39 research outputs found

    쑰건뢀 μžκΈ°νšŒκ·€ν˜• 인곡신경망을 μ΄μš©ν•œ μ œμ–΄ κ°€λŠ₯ν•œ κ°€μ°½ μŒμ„± ν•©μ„±

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : μœ΅ν•©κ³Όν•™κΈ°μˆ λŒ€ν•™μ› 지λŠ₯μ •λ³΄μœ΅ν•©ν•™κ³Ό, 2022. 8. 이ꡐꡬ.Singing voice synthesis aims at synthesizing a natural singing voice from given input information. A successful singing synthesis system is important not only because it can significantly reduce the cost of the music production process, but also because it helps to more easily and conveniently reflect the creator's intentions. However, there are three challenging problems in designing such a system - 1) It should be possible to independently control the various elements that make up the singing. 2) It must be possible to generate high-quality sound sources, 3) It is difficult to secure sufficient training data. To deal with this problem, we first paid attention to the source-filter theory, which is a representative speech production modeling technique. We tried to secure training data efficiency and controllability at the same time by modeling a singing voice as a convolution of the source, which is pitch information, and filter, which is the pronunciation information, and designing a structure that can model each independently. In addition, we used a conditional autoregressive model-based deep neural network to effectively model sequential data in a situation where conditional inputs such as pronunciation, pitch, and speaker are given. In order for the entire framework to generate a high-quality sound source with a distribution more similar to that of a real singing voice, the adversarial training technique was applied to the training process. Finally, we applied a self-supervised style modeling technique to model detailed unlabeled musical expressions. We confirmed that the proposed model can flexibly control various elements such as pronunciation, pitch, timbre, singing style, and musical expression, while synthesizing high-quality singing that is difficult to distinguish from ground truth singing. Furthermore, we proposed a generation and modification framework that considers the situation applied to the actual music production process, and confirmed that it is possible to apply it to expand the limits of the creator's imagination, such as new voice design and cross-generation.