30 research outputs found

    ์Œ์•…์  ์š”์†Œ์— ๋Œ€ํ•œ ์กฐ๊ฑด๋ถ€ ์ƒ์„ฑ์˜ ๊ฐœ์„ ์— ๊ด€ํ•œ ์—ฐ๊ตฌ: ํ™”์Œ๊ณผ ํ‘œํ˜„์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(๋””์ง€ํ„ธ์ •๋ณด์œตํ•ฉ์ „๊ณต), 2023. 2. ์ด๊ต๊ตฌ.Conditional generation of musical components (CGMC) creates a part of music based on partial musical components such as melody or chord. CGMC is beneficial for discovering complex relationships among musical attributes. It can also assist non-experts who face difficulties in making music. However, recent studies for CGMC are still facing two challenges in terms of generation quality and model controllability. First, the structure of the generated music is not robust. Second, only limited ranges of musical factors and tasks have been examined as targets for flexible control of generation. In this thesis, we aim to mitigate these two challenges to improve the CGMC systems. For musical structure, we focus on intuitive modeling of musical hierarchy to help the model explicitly learn musically meaningful dependency. To this end, we utilize alignment paths between the raw music data and the musical units such as notes or chords. For musical creativity, we facilitate smooth control of novel musical attributes using latent representations. We attempt to achieve disentangled representations of the intended factors by regularizing them with data-driven inductive bias. This thesis verifies the proposed approaches particularly in two representative CGMC tasks, melody harmonization and expressive performance rendering. A variety of experimental results show the possibility of the proposed approaches to expand musical creativity under stable generation quality.์Œ์•…์  ์š”์†Œ๋ฅผ ์กฐ๊ฑด๋ถ€ ์ƒ์„ฑํ•˜๋Š” ๋ถ„์•ผ์ธ CGMC๋Š” ๋ฉœ๋กœ๋””๋‚˜ ํ™”์Œ๊ณผ ๊ฐ™์€ ์Œ์•…์˜ ์ผ๋ถ€๋ถ„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋‚˜๋จธ์ง€ ๋ถ€๋ถ„์„ ์ƒ์„ฑํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ์ด ๋ถ„์•ผ๋Š” ์Œ์•…์  ์š”์†Œ ๊ฐ„ ๋ณต์žกํ•œ ๊ด€๊ณ„๋ฅผ ํƒ๊ตฌํ•˜๋Š” ๋ฐ ์šฉ์ดํ•˜๊ณ , ์Œ์•…์„ ๋งŒ๋“œ๋Š” ๋ฐ ์–ด๋ ค์›€์„ ๊ฒช๋Š” ๋น„์ „๋ฌธ๊ฐ€๋“ค์„ ๋„์šธ ์ˆ˜ ์žˆ๋‹ค. ์ตœ๊ทผ ์—ฐ๊ตฌ๋“ค์€ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜์—ฌ CGMC ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ์„ ๋†’์—ฌ์™”๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋“ค์—๋Š” ์•„์ง ์ƒ์„ฑ ํ’ˆ์งˆ๊ณผ ์ œ์–ด๊ฐ€๋Šฅ์„ฑ ์ธก๋ฉด์—์„œ ๋‘ ๊ฐ€์ง€์˜ ํ•œ๊ณ„์ ์ด ์žˆ๋‹ค. ๋จผ์ €, ์ƒ์„ฑ๋œ ์Œ์•…์˜ ์Œ์•…์  ๊ตฌ์กฐ๊ฐ€ ๋ช…ํ™•ํ•˜์ง€ ์•Š๋‹ค. ๋˜ํ•œ, ์•„์ง ์ข์€ ๋ฒ”์œ„์˜ ์Œ์•…์  ์š”์†Œ ๋ฐ ํ…Œ์Šคํฌ๋งŒ์ด ์œ ์—ฐํ•œ ์ œ์–ด์˜ ๋Œ€์ƒ์œผ๋กœ์„œ ํƒ๊ตฌ๋˜์—ˆ๋‹ค. ์ด์— ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” CGMC์˜ ๊ฐœ์„ ์„ ์œ„ํ•ด ์œ„ ๋‘ ๊ฐ€์ง€์˜ ํ•œ๊ณ„์ ์„ ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ, ์Œ์•… ๊ตฌ์กฐ๋ฅผ ์ด๋ฃจ๋Š” ์Œ์•…์  ์œ„๊ณ„๋ฅผ ์ง๊ด€์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๋Š” ๋ฐ ์ง‘์ค‘ํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ๋ž˜ ๋ฐ์ดํ„ฐ์™€ ์Œ, ํ™”์Œ๊ณผ ๊ฐ™์€ ์Œ์•…์  ๋‹จ์œ„ ๊ฐ„ ์ •๋ ฌ ๊ฒฝ๋กœ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ชจ๋ธ์ด ์Œ์•…์ ์œผ๋กœ ์˜๋ฏธ์žˆ๋Š” ์ข…์†์„ฑ์„ ๋ช…ํ™•ํ•˜๊ฒŒ ๋ฐฐ์šธ ์ˆ˜ ์žˆ๋„๋ก ํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ์ž ์žฌ ํ‘œ์ƒ์„ ํ™œ์šฉํ•˜์—ฌ ์ƒˆ๋กœ์šด ์Œ์•…์  ์š”์†Œ๋“ค์„ ์œ ์—ฐํ•˜๊ฒŒ ์ œ์–ดํ•˜๊ณ ์ž ํ•œ๋‹ค. ํŠนํžˆ ์ž ์žฌ ํ‘œ์ƒ์ด ์˜๋„๋œ ์š”์†Œ์— ๋Œ€ํ•ด ํ’€๋ฆฌ๋„๋ก ํ›ˆ๋ จํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋น„์ง€๋„ ํ˜น์€ ์ž๊ฐ€์ง€๋„ ํ•™์Šต ํ”„๋ ˆ์ž„์›Œํฌ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ž ์žฌ ํ‘œ์ƒ์„ ์ œํ•œํ•˜๋„๋ก ํ•œ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์—์„œ๋Š” CGMC ๋ถ„์•ผ์˜ ๋Œ€ํ‘œ์ ์ธ ๋‘ ํ…Œ์Šคํฌ์ธ ๋ฉœ๋กœ๋”” ํ•˜๋ชจ๋‚˜์ด์ œ์ด์…˜ ๋ฐ ํ‘œํ˜„์  ์—ฐ์ฃผ ๋ Œ๋”๋ง ํ…Œ์Šคํฌ์— ๋Œ€ํ•ด ์œ„์˜ ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฒ€์ฆํ•œ๋‹ค. ๋‹ค์–‘ํ•œ ์‹คํ—˜์  ๊ฒฐ๊ณผ๋“ค์„ ํ†ตํ•ด ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•๋ก ์ด CGMC ์‹œ์Šคํ…œ์˜ ์Œ์•…์  ์ฐฝ์˜์„ฑ์„ ์•ˆ์ •์ ์ธ ์ƒ์„ฑ ํ’ˆ์งˆ๋กœ ํ™•์žฅํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ์‹œ์‚ฌํ•œ๋‹ค.Chapter 1 Introduction 1 1.1 Motivation 5 1.2 Definitions 8 1.3 Tasks of Interest 10 1.3.1 Generation Quality 10 1.3.2 Controllability 12 1.4 Approaches 13 1.4.1 Modeling Musical Hierarchy 14 1.4.2 Regularizing Latent Representations 16 1.4.3 Target Tasks 18 1.5 Outline of the Thesis 19 Chapter 2 Background 22 2.1 Music Generation Tasks 23 2.1.1 Melody Harmonization 23 2.1.2 Expressive Performance Rendering 25 2.2 Structure-enhanced Music Generation 27 2.2.1 Hierarchical Music Generation 27 2.2.2 Transformer-based Music Generation 28 2.3 Disentanglement Learning 29 2.3.1 Unsupervised Approaches 30 2.3.2 Supervised Approaches 30 2.3.3 Self-supervised Approaches 31 2.4 Controllable Music Generation 32 2.4.1 Score Generation 32 2.4.2 Performance Rendering 33 2.5 Summary 34 Chapter 3 Translating Melody to Chord: Structured and Flexible Harmonization of Melody with Transformer 36 3.1 Introduction 36 3.2 Proposed Methods 41 3.2.1 Standard Transformer Model (STHarm) 41 3.2.2 Variational Transformer Model (VTHarm) 44 3.2.3 Regularized Variational Transformer Model (rVTHarm) 46 3.2.4 Training Objectives 47 3.3 Experimental Settings 48 3.3.1 Datasets 49 