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    ํฐ ๊ทธ๋ž˜ํ”„ ์ƒ์—์„œ์˜ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€ ๋žญํฌ์— ๋Œ€ํ•œ ๋น ๋ฅธ ๊ณ„์‚ฐ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2020. 8. ์ด์ƒ๊ตฌ.Computation of Personalized PageRank (PPR) in graphs is an important function that is widely utilized in myriad application domains such as search, recommendation, and knowledge discovery. Because the computation of PPR is an expensive process, a good number of innovative and efficient algorithms for computing PPR have been developed. However, efficient computation of PPR within very large graphs with over millions of nodes is still an open problem. Moreover, previously proposed algorithms cannot handle updates efficiently, thus, severely limiting their capability of handling dynamic graphs. In this paper, we present a fast converging algorithm that guarantees high and controlled precision. We improve the convergence rate of traditional Power Iteration method by adopting successive over-relaxation, and initial guess revision, a vector reuse strategy. The proposed method vastly improves on the traditional Power Iteration in terms of convergence rate and computation time, while retaining its simplicity and strictness. Since it can reuse the previously computed vectors for refreshing PPR vectors, its update performance is also greatly enhanced. Also, since the algorithm halts as soon as it reaches a given error threshold, we can flexibly control the trade-off between accuracy and time, a feature lacking in both sampling-based approximation methods and fully exact methods. Experiments show that the proposed algorithm is at least 20 times faster than the Power Iteration and outperforms other state-of-the-art algorithms.๊ทธ๋ž˜ํ”„ ๋‚ด์—์„œ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ (P ersonalized P age R ank, PPR ๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์€ ๊ฒ€์ƒ‰ , ์ถ”์ฒœ , ์ง€์‹๋ฐœ๊ฒฌ ๋“ฑ ์—ฌ๋Ÿฌ ๋ถ„์•ผ์—์„œ ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ํ™œ์šฉ๋˜๋Š” ์ค‘์š”ํ•œ ์ž‘์—… ์ด๋‹ค . ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒƒ์€ ๊ณ ๋น„์šฉ์˜ ๊ณผ์ •์ด ํ•„์š”ํ•˜๋ฏ€๋กœ , ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ํšจ์œจ์ ์ด๊ณ  ํ˜์‹ ์ ์ธ ๋ฐฉ๋ฒ•๋“ค์ด ๋‹ค์ˆ˜ ๊ฐœ๋ฐœ๋˜์–ด์™”๋‹ค . ๊ทธ๋Ÿฌ๋‚˜ ์ˆ˜๋ฐฑ๋งŒ ์ด์ƒ์˜ ๋…ธ๋“œ๋ฅผ ๊ฐ€์ง„ ๋Œ€์šฉ๋Ÿ‰ ๊ทธ๋ž˜ํ”„์— ๋Œ€ํ•œ ํšจ์œจ์ ์ธ ๊ณ„์‚ฐ์€ ์—ฌ์ „ํžˆ ํ•ด๊ฒฐ๋˜์ง€ ์•Š์€ ๋ฌธ์ œ์ด๋‹ค . ๊ทธ์— ๋”ํ•˜์—ฌ , ๊ธฐ์กด ์ œ์‹œ๋œ ์•Œ๊ณ ๋ฆฌ๋“ฌ๋“ค์€ ๊ทธ๋ž˜ํ”„ ๊ฐฑ์‹ ์„ ํšจ์œจ์ ์œผ๋กœ ๋‹ค๋ฃจ์ง€ ๋ชปํ•˜์—ฌ ๋™์ ์œผ๋กœ ๋ณ€ํ™”ํ•˜๋Š” ๊ทธ๋ž˜ํ”„๋ฅผ ๋‹ค๋ฃจ๋Š” ๋ฐ์— ํ•œ๊ณ„์ ์ด ํฌ๋‹ค . ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋†’์€ ์ •๋ฐ€๋„๋ฅผ ๋ณด์žฅํ•˜๊ณ  ์ •๋ฐ€๋„๋ฅผ ํ†ต์ œ ๊ฐ€๋Šฅํ•œ , ๋น ๋ฅด๊ฒŒ ์ˆ˜๋ ดํ•˜๋Š” ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ ๊ณ„์‚ฐ ์•Œ๊ณ ๋ฆฌ๋“ฌ์„ ์ œ์‹œํ•œ๋‹ค . ์ „ํ†ต์ ์ธ ๊ฑฐ๋“ญ์ œ๊ณฑ๋ฒ• (Power ์— ์ถ•์ฐจ๊ฐ€์†์™„ํ™”๋ฒ• (Successive Over Relaxation) ๊ณผ ์ดˆ๊ธฐ ์ถ”์ธก ๊ฐ’ ๋ณด์ •๋ฒ• (Initial Guess ์„ ํ™œ์šฉํ•œ ๋ฒกํ„ฐ ์žฌ์‚ฌ์šฉ ์ „๋žต์„ ์ ์šฉํ•˜์—ฌ ์ˆ˜๋ ด ์†๋„๋ฅผ ๊ฐœ์„ ํ•˜์˜€๋‹ค . ์ œ์‹œ๋œ ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด ๊ฑฐ๋“ญ์ œ๊ณฑ๋ฒ•์˜ ์žฅ์ ์ธ ๋‹จ์ˆœ์„ฑ๊ณผ ์—„๋ฐ€์„ฑ์„ ์œ ์ง€ ํ•˜๋ฉด์„œ ๋„ ์ˆ˜๋ ด์œจ๊ณผ ๊ณ„์‚ฐ์†๋„๋ฅผ ํฌ๊ฒŒ ๊ฐœ์„  ํ•œ๋‹ค . ๋˜ํ•œ ๊ฐœ์ธํ™”๋œ ํŽ˜์ด์ง€๋žญํฌ ๋ฒกํ„ฐ์˜ ๊ฐฑ์‹ ์„ ์œ„ํ•˜์—ฌ ์ด์ „์— ๊ณ„์‚ฐ ๋˜์–ด ์ €์žฅ๋œ ๋ฒกํ„ฐ๋ฅผ ์žฌ์‚ฌ์šฉํ•˜ ์—ฌ , ๊ฐฑ์‹  ์— ๋“œ๋Š” ์‹œ๊ฐ„์ด ํฌ๊ฒŒ ๋‹จ์ถ•๋œ๋‹ค . ๋ณธ ๋ฐฉ๋ฒ•์€ ์ฃผ์–ด์ง„ ์˜ค์ฐจ ํ•œ๊ณ„์— ๋„๋‹ฌํ•˜๋Š” ์ฆ‰์‹œ ๊ฒฐ๊ณผ๊ฐ’์„ ์‚ฐ์ถœํ•˜๋ฏ€๋กœ ์ •ํ™•๋„์™€ ๊ณ„์‚ฐ์‹œ๊ฐ„์„ ์œ ์—ฐํ•˜๊ฒŒ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ด๋Š” ํ‘œ๋ณธ ๊ธฐ๋ฐ˜ ์ถ”์ •๋ฐฉ๋ฒ•์ด๋‚˜ ์ •ํ™•ํ•œ ๊ฐ’์„ ์‚ฐ์ถœํ•˜๋Š” ์—ญํ–‰๋ ฌ ๊ธฐ๋ฐ˜ ๋ฐฉ๋ฒ• ์ด ๊ฐ€์ง€์ง€ ๋ชปํ•œ ํŠน์„ฑ์ด๋‹ค . ์‹คํ—˜ ๊ฒฐ๊ณผ , ๋ณธ ๋ฐฉ๋ฒ•์€ ๊ฑฐ๋“ญ์ œ๊ณฑ๋ฒ•์— ๋น„ํ•˜์—ฌ 20 ๋ฐฐ ์ด์ƒ ๋น ๋ฅด๊ฒŒ ์ˆ˜๋ ดํ•œ๋‹ค๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ์œผ๋ฉฐ , ๊ธฐ ์ œ์‹œ๋œ ์ตœ๊ณ  ์„ฑ๋Šฅ ์˜ ์•Œ๊ณ ๋ฆฌ ๋“ฌ ๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ๊ฒƒ ๋˜ํ•œ ํ™•์ธ๋˜์—ˆ๋‹ค1 Introduction 1 2 Preliminaries: Personalized PageRank 4 2.1 Random Walk, PageRank, and Personalized PageRank. 