊ตฌ์„ฑ๊ณผ ์‹œ์‚ฌ์  51 ์ œ4์ ˆ ๋ถ€์‚ฐํ•ญ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ๊ตฌ์„ฑ์— ๋”ฐ๋ฅธ ๋ฌธ์ œ์  54 1. ํ™˜์ ๋น„์šฉ ์ฆ๊ฐ€์— ๋”ฐ๋ฅธ ํ•ญ๋งŒ๊ฐ€๊ฒฉ๊ฒฝ์Ÿ๋ ฅ ์ €ํ•˜ 55 2. ์„ ์„ํ™œ์šฉ์˜ ๋น„ํšจ์œจ์„ฑ ์ฆ๋Œ€ 56 3. ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ๊ฐ„ ๊ณผ๋„ํ•œ ํ•˜์—ญ์š”์œจ๊ฒฝ์Ÿ์— ๋”ฐ๋ฅธ ์ˆ˜์ต์„ฑ ์•…ํ™” 57 4. ๋ถˆํ™•์‹คํ•œ ๋ฌผ๋™๋Ÿ‰ ์„ฑ์žฅ๊ธฐ๋ฐ˜ 60 5. ํ•ญ๋งŒ๊ณต์‚ฌ(PA)์˜ ํ•ญ๋งŒ๊ด€๋ฆฌ ํ†ต์ œ๋ ฅ ๋ถ€์žฌ 61 ์ œ3์žฅ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ์žฌ๊ตฌ์„ฑ์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ๊ณผ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ์žฌ๊ตฌ์„ฑ ๋ฐฉ์•ˆ 62 ์ œ1์ ˆ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ์žฌ๊ตฌ์„ฑ์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 62 1. ๋ถ€์‚ฐํ•ญ ๊ฒฝ์Ÿ๋ ฅ ์ œ๊ณ ๋ฐฉ์•ˆ ๊ด€๋ จ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 62 2. ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„ ํ†ตํ•ฉ ๊ด€๋ จ ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 68 ์ œ2์ ˆ ๋ถ€์‚ฐํ•ญ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„ ์šด์˜ ํŠน์ง•๊ณผ SWOT ๋ถ„์„ 73 1. ์ง€๋ฆฌ์  ํŠน์ง• 73 2. ํ™˜์ ํ™”๋ฌผ ์ค‘์‹ฌํ•ญ๋งŒ 76 3. ๋ถํ•ญ-์‹ ํ•ญ ํˆฌํฌํŠธ(Two port) ์‹œ์Šคํ…œ 79 4. ํ•ญ๋งŒ๋น„์šฉ(Terminal Handling Charge) 80 5. ๋ถ€์‚ฐํ•ญ์˜ SWOT ๋ถ„์„ 84 ์ œ3์ ˆ ๋ถ€์‚ฐํ•ญ ๊ฒฝ์Ÿ๋ ฅ ๊ฐ•ํ™”๋ฅผ ์œ„ํ•œ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ์žฌ๊ตฌ์„ฑ ๋ฐฉ์•ˆ 93 1. ๊ธ€๋กœ๋ฒŒ์„ ์‚ฌ ์ค‘์‹ฌ ์žฌํŽธ 93 2. ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ์˜ ํ†ตํ•ฉ 97 3. ๊ธˆ์œตํˆฌ์ž์ž ๋น„์ค‘ ์ถ•์†Œ 100 4. ๊ตญ์ ์„ ์‚ฌ์˜ ์ž๊ฐ€ํ„ฐ๋ฏธ๋„ ํ™•๋ณด 101 5. ํ•ญ๋งŒ๊ณต์‚ฌ์˜ ๊ณต๊ณต์ •์ฑ… ์‹คํ–‰๋ ฅ ํ™•๋ณด 103 ์ œ4์ ˆ ๋ถ€์‚ฐํ•ญ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ์žฌ๊ตฌ์„ฑ ๊ธฐ๋Œ€ํšจ๊ณผ 105 1. ํ•ญ๋งŒ๋ฌผ๋™๋Ÿ‰ ์ฆ๋Œ€ํšจ๊ณผ 105 2. ํ•ญ๋งŒํšจ์œจ์„ฑ ์ œ๊ณ ํšจ๊ณผ 108 3. ๊ตญ์  ์ปจํ…Œ์ด๋„ˆ์„ ์‚ฌ ๊ฒฝ์Ÿ๋ ฅ ์ œ๊ณ ํšจ๊ณผ 111 4. ํ•ญ๋งŒ๊ณต์‚ฌ์˜ ์ •์ฑ…์‹คํ–‰๋ ฅ(๊ณต๊ณต์„ฑ) ๊ฐ•ํ™”ํšจ๊ณผ 115 ์ œ4์žฅ ๋ถ€์‚ฐํ•ญ ํ„ฐ๋ฏธ๋„์šด์˜์‚ฌ ์žฌ๊ตฌ์„ฑ ํšจ๊ณผ์— ๊ด€ํ•œ ์‹ค์ฆ๋ถ„์„ 118 ์ œ1์ ˆ ์—ฐ๊ตฌ๋ชจํ˜• ์„ค์ •๊ณผ ๋ณ€์ˆ˜์ธก์ • 118 1. ์—ฐ๊ตฌ๋ชจํ˜•๊ณผ ์—ฐ๊ตฌ๊ฐ€์„ค ์„ค์ • 118 2. ๋ณ€์ˆ˜์˜ ์กฐ์ž‘์  ์ •์˜์™€ ์ธก์ •๋ฐฉ๋ฒ• 123 ์ œ2์ ˆ ๊ธฐ์ˆ ํ†ต๊ณ„์™€ ๋นˆ๋„๋ถ„์„ 129 1. ๊ธฐ์ˆ ํ†ต๊ณ„๋ถ„์„ 129 2. ๋นˆ๋„๋ถ„์„ 133 ์ œ3์ ˆ ์‘๋‹ต๊ทธ๋ฃน๋ณ„ ์ธ์‹๋„ ์ฐจ์ด๋ถ„์„ 157 ์ œ4์ ˆ ์‹ ๋ขฐ์„ฑ ๋ฐ ํƒ€๋‹น์„ฑ ๊ฒ€์ • 188 ์ œ5์ ˆ ๊ฐ€์„ค์˜ ๊ฒ€์ • 193 1. ๊ฐ€์„คโ… ์˜ ๊ฒ€์ • 193 2. ๊ฐ€์„คโ…ก์˜ ๊ฒ€์ • 199 3. ๊ฐ€์„คโ…ข์˜ ๊ฒ€์ • 206 ์ œ6์ ˆ ๋ถ„์„๊ฒฐ๊ณผ์˜ ์š”์•ฝ 215 ์ œ5์žฅ ๊ฒฐ ๋ก  217 ์ œ1์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 217 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๊ฒฐ๋ก  221 ์ œ3์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฐฉํ–ฅ 223 ์ฐธ๊ณ ๋ฌธํ—Œ 225Docto

    ๋ฒ ์ด์ง€์•ˆ ๋„คํŠธ์›Œํฌ๋ฅผ ํ™œ์šฉํ•œ ๊ตํ†ต์ƒํƒœ์˜ ํ™•๋ฅ ๋ก ์  ์˜ˆ์ธก

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2017. 8. ๊ณ ์Šน์˜.Traffic state prediction is an important issue in traffic operations. One of the main purposes of traffic operations is to prevent flow breakdown. Therefore, it is necessary to perform traffic state predictions that reflects the stochastic process of traffic flow. However, traffic state transition is affected complexly and simultaneously by many factors, which lead to a lack of understanding and accurate prediction. Meanwhile, the Bayesian network is a methodology that not only is suitable for a problem with uncertainty but also can improve the understanding of a problem. Also, it is possible to derive fair probability with incomplete information, which allows the analysis of various situations. In this study, we developed a traffic state prediction model using the Bayesian network to reflect dynamic and stochastic traffic flow characteristics. In order to improve the structure of the Bayesian network, which has been used simply in transportation problems, we proposed a modeling procedure using mixture of Gaussians (MOGs). Also, spatially extended variables were used to consider the spatiotemporal evolution of traffic flow pattern. In particular, traffic state identification was performed by estimating the link speed in order to consider the spatial propagation of congestion. In the performance evaluation, the Bayesian network has better performance than logistic regression and has the same level of performance as artificial neural network based on machine learning. Also, by performing sensitivity analyses, we provided the understanding of traffic state prediction and the guidelines for model improvement. Therefore, the Bayesian network developed in this study can be considered as a traffic state prediction model with good prediction accuracy and provides insights for traffic state prediction.Chapter 1. Introduction 1 1.1 Research background and purpose 1 1.2 Research scope and procedure 4 Chapter 2. Literature Review 8 2.1 Characteristics of traffic state 8 2.2 Traffic state estimation and prediction 14 2.3 Bayesian network 37 2.4 Originality of this research 41 Chapter 3. Data Collection and Preparation 46 3.1 Data collection and validity check 46 3.2 Traffic state identification 47 3.3 Data Description 63 Chapter 4. Bayesian Network Modeling 66 4.1 Modeling procedure 66 4.2 Description of interface mechanism 69 4.3 Module design 74 4.4 Eliciting the structure 81 4.5 Verification 81 4.6 Parameter learning 85 Chapter 5. Model Evaluation 87 5.1 Evaluation results 87 5.2 Comparison with other methodologies 92 5.3 Sensitivity analysis 104 Chapter 6. Conclusions 127 6.1 Summary 127 6.2 Guidelines for traffic state prediction 128 6.3 Limitations of the study 129 6.4 Applications and future research 130 References 135Docto

    ๊ณต๊ณต๊ธฐ๊ด€ ์ง€๋ฐฉ์ด์ „์— ๋”ฐ๋ฅธ ์ข…์‚ฌ์ž์˜ ์‹ฌ๋ฆฌ๋ณ€ํ™”๊ฐ€ ์กฐ์ง๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ–‰์ •๋Œ€ํ•™์› : ๊ณต๊ธฐ์—…์ •์ฑ…ํ•™๊ณผ, 2016. 8. ์ž„๋„๋นˆ.์ •๋ถ€๋Š” ๊ตญ๊ฐ€๊ท ํ˜•๋ฐœ์ „๊ณผ ์ง€๋ฐฉ๋ถ„๊ถŒ์ด๋ผ๋Š” ๋ช…๋ชฉํ•˜์— ์ˆ˜๋„๊ถŒ ์†Œ์žฌ ๊ณต๊ณต๊ธฐ๊ด€์„ ์ง€๋ฐฉ์œผ๋กœ ์ด์ „ํ•œ๋‹ค๋Š” ์ •์ฑ…์„ ์ˆ˜๋ฆฝํ•˜์˜€๊ณ , ์ด๋ฅผ ์‹คํ–‰ํ•˜์—ฌ 2016๋…„ ๋ง๊นŒ์ง€ ๋Œ€๋ถ€๋ถ„์˜ ๊ณต๊ณต๊ธฐ๊ด€์ด ๋ถ€์‚ฐ, ๋Œ€๊ตฌ, ๋‚˜์ฃผ ๋“ฑ์œผ๋กœ ์ง€๋ฐฉ์ด์ „์„ ์™„๋ฃŒํ•  ์˜ˆ์ • ๋˜๋Š” ์ด๋ฏธ ์ง€๋ฐฉ์œผ๋กœ ์ด์ „์„ ์™„๋ฃŒํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ณต๊ณต๊ธฐ๊ด€์˜ ์ง€๋ฐฉ์ด์ „์€ ์ง€์—ญ ๊ฒฝ์ œ๋ฅผ ์‚ด๋ฆฌ๊ณ  ์ˆ˜๋„๊ถŒ๊ณผ ์ง€์—ญ๊ฐ„์˜ ๋ถˆ๊ท ํ˜• ํ•ด์†Œ์™€ ๊ตญ๊ฐ€์™€ ์ง€์—ญ์‚ฌํšŒ์˜ ๊ฒฝ์Ÿ๋ ฅ์„ ๋†’์ด๋Š” ์—ญํ• ์„ ํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ํ•˜์ง€๋งŒ ๊ณต๊ณต๊ธฐ๊ด€ ์ข…์‚ฌ์ž๋“ค์€ ๊ฑฐ์ฃผ์ง€๋ฅผ ์ง€๋ฐฉ์œผ๋กœ ์ด์ „ํ•จ์— ๋”ฐ๋ผ ์ƒˆ๋กœ์šด ํ™˜๊ฒฝ์— ์ ์‘ํ•ด์•ผ ํ•œ๋‹ค๋Š” ์ ์—์„œ ์‹ฌ๋ฆฌ์ ์ธ ๋™์š”๋‚˜ ์‹ฌ๋ฆฌ์ƒํƒœ์˜ ๋ณ€ํ™”๋ฅผ ๊ฒฝํ—˜ํ•˜๊ฒŒ ๋  ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์š”์ธ๋“ค์€ ๊ฒฐ๊ตญ ๊ณต๊ณต๊ธฐ๊ด€ ์ข…์‚ฌ์ž๋“ค์˜ ์กฐ์ง๋ชฐ์ž…์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ฒŒ ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ถ€์‚ฐํ˜์‹ ๋„์‹œ๋กœ ์ด์ „ํ•œ K๊ณต๊ณต๊ธฐ๊ด€ ์ง์›์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์—ฌ ์ง€๋ฐฉ์ด์ „์œผ๋กœ ์ธํ•œ ์ง์›๋“ค์˜ ์‹ฌ๋ฆฌ์ƒํƒœ๋ฅผ ๊ธฐ๋Œ€์‹ฌ๋ฆฌ์™€ ๋ถˆ์•ˆ์‹ฌ๋ฆฌ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ, ์ง€๋ฐฉ์ด์ „ ์ด์ „๊ณผ ์ดํ›„์˜ ์‹ฌ๋ฆฌ์ƒํƒœ ๋ณ€ํ™”๊ฐ€ ์กฐ์ง๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์˜จ๋ผ์ธ ์„ค๋ฌธ์กฐ์‚ฌ์˜ ๋ฐฉ๋ฒ•์œผ๋กœ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์„ค๋ฌธ์กฐ์‚ฌ๋Š” ์ง€๋ฐฉ์ด์ „ ์ด์ „๋ถ€ํ„ฐ ๊ธฐ๊ด€์—์„œ ๊ทผ๋ฌดํ•œ ์ง์›๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๊ณ , ์š”์ธ๋ถ„์„, ํƒ€๋‹น์„ฑ ๋ฐ ์‹ ๋ขฐ๋„ ๋ถ„์„, ๋นˆ๋„๋ถ„์„, ์ง‘๋‹จ๋ณ„ ํ‰๊ท ๋ถ„์„, ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„, ๋…๋ฆฝํ‘œ๋ณธ t๊ฒ€์ •, ๋Œ€์‘ํ‘œ๋ณธ t๊ฒ€์ •, ํšŒ๊ท€๋ถ„์„ ๋“ฑ์˜ ๋ฐฉ๋ฒ•์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š”, ์ฒซ์งธ, ํƒ€๋‹น์„ฑ๋ถ„์„๊ณผ ์‹ ๋ขฐ์„ฑ๋ถ„์„์„ ๋ฐ”ํƒ•์œผ๋กœ ์ด 7๊ฐœ ์š”์ธ์˜ ํ‰๊ท ๊ฐ’๊ณผ ํ‘œ์ค€ํŽธ์ฐจ๋ฅผ ๋น„๊ตํ•˜์˜€๋Š”๋ฐ ๊ณต๊ณต๊ธฐ๊ด€ ์ง€๋ฐฉ์ด์ „ ์ด์ „๊ณผ ์ดํ›„์˜ ๊ธฐ๋Œ€์‹ฌ๋ฆฌ์˜ ํ‰๊ท ๊ฐ’์˜ ์ฐจ์ด๊ฐ€ ํฐ ๊ฒƒ์— ๋ฐ˜ํ•˜์—ฌ ๋ถˆ์•ˆ์‹ฌ๋ฆฌ์˜ ๊ฒฝ์šฐ ํ‰๊ท ๊ฐ’์˜ ๋ณ€ํ™”๊ฐ€ ๊ฑฐ์˜ ์—†๋‹ค๋Š” ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‘˜์งธ, ํ•˜์œ„๊ตฌ์„ฑ์š”์ธ์— ๋Œ€ํ•˜์—ฌ ๋ฒ”์ฃผ๋ณ„ ํ‰๊ท ๊ฐ’์„ t๊ฒ€์ •๊ณผ ์ผ์›๋ถ„์‚ฐ๋ถ„์„์„ ์ด์šฉํ•˜์—ฌ ๋น„๊ตํ•˜์˜€๋Š”๋ฐ, ๊ณต๊ณต๊ธฐ๊ด€ ์ง€๋ฐฉ์ด์ „ ์ด์ „๊ณผ ์ดํ›„์˜ ์‹ฌ๋ฆฌ์š”์ธ์€ ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ํŠน์„ฑ์— ๋”ฐ๋ผ ์ฐจ์ด๊ฐ€ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์…‹์งธ, ์ธ๊ตฌํ†ต๊ณ„ํ•™์  ๋ณ€์ˆ˜๋ณ„๋กœ ๊ณต๊ณต๊ธฐ๊ด€ ์ง€๋ฐฉ์ด์ „ ์ด์ „๊ณผ ์ดํ›„์˜ ์‹ฌ๋ฆฌ์š”์ธ ๊ฐ„ ์ฐจ์ด๊ฐ€ ์–ด๋–ป๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ์ง€๋ฅผ ๋ถ„์„ํ•˜์˜€๋Š”๋ฐ, ๊ธฐ๋Œ€์‹ฌ๋ฆฌ์—์„œ๋งŒ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๊ฐ€ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋„ท์งธ, ์ง์›๋“ค์˜ ๊ธฐ๋Œ€์‹ฌ๋ฆฌ๊ฐ€ ๋†’์„์ˆ˜๋ก ์ •์„œ์  ์กฐ์ง๋ชฐ์ž… ์ˆ˜์ค€๊ณผ ๊ทœ๋ฒ”์  ์กฐ์ง๋ชฐ์ž… ์ˆ˜์ค€์ด ๋†’์•„์ง€๊ณ , ์ง์›๋“ค์˜ ๋ถˆ์•ˆ์‹ฌ๋ฆฌ๋Š” ์กฐ์ง๋ชฐ์ž… ์ˆ˜์ค€์— ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ์•Š๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ ๋˜์—ˆ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ํ•„์š”์„ฑ 1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 2. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ 2 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ๊ณผ ๋ฐฉ๋ฒ• 2 1. ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ 2 2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 3 ์ œ 2 ์žฅ ์ด๋ก ์  ๋…ผ์˜์™€ ์„ ํ–‰์—ฐ๊ตฌ์˜ ๊ฒ€ํ†  3 ์ œ 1 ์ ˆ ๊ณต๊ณต๊ธฐ๊ด€ ์ง€๋ฐฉ์ด์ „ ๊ฐœ์š” 3 1. ๊ณต๊ณต๊ธฐ๊ด€ ์ง€๋ฐฉ์ด์ „์˜ ์ถ”์ง„๋ฐฐ๊ฒฝ 3 2. ํ˜์‹ ๋„์‹œ ๊ฐœ๋… 10 3. ํ˜์‹ ๋„์‹œ ๊ฑด์„ค์˜ ๊ธฐ๋Œ€ํšจ๊ณผ 10 4. ๋ถ€์‚ฐํ˜์‹ ๋„์‹œ์˜ ๊ฑด์„ค๋ฐฉํ–ฅ 10 ์ œ 2 ์ ˆ ์„ ํ–‰์—ฐ๊ตฌ์˜ ๊ฒ€ํ†  11 1. ๊ณต๊ณต๊ธฐ๊ด€ ์ง€๋ฐฉ์ด์ „์— ๊ด€ํ•œ ์—ฐ๊ตฌ 11 2. ์ง€๋ฐฉ์ด์ „๊ณผ ๋งŒ์กฑ๋„์— ๊ด€ํ•œ ์—ฐ๊ตฌ 12 3. ์ง€๋ฐฉ์ด์ „๊ณผ ์กฐ์ง๋ชฐ์ž…์— ๊ด€ํ•œ ์—ฐ๊ตฌ 13 ์ œ 3 ์žฅ ์—ฐ๊ตฌ์„ค๊ณ„ ๋ฐ ์—ฐ๊ตฌ๊ฐ€์„ค 14 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์„ค๊ณ„ 14 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๊ฐ€์„ค 15 ์ œ 3 ์ ˆ ๋ณ€์ˆ˜์„ค์ • ๋ฐ ์ธก์ • 16 1. ๋ณ€์ˆ˜์„ค์ • 16 2. ๋ณ€์ˆ˜์˜ ์ธก์ • 16 ์ œ 4 ์ ˆ ์ž๋ฃŒ์ˆ˜์ง‘ ๋ฐ ๋ถ„์„๋ฐฉ๋ฒ• 21 ์ œ 4 ์žฅ ๊ฒฐ๊ณผ๋ถ„์„ 21 ์ œ 1 ์ ˆ ํ‘œ๋ณธ์˜ ํŠน์„ฑ๊ณผ ์ธก์ •๋„๊ตฌ์˜ ํƒ€๋‹น์„ฑ ๋ฐ ์‹ ๋ขฐ์„ฑ 21 1. ํ‘œ๋ณธ์˜ ํŠน์„ฑ 21 2. ํƒ€๋‹น์„ฑ ๋ฐ ์‹ ๋ขฐ์„ฑ ๋ถ„์„ 23 3. ์†Œ๊ฒฐ 28 ์ œ 2 ์ ˆ ๊ธฐ์ˆ ํ†ต๊ณ„ ํ‰๊ท ๋น„๊ต ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ 28 1. ๊ธฐ์ˆ ํ†ต๊ณ„๋ถ„์„ 28 2. ํ•˜์œ„๊ตฌ์„ฑ์š”์ธ์— ๋Œ€ํ•œ ๋ฒ”์ฃผ๋ณ„ ํ‰๊ท ๊ฐ’ ๋น„๊ต 29 3. ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ 43 ์ œ 3 ์ ˆ ์ง€๋ฐฉ์ด์ „ ์ด์ „๊ณผ ์ดํ›„์˜ ์‹ฌ๋ฆฌ์š”์ธ์˜ ์ฐจ์ด ๋ถ„์„ 45 1. ์ง€๋ฐฉ์ด์ „ ์ด์ „๊ณผ ์ดํ›„์˜ ์‹ฌ๋ฆฌ์š”์ธ๊ฐ„ ์ฐจ์ด ๋ถ„์„ 45 2. ์ธ๊ตฌํ†ต๊ณ„๋ณ€์ˆ˜๋ณ„ ์ง€๋ฐฉ์ด์ „ ์ด์ „๊ณผ ์ดํ›„์˜ ์‹ฌ๋ฆฌ์š”์ธ ๊ฐ„ ์ฐจ์ด๋ถ„์„ 47 ์ œ 4 ์ ˆ ํšŒ๊ท€๋ถ„์„ 54 1. ์ง€๋ฐฉ์ด์ „ ์ด์ „์˜ ์‹ฌ๋ฆฌ์š”์ธ๊ณผ ์กฐ์ง๋ชฐ์ž…๊ณผ์˜ ์˜ํ–ฅ๋ถ„์„ 54 2. ์ง€๋ฐฉ์ด์ „ ์ดํ›„์˜ ์‹ฌ๋ฆฌ์š”์ธ๊ณผ ์กฐ์ง๋ชฐ์ž…๊ณผ์˜ ์˜ํ–ฅ๋ถ„์„ 59 ์ œ 5 ์žฅ ๊ฒฐ๋ก  65 ์ œ 1 ์ ˆ ๋ถ„์„๊ฒฐ๊ณผ์˜ ์š”์•ฝ 65 ์ œ 2 ์ ˆ ๊ฒฐ๋ก  ๋ฐ ์ •์ฑ…์  ํ•จ์˜ 67 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฐฉํ–ฅ 71 ์ฐธ๊ณ  ๋ฌธํ—Œ 72 ์„ค๋ฌธ์ง€ 76 Abstract 84Maste
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