3.3.2 Comparative Methods 50 3.3.3 Training 50 3.3.4 Metrics 51 3.4 Evaluation 56 3.4.1 Chord Coherence and Diversity 57 3.4.2 Harmonic Similarity to Human 59 3.4.3 Controlling Chord Complexity 60 3.4.4 Subjective Evaluation 62 3.4.5 Qualitative Results 67 3.4.6 Ablation Study 73 3.5 Conclusion and Future Work 74 Chapter 4 Sketching the Expression: Flexible Rendering of Expressive Piano Performance with Self-supervised Learning 76 4.1 Introduction 76 4.2 Proposed Methods 79 4.2.1 Data Representation 79 4.2.2 Modeling Musical Hierarchy 80 4.2.3 Overall Network Architecture 81 4.2.4 Regularizing the Latent Variables 84 4.2.5 Overall Objective 86 4.3 Experimental Settings 87 4.3.1 Dataset and Implementation 87 4.3.2 Comparative Methods 88 4.4 Evaluation 88 4.4.1 Generation Quality 89 4.4.2 Disentangling Latent Representations 90 4.4.3 Controllability of Expressive Attributes 91 4.4.4 KL Divergence 93 4.4.5 Ablation Study 94 4.4.6 Subjective Evaluation 95 4.4.7 Qualitative Examples 97 4.4.8 Extent of Control 100 4.5 Conclusion 102 Chapter 5 Conclusion and Future Work 103 5.1 Conclusion 103 5.2 Future Work 106 5.2.1 Deeper Investigation of Controllable Factors 106 5.2.2 More Analysis of Qualitative Evaluation Results 107 5.2.3 Improving Diversity and Scale of Dataset 108 Bibliography 109 ์ดˆ ๋ก 137๋ฐ•

    ๋Œ€์žฅ์•” ์ข…์–‘๋ฉด์—ญ๋ฏธ์„ธํ™˜๊ฒฝ์— ๋Œ€ํ•œ ๋ฉด์—ญ์กฐ์งํ™”ํ•™์—ผ์ƒ‰ ์Šฌ๋ผ์ด๋“œ ์ด๋ฏธ์ง€ ๋ถ„์„ ๊ธฐ๋ฐ˜์˜ ์ •๋Ÿ‰์  ๊ณ ์ฐฐ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2021. 2. ๊ฐ•๊ฒฝํ›ˆ.Purpose: Despite the well-known prognostic value of the tumorโ€“immune microenvironment (TIME) in colorectal cancers (CRCs), objective and readily applicable methods for quantifying tumor-infiltrating lymphocytes (TIL) and the tumorโ€“stroma ratio (TSR) are not yet available. Experimental Design: We established an open-source software based analytic pipeline for quantifying TILs and the TSR from whole-slide images obtained after CD3 and CD8 immunohistochemical staining. Using random forest classifiers, the method separately quantified intraepithelial TILs (iTIL) and stromal TILs (sTIL). We applied this method to discovery and validation cohorts of 578 and 283 stage III or high-risk stage II CRC patients, respectively, who were subjected to curative surgical resection and oxlaliplatin-based adjuvant chemotherapy. Results: Automatic quantification of iTILs and sTILs showed a moderate concordance with that obtained after visual inspection by pathologists. The K-meansโ€“based consensus clustering of 197 TIME parameters that showed robustness against variations in tumor area annotation caused CRCs to be grouped into five distinctive subgroups, reminiscent of those for consensus molecular subtypes (CMS1-4 and mixed/intermediate group). In accordance with the original CMS report, the CMS4-like subgroup (cluster 4) was significantly associated with a worse 5-year relapse-free survival and proved to be an independent prognostic factor. The clinicopathologic and prognostic features of the TIME subgroups were reproduced in an independent validation cohort. Conclusions: Machine-learningโ€“based analysis of whole-slide histopathologic images can be useful for extracting quantitative information about the TIME. This information can classify CRCs into clinicopathologically relevant subgroups without performing molecular analyses of the tumors.์ข…์–‘๋ฉด์—ญ๋ฏธ์„ธํ™˜๊ฒฝ(Tumor-immune microenvironment)์ด ๋Œ€์žฅ์•”์—์„œ ์ค‘์š”ํ•œ ์˜ˆํ›„์ธ์ž๋ผ๋Š” ์‚ฌ์‹ค์€ ์ด์ „๋ถ€ํ„ฐ ์ž˜ ์•Œ๋ ค์ ธ ์žˆ์—ˆ์ง€๋งŒ, ์ข…์–‘์นจ์œค๋ฆผํ”„๊ตฌ(Tumor-infiltrating lymphocyte, TIL)์™€ ์ข…์–‘ ๋‚ด ๊ธฐ์งˆ ๋ถ„์œจ (tumor-stroma ratio, TSR)์— ๋Œ€ํ•œ ๊ฐ๊ด€์ ์ด๊ณ ๋„ ๊ฐ„๋‹จํ•œ ์ธก์ •๋ฒ•์€ ์ง€๊ธˆ๊นŒ์ง€ ๋ฐœํ‘œ๋œ ๋ฐ” ์—†์—ˆ๋‹ค. ์ด์— ์šฐ๋ฆฌ๋Š” ์ข…์–‘ ์กฐ์ง์— ๋Œ€ํ•œ CD3, CD8 ๋ฉด์—ญ์กฐ์งํ™”ํ•™์—ผ์ƒ‰ ์Šฌ๋ผ์ด๋“œ์˜ ์ „์ฒด ์ด๋ฏธ์ง€๋กœ๋ถ€ํ„ฐ TIL๊ณผ TSR์„ ์ •๋Ÿ‰ํ•  ์ˆ˜ ์žˆ๋Š” ๊ณต๊ฐœ์†Œํ”„ํŠธ์›จ์–ด ๊ธฐ๋ฐ˜์˜ ๋ถ„์„ ํŒŒ์ดํ”„๋ผ์ธ์„ ๊ตฌ์ถ•ํ•˜์˜€๋‹ค. ๋Œ€ํ‘œ์  ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฒ•์ธ ๋žœ๋คํฌ๋ ˆ์ŠคํŠธ (Random forest)๋ฅผ ์ด์šฉํ•˜์—ฌ ์ฃผ์–ด์ง„ ์ด๋ฏธ์ง€ ์ƒ์—์„œ ์ข…์–‘๊ณผ ๊ธฐ์งˆ์„ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๊ณ , ํ•œ ํ™˜์ž ๋‹น TIL๊ณผ TSR์— ๋Œ€ํ•œ 208์ข…์˜ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ์ด ๋ถ„์„๊ธฐ๋ฒ•์„ ์„œ์šธ๋Œ€ํ•™๊ต๋ณ‘์›์—์„œ 2005๋…„๋ถ€ํ„ฐ 2012๋…„ ์‚ฌ์ด์— ๋Œ€์žฅ์•” ์ˆ˜์ˆ ์„ ๋ฐ›๊ณ  2๊ธฐ ๊ณ ์œ„ํ—˜๊ตฐ ๋˜๋Š” 3๊ธฐ๋กœ ์ง„๋‹จ๋˜์–ด ์˜ฅ์‚ด๋ฆฌํ”Œ๋ผํ‹ด(oxaliplatin) ๊ธฐ๋ฐ˜์˜ ํ•ญ์•”์น˜๋ฃŒ๋ฅผ ๋ฐ›์€ 578๋ช…์˜ ํ™˜์ž๊ตฐ์— ์ ์šฉํ•˜์˜€๊ณ , 208์ข…์˜ ํŒŒ๋ผ๋ฏธํ„ฐ ์ค‘ ๋ฐ˜๋ณต ๋ถ„์„์—๋„ ๊ฐ’์ด ์‹ฌํ•˜๊ฒŒ ํ”๋“ค๋ฆฌ์ง€ ์•Š๋Š” 197์ข…์˜ ํŒŒ๋ผ๋ฏธํ„ฐ์— ๋Œ€ํ•œ ๊ตฐ์ง‘๋ถ„์„ (Clustering analysis)์„ ์‹œํ–‰ํ•˜์—ฌ 578๋ช…์˜ ํ™˜์ž๋“ค์„ ๋‹ค์„ฏ ์•„ํ˜•์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ๊ฐ ์•„ํ˜•๋“ค์˜ ์ž„์ƒ๋ณ‘๋ฆฌํ•™์  ํŠน์„ฑ์ด ๋Œ€์žฅ์•”์˜ ๋ถ„์ž์  ์•„ํ˜•์œผ๋กœ ๊ธฐ์กด์— ์ •๋ฆฝ๋˜์–ด ์žˆ๋Š” consensus molecular subtype (CMS)์˜ ๊ฐ ์•„ํ˜•๋“ค์˜ ๊ทธ๊ฒƒ๊ณผ 1:1 ๋Œ€์‘ ๊ด€๊ณ„๋ฅผ ๋ณด์ธ๋‹ค๋Š” ์‚ฌ์‹ค์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. CMS ์•„ํ˜• ์ค‘์—์„œ๋Š” ์กฐ์ง ๋‚ด ์„ฌ์œ ํ™” ์ •๋„๊ฐ€ ์‹ฌํ•œ ๋„ค๋ฒˆ์งธ ์•„ํ˜•์ด ๊ฐ€์žฅ ๋‚˜์œ ์˜ˆํ›„๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ์—ˆ๋Š”๋ฐ, ๋ณธ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์—์„œ๋„ ์กฐ์ง ๋‚ด ์„ฌ์œ ํ™” ์ •๋„๊ฐ€ ์‹ฌํ•œ ์•„ํ˜•์ด ๋‚˜์œ 5๋…„ ๋ฌด์žฌ๋ฐœ ์ƒ์กด์œจ (relapse-free survival)๊ณผ ์œ ์˜๋ฏธํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€๊ณ  ์ด๊ฒƒ์ด TNM ๋ณ‘๊ธฐ ๋ฐ ์ข…์–‘ ๋ถ„ํ™”๋„์— ๋Œ€ํ•œ ๋ณด์ • ํ›„์—๋„ ๋…๋ฆฝ์  ์˜ˆํ›„์˜ˆ์ธก์ธ์ž๋กœ ์ž‘์šฉํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ์ด๋Ÿฌํ•œ ์ž„์ƒ๋ณ‘๋ฆฌํ•™์  ํŠน์„ฑ ๋ฐ ์˜ˆํ›„์  ํŠน์„ฑ์ด ๋ถ„๋‹น์„œ์šธ๋Œ€ํ•™๊ต๋ณ‘์›์—์„œ 2007๋…„๋ถ€ํ„ฐ 2012๋…„ ์‚ฌ์ด์— ๋ชจ์ง‘๋œ 283๋ช…์˜ ๋…๋ฆฝ์  ํ™˜์ž๊ตฐ์—์„œ๋„ ์žฌํ˜„๋จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Š” ๊ธฐ๊ณ„ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ์กฐ์ง ๋ณ‘๋ฆฌ ์ด๋ฏธ์ง€ ๋ถ„์„์ด ์ข…์–‘๋ฉด์—ญ๋ฏธ์„ธํ™˜๊ฒฝ์— ๋Œ€ํ•œ ์ •๋Ÿ‰์  ์ •๋ณด๋ฅผ ์–ป๊ธฐ ์œ„ํ•œ ์œ ์šฉํ•œ ๋ฐฉ์‹์ž„์„ ํ™•์ธํ•œ ๊ฒƒ์ด๋ฉฐ, ์ด๋Ÿฌํ•œ ์ •๋Ÿ‰์  ์ •๋ณด๋ฅผ ์ด์šฉํ•˜๋ฉด ๋ถ„์ž์ƒ๋ฌผํ•™์  ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์ง€ ์•Š๊ณ ๋„ ๋Œ€์žฅ์•” ํ™˜์ž๋“ค์„ ์ž„์ƒ์ ์œผ๋กœ ์œ ์˜๋ฏธํ•œ ์•„ํ˜•์œผ๋กœ ๋ถ„๋ฅ˜ํ•  ์ˆ˜ ์žˆ์Œ์„ ์ž…์ฆํ•œ ๊ฒƒ์ด๋ผ ํ•  ์ˆ˜ ์žˆ๋‹ค.Abstract i Table of Contents iii Chapter 1. Introduction 1 Chapter 2. Materials and Methods 3 2.1. Patients and samples 3 2.2. Immunohistochemistry 4 2.3. Construction of machine learning classifiers for identifying the tumor, stroma, and lymphocytes 4 2.4. Validation of discrimination between iTILs and sTILs 5 2.5. Whole-slide quantification of tumor, stroma, and lymphocytes 6 2.6. Identification of tumor subtypes based on TIME parameters 8 2.7. Molecular analysis 8 2.8. Statistical analysis 10 Chapter 3. Results 11 3.1. Establishment of an analytic pipeline for quantification of TILs and TSR from whole-slide immunohistochemical images 11 3.2. Quantitative description for TIME of stage III and high-risk stage II CRCs indicative of curative surgical resection and oxaliplatin-based adjuvant chemotherapy 13 3.3 Subtyping of CRC based on quantitative features of TIME 15 3.4 Differential prognostic implications of five TIME clusters 26 Chapter 4. Discussion 32 Bibliography 36 Acknowledgement 42 ๊ตญ๋ฌธ์ดˆ๋ก 43Docto

    (The) effects of hospice care on quality of life in terminal cancer patients.

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    ๊ฐ„ํ˜ธํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€]ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋Š” ์‹ ์ฒด์ , ์ •์‹ ์ , ์‚ฌํšŒ์ , ์˜์ ์ธ ์ง€์ง€๋ฅผ ํ†ตํ•˜์—ฌ ํ™˜์ž์˜ ์‚ถ์˜ ์งˆ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ง๊ธฐ ์•”ํ™˜์ž์˜ ์‚ถ์˜ ์งˆ(์‹ ์ฒด, ์ •์‹ , ์‹ค์กด, ์ง€์ง€, ์˜์ ์ธ ์ธก๋ฉด)์„ ํ‰๊ฐ€ํ•ด๋ด„์œผ๋กœ์จ ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ์˜ ํšจ๊ณผ๋ฅผ ์•Œ์•„๋ณด๋ฉฐ, ๋Œ€์ƒ์ž์˜ ์ผ๋ฐ˜์ ์ธ ํŠน์„ฑ๊ณผ ์‚ถ์˜ ์งˆ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•ด ๋ณด๊ณ ์ž ์‹œ๋„๋œ ๋น„๋™๋“ฑ์„ฑ ๋Œ€์กฐ๊ตฐ ์‚ฌํ›„์„ค๊ณ„ ์—ฐ๊ตฌ์ด๋‹ค. ์—ฐ๊ตฌ๋Œ€์ƒ์ž๋Š” ์ด 60๋ช…(4๊ฐœ์˜ ํ˜ธ์Šคํ”ผ์Šค ๊ธฐ๊ด€์— ์˜๋ขฐ๋œ 30๋ช…์˜ ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๋ง๊ธฐ ์•”ํ™˜์ž, 2๊ฐœ์˜ ์ข…ํ•ฉ๋ณ‘์›์— ์ž…์›ํ•œ 30๋ช…์˜ ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์ง€ ์•Š์€ ํ™˜์ž)์œผ๋กœ ์—ฐ๊ตฌ๊ธฐ์ค€์— ๋งž๋Š” ํ™˜์ž๋“ค์ด ๋Œ€์ƒ์ž๋กœ ์„ ์ •๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ๋„๊ตฌ๋Š” ์งˆ๋ฌธ์ง€๋ฅผ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ ์ง์ ‘๋ฉด๋‹ด์„ ํ†ตํ•ด ์ˆ˜์ง‘๋˜์—ˆ๋‹ค. ์งˆ๋ฌธ์ง€๋Š” ์ผ๋ฐ˜์  ํŠน์„ฑ์— ๊ด€ํ•œ 10๋ฌธํ•ญ๊ณผ, ์‚ถ์˜ ์งˆ์— ๊ด€ํ•œ 22๋ฌธํ•ญ(์‹ ์ฒด์  2, ์ •์‹ ์  4, ์‹ค์กด์  6, ์ง€์ง€์  6, ์˜์  4๋ฌธํ•ญ)์œผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ์œผ๋ฉฐ, ์ด๋Š” Cohen์™ธ(1996) ๋ฐ ์œค๊ฒฝ์ด(1997)์˜ ๋„๊ตฌ๋ฅผ ์ˆ˜์ • ๋ณด์™„ํ•˜์—ฌ ๋งŒ๋“  ๊ฒƒ์œผ๋กœ ์‚ฌ์ „์กฐ์‚ฌ ํ›„ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์ž๋ฃŒ์ˆ˜์ง‘ ๊ธฐ๊ฐ„์€ 2000๋…„ 9์›” 18์ผ๋ถ€ํ„ฐ 10์›” 28์ผ๊นŒ์ง€์ด๋ฉฐ, ํ†ต๊ณ„์  ๋ฐฉ๋ฒ•์—๋Š” ์‹ค์ˆ˜์™€ ๋ฐฑ๋ถ„์œจ, ฯ‡2, t-test๋กœ, ANOVA, Pearson correlation์ด ์ด์šฉ๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฃผ๊ฐ€์„ค "ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๋ง๊ธฐ ์•”ํ™˜์ž์˜ ์ „์ฒด์ ์ธ ์‚ถ์˜ ์งˆ์€ ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์ง€ ์•Š์€ ๊ตฐ๋ณด๋‹ค ๋†’์„ ๊ฒƒ์ด๋‹ค."๋Š” ์ง€์ง€๋˜์—ˆ๋‹ค(t=4.948, p=.000). ๋ถ€๊ฐ€์„ค ์ค‘ "ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๊ตฐ์ด ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์ง€ ์•Š์€ ๊ตฐ๋ณด๋‹ค ์ •์‹ ์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์ด ๋†’์„ ๊ฒƒ์ด๋‹ค."(t=3.106, p=.003)์™€ "ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๊ตฐ์ด ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์ง€ ์•Š์€ ๊ตฐ๋ณด๋‹ค ์ง€์ง€์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์ด ๋†’์„ ๊ฒƒ์ด๋‹ค."(t=5.514, p=.000), "ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๊ตฐ์ด ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์ง€ ์•Š์€ ๊ตฐ๋ณด๋‹ค ์˜์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์ด ๋†’์„ ๊ฒƒ์ด๋‹ค."(t=4.588, p=.000)๋Š” ์ง€์ง€๋˜์—ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ "ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๊ตฐ์ด ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์ง€ ์•Š์€ ๊ตฐ๋ณด๋‹ค ์‹ ์ฒด์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์ด ๋†’์„ ๊ฒƒ์ด๋‹ค."(t=1.003, p=.320)์™€ "ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๊ตฐ์ด ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์ง€ ์•Š์€ ๊ตฐ๋ณด๋‹ค ์‹ค์กด์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์ด ๋†’์„ ๊ฒƒ์ด๋‹ค."(t=1.975, p=.053)๋Š” ๊ธฐ๊ฐ๋˜์—ˆ๋‹ค. ๋Œ€์ƒ์ž๋“ค์˜ ์ผ๋ฐ˜์ ์ธ ํŠน์„ฑ ์ค‘ ์‚ถ์˜ ์งˆ๊ณผ ๊ด€๋ จ์ด ์žˆ๋Š” ๊ฒƒ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ „๋ฐ˜์ ์ธ ์‚ถ์˜ ์งˆ์— ์žˆ์–ด ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๊ตฐ์—์„œ ์ข…๊ต๋ฅผ ๊ฐ€์ง„ ๊ฒฝ์šฐ(F= 4.258, p=.048)์™€ ๊ต์œก์ •๋„๊ฐ€ ๋†’์„ ์ˆ˜๋ก(F=3.595, p=.020), ๊ทธ๋ฆฌ๊ณ  ์ข…๊ต๋ฅผ ๊ฐ€์ง„ ๊ธฐ๊ฐ„์ด ๊ธธ์ˆ˜๋ก(r=.475, p=.014) ์ „๋ฐ˜์ ์ธ ์‚ถ์˜ ์งˆ ์ ์ˆ˜๊ฐ€ ๋†’์•˜๋‹ค. ์‹ ์ฒด์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์€ ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์ง€ ์•Š์€ ๊ตฐ์—์„œ ์—ฐ๋ น์ด ๋†’์„์ˆ˜๋ก ๋‚ฎ์•˜๋‹ค(r=-.362, p=.040) . ์‹ค์กด์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์— ์žˆ์–ด ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๊ตฐ์—์„œ ์—ฌ์žํ™˜์ž์˜ ๊ฒฝ์šฐ(F=4.200, p=.050), ๊ทธ๋ฆฌ๊ณ  ์ข…๊ต๋ฅผ ๊ฐ€์ง„ ๊ธฐ๊ฐ„์ด ๊ธธ์ˆ˜๋ก(r=.486, p=.012) ์‹ค์กด์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ ์ ์ˆ˜๊ฐ€ ๋†’์•˜๋‹ค. ์˜์ ์ธ ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์€ ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๊ตฐ์—์„œ ๊ต์œก์ •๋„๊ฐ€ ๋†’์„์ˆ˜๋ก(F=3.301, p=.027), ์—ฐ๋ น์ด ๋‚ฎ์„์ˆ˜๋ก(r=.-362, p=.049) ์˜์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ ์ ์ˆ˜๊ฐ€ ๋†’์•˜๋‹ค. ๋˜ํ•œ ์˜์ ์ธ ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์€ ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์ง€ ์•Š์€ ๊ตฐ์—์„œ ๊ฒฝ์ œ์ƒํƒœ๊ฐ€ ๋†’์„์ˆ˜๋ก(F=3.423, p=.047), ๊ทธ๋ฆฌ๊ณ  ์ข…๊ต๋ฅผ ๊ฐ–๊ณ  ์žˆ๋Š” ๊ฒฝ์šฐ(F=9.730, p=.004) ์˜์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ ์ ์ˆ˜๊ฐ€ ๋†’์•˜๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋ฅผ ๋ฐ›์€ ๊ตฐ๊ณผ ๋ฐ›์ง€ ์•Š์€ ๊ตฐ ๋ชจ๋‘์—์„œ ์˜์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์€ ์ข…๊ต๋ฅผ ๊ฐ€์ง„ ๊ธฐ๊ฐ„์ด ๊ธธ์ˆ˜๋ก ๋†’์•˜๋‹ค(์‹คํ—˜๊ตฐ r=.393, p=.047, ๋Œ€์กฐ๊ตฐ r=.520, p=.003). ๊ฒฐ๋ก ์ ์œผ๋กœ ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๋Š” ์ •์‹ ์ , ์ง€์ง€์ , ์˜์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์„ ํฌํ•จํ•œ ์ „์ฒด์ ์ธ ์‚ถ์˜ ์งˆ์— ๊ธ์ •์ ์ธ ํšจ๊ณผ๋ฅผ ๊ฐ€์ ธ์˜ค๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ ์ฒด์  ๋ฐ ์‹ค์กด์  ์ฐจ์›์— ์žˆ์–ด์„œ๋Š” ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ๊ฐ€ ์œ ์˜ํ•œ ํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋‚ด์ง€ ๋ชปํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ์‹ ์ฒด์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ ํ–ฅ์ƒ์„ ์œ„ํ•ด์„œ ํ˜ธ์Šคํ”ผ์Šค ๊ฐ„ํ˜ธ์‚ฌ๋Š” ๋ณด๋‹ค ์ ๊ทน์ ์œผ๋กœ ์‹ ์ฒด์ฆ์ƒ๊ด€๋ฆฌ์— ์ฐธ์—ฌํ•˜์—ฌ์•ผ ํ•˜๋ฉฐ, ๋…์ž์ ์ธ ๊ฐ„ํ˜ธ์ค‘์žฌ ๊ฐœ๋ฐœ์— ํž˜์จ์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ๋˜ํ•œ ์‹ค์กด์  ์ฐจ์›์˜ ์‚ถ์˜ ์งˆ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋ณด๋‹ค ํฌ๊ด„์ ์ธ ์˜์  ๊ฐ„ํ˜ธ์— ๋Œ€ํ•œ ๊ฐœ๋… ์ •๋ฆฝ๊ณผ ์ƒˆ๋กœ์šด ๊ฐ„ํ˜ธ์ค‘์žฌ๋ฐฉ๋ฒ•์ด ์š”๊ตฌ๋œ๋‹ค. [์˜๋ฌธ]Hospice care aims to improve the quality of life of patients through physical, psychological, social and spiritual support. So, this study was conducted to find out the effects of hospice care by evaluating the terminally-ill patients' quality of life(physical, psychological, existential, support, and spiritual aspect) and to analyze the relationship between the quality of life and the general characteristics of subjects. The study design is nonequivalent control group posttest only design. From September 18 to October 28 2000, 60 terminal cancer patients(30 hospice patients in four hospice institutes, 30 nonhospice patients in two general hospitals) were selected according to the research criteria as subjects for this study. The data were collected using the questionnaire with interview. The tools used for the research were 10-item questionnaire regarding general characteristics, 22-item questionnaire about quality of life(2-item for physical, 4 for psychological, 6 for existential, 6 for support, 4 for spiritual aspect). The questionnaire were to measure the quality of life by means of the measure instrument of Cohen. et al.(1996), Kyoung-E. Youn(1997). Frequency and percentage, ฯ‡2, t-test, ANOVA, Pearson correlation were used. The results of the study were as follows. Main hypothesis 'The global overall quality of life of the hospice patients will be higher than that of nonhospice patients' was supported((t=4.948, p=.000). Sub hypothesis 'The quality of life of the hospice patients in psychological aspects will be higher than that of nonhospice'(t=3.106, p=.003), 'The quality of life of the hospice patients in support aspects will be higher than that of nonhospice'(t=5.514, p=.000) and 'The quality of life of the hospice patients in spiritual aspects will be higher than that of nonhospice'(t=4.588, p=.000) were supported. But 'The quality of life of the hospice patients in physical aspects will be higher than that of nonhospice' (t=1.003, p=.320) and 'The quality of life of the hospice patients in existential aspects will be higher than that of nonhospice'(t=1.975, p=.053) were not supported. Relations between the general characteristics of subjects and the quality of life of each group were as follows. Global quality of life is higher when the patients have religion(F=4.258, p=.048), high education(F=3.595, p=.020) and it depends on a length of having religion (r=.475, p=.014) in experimental group. Physical aspects of quality of life are higher when the patients are young(r=-.362, p=.040) in control group. Existential aspects of quality of life are higher when the patients are female(F=4.200, p=.050), and they depend on a length of having religion(r=.486, p=.012) in experimental group. Spiritual aspects of quality of life are higher when the patients have high education(F=3.301, p=.027) and are young(r=.-362, p=.049) in experimental group. In both group, spiritual aspects of quality of life are higher depending on a length of having religion(experimental group r=.393, p=.047, control group r=.520, p=.003). Spiritual aspects of quality of life are also higher when the patients have religion(F=9.730, p=.004) and economy status of the patients is higher(F=3.423, p=.047) in control group. It concluded that the hospice care has positive effects on general quality of life including psychological, support, and spiritual aspects. But in physical and existential aspect of quality of life, hospice care has not shown significant effects. So, for the progress of the physical aspect of quality of life, hospice nurses are to participate actively in physical symptom control and to develop independent nursing intervention. For the progress of the existential aspect of quality of life, comprehensive spiritual care is conceptualized and new nursing interventions are developed.ope

    (The) disease experiences of young adults with chronic diseases

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    ๊ฐ„ํ˜ธํ•™๊ณผ/๋ฐ•์‚ฌ[ํ•œ๊ธ€] ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋Œ€์ƒ์ž์˜ ๊ฒฝํ—˜์„ ์ดํ•ดํ•˜๋Š”๋ฐ ์ดˆ์ ์„ ๋‘๋Š” Parse์˜ ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๋ก ์„ ์ ์šฉํ•˜์—ฌ ์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž์˜ ์งˆ๋ณ‘ ๊ฒฝํ—˜์„ ํƒ์ƒ‰ํ•˜๋ฉฐ ๊ทธ ๊ณผ์ •๊ณผ ์˜๋ฏธ๋ฅผ ์‹ฌ์ธต์ ์œผ๋กœ ํŒŒ์•…ํ•˜๊ณ  ์ด๋ฅผ ํ†ตํ•ด ์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž๋“ค์˜ ๊ฒฝํ—˜์— ๋Œ€ํ•œ ์˜๋ฏธ์ฒด๊ณ„๋ฅผ ์ง„์ˆ ํ•˜๊ณ , ์ข…ํ•ฉ, ๋ถ„์„ํ•จ์œผ๋กœ์จ ์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž๋ฅผ ์œ„ํ•œ ์‹ค๋ฌด ๊ฐ„ํ˜ธ ์ด๋ก ์„ ๊ฐœ๋ฐœํ•˜๋Š”๋ฐ ๊ธฐ์ดˆ ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜๊ณ ์ž ํ•œ๋‹ค.์—ฐ๊ตฌ๊ธฐ๊ฐ„์€ 2006๋…„ 4์›”๋ถ€ํ„ฐ 2007๋…„ 2์›”๊นŒ์ง€์ด๋ฉฐ, 20์„ธ์—์„œ 35์„ธ ์‚ฌ์ด์˜ ์—ฐ๋ น์ธต์—์„œ '๊ณ ํ˜ˆ์••'์ด๋‚˜ '๋‹น๋‡จ๋ณ‘'์„ ์ฒ˜์Œ์œผ๋กœ ์ง„๋‹จ ๋ฐ›์€ ์ž ์ด 10๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. Parse์˜ ์—ฐ๊ตฌ๋ฐฉ๋ฒ•๋ก ์— ๋”ฐ๋ผ ์—ฐ๊ตฌ์ž์™€ ์ฐธ์—ฌ์ž์˜ ๋‚˜-๋„ˆ ๊ด€๊ณ„ ๊ณผ์ •์„ ํ†ตํ•ด ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜๊ณ , ์ฐธ์—ฌ์ž์˜ ์–ธ์–ด์—์„œ ์ถ”์ถœํ•œ ๋‚ด์šฉ์„ ์—ฐ๊ตฌ์ž์˜ ์–ธ์–ด๋กœ ์ถ”์ƒํ™”ํ•˜์—ฌ ์ข…ํ•ฉํ•œ ๊ฒฐ๊ณผ ๋ช…์ œ๋ฅผ ๋งŒ๋“ค์—ˆ์œผ๋ฉฐ, ๊ฐ๊ฐ์˜ ๋ช…์ œ์—์„œ ํ•ต์‹ฌ๊ฐœ๋…์„ ๋ฝ‘์•„๋‚ธ ํ›„, ์ด๋ฅผ ๊ฒฝํ—˜์˜ ๊ตฌ์กฐ๋กœ ์ข…ํ•ฉํ•˜์˜€๋‹ค. ๋ฐœ๊ฒฌ์  ํ•ด์„๋‹จ๊ณ„์—์„œ๋Š” ๊ตฌ์กฐ์  ์ „ํ™˜๊ณผ ๊ฐœ๋…์  ํ†ตํ•ฉ๊ณผ์ •์„ ๊ฑฐ์ณค์œผ๋ฉฐ, ์ด๋ฅผ ํ†ตํ•ด ๋‚˜ํƒ€๋‚œ ๊ฐœ๋…๋“ค์„ ์„œ๋กœ ์—ฐ๊ฒฐ์‹œ์ผœ ๊ฒ€์ฆ ๊ฐ€๋Šฅํ•˜๋„๋ก ์ด๋ก ์  ๊ตฌ์กฐ๋กœ ์ง„์ˆ ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฐฉ๋ฒ•์˜ ํ‰๊ฐ€๋Š” Guba ์™€ Lincoln(1985), Sandelowski (1986)๊ฐ€ ์ œ์‹œํ•œ ์‹ ๋น™์„ฑ, ์ ์šฉ์„ฑ, ์ผ๊ด€์„ฑ, ์ค‘๋ฆฝ์„ฑ์˜ ๊ธฐ์ค€์„ ์ ์šฉํ•˜์—ฌ ์‹ ๋ขฐ์„ฑ๊ณผ ํƒ€๋‹น์„ฑ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค.์ฐธ์—ฌ์ž 10๋ช…์˜ ๋ช…์ œ๋กœ๋ถ€ํ„ฐ ์ถ”์ถœ๋œ ํ•ต์‹ฌ๊ฐœ๋…์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค.1. ๋‚˜์ด์™€ ๊ฑธ๋งž์ง€ ์•Š์€ ๋ณ‘์œผ๋กœ ์ธ์ง€2. ์งˆ๋ณ‘์˜ ์˜๋ฏธ, ์›์ธ, ์‹ฌ๊ฐ์„ฑ ์ธ์‹์œผ๋กœ ์งˆ๋ณ‘ ์ˆ˜์šฉ3. ๋ณ€ํ™”์— ์˜ํ•œ ๊ณ ํ†ต๊ณผ ์•ฝ๋ฌผ ๋‘๋ ค์›€์— ๋”ฐ๋ฅธ ์น˜๋ฃŒ ์ €ํ•ญ4. ์ž์‹ ๊ณผ์˜ ์‹ธ์›€์—์„œ ๊ฐˆ๋“ฑ๊ณผ ์กฐ์ ˆ์˜ ๋ฐ˜๋ณต5. ๋ชจํ˜ธํ•œ ๋ฌด๋ ฅ๊ฐ๊ณผ ๋ฐœ๋‹ฌ๊ณผ์—…(์ด์„ฑ๊ณผ์˜ ์นœ๋ฐ€๊ฐ, ์ง์—…์„ ํƒ)์˜ ์ขŒ์ ˆ์— ์˜ํ•œ๋Œ€ํ˜ผ๋ž€6. ์งˆ๋ณ‘, ๋‚˜, ํƒ€์ธ์˜ ์ดํ•ด์™€ ํ˜‘๋ ฅ์œผ๋กœ ์งˆ๋ณ‘๊ณผ ์นœ๊ตฌ ๋˜์–ด ํ•จ๊ป˜ํ•จ.7. ์‚ถ์˜ ์˜๋ฏธ์™€ ๊ฐ€์น˜ ํ™•์ธ, ์ž์‹ ๋งŒ์˜ ๋ฐฉ๋ฒ•์œผ๋กœ ์Šค์Šค๋กœ๊ฐ€ ์น˜๋ฃŒ์ž๊ฐ€ ๋˜๊ธธ ํฌ๋ง์ด๋ฅผ ํ†ตํ•ด ๋ฐœ๊ฒฌ๋œ ์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž์˜ ์งˆ๋ณ‘ ๊ฒฝํ—˜์˜ ๊ตฌ์กฐ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค."์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž์˜ ์งˆ๋ณ‘ ๊ฒฝํ—˜์€ ๋‚˜์ด์™€ ๊ฑธ๋งž์ง€ ์•Š์€ ๋ณ‘์œผ๋กœ ์ธ์ง€ํ•˜๊ณ , ์งˆ๋ณ‘์˜ ์˜๋ฏธ, ์›์ธ, ์‹ฌ๊ฐ์„ฑ ์ธ์‹์œผ๋กœ ์งˆ๋ณ‘์„ ์ˆ˜์šฉํ•˜๋‚˜, ๋ณ€ํ™”์— ์˜ํ•œ ๊ณ ํ†ต ๋ฐ ์•ฝ๋ฌผ ๋‘๋ ค์›€์— ๋”ฐ๋ฅธ ์น˜๋ฃŒ ์ €ํ•ญ์„ ๊ฒฝํ—˜ํ•˜๋ฉฐ, ์ž์‹ ๊ณผ์˜ ์‹ธ์›€์—์„œ ๊ฐˆ๋“ฑ๊ณผ ์กฐ์ ˆ์„ ๋ฐ˜๋ณตํ•ด๊ฐ€์ง€๋งŒ ๋ชจํ˜ธํ•œ ๋ฌด๋ ฅ๊ฐ๊ณผ ๋ฐœ๋‹ฌ๊ณผ์—…(์ด์„ฑ๊ณผ์˜ ์นœ๋ฐ€๊ฐ, ์ง์—…์„ ํƒ)์˜ ์ขŒ์ ˆ์— ์˜ํ•ด ๋Œ€ํ˜ผ๋ž€์„ ๊ฒช๋Š” ๊ฐ€์šด๋ฐ, ์งˆ๋ณ‘, ๋‚˜, ์˜๋ฏธ ์žˆ๋Š” ํƒ€์ธ์˜ ์ดํ•ด์™€ ํ˜‘๋ ฅ์œผ๋กœ ์งˆ๋ณ‘๊ณผ ์นœ๊ตฌ ๋˜์–ด ํ•จ๊ป˜ํ•จ์œผ๋กœ์จ ์‚ถ์˜ ์˜๋ฏธ์™€ ๊ฐ€์น˜๋ฅผ ํ™•์ธํ•˜๊ณ  ์ž์‹ ๋งŒ์˜ ๋ฐฉ๋ฒ•์œผ๋กœ ์Šค์Šค๋กœ๊ฐ€ ์น˜๋ฃŒ์ž๊ฐ€ ๋˜๊ธธ ํฌ๋งํ•˜๋Š” ๊ณผ์ •์ด๋‹ค."์ด์ƒ์—์„œ ๋‚˜ํƒ€๋‚œ ์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž์˜ ์งˆ๋ณ‘ ๊ฒฝํ—˜์˜ ๊ตฌ์กฐ๋ฅผ Parse์˜ ์ธ๊ฐ„๋˜์–ด๊ฐ ์ด๋ก ๊ณผ ์—ฐ๊ฒฐํ•˜์—ฌ ๊ตฌ์กฐ์  ์ „ํ™˜๊ณผ ๊ฐœ๋…์  ํ†ตํ•ฉ์„ ํ•˜๋Š” ๋ฐœ๊ฒฌ์  ํ•ด์„์„ ์ด๋Œ์–ด ๋ƒˆ๋Š”๋ฐ, ๋จผ์ € ๊ตฌ์กฐ์  ์ „ํ™˜์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค."์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž์˜ ์งˆ๋ณ‘ ๊ฒฝํ—˜์€ ๋ถ€์กฐํ™”๋ฅผ ํ‘œํ˜„ํ•˜๊ณ  ๊ฑด๊ฐ•-์งˆ๋ณ‘์˜ ๊ฐ€์น˜ ์„ ํƒ์œผ๋กœ ํ˜„์‹ค์„ ์•Œ๋ฉฐ ๊ณ ํ†ต๊ณผ ๋‘๋ ค์›€์„ ์˜ˆ๊ฒฌํ•˜๋Š” ๊ฒƒ์œผ๋กœ(์˜๋ฏธํ™”), ๋ง์„ค์ž„๊ณผ ์ˆ˜ํ–‰์ด ๊ณต์กดํ•˜๊ณ  ๋‚˜ํƒ€๋‚˜๊ฑฐ๋‚˜ ์ˆจ๊ฒจ์ง„ ๋ฌด๊ธฐ๋ ฅ๊ณผ ๊ฐ€๊น๊ฑฐ๋‚˜ ๋จผ ์ด์ƒ์— ํ˜ผ๋ˆ์„ ํ‘œ์ถœํ•˜๋‚˜(์œจ๋™์„ฑ), ๊ด€๊ณ„ํ˜•์„ฑ์˜ ์ถ”์ง„์„ ์œ„ํ•ด ์นœ๊ตฌ ์‚ผ๊ณ  ๊ฐ€์น˜์˜ ํ™•์‹ ๊ณผ ๊ณ ์œ ํ•จ์˜ ๋ฐœ๊ฒฌ์œผ๋กœ ๊ฑฐ๋“ญ๋‚˜๋Š”(๊ณต๋™์ดˆ์›”) ๊ณผ์ •์ด๋‹ค. "์ด๋Ÿฌํ•œ ๊ตฌ์กฐ์  ์ „ํ™˜์€ ๋˜ํ•œ ๊ฐœ๋…์ ์œผ๋กœ Parse ์ด๋ก ๊ณผ ์—ฐ๊ฒฐ๋˜๋Š”๋ฐ, ์—ฌ๊ธฐ์„œ๋Š” '์–ธ์–ดํ™”, ๊ฐ€์น˜ํ™”, ์ƒ์ƒํ™”, ๊ฐ€๋Šฅ-์ œํ•œ, ๋…ธ์ถœ-์€ํ, ์—ฐ๊ฒฐ-๋ถ„๋ฆฌ, ๊ฐ•ํ™”์„ฑ, ๋…์ฐฝ์„ฑ, ๋ณ€ํ˜•์„ฑ'์˜ Pare์ด๋ก ์˜ 9๊ฐ€์ง€ ๊ฐœ๋…์ด ๋ชจ๋‘ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒโ…ด์ด ํ™•์ธ๋˜์—ˆ๋‹ค.๋ณธ ์—ฐ๊ตฌ์˜ ๋ฐœ๊ฒฌ์  ํ•ด์„์„ ํ†ตํ•ด ๋‚˜ํƒ€๋‚œ ๊ฐœ๋…๋“ค์„ ์„œ๋กœ ์—ฐ๊ฒฐ์‹œ์ผœ ๊ฒ€์ฆ ๊ฐ€๋Šฅํ•œ ์ด๋ก ์  ๊ตฌ์กฐ๋ฅผ ์ง„์ˆ ํ•ด๋ณด๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. โ…ด1. ์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์‹คํ˜„ํ•œ๋‹ค๋Š” ๊ฒƒ์€ ๋…ธ์ถœ-์€ํ์˜ ์–ธ์–ดํ™”๋ฅผ ํ†ตํ•ด ๋‚˜ํƒ€๋‚œ๋‹ค.2. ์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž์˜ ๋…์ฐฝ์„ฑ์€ ๊ฐ€๋Šฅ-์ œํ•œ์˜ ๊ฐ€์น˜ํ™”์—์„œ ๊ฐœ๋ฐœ๋œ๋‹ค.3. ์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž์˜ ๋ณ€ํ˜•์„ฑ์€ ์ƒ์ƒํ™”๋ฅผ ์—ฐ๊ฒฐ-๋ถ„๋ฆฌํ•˜๋Š” ๊ฐ€์šด๋ฐ ์‹คํ˜„๋œ๋‹ค.๊ฒฐ๋ก ์ ์œผ๋กœ ์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž์˜ ์งˆ๋ณ‘ ๊ฒฝํ—˜์€ ์ธ๊ฐ„-์šฐ์ฃผ-ํ™˜๊ฒฝ๊ณผ์˜ ์ƒํ˜ธ์ž‘์šฉ ์†์—์„œ ์ง€์†์ ์œผ๋กœ ๋ณ€ํ™”ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๊ทธ๋“ค์€ ์ Š์Œ์œผ๋กœ ์ธํ•ด ๊ณ ํ†ต์ด ๊ฐ€์ค‘๋˜๊ณ  ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ๊ฐˆ๋“ฑ๊ณผ ํ˜ผ๋ž€์„ ๊ฒฝํ—˜ํ•˜๋‚˜ ๋ฐ˜๋ณต์ ์œผ๋กœ ์˜๋ฏธ๋ฅผ ๋ฐœ๊ฒฌํ•˜๊ณ  ๊ฐ€์น˜๋ฅผ ํ™•์ธํ•จ์œผ๋กœ์จ ์ž๊ธฐ ๊ด€๋ฆฌ์™€ ๋ณ€ํ™”๋ฅผ ํ–ฅํ•ด ๋…ธ๋ ฅํ•˜๋Š” ์ธ๊ฐ„๋˜์–ด๊ฐ์˜ ๊ณผ์ •์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค.๋”ฐ๋ผ์„œ ๊ฐ„ํ˜ธ์‚ฌ๊ฐ€ ์„ฑ์ธ ์ „๊ธฐ ๋งŒ์„ฑ์งˆํ™˜์ž์™€ ์ง„์ •์œผ๋กœ ํ•จ๊ป˜ ํ•œ๋‹ค๋Š” ๊ฒƒ์€ Parse๊ฐ€ ์ฃผ์žฅํ•œ ๋ฐ”์™€ ๊ฐ™์ด ๊ทธ๋“ค์ด ๋งํ•˜๋Š” ์ƒํ™ฉ์˜ ์˜๋ฏธ์— ๋Œ€ํ•ด ํŒ๋‹จํ•˜๊ฑฐ๋‚˜ ๋ถ„๋ฅ˜ํ•˜์ง€ ์•Š์œผ๋ฉฐ, ๋ณ€ํ™”๋ฅผ ์œ ๋„ํ•˜์ง€๋„ ์•Š๊ณ , ์˜ค์ง ๊ทธ๋“ค๊ณผ ํ•จ๊ป˜ ์ƒˆ๋กœ์šด ๊ฐ€๋Šฅ์„ฑ์„ ๊ณต๋™์ฐฝ์กฐํ•˜๊ธฐ ์œ„ํ•ด ์ฃผ์ฒด ๋Œ€ ์ฃผ์ฒด๋กœ์„œ์˜ ๊ด€๊ณ„๋ฅผ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ๊ทธ๋“ค์ด ์ฒ˜ํ•ด์žˆ๋Š” ๋งฅ๋ฝ์—์„œ ๊ทธ๋“ค์ด ๋…ํŠนํ•˜๊ฒŒ ๊ฒฝํ—˜ํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ๋“ค์˜ ์˜๋ฏธ๋ฅผ ํƒ์ƒ‰ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋Œ€์ƒ์ž๋ฅผ ์žˆ๋Š” ๊ทธ๋Œ€๋กœ ๋ฐ”๋ผ๋ณด๋ฉด์„œ ํ•จ๊ป˜ ์žˆ์–ด์ฃผ๋Š” ๊ฒƒ์ด์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. [์˜๋ฌธ]The purpose of this study was to explore young adults' lived experience of chronic disease and apprehend the connectedness the structure of the experience with Parse's Human Becoming Theory, and develop a tentative nursing practice theory for young adults with chronic disease.Data collection for this study was conducted from August 2006 to February 2007. Research participants were 10 young adults between the ages of 20~35 years who were first diagnosed as having diabetes or hypertension. Parse's Human Becoming Theory provided the theoretical perspective and guided the descriptive exploratory methodology that was used. The data were collected using a dialogical engagement process and were analyzed using extraction-synthesis and heuristic interpretation. The criteria of Guba and Lincoln (1985), and Sandelowski (1986), i.e., credibility, transferability, audibility, and confirmability were used to test the validity and reliability of the data.The following 7 core concepts were extracted from the 10 participants' propositions.1. Perception of the disease as not appropriate for young adults.2. Accepting the disease upon recognizing its meaning, cause, andseriousness.3. Resistance to treatment due to suffering arising from lifestyle changesand fear of medications.4. Repeating conflict and regulation in the process of self-struggle.5. Confusion caused by an obscure powerlessness and the breakdownof development tasks (e.g., intimacy with the opposite sex, job choices).6. Befriending and partnering with the disease through understanding ofthe disease, self, and others and cooperative support.7. Confirmation of life's meaning and worth and hope for developingunique ways to heal themselves.From the core concepts the structure of the disease experiences of young adults with chronic disease can be synthesized as follows:"Young adults with chronic disease did not perceive the disease as appropriate for young adults but accepted the disease upon recognizing its meaning, cause, and seriousness. After temporarily resisting treatment due to suffering arising from lifestyle changes and fear of medications they repeatedly experienced conflict and regulation in the process of self-struggle, and noted confusion caused by an obscure powerlessness and the breakdown of development tasks (e.g. intimacy with the opposite sex, job choices). However, they wanted to befriend and partner with the disease through understanding of the disease, self, and others and cooperative support, and they finally confirm of life's meaning and worth and hope for developing unique ways to heal themselves."Heuristic interpretation involves two processes, structural transposition and conceptual integration. The structural transposition is "young adults with chronic disease express an imbalance, knowing reality through their choices in health-disease values, and anticipating pain and fear (Meaning). Although they hesitate and perform simultaneously and express confusion in appearing-hiding powerlessness and near-far ideal(Rhythmicity), they befriend the disease to develop relationships and want to be reborn through confirming their values and discovering their originality (Co-trancendence)".The conceptual integration revealed that this structual transposition were conceptually connected with all of Parse's 9 concepts - valuing, imaging, languaging, revealing-concealing, enabling-limiting, connecting-separating, transforming, originating, and powering.The following 3 theoretical structures were identified to verify the interrelationship of the concepts which defined through heuristic interpretation.1. Powering of young adults with chronic disease is showed throughlanguaging of revealing-concealing.2. Originating young adults with chronic disease is developed fromvaluing enabling-limiting.3. Transforming young adults with chronic disease is realized throughconnecting-separating imaging.In conclusion, the disease experience of young adults with chronic disease reflects a continuous change through human-universe-environment relationships. Despite much suffering relating to their young age and experiencing conflict and confusion, young adults with chronic disease repeatedly try to self-regulate and change through confirmation their meaning and worth, reflecting the human becoming process.prohibitio

    Local Key Estimation from Audio Using Rule-based Method Depending on Chord Information

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(๋””์ง€ํ„ธ์ •๋ณด์œตํ•ฉ์ „๊ณต), 2015. 8. ์ด๊ต๊ตฌ.์˜ค๋Š˜๋‚  ๋””์ง€ํ„ธ ๋งค์ฒด๊ฐ€ ๋ฐœ๋‹ฌํ•˜๋ฉด์„œ ์‚ฌ๋žŒ๋“ค์ด ์ˆ˜๋งŽ์€ ์Œ์•…์„ ํ•œ๊บผ๋ฒˆ์— ๊ฒ€์ƒ‰ํ•˜๊ณ  ์ ‘ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ, ์†Œ๋น„์ž๋“ค์ด ์›ํ•˜๋Š” ์Œ์•…์„ ํšจ๊ณผ์ ์œผ๋กœ ๊ฒ€์ƒ‰ํ•˜๊ฑฐ๋‚˜ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ๋‚ด ์Œ์›๋“ค์„ ์ž๋™์ ์œผ๋กœ ๋ถ„๋ฅ˜, ๋ถ„์ ˆ, ํ˜น์€ ์š”์•ฝํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•œ ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์ด ์ค‘์š”ํ•ด์ง€๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋Œ€ํ‘œ์ ์œผ๋กœ ๋– ์˜ค๋ฅธ ์—ฐ๊ตฌ ์ฃผ์ œ๋Š” ์Œ์•… ๊ตฌ์กฐ๋ฅผ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์ด๋ฉฐ, ์ด ์ฃผ์ œ์™€ ๋ฐ€์ ‘ํ•œ ์—ฐ๊ด€์ด ์žˆ๋Š” ์Œ์•…์  ์š”์†Œ๋Š” ๋ฐ”๋กœ ์Œ์•…์˜ ์กฐ(key)์ด๋‹ค. ์กฐ๋Š” ์Œ์•… ์ผ๋ถ€ ํ˜น์€ ์ „์ฒด์˜ ํ™”์„ฑ์  ๋งฅ๋ฝ์„ ์˜๋ฏธํ•˜๋Š” ๊ฒƒ์œผ๋กœ, ์Œ์•…์˜ ๋ถ„์œ„๊ธฐ ๋ฐ ๊ตฌ์กฐ๋ฅผ ์ธ์ง€ํ•˜๊ฑฐ๋‚˜ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฐ ํฐ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋”ฐ๋ผ์„œ, ์Œ์•…์œผ๋กœ ์ž๋™์ ์œผ๋กœ ์กฐ๋ฅผ ์ฐพ์•„๋‚ด๋Š” ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์Œ์•…์ •๋ณด๊ฒ€์ƒ‰ ๋ถ„์•ผ์—์„œ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜์–ด์™”๋‹ค. ๋งŽ์€ ์ˆ˜์˜ ์—ฐ๊ตฌ๋“ค์ด ์ด๋ฃจ์–ด์กŒ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ๊ทธ ๋™์•ˆ ์ œ์•ˆ๋˜์—ˆ๋˜ ์‹œ์Šคํ…œ๋“ค์€ ์•ˆ์ •์ ์ด๊ณ  ๋†’์€ ์ •ํ™•๋„๋กœ ์กฐ๋ฅผ ๊ฒ€์ถœํ•˜๋Š” ๋ฐ ํ•œ๊ณ„๋ฅผ ๊ฒช๊ณ  ์žˆ๋‹ค. ๊ทธ๋ ‡๊ธฐ ๋•Œ๋ฌธ์— ์กฐ ๊ฒ€์ถœ ์‹œ์Šคํ…œ์˜ ๊ฐœ์„ ์€ ์˜ค๋Š˜๋‚ ์—๋„ ์—ฌ์ „ํžˆ ์ค‘์š”ํ•œ ๊ณผ์ œ๋กœ ๋‚จ์•„์žˆ๋‹ค. ๊ด€๋ จ ์—ฐ๊ตฌ๋“ค์€ ๊ทœ์น™ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹๊ณผ ํ†ต๊ณ„ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์˜ ์—ฐ๊ตฌ๋“ค๋กœ ๋‚˜๋‰  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด ์ค‘ ํ†ต๊ณ„ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์€ ์ตœ๊ทผ ๊ธฐ๊ณ„ํ•™์Šต ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋ฐœ๋‹ฌ์— ๋”ฐ๋ผ ๋น„์•ฝ์ ์œผ๋กœ ๋ฐœ์ „ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด ๋ฐฉ์‹์—๋Š” ๋ฐ์ดํ„ฐ ํ™•๋ณด์˜ ์–ด๋ ค์›€๊ณผ ๊ณผ์ ํ•ฉ(overfitting)์˜ ์œ„ํ—˜์„ฑ์ด๋ผ๋Š” ๋‹จ์ ์ด ์กด์žฌํ•œ๋‹ค. ์ด๋Š” ํ†ต๊ณ„ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์—๋งŒ ์˜์กดํ•˜์—ฌ ์กฐ ๊ฒ€์ถœ์„ ์‹œ๋„ํ•˜๋Š” ๊ฒƒ์ด ํ•œ๊ณ„๊ฐ€ ์žˆ์œผ๋ฉฐ, ๋”ฐ๋ผ์„œ ๊ทœ์น™ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์˜ ๋ฐœ์ „์ด ํ•จ๊ป˜ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•จ์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ตœ๊ทผ ํ™”์„ฑ์˜ ์œ„๊ณ„๋ฅผ ํ™œ์šฉํ•œ ์กฐ ๊ฒ€์ถœ ์—ฐ๊ตฌ๋“ค์ด ๊ทœ์น™ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์˜ ์ž ์žฌ์„ฑ์„ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค. ๋Œ€์‹ , ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋“ค์€ ๋Œ€์ฒด์ ์œผ๋กœ MIDI(Musical Instrument Digital Interface)๋‚˜ ํ…์ŠคํŠธ ๋“ฑ์˜ ๊ธฐํ˜ธ์ (symbolized) ํ˜•ํƒœ์˜ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์Œ์•…์ •๋ณด๊ฒ€์ƒ‰ ๋ถ„์•ผ์—์„œ์˜ ์‹ค์ œ์ ์ธ ์‘์šฉ์„ ์œ„ํ•ด์„œ๋Š” ์Œ์› ํ˜น์€ ์˜ค๋””์˜ค ์‹ ํ˜ธ๋ฅผ ํ†ตํ•ด์„œ๋„ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๋Š” ์ผ์ด ํ•„์š”ํ•˜๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ์‹ค์ œ ์Œ์›์„ ๋Œ€์ƒ์œผ๋กœ ์กฐ๋ฅผ ๊ฒ€์ถœํ•˜๋Š” ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•˜๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ๊ทœ์น™ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์„ ์–ด๋–ป๊ฒŒ ๋ฐœ์ „์‹œ์ผœ์•ผ ํ•  ๊ฒƒ์ธ์ง€์— ๋Œ€ํ•œ ๊ณ ์ฐฐ์ด ํ•„์š”ํ•œ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ํ•„์š”์„ฑ์— ์ž…๊ฐํ•˜์—ฌ, ์Œ์•…์˜ ํ™”์Œ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•œ ๊ทœ์น™ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์˜ ์ƒˆ๋กœ์šด ์กฐ ๊ฒ€์ถœ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•˜๊ณ  ์‹ค์ œ ์Œ์›์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ทธ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์ด ๋‘ ๋‹จ๊ณ„๋กœ ๋‚˜๋‰œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•˜๋Š” ๋‹จ๊ณ„๋กœ, ์ˆ˜์ง‘ํ•œ ์Œ์›์œผ๋กœ๋ถ€ํ„ฐ ์‹œ๊ฐ„์ ์ธ ํ™”์Œ ์ •๋ณด๋ฅผ ์ •๋Ÿ‰์ ์œผ๋กœ ์ถ”์ถœํ•œ ๋’ค, ๊ทธ ํ™”์Œ ์ •๋ณด๋ฅผ ์ด์šฉํ•˜์—ฌ ์Œ์›์˜ ์ง€์—ฝ์  ์กฐ๋ฅผ ๊ฒ€์ถœํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ์‹œ์Šคํ…œ ๊ตฌ์ถ•์— ๊ด€๋ จํ•˜์—ฌ ์ƒˆ๋กœ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•๋“ค์„ ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋“ค๊ณผ ๋น„๊ตํ•˜๊ณ  ๊ฐ๊ฐ์˜ ํšจ๋Šฅ์„ฑ์„ ๊ฒ€์ฆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์Œ์•… ๊ตฌ์กฐ ๋ถ„์„ ๋ฐ ์Œ์•…์ •๋ณด๊ฒ€์ƒ‰(Music Information Retrieval) ๋ถ„์•ผ์— ํ•™๋ฌธ์ ์œผ๋กœ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์†Œ๋น„์ž๋“ค์ด ๊ฐœ์ธ์˜ ๋ชฉ์ ์— ๋”ฐ๋ผ ์Œ์›์„ ํšจ์œจ์ ์œผ๋กœ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๋„์™€์ฃผ๋Š” ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ ์„ค๊ณ„๋ฅผ ์œ„ํ•œ ๊ธฐ์ˆ ์  ํ† ๋Œ€๊ฐ€ ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.๋ชฉ ์ฐจ ์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ ๋ชฉ์  5 ์ œ 2 ์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ 7 ์ œ 1 ์ ˆ ๊ทœ์น™ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์˜ ์กฐ ๊ฒ€์ถœ ์—ฐ๊ตฌ 7 2.1.1. Template-matching ๊ธฐ๋ฒ•........................................................... 7 2.1.1.1. ์กฐ ํ”„๋กœํŒŒ์ผ (Key-profile).......................................... 8 2.1.1.2. Template-matching ๊ธฐ๋ฒ• ์‘์šฉ ์—ฐ๊ตฌ..........................12 2.1.2. ํ™”์„ฑ ๋ชจ๋ธ (Harmony model) ๊ธฐ๋ฒ•............................................13 2.1.2.1. The Spiral Array ๋ชจ๋ธ................................................14 2.1.2.2. Tonal Grammar Tree ๋ชจ๋ธ.........................................17 ์ œ 2 ์ ˆ ํ†ต๊ณ„ ๊ธฐ๋ฐ˜ ๋ฐฉ์‹์˜ ์กฐ ๊ฒ€์ถœ ์—ฐ๊ตฌ 19 2.2.1. Hidden Markov Model ๊ธฐ๋ฒ•....................................................19 2.2.2. Hidden Markov Model ๊ธฐ๋ฒ• ์‘์šฉ ์—ฐ๊ตฌ...................................21 ์ œ 3 ์ ˆ ์š”์•ฝ 23 ์ œ 3 ์žฅ ์‹œ์Šคํ…œ ๊ตฌ์„ฑ 25 ์ œ 1 ์ ˆ ํ™”์Œ ๊ฒ€์ถœ 26 3.1.1. ์Œ ์ถ”์ถœ......................................................................................26 3.1.1.1. Harmonic Pitch Class Profile (HPCP)..........................26 3.1.2. ํ™”์Œ ๊ฒ€์ถœ...................................................................................29 3.1.2.1. ๊ฒ€์ถœ๋œ ํ™”์Œ ์ข…๋ฅ˜.........................................................30 3.1.2.2. ๊ฒ€์ถœ ๋ฐฉ๋ฒ•.....................................................................33 ์ œ 2 ์ ˆ ์กฐ ๊ฒ€์ถœ 35 3.2.1. ์ƒˆ๋กœ์šด ํ”„๋กœํŒŒ์ผ ์„ค์ •................................................................35 3.2.2. ์œˆ๋„์šฐ ํฌ๊ธฐ ์„ค์ •.......................................................................42 3.2.3. ๊ฒ€์ถœ ๋ฐฉ๋ฒ•...................................................................................47 ์ œ 4 ์žฅ ์‹คํ—˜ 49 ์ œ 1 ์ ˆ ์‹คํ—˜ ๋ฐฉ๋ฒ•..............................................................................49 4.1.1. ์‹คํ—˜ ์ฒ™๋„.......................................................................................49 4.1.2. ์‹คํ—˜ ๋ฐฉ๋ฒ•.......................................................................................51 4.1.2.1. ์‹คํ—˜ ๋Œ€์ƒ.....................................................................51 4.1.2.2. ์‹คํ—˜ ๊ตฌ์„ฑ.....................................................................53 ์ œ 2 ์ ˆ ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 55 4.2.1. ์œˆ๋„์šฐ ํฌ๊ธฐ...................................................................................55 4.2.2. ํ”„๋กœํŒŒ์ผ ์ข…๋ฅ˜...............................................................................58 4.2.3. ์ข…ํ•ฉ...............................................................................................62 ์ œ 5 ์žฅ ๊ฒฐ๋ก  65 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ์š”์•ฝ...............................................................................65 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ ์˜์˜...............................................................................66 ์ œ 3 ์ ˆ ํ•œ๊ณ„์  ๋ฐ ์ถ”ํ›„ ๋ฐฉํ–ฅ..............................................................67 ์ฐธ๊ณ ๋ฌธํ—Œ 69 Abstract 76Maste

    ์—ํƒ„์˜ฌ์˜ ๋งŒ์„ฑ์  ๋…ธ์ถœ์— ์˜ํ•œ Xenopus oocyte์— ๋ฐœํ˜„๋œ ํฅ๋ถ„์„ฑ ์•„๋ฏธ๋…ธ์‚ฐ ์šด๋ฐ˜์ž 4ํ˜•์˜ ํ™œ์„ฑ๋„ ์ €ํ•˜

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    Thesis(doctors)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ๋งˆ์ทจ๊ณผํ•™์ „๊ณต,2008.2.Docto
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