5 2.1.1 Basics on Random Walk 5 2.1.2 PageRank. 6 2.1.3 Personalized PageRank 8 2.2 Characteristics of Personalized PageRank. 9 2.3 Applications of Personalized PageRank. 12 2.4 Previous Work on Personalized PageRank Computation. 17 2.4.1 Basic Algorithms 17 2.4.2 Enhanced Power Iteration 18 2.4.3 Bookmark Coloring Algorithm. 20 2.4.4 Dynamic Programming 21 2.4.5 Monte-Carlo Sampling. 22 2.4.6 Enhanced Direct Solving 24 2.5 Summary 26 3 Personalized PageRank Computation with Initial Guess Revision 30 3.1 Initial Guess Revision and Relaxation 30 3.2 Finding Optimal Weight of Successive Over Relaxation for PPR. 34 3.3 Initial Guess Construction Algorithm for Personalized PageRank. 36 4 Fully Personalized PageRank Algorithm with Initial Guess Revision 42 4.1 FPPR with IGR. 42 4.2 Optimization. 49 4.3 Experiments. 52 5 Personalized PageRank Query Processing with Initial Guess Revision 56 5.1 PPR Query Processing with IGR 56 5.2 Optimization. 64 5.3 Experiments. 67 6 Conclusion 74 Bibliography 77 Appendix 88 Abstract (In Korean) 90Docto

    ์‹œํ€€์Šค ๊ธฐ๋ฐ˜ 3์ฐจ์› ๋‹ค์ธ ์ž์„ธ ์ถ”์ •์„ ์œ„ํ•œ ๊ธฐํ•˜ํ•™์  ๋ฐ์ดํ„ฐ ์ฆ๊ฐ• ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋ฐ์ดํ„ฐ์‚ฌ์ด์–ธ์Šค๋Œ€ํ•™์› ๋ฐ์ดํ„ฐ์‚ฌ์ด์–ธ์Šคํ•™๊ณผ, 2023. 2. ์ด์ค€์„.3D pose estimation is an invaluable task in computer vision with various practical applications. Recently, a Transformer-based sequence-to-sequence model, MixSTE [60], has been successfully applied to 3D single-person pose estimation by decoupling the 2Dto-3D modeling from pixel-level details. We propose a natural extension of this model from single-person to multi-person problem, adding a novel inter-personal attention for 2D-to-3D lifting. Naturally referring to neighboring frames, this design is highly robust in handling occlusions. However, 3D multi-person pose estimation is still challenging due to extreme data scarcity. From an observation that our 2D-to-3D lifting approach is free from pixel-level details, we propose a novel geometry-aware data augmentation that allows us to infinitely generate diverse training examples from existing single-person trajectories. From extensive experiments on standard benchmarks, we verify that our model and data augmentation method achieve the state-of-the-art, not just on accuracy but also on smoothness. We also qualitatively demonstrate the effectiveness of our approach both on public benchmarks and with in-the-wild videos.์ปดํ“จํ„ฐ ๋น„์ „์— ๊ธฐ๋ฐ˜ํ•œ 3์ฐจ์› ์ž์„ธ ์ถ”์ •(3D Pose Estimation)์€ ๋งค์šฐ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์— ์‘์šฉ๋  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ํฐ ๊ฐ€์น˜๊ฐ€ ์žˆ๋‹ค. ์ตœ๊ทผ, ํŠธ๋žœ์Šคํฌ๋จธ(Transformer) ๋ชจ๋ธ ๊ธฐ๋ฐ˜์˜ ์‹œํ€€์Šค-์‹œํ€€์Šค(Sequence-tosequence) ๋ชจ๋ธ์ธ MixSTE [60] ์€ ๋‹จ์ผ ๊ฐ์ฒด(์‚ฌ๋žŒ) 3์ฐจ์› ์ž์„ธ ์ถ”์ •์—์„œ 2์ฐจ์› ์ž์„ธ๋กœ๋ถ€ํ„ฐ์˜ 3์ฐจ์› ์ž์„ธ ์ถ”์ •(2D-to-3D Lifting)์˜ ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์„ฑ๊ณต์ ์ธ ๊ฒฐ๊ณผ๋ฅผ ๊ฑฐ๋‘” ๋ฐ” ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด์˜ ํ™•์žฅ์œผ๋กœ์จ ๋‹ค์ค‘ ๊ฐ์ฒด 3์ฐจ์› ์ž์„ธ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃจ๋ฉฐ, ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ๋น„๊ตํ•ด ๋“ฑ์žฅํ•˜๋Š” ๊ฐ์ฒด๊ฐ„ ์ •๋ณด์˜ ์ƒํ˜ธ ์ฐธ์กฐ(Inter-Personal Attention) ๋ชจ๋“ˆ์„ ์ƒˆ๋กœ์ด ์ถ”๊ฐ€ํ•˜์˜€๋‹ค. ๋ชจ๋ธ ๊ตฌ์กฐ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ƒํ˜ธ ์ธ์ ‘ ํ”„๋ ˆ์ž„ ์ •๋ณด๋ฅผ ์ž์—ฐ์Šค๋Ÿฝ๊ฒŒ ์ฐธ์กฐํ•จ์œผ๋กœ์จ, ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ณ ์•ˆํ•œ ๋ชจ๋ธ์€ ์ƒํ˜ธ ๊ฐ€๋ ค์ง ํ˜„์ƒ์— ๊ฐ•์ธํ•œ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. ํ•˜์ง€๋งŒ, ๋‹ค์ค‘ ๊ฐ์ฒด 3์ฐจ์› ์ž์„ธ ์ถ”์ •์€ ๋ฐ์ดํ„ฐ ๋ถ€์กฑ ํ˜„์ƒ์ด๋ผ๋Š” ๊ณ ์งˆ์ ์ธ ๋ฌธ์ œ๋ฅผ ์ง€๋‹Œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ•๋ก ์€ ํ”ฝ์…€ ์ˆ˜์ค€์˜ ๋””ํ…Œ์ผ์—์„œ ๋ฒ—์–ด๋‚˜, 2์ฐจ์› ์ž์„ธ์™€ 3์ฐจ์› ์ž์„ธ ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ๋‹ค๋ฃจ๊ธฐ์—, ์ฃผ์–ด์ง„ ๋ฐ์ดํ„ฐ์™€ ์นด๋ฉ”๋ผ ํŒŒ๋ผ๋ฏธํ„ฐ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์‹ค์ƒ ๋ฌด์ œํ•œ์ ์œผ๋กœ ์ฆ๊ฐ•ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ•์ ์„ ์ง€๋‹Œ๋‹ค. ๋ณธ ๋ถ„์•ผ์—์„œ ์„ฑ๋Šฅ ์ธก์ • ๋ฐ ๋น„๊ต๋ฅผ ์œ„ํ•œ ๋Œ€ํ‘œ์ ์ธ ์‹คํ—˜์šฉ ๋ฐ์ดํ„ฐ์…‹์—์„œ ์„ฑ๋Šฅ์„ ์ธก์ •ํ•œ ๊ฒฐ๊ณผ, ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ณ ์•ˆํ•œ ๋ชจ๋ธ์€ ์ •ํ™•๋„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ถœ๋ ฅ ๊ฒฐ๊ณผ์˜ ๋ถ€๋“œ๋Ÿฌ์›€ ๋‘ ์ธก๋ฉด์—์„œ ๋ชจ๋‘ ์—ฌํƒ€ ๊ธฐ์กด ๋ชจ๋ธ๊ณผ ๋น„๊ตํ•ด ๊ฐ€์žฅ ํ›Œ๋ฅญํ•œ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. ๋‚˜์•„๊ฐ€, ํ…Œ์ŠคํŠธ์šฉ ๋ฐ์ดํ„ฐ์…‹ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‹ค์–‘ํ•œ ์‹œ์ค‘ ๋น„๋””์˜ค์—์„œ๋„ ํ›Œ๋ฅญํ•œ ์„ฑ๋Šฅ์„ ๋ณด์ž„์œผ๋กœ์จ ์—ฐ๊ตฌ์˜ ์ƒ์—…์  ๊ฐ€์น˜ ๋˜ํ•œ ์ž…์ฆํ•˜์˜€๋‹ค.Chapter 1. Introduction 1 Chapter 2. Related Work 5 Chapter 3. Problem Formulation and Notations 8 Chapter 4. The POTR-3D Model 9 Chapter 5. Geometry-Aware Data Augmentation 16 Chapter 6. Experiments 22 Chapter 7. Summary 35 Bibliography 37 Abstract in Korean 44์„

    A Study on the Effect of Navy CollaborativeLeadership on Organizational Effectiveness

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    Korea is currently faced with Chinaโ€™s military growth, denuclearization talks with North Korea, preparations against non-military transnational threats, and changes in the security environment where there is an increased use of peace-keeping forces. Furthermore, Korea must create a powerful military force that can be victorious by strengthening the efficiency of military organizations and future-orientation based on information science technologies, while also being reborn as a military that can defeat any threat. A key element to solve this imperative task is leadership. Military leadership today is changing from the push method to encourage subordinates to achieve the goal of the organization by exercising direct influence towards a pull method of setting examples as a leader for hard and difficult tasks so that subordinates can follow suit. It is shifting from being based on rank that uses authority by position as the main source of influence to being based on functions that use oneโ€™s operational functions as the source of influence. Trust, communication and the level of cooperation is closely associated within an organization. Due to globalization, emphasis on environment, spread of information technologies, and the shift to a scientific and social paradigm, todayโ€™s society emphasizes the importance of leadership within an organization. Recent leadership research trends are shifting from single leadership by individuals to shared leadership, and from position-based leaders to team work processes, and from competition with other departments to mutual cooperation, and from systematic leadership to flexible leadership. Collaborative leadership is the series of processes that forms trust among members, communicating horizontally, and delegating authorities to members during the course of decision-making and pursuing operations so that members can voluntarily participate in duties so that the leader can complete a given mission. Studies on collaborative leadership are being carried out in some foreign countries and excluding conceptual research at the naval leadership center and a handful of studies made in Koreaโ€™s administrative academic circles, there are very few research outcomes in Korea. Accordingly, in order to construct a theoretical basis through literary research, this study reviewed domestic and foreign research theses including collaborative leadership books published by the naval education institutes to explore the sub-components of collaborative leadership and analyzed its impact on organizational effectiveness (job satisfaction, Organizational Commitment) using statistical methods. Furthermore, in empirical research, surveys were administered to officers of naval ships and land units located in C to empirically analyze the impact of collaborative leadership on organizational effectiveness to verify the hypothesis through statistical analysis. When considering the special characteristics of military organizations, there must be a leadership theory targeting a military organization. The army has developed mission-based commands so that all commanding officers can lead missions within their given authority, but mission-based commands have not been theoretically established to fit naval missions and work environments. Naval missions are often conducted over-the-horizon and outside of visible ranges, and traditionally, battle functions under network systems and cooperative combat capacities with different echelons are given particular importance. Naval fleets are comprised of complex weapons systems and it is has a complex composition of various departments and personnel that are in charge of specialized functions and roles. Therefore, operation of naval vessels can also be successful only through organic cooperation of various specialized work competence systems and thus, I judged that it was necessary to conduct empirical research on the effectiveness of collaborative leadership to emphasize the critical importance of applying collaborative leadership in the navy. Hence, the purpose of this study is to examine the impact of collaborative leadership on organizational effectiveness and to empirically analyze sub-factors that affect organizational effectiveness. The results of this study can be summarized as follows. Hypothesis 1 is โ€˜Collaborative leadership should affect job satisfactionโ€™ and a more detailed Hypothesis 1-1 is โ€˜The level of procedural collaboration within an organization should affect the level of job satisfactionโ€™. Results of verification of this showed that procedural collaboration had a positive (+) impact on work satisfaction (ฮฒ=.532, p<.001) and the descriptive power was analyzed to be 28.2%, thus having the same results as the hypothesis, and therefore, Hypothesis 1-1 was adopted. Hypothesis 1-2 was โ€˜The level of cognitive collaboration within an organization should affect job satisfactionโ€™. Results of verification on this showed that cognitive collaboration had a positive (+) impact on job satisfaction (ฮฒ=.418, p<.001) and therefore, Hypothesis 1-2 was also adopted. Hypothesis 1-3 was โ€˜The level of structural collaboration within an organization should affect work satisfactionโ€™. Results of verification on this showed that structural collaboration had a positive (+) impact (ฮฒ=.485, p<.001) and its descriptive power was analyzed to be 23.4%, and since the results were consistent with the hypothesis, Hypothesis 1-3 was adopted. Hypothesis 2 wasโ€˜Collaborative leadership should affect organizational commitmentโ€™. The more detailed Hypothesis 2-1 was โ€˜The level of procedural collaboration within an organization should affect organizational commitmentโ€™. Results of verification on this showed that procedural collaboration had a positive (+) impact on Organizational Commitment (ฮฒ=.561, p<.001) and descriptive power on this was analyzed to be 31.1%, thus having results consistent with the hypothesis, and therefore, Hypothesis 2-1 was adopted. Hypothesis 2-2 was โ€˜The level of cognitive collaboration within an organization should affect organizational commitmentโ€™. Results of verification on this showed that cognitive collaboration had a positive (+) impact on organizational commitment (ฮฒ=.437, p<.001) and the descriptive power was analyzed to be 19.0% and results were consistent with the hypothesis, so Hypothesis 2-2 was also adopted. Hypothesis 2-3 was โ€˜The level of structural collaboration within an organization should affect organizational commitmentโ€™. Results of verification on this showed that structural collaboration had a positive (+) impact (ฮฒ=.485, p<.001) and the descriptive power for this was 20.3%, thus being consistent with the hypothesis, and therefore, Hypothesis 2-3 was also adopted. Among the sub-components of collaborative leadership, procedural collaboration had high impact on job satisfaction (R2=.282, p<.001) and organizational commitment (R2=.313, p<.001), and this shows that procedural collaboration is a very important component in applying collaborative leadership in naval organizations. Meanwhile, the level of cognitive cooperation was the component with the smallest impact on job satisfaction (R2=.173 p<.001) and organizational commitment (R2=.190, p<.001). It is evident that in order to overcome this, it is necessary to complete duties through assertive development of competencies such as continuous learning and education for individuals, while also providing opportunities to enhance the expertise of individuals. This study investigated the sub-components of collaborative leadership that stopped short as conceptual studies and sought after sub-components that included the concepts of trust, horizontal communication and delegation of authorities proposed by the naval leadership center. Furthermore, it empirically analyzed the relationship of effects between collaborative leadership and organizational effectiveness for military officers currently serving in the navy to provide basic data for practical use of collaborative leadership, thus making this study significant. Furthermore, the components of collaborative leadership were checked based on various definitions in preceding studies, while scales for measuring this were revised and supplemented to fit naval organizations to systematically verify the feasibility of the contents and composition, thus making this study significant. This study is practically the first study that set up the concept of collaborative leadership. It is anticipated that research on collaborative leadership fitting the characteristics of the navy will continue to expand in the future and such research results will contribute to the development of naval organizations.์ œ1์žฅ ์„œ ๋ก  1 ์ œ1์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  5 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• ๋ฐ ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 6 ์ œ2์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 7 ์ œ1์ ˆ ๋ฆฌ๋”์‹ญ ๊ฐœ๋… 7 1. ๋ฆฌ๋”์‹ญ์˜ ์ •์˜ 7 2. ๋ฆฌ๋”์‹ญ ์—ฐ๊ตฌ์˜ ๋ณ€์ฒœ ๊ณผ์ • 7 ์ œ2์ ˆ ํ˜‘๋ ฅ์ ๋ฆฌ๋”์‹ญ 10 1. ํ˜‘๋ ฅ์ ๋ฆฌ๋”์‹ญ์˜ ๊ฐœ๋… ๋ฐ ์ •์˜ 10 2. ํ˜‘๋ ฅ์ ๋ฆฌ๋”์‹ญ์˜ ์„ ํ–‰ ์—ฐ๊ตฌ 14 3. ํ˜‘๋ ฅ์ ๋ฆฌ๋”์‹ญ์˜ ๊ตฌ์„ฑ ์š”์†Œ 17 ์ œ3์ ˆ ํ˜‘๋ ฅ์ ๋ฆฌ๋”์‹ญ๊ณผ ์กฐ์ง์œ ํšจ์„ฑ 26 1. ์กฐ์ง์œ ํšจ์„ฑ์˜ ๊ฐœ๋… 26 2. ํ˜‘๋ ฅ์ ๋ฆฌ๋”์‹ญ๊ณผ ์ง๋ฌด๋งŒ์กฑ 30 3. ํ˜‘๋ ฅ์ ๋ฆฌ๋”์‹ญ๊ณผ ์กฐ์ง๋ชฐ์ž… 32 ์ œ3์žฅ ๊ตญ๋ฐฉํ™˜๊ฒฝ ๋ณ€ํ™”์™€ ํ•ด๊ตฐ๋ฆฌ๋”์‹ญ 34 ์ œ1์ ˆ ๊ตญ๋ฐฉํ™˜๊ฒฝ ๋ณ€ํ™” 34 1. ๊ตญ๋ฐฉ๊ฐœํ˜ ๊ฐœ์š” 34 2. ๊ตญ๋ฐฉํ™˜๊ฒฝ ๋ณ€ํ™” ์ถ”์„ธ 35 ์ œ2์ ˆ ๊ตฐ ๋ฆฌ๋”์‹ญ ๋ณ€ํ™” 41 1. ๊ตฐ ๋ฆฌ๋”์‹ญ์˜ ์ •์˜ 41 2. ๊ตฐ ๋ฆฌ๋”์‹ญ์˜ ํŠน์„ฑ 42 3. ๊ตฐ ๋ฆฌ๋”์‹ญ์˜ ๋ณ€ํ™” 43 4. ๊ตฐ ๋ฆฌ๋”์‹ญ ์—ฐ๊ตฌ ๊ฒฝํ–ฅ์˜ ๋ฌธ์ œ์  45 ์ œ3์ ˆ ํ•ด๊ตฐ ๋ฆฌ๋”์‹ญ์˜ ํŠน์„ฑ 47 1. ํ•ด๊ตฐ ์ž‘์ „ํ™˜๊ฒฝ์— ์ ํ•ฉํ•œ ๋ฆฌ๋”์‹ญ ํŠน์„ฑ 47 2. ํ•ด์–‘ํ™˜๊ฒฝ์— ์ ํ•ฉํ•œ ๋ฆฌ๋”์‹ญ ํŠน์„ฑ 48 3. ํ•ด๊ตฐ๋ฆฌ๋”์‹ญ ๊ต์œกํ˜„ํ™ฉ ๋ถ„์„ 50 ์ œ4์žฅ ์—ฐ๊ตฌ์˜ ์„ค๊ณ„ 52 ์ œ1์ ˆ ์—ฐ๊ตฌ๋ชจํ˜• ๋ฐ ๊ฐ€์„ค์˜ ์„ค์ • 52 1. ์—ฐ๊ตฌ๋ชจํ˜• ์„ค๊ณ„ 52 2. ๊ฐ€์„ค์„ค์ • 52 3. ๋ณ€์ˆ˜์˜ ์กฐ์ž‘์  ์ •์˜ 54 ์ œ5์žฅ ์‹ค์ฆ๋ถ„์„ 57 ์ œ1์ ˆ ์กฐ์‚ฌ์˜ ๊ฐœ์š” 57 1. ์„ค๋ฌธ์กฐ์‚ฌ์˜ ๊ฐœ์š” 57 2. ์ˆ˜์ง‘๋œ ์ž๋ฃŒ์˜ ํŠน์„ฑ 59 ์ œ2์ ˆ ํƒ€๋‹น์„ฑ ๋ฐ ์‹ ๋ขฐ๋„ ๊ฒ€์ฆ 62 1. ํƒ€๋‹น์„ฑ ๊ฒ€์ฆ 62 2. ์‹ ๋ขฐ์„ฑ ๊ฒ€์ฆ 68 ์ œ3์ ˆ ๊ฐ€์„ค์˜ ๊ฒ€์ฆ๊ฒฐ๊ณผ 69 1. ๊ฐ€์„ค๊ฒ€์ฆ ๊ฒฐ๊ณผ 69 2. ์ถ”๊ฐ€ ํ†ต๊ณ„๋ถ„์„ 76 ์ œ 6 ์žฅ ๊ฒฐ ๋ก  92 ์ œ1์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 92 ์ œ2์ ˆ ์—ฐ๊ตฌ์˜ ์‹œ์‚ฌ์  95 ์ œ3์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฐฉํ–ฅ 97 ๊ฐ์‚ฌ์˜ ๊ธ€ 98 ์ฐธ๊ณ ๋ฌธํ—Œ 99 ์„ค ๋ฌธ ์ง€ 113Docto

    The Prophylactic Impact of Low Molecular Weight Heparin on Occurrence of Venous Thromboembolism after Colorectal Cancer Resection

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    Purpose: In western society, prophylaxis for venous thromboembolism (VIE) is the standard treatment under colorectal surgery for colorectal cancer. However, the incidence of VIE after colorectal surgery and the effect of prophylactic methods are not well known in Korea. The aim of this study is to evaluate the incidence of VIE and assess the efficacy and safety of low molecular weight heparin (enoxaparin) after major colorectal surgery in Korean patients with compression stockings. Methods: From Jan. 2006 to Dec. 2008, 1,727 consecutive patients underwent major colorectal surgery. Thirty-six were excluded due to the therapeutic use of enoxaparin. A final number of 1,691 patients were included. Graduated compression stockings were used in all patients and 654 were perioperatively given enoxaparin. Only compression stocking group (group A) and compression stocking with enoxaparin group (group B) were compared in terms of VTE. The event of VIE within 6 months after surgery was counted by clinical symptoms, then imaging findings were used for confirmation. Results: Total VIE developed in 10 patients (0.6%). Three with deep vein thrombosis had pulmonary embolism. Two had only pulmonary embolism. The rates of VTE were not different between group A and B (0.8% vs. 0.3%, P=0.333). Also, postoperative major bleeding was not significantly different. However, postoperative transfusion was higher in group B (P<0.001). Conclusion: The incidence of VTE was very low after colorectal surgery in Korean patients with compression stockings. The additional use of enoxaparin for colorectal cancer patients with compression stockings seems to have little benefit for VIE prophylaxis. ์ˆ˜์ˆ  ํ›„์— ๋ฐœ์ƒ๋˜๋Š” ์ •๋งฅ ํ˜ˆ์ „์ƒ‰์ „์ฆ์€ ์ž„์ƒ์ ์œผ๋กœ ์ค‘์š”ํ•˜๋ฉด์„œ๋„ ์˜ˆ๋ฐฉ ๊ฐ€๋Šฅํ•œ ํ•ฉ๋ณ‘์ฆ์œผ๋กœ ์•Œ๋ ค์ ธ ์™”๋‹ค. ์„œ์–‘์—์„œ๋Š” ์ผ๋ฐ˜ ์ˆ˜์ˆ ์˜ ๊ฒฝ์šฐ ํ•˜์ง€ ์‹ฌ๋ถ€ ์ •๋งฅํ˜ˆ์ „์ฆ ๋ฐœ์ƒ๋ฅ ์ด 20%์ •๋„์ด๋ฉฐ ๋Œ€์žฅ์ ˆ์ œ์ˆ ์˜ ๊ฒฝ์šฐ๋Š” 30% ์ •๋„๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค.(1)๋ฐ˜๋ฉด, ๋™์–‘์ธ๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์—ฐ๊ตฌ๋“ค์—์„œ ์ˆ˜์ˆ  ํ›„ ์ž„์ƒ์ ์œผ๋กœ ์ง„๋‹จ๋œ ํ•˜์ง€ ์‹ฌ๋ถ€ ์ •๋งฅํ˜ˆ์ „์ฆ์€ 0.27%์˜ ํ™˜์ž์—์„œ ๋ฐœ์ƒํ•˜์˜€๊ณ , ํ๋™๋งฅ ์ƒ‰์ „์ฆ์€ 0.8% ์ด๋‚ด๋กœ ์„œ์–‘์— ๋น„ํ•ด ๋‚ฎ๊ฒŒ ๋ณด๊ณ ๋˜์—ˆ๋‹ค.(2-5) ํŠนํžˆ, ๋Œ€์žฅ์•” ์ˆ˜์ˆ ์ด ๋‹ค๋ฅธ ์ผ๋ฐ˜ ๊ฐœ๋ณต ์ˆ˜์ˆ ์— ๋น„ํ•ด์„œ ์ •๋งฅ ํ˜ˆ์ „์ƒ‰์ „์ฆ ๋ฐœ์ƒ์ด ๋งŽ์€ ๊ฒƒ์œผ๋กœ ๋ณด๊ณ ๋˜๊ณ  ์žˆ๊ณ , ํ๋™๋งฅ ์ƒ‰์ „์ฆ์˜ ๋นˆ๋„ ๋˜ํ•œ ๋†’๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ๋‹ค.(6)์•„์‹œ์•„์—์„œ ๋Œ€์žฅ์•” ์ˆ˜์ˆ  ํ›„์— ์ž„์ƒ์ ์œผ๋กœ ์ง„๋‹จ๋œ ํ•˜์ง€ ์‹ฌ๋ถ€ ์ •๋งฅํ˜ˆ์ „์ฆ์˜ ๋นˆ๋„๋Š” 4.7%, ๊ทธ๋ฆฌ๊ณ  ํ๋™๋งฅ ์ƒ‰์ „์ฆ์€ 1.7โˆผ3.8%๋กœ ๋ณด๊ณ ๋˜์—ˆ์œผ๋ฉฐ,(7,8) ์„œ์–‘์—์„œ๋Š” ์ˆ˜์ˆ  ํ›„ ํ•˜์ง€ ์‹ฌ๋ถ€์ •๋งฅํ˜ˆ์ „์ฆ์ด 3.1โˆผ51.7%์— ์ด๋ฅธ๋‹ค๋Š” ๋ณด๊ณ ๊ฐ€ ์žˆ๋‹ค.