κ°€μ°½ 합성은 주어진 μž…λ ₯ μ•…λ³΄λ‘œλΆ€ν„° μžμ—°μŠ€λŸ¬μš΄ κ°€μ°½ μŒμ„±μ„ ν•©μ„±ν•΄λ‚΄λŠ” 것을 λͺ©ν‘œλ‘œ ν•œλ‹€. κ°€μ°½ ν•©μ„± μ‹œμŠ€ν…œμ€ μŒμ•… μ œμž‘ λΉ„μš©μ„ 크게 쀄일 수 μžˆμ„ 뿐만 μ•„λ‹ˆλΌ μ°½μž‘μžμ˜ μ˜λ„λ₯Ό 보닀 쉽고 νŽΈλ¦¬ν•˜κ²Œ λ°˜μ˜ν•  수 μžˆλ„λ‘ λ•λŠ”λ‹€. ν•˜μ§€λ§Œ μ΄λŸ¬ν•œ μ‹œμŠ€ν…œμ˜ 섀계λ₯Ό μœ„ν•΄μ„œλŠ” λ‹€μŒ μ„Έ κ°€μ§€μ˜ 도전적인 μš”κ΅¬μ‚¬ν•­μ΄ μ‘΄μž¬ν•œλ‹€. 1) 가창을 μ΄λ£¨λŠ” λ‹€μ–‘ν•œ μš”μ†Œλ₯Ό λ…λ¦½μ μœΌλ‘œ μ œμ–΄ν•  수 μžˆμ–΄μ•Ό ν•œλ‹€. 2) 높은 ν’ˆμ§ˆ μˆ˜μ€€ 및 μ‚¬μš©μ„±μ„ 달성해야 ν•œλ‹€. 3) μΆ©λΆ„ν•œ ν›ˆλ ¨ 데이터λ₯Ό ν™•λ³΄ν•˜κΈ° μ–΄λ ΅λ‹€. μ΄λŸ¬ν•œ λ¬Έμ œμ— λŒ€μ‘ν•˜κΈ° μœ„ν•΄ μš°λ¦¬λŠ” λŒ€ν‘œμ μΈ μŒμ„± 생성 λͺ¨λΈλ§ 기법인 μ†ŒμŠ€-ν•„ν„° 이둠에 μ£Όλͺ©ν•˜μ˜€λ‹€. κ°€μ°½ μ‹ ν˜Έλ₯Ό μŒμ • 정보에 ν•΄λ‹Ήν•˜λŠ” μ†ŒμŠ€μ™€ 발음 정보에 ν•΄λ‹Ήν•˜λŠ” ν•„ν„°μ˜ ν•©μ„±κ³±μœΌλ‘œ μ •μ˜ν•˜κ³ , 이λ₯Ό 각각 λ…λ¦½μ μœΌλ‘œ λͺ¨λΈλ§ν•  수 μžˆλŠ” ꡬ쑰λ₯Ό μ„€κ³„ν•˜μ—¬ ν›ˆλ ¨ 데이터 νš¨μœ¨μ„±κ³Ό μ œμ–΄ κ°€λŠ₯성을 λ™μ‹œμ— ν™•λ³΄ν•˜κ³ μž ν•˜μ˜€λ‹€. λ˜ν•œ μš°λ¦¬λŠ” 발음, μŒμ •, ν™”μž λ“± 쑰건뢀 μž…λ ₯이 주어진 μƒν™©μ—μ„œ μ‹œκ³„μ—΄ 데이터λ₯Ό 효과적으둜 λͺ¨λΈλ§ν•˜κΈ° μœ„ν•˜μ—¬ 쑰건뢀 μžκΈ°νšŒκ·€ λͺ¨λΈ 기반의 심측신경망을 ν™œμš©ν•˜μ˜€λ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ λ ˆμ΄λΈ”λ§ λ˜μ–΄μžˆμ§€ μ•Šμ€ μŒμ•…μ  ν‘œν˜„μ„ λͺ¨λΈλ§ν•  수 μžˆλ„λ‘ μš°λ¦¬λŠ” μžκΈ°μ§€λ„ν•™μŠ΅ 기반의 μŠ€νƒ€μΌ λͺ¨λΈλ§ 기법을 μ œμ•ˆν–ˆλ‹€. μš°λ¦¬λŠ” μ œμ•ˆν•œ λͺ¨λΈμ΄ 발음, μŒμ •, μŒμƒ‰, 창법, ν‘œν˜„ λ“± λ‹€μ–‘ν•œ μš”μ†Œλ₯Ό μœ μ—°ν•˜κ²Œ μ œμ–΄ν•˜λ©΄μ„œλ„ μ‹€μ œ κ°€μ°½κ³Ό ꡬ뢄이 μ–΄λ €μš΄ μˆ˜μ€€μ˜ κ³ ν’ˆμ§ˆ κ°€μ°½ 합성이 κ°€λŠ₯함을 ν™•μΈν–ˆλ‹€. λ‚˜μ•„κ°€ μ‹€μ œ μŒμ•… μ œμž‘ 과정을 κ³ λ €ν•œ 생성 및 μˆ˜μ • ν”„λ ˆμž„μ›Œν¬λ₯Ό μ œμ•ˆν•˜μ˜€κ³ , μƒˆλ‘œμš΄ λͺ©μ†Œλ¦¬ λ””μžμΈ, ꡐ차 생성 λ“± μ°½μž‘μžμ˜ 상상λ ₯κ³Ό ν•œκ³„λ₯Ό λ„“νž 수 μžˆλŠ” μ‘μš©μ΄ κ°€λŠ₯함을 ν™•μΈν–ˆλ‹€.1 Introduction 1 1.1 Motivation 1 1.2 Problems in singing voice synthesis 4 1.3 Task of interest 8 1.3.1 Single-singer SVS 9 1.3.2 Multi-singer SVS 10 1.3.3 Expressive SVS 11 1.4 Contribution 11 2 Background 13 2.1 Singing voice 14 2.2 Source-filter theory 18 2.3 Autoregressive model 21 2.4 Related works 22 2.4.1 Speech synthesis 25 2.4.2 Singing voice synthesis 29 3 Adversarially Trained End-to-end Korean Singing Voice Synthesis System 31 3.1 Introduction 31 3.2 Related work 33 3.3 Proposed method 35 3.3.1 