(9-11)์•„์ง๊นŒ์ง€ ๊ตญ๋‚ด์—์„œ๋Š” ๋Œ€์žฅ์ ˆ์ œ์ˆ ํ›„์˜ ์ •๋งฅ ํ˜ˆ์ „์ƒ‰์ „์ฆ์˜ ๋ฐœ์ƒ๋ฅ ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ๊ฑฐ์˜ ์—†์—ˆ์œผ๋ฉฐ, ์ €๋ถ„์ž๋Ÿ‰ ํ—คํŒŒ๋ฆฐ(low molecular weight heparin)์„ ๋Œ€์žฅ์ ˆ์ œ์ˆ ์„ ์‹œํ–‰ํ•˜๋Š” ๋ชจ๋“  ํ™˜์ž์—์„œ ํˆฌ์—ฌํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•œ ์˜๊ฒฌ๋„ ์ •๋ฆฝ๋˜์ง€ ์•Š์€ ์ƒํƒœ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋Œ€์žฅ์•”์œผ๋กœ ๋Œ€์žฅ์ ˆ์ œ์ˆ ์„ ์‹œํ–‰ํ•œ ํ™˜์ž๋“ค์„ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜์ˆ  ์‹œ ์••๋ฐ•์Šคํƒ€ํ‚น(graduated compression stocking)๋งŒ ์‚ฌ์šฉํ•œ ๊ตฐ๊ณผ ์••๋ฐ•์Šคํƒ€ํ‚น๊ณผ ์ €๋ถ„์ž๋Ÿ‰ ํ—คํŒŒ๋ฆฐ์„ ๊ฐ™์ด ์‚ฌ์šฉํ•œ ๊ตฐ์œผ๋กœ ๋‚˜๋ˆ„์–ด์„œ ์ •๋งฅ ํ˜ˆ์ „์ƒ‰์ „์ฆ์˜ ๋ฐœ์ƒ ๋นˆ๋„์™€ ์ €๋ถ„์ž๋Ÿ‰ ํ—คํŒŒ๋ฆฐ์˜ ํšจ์šฉ์„ฑ ๋ฐ ์ถœํ˜ˆ ์œ„ํ—˜์— ๋Œ€ํ•ด์„œ ๋น„๊ตํ•ด ๋ณด์•˜๋‹ค.Cheung HYS, 2008, ASIAN J SURG, V31, P63Beekman R, 2006, CAN J SURG, V49, P197Bauduer F, 2005, MOL GENET METAB, V86, P91, DOI 10.1016/j.ymgme.2005.04.002Almawi W, 2005, J THROMB THROMBOLYS, V19, P189, DOI 10.1007/s11239-005-1313-xCheuk BLY, 2004, BRIT J SURG, V91, P424, DOI 10.1002/bjs.4454Otten HMMB, 2004, ARCH INTERN MED, V164, P190Kim YH, 2003, J BONE JOINT SURG BR, V85B, P661, DOI 10.1302/0301-620X.85B5.14012ANDERSON FA, 2003, CIRCULATION, V107, P19WILLEJORGENSEN P, 2003, COCHRANE DB SYST REV, DOI DOI 10.1002/14651858.CD001217CHUNG CC, 2003, COLORECTAL DIS, V5, P528GREENE FL, 2002, AJCC CANC STAGING MALee FY, 2001, ANZ J SURG, V71, P637McLeod RS, 2001, ANN SURG, V233, P438Marshall NJ, 2000, AUST NZ J SURG, V70, P6392000, DIS COLON RECTUM, V43, P1037Ho YH, 1999, DIS COLON RECTUM, V42, P196Schwenk W, 1998, SURG ENDOSC-ULTRAS, V12, P7Yoo MC, 1997, INT ORTHOP, V21, P399HO YH, 1996, DEEP VENOUS THROMBOS, P38REES DC, 1995, LANCET, V346, P1133IDO K, 1995, SURG ENDOSC-ULTRAS, V9, P310GREM JL, 1994, J CLIN ONCOL, V12, P560KUM CK, 1993, ANN ACAD MED SINGAP, V22, P895HUBER O, 1992, ARCH SURG-CHICAGO, V127, P310KIM YH, 1988, J BONE JOINT SURG AM, V70A, P878TORNGREN S, 1982, DIS COLON RECTUM, V25, P563TSAKOK FH, 1974, ANN AC AD MED SINGAP, V3, P399HWANG WS, 1968, SINGAPORE MED J, V9, P276TINCKLER LF, 1964, BRIT MED J, V1, P502

    ์†Œ๋น„์ž์˜ ๊ฐ€๊ฒฉ๋ฐ˜์‘์„ฑ์„ ๊ณ ๋ คํ•œ ํ”ผํฌ์š”๊ธˆ์ œ ํŒŒ๋ผ๋ฏธํ„ฐ ์„ค๊ณ„

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2016. 2. ์œค์šฉํƒœ.Recent introduction of deregulation in the power industries offers electricity retailers the chance to make profit through electricity retail business. Accordingly, the retailers need to establish proper strategies for designing and implementing their pricing schemes to grab the chance. Many other pricing schemes can be used for the purposes, however, critical peak pricing (CPP) is chosen in this thesis as a tool for the profit maximization for the following reasons. One, to our knowledge, it is less highlighted compared to other retail pricing schemes despite of its advantages. The other, it contains several designable components for the retailer perspective, for example, base and peak rates, number of events, and event duration. In terms of the profit maximization, this thesis aims at presenting several analyses and intuitive guides for designing CPP scheme based on the price responsiveness of customers. The guides and designs include selection of the CPP parameters such as peak rate, number of events, and event duration. To achieve the goals, we first investigated how these parameters affect profit of the retailers. In this process, simple price-response model is adopted for the analyses. The results of the analyses allow us to provide the following three important guidelines. First, the optimal peak rate is approximately inversely proportional to price responsiveness of customers. Second, the optimal peak rate does not change a lot as the number of events varies. Third, the optimal peak rate for single critical event case can be safely used even in case of the multi-events situations in terms of the profit. Fourth, we can ensure existence of a minimum number of events not to lose profit compared the uniform pricing or real-time pricing schemes. Finally, it is a reasonable choice to set event duration as an hour and number of the events as equal time period to total events hour, instead of evaluating each combination of two variables. They are verified by the illustrative examples. In addition to examining the profit maximization of the retailers, we also explored the benefits that a CPP scheme would afford customers. The results showed that CPP could be advantageous to both customers and the retailer if the customers were sufficiently responsive to price fluctuations. It should be emphasized that such a win-win situation may be only achieved if the CPP scheme is properly designedthis, in turn, requires an analysis of the effects of the CPP parameters on profit and a methodology for selecting appropriate parameter values, which were presented in this thesis. On the other hands, we pointed out that CPP is not free from the payback phenomenon: a rise in consumption after a critical event, like other time-varying retail pricing schemes. Also, we showed that this payback has a negative effect on profits and thus must be appropriately considered when designing a CPP scheme by the illustrative example which proves that CPP scheme which is designed without considering payback may not be an optimal anymore if the payback takes place. However, few studies have examined CPP scheme design considering payback. This thesis thus characterizes payback using three parameters (duration, amount, and pattern) and examines payback effects on the optimal schedule of critical events and on the optimal peak rate for two specific payback patterns. All the analysis is verified through numerical simulations. The results demonstrate the need to properly consider payback parameters when designing a profit-maximizing CPP scheme.Chapter 1. Introduction 1 1.1 MOTIVATIONS AND PURPOSES 1 1.2 LITERATURE SURVEY AND CONTRIBUTIONS 6 1.3 THESIS ORGANIZATION 9 Chapter 2. Backgrounds 10 2.1 STRUCTURE OF THE ELECTRICITY WHOLESALE MARKET AND RETAIL SECTOR 10 2.1.1 ENTITIES IN THE ELECTRICITY RETAIL SECTOR 10 2.1.2 ASSUMPTIONS ON ELECTRICITY MARKET 12 2.2 CRITICAL EVENTS SCHEDULING PROBLEM OF THE LSE 14 2.3 PRICE RESPONSIVENESS MODEL OF THE CUSTOMERS 17 Chapter 3. Profit-Maximizing CPP Scheme considering Customers Price-Responsiveness 20 3.1 PROFIT INDEX 20 3.2 SELECTION OF THE OPTIMAL PEAK RATE 22 3.2.1 ANALYSIS FOR SIMPLIFIED CASE 22 3.2.2 EXPANSION FOR GENERALIZED CASE 23 3.3 SELECTION OF THE NUMBER OF EVENTS 27 3.3.1 DETERMINING MINIMUM NUMBER OF THE EVENTS 27 3.3.2 ANALYSIS ON MINIMUM NUMBER OF THE EVENTS 31 3.3.3 REMARK ON MAXIMUM NUMBER OF THE EVENTS 32 3.4 SELECTION OF THE EVENTS DURATION 33 Chapter 4. Payback Effects on CPP Implementation 35 4.1 CHARACTERIZING PAYBACK PHENOMENON 35 4.1.1 PAYBACK RATIO 36 4.1.2 PAYBACK FUNCTION 37 4.2 PAYBACK EFFECTS ON CPP DESIGN 40 4.2.1 PAYBACK EFFECTS ON EVENT SCHEDULING PROBLEM 40 4.2.2 PAYBACK EFFECTS ON OPTIMAL PEAK RATE 42 Chapter 5. Illustrative Examples 48 5.1 DESIGNING PROFIT-MAXIMIZING CPP SCHEME 48 5.1.1 CONFIGURATIONS 48 5.1.2 EVENTS SCHEDULING PROBLEM AND PROFIT INDEX 50 5.1.3 OPTIMAL PEAK RATE 51 5.1.4 NUMBER OF THE EVENTS 55 5.1.5 EVENT DURATION 59 5.1.6 DISCUSSION ON THE BENEFIT OF CUSTOMERS 61 5.2 RESULTS IN PAYBACK PHENOMENON 63 5.2.1 CONFIGURATIONS 63 5.2.2 PAYBACK EFFECTS ON THE OPTIMAL EVENT SCHEDULE 64 5.2.3 PAYBACK EFFECTS ON THE OPTIMAL PEAK RATE 65 Chapter 6. Conclusions and Future Works 70 6.1 CONCLUSIONS 70 6.2 FUTURE WORKS 72 Appendix 74 A. NOVEL ALGORITHM FOR EVENTS SCHEDULING PROBLEM 74 B. NOMENCLATURE 76 B.1 VARIABLES 76 B.2 LIST OF ABBREVIATIONS 79 BIBLIOGRPHY 81 ๋…ผ๋ฌธ ์ดˆ๋ก 89Docto

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