Input representation 35 3.3.2 Mel-synthesis network 36 3.3.3 Super-resolution network 38 3.4 Experiments 42 3.4.1 Dataset 42 3.4.2 Training 42 3.4.3 Evaluation 43 3.4.4 Analysis on generated spectrogram 46 3.5 Discussion 49 3.5.1 Limitations of input representation 49 3.5.2 Advantages of using super-resolution network 53 3.6 Conclusion 55 4 Disentangling Timbre and Singing Style with multi-singer Singing Synthesis System 57 4.1Introduction 57 4.2 Related works 59 4.2.1 Multi-singer SVS system 60 4.3 Proposed Method 60 4.3.1 Singer identity encoder 62 4.3.2 Disentangling timbre & singing style 64 4.4 Experiment 64 4.4.1 Dataset and preprocessing 64 4.4.2 Training & inference 65 4.4.3 Analysis on generated spectrogram 65 4.4.4 Listening test 66 4.4.5 Timbre & style classification test 68 4.5 Discussion 70 4.5.1 Query audio selection strategy for singer identity encoder 70 4.5.2 Few-shot adaptation 72 4.6 Conclusion 74 5 Expressive Singing Synthesis Using Local Style Token and Dual-path Pitch Encoder 77 5.1 Introduction 77 5.2 Related work 79 5.3 Proposed method 80 5.3.1 Local style token module 80 5.3.2 Dual-path pitch encoder 85 5.3.3 Bandwidth extension vocoder 85 5.4 Experiment 86 5.4.1 Dataset 86 5.4.2 Training 86 5.4.3 Qualitative evaluation 87 5.4.4 Dual-path reconstruction analysis 89 5.4.5 Qualitative analysis 90 5.5 Discussion 93 5.5.1 Difference between midi pitch and f0 93 5.5.2 Considerations for use in the actual music production process 94 5.6 Conclusion 95 6 Conclusion 97 6.1 Thesis summary 97 6.2 Limitations and future work 99 6.2.1 Improvements to a faster and robust system 99 6.2.2 Explainable and intuitive controllability 101 6.2.3 Extensions to common speech synthesis tools 103 6.2.4 Towards a collaborative and creative tool 104λ°•

    무신미 EMS λŒμ—°λ³€μ΄μ²΄μ˜ μœ μ „μ  뢄석

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : 농업생λͺ…κ³Όν•™λŒ€ν•™ 식물생산과학뢀, 2018. 2. 강병철.Capsaicinoid is the alkaloid compounds produced in peppers (Capsicum spp.). They are responsible for pepper pungency or hotness and is one of the important traits in breeding programs. Although many studies have been performed to elucidate its biosynthesis, the biosynthetic pathway is largely based on studies on the similar pathways of other plants. To understand the biosynthesis of capsaicinoid, a nonpungent mutant 221-2-1a, developed from pungent 'Yuwol-cho' were analyzed. 221-2-1a was found to have no mutation in the coding sequence of Pun1, but the levels of capsaicinoid in their fruits were drastically decreased compared to that of Yuwol-cho. To identify the gene(s) responsible for the non-pungent trait in 221-2-1a. Gene expressions of 12 genes involved in capsaicinoid biosynthesis were compared between 221-2-1a and Yuwol-cho together with several selected cultivars. Seven out of 12 genes (pAMT, BCAT, ACL, KAS, FatA, PAL, and Pun1) showed a significant decrease in their expression levels in 221-2-1a compared to pungent cultivars. Furthermore, the inheritance of pungency was studied in a population derived from a between Yuwolcho and 221-2-1a. The inheritance study showed that the nonpungency in 221-2-1a is controlled by two recessive genes. To identify the genes responsible for non-pungency trait, samples of Yuwol-cho and bulked F3 were sequenced and analyzed by MutMap. A total of 11 SNPs were identified in the intergenic sequences and the candidates were annotated. Although candidate genes were not capsaicinoid biosynthesic genes, the candidate genes are believed to be ideal targets in studies to carryout in the future.I. LITERATURE REVIEW 01 II. INTRODUCTION 07 III. MATERIALS AND METHODS 10 3.1 Plant materials 10 3.2 Sample preparation for capsaicinoid analysis.. 10 3.3 Gibbs screening and HPLC analysis . 11 3.4 Isolation of RNA and cDNA synthesis 13 3.5 Quantitative real-time PCR analysis. 13 3.6 Polymerase chain reaction (PCR) amplification and sequence analysis of Pun1. 17 3.7 Genomic DNA extraction 17 3.8 Whole genome sequencing of wild type and mutant bulk 18 3.9 Alignment of reference sequence and MutMap.. 18 IV. RESULTS 20 4.1 Capsaicinoid measurement in placenta tissues . 20 4.2 Expression analysis of capsaicinoid pathway genes. 23 4.3 Comparison of Pun1 exon sequences . 28 4.4 Evaluation of pungency segregation. 30 4.5 Sequencing of Yuwol-cho and bulked F3 DNA 34 4.6 MutMap analysis for candidate region identification 36 V. DISCUSSION 40 VI. REFERENCES. 44 VII. ABSTRACT IN KOREAN 55 VIII. APPENDIX. 57Maste

    A Numerical Simulation of Near-Cloud Turbulence Associated with Tropical Cyclone Hagibis

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    ν•™μœ„λ…Όλ¬Έ(석사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : μžμ—°κ³Όν•™λŒ€ν•™ ν˜‘λ™κ³Όμ • 계산과학전곡, 2023. 2. κΉ€μ •ν›ˆ.2019λ…„ 10μ›” 11일 0840μ—μ„œ 0900 UTC 기간에 λΆμ„œ νƒœν‰μ–‘ 상곡을 μ§€λ‚˜λ˜ 항곡기가 νƒœν’ ν•˜κΈ°λΉ„μŠ€ λΆμ„œμͺ½ μ „λ©΄μ˜ μ•½ 11 km κ³ λ„μ—μ„œ λ‹€λ°œμ„±μ˜ λ‚œλ₯˜λ₯Ό μ‘°μš°ν•˜μ˜€λ‹€. λ‚œλ₯˜ λ°œμƒ 지점은 νƒœν’ μ€‘μ‹¬μœΌλ‘œλΆ€ν„° 500 km 이상 λ–¨μ–΄μ Έ μžˆμ—ˆμœΌλ©°, 항곡기 κ΄€μΈ‘ μžλ£Œμ— λ”°λ₯΄λ©΄ μ—λ„ˆμ§€ μ†Œμ‚°λ₯ μ΄ 0.22 m2/3s-1 이상인 쀑강도 크기λ₯Ό κ°–λŠ” λ‚œλ₯˜λ₯Ό ν¬ν•¨ν•˜μ˜€λ‹€. λ”°λΌμ„œ λ³Έ μ—°κ΅¬μ—μ„œλŠ” λ³Έ λ‚œλ₯˜ μ‚¬λ‘€μ˜ λ°œμƒ λ©”μ»€λ‹ˆμ¦˜μ„ μ‚΄νŽ΄λ³΄κΈ° μœ„ν•΄ Weather and Forecasting (WRF)을 μ΄μš©ν•œ 수치λͺ¨λΈ μ‹€ν—˜μ„ μˆ˜ν–‰ν•˜μ˜€λ‹€. μˆ˜μΉ˜μ‹€ν—˜μ€ λ‚œλ₯˜ κ΄€μΈ‘ 지점을 μ€‘μ‹¬μœΌλ‘œ μˆ˜ν‰κ²©μžκ°€ 각각 15, 5, 1, 0.2 km인 μ˜μ—­ 4개둜 κ΅¬μ„±ν•˜μ˜€μœΌλ©°, 연직 ν•΄μƒλ„λŠ” λ‚œλ₯˜ λ°œμƒ ꡬ간인 8-13 kmμ—μ„œ μ•½ 280 m둜 μ„€μ •ν•˜μ˜€λ‹€. 자유 λŒ€κΈ°μ—μ„œμ˜ 연직 ν˜Όν•© λͺ¨μˆ˜ν™”λ₯Ό μœ„ν•΄ Mellor-Yamada 2.5μ°¨ λ‚œλ₯˜ μ’…κ²° 방법둠을 μ΄μš©ν•˜μ—¬ μ•„κ²©μž 규λͺ¨μ˜ λ‚œλ₯˜ μš΄λ™μ—λ„ˆμ§€(subgrid-scale turbulent kinetic energy, μ΄ν•˜ SGS TKE)λ₯Ό μ‚°μΆœν•˜λŠ” Mellor-Yamada-JanjiΔ‡ λ°©μ•ˆμ„ 각 μ˜μ—­μ— μ μš©ν•˜μ˜€λ‹€. κ·Έ κ²°κ³Ό SGS TKEκ°€ 0.25 m2s-2 이상인 κ°•ν•œ λ‚œλ₯˜κ°€ κ΅­μ§€μ μœΌλ‘œ μ„Έ ꡬ간 1) z = 13-15 km, 2) z = 10-12 km, 3) z = 6-8 kmμ—μ„œ λͺ¨μ˜λ˜μ—ˆμœΌλ©°, λ³Έ μ—°κ΅¬μ—μ„œλŠ” μ€‘κ°•λ„μ˜ λ‚œλ₯˜κ°€ κ΄€μΈ‘λœ 2) ꡬ간을 μ€‘μ μ μœΌλ‘œ λΆ„μ„ν•˜μ˜€λ‹€. λ‚œλ₯˜ λ°œμƒ 2μ‹œκ°„ 이전뢀터 λͺ¨λ£¨μš΄ ν•˜μΈ΅μ— μœ„μΉ˜ν•œ 2) κ΅¬κ°„μ—μ„œλŠ” νƒœν’ μƒμΈ΅μ˜ κ³ κΈ°μ••μ„± 유좜둜 μœ λ„λœ κ°•ν•œ 연직 μ‹œμ–΄μ— μ˜ν•œ Kelvin-Helmholtz λΆˆμ•ˆμ •μ΄ μ§€μ†μ μœΌλ‘œ μ‘΄μž¬ν•˜μ˜€λ‹€. 이후 0800 UTC λΆ€ν„° 정적 μ•ˆμ •λ„κ°€ κ°μ†Œν•˜μ—¬ 0840-0900 UTCμ—λŠ” 2) ꡬ간 λ‚œλ₯˜ λ°œμƒμ˜ 직접적 원인인 λŒ€λ₯˜ λΆˆμ•ˆμ •μ΄ λ°œμƒν•˜μ˜€λ‹€. μ΄λ•Œ νƒœν’ μƒμΈ΅μ˜ κ³ κΈ°μ•• 흐름에 μ˜ν•œ 쀑립 ν˜Ήμ€ μ•½ν•œ κ΄€μ„± λΆˆμ•ˆμ •μ΄ μ§€μ†μ μœΌλ‘œ μ‘΄μž¬ν–ˆλ˜ 2) κ΅¬κ°„μ—μ„œλŠ” λ‚œλ₯˜ κ΄€μΈ‘ μ‹œκΈ°μ— νƒœν’μ˜ λΆμƒμœΌλ‘œ κ³ κΈ°μ••μ„± 흐름이 κ°•ν™”λ˜μ–΄ κ΄€μ„± λΆˆμ•ˆμ •λ„κ°€ μ¦κ°€ν•˜μ˜€λ‹€. 즉 κ΄€μ„± λΆˆμ•ˆμ • κ°•ν™”μ‹œκΈ°μ™€ λŒ€λ₯˜ λΆˆμ•ˆμ •μ˜ λ°œν˜„ μ‹œκΈ°κ°€ μΌμΉ˜ν•˜μ˜€μœΌλ©°, 이λ₯Ό 톡해 κ΄€μ„± λΆˆμ•ˆμ •μ΄ 2) ꡬ간 λ‚œλ₯˜ λ°œμƒμ˜ 주원인인 λŒ€λ₯˜ λΆˆμ•ˆμ • λ°œν˜„μ— μ€‘μš” μš”μ†Œλ‘œ μž‘μš©ν•œ κ²ƒμœΌλ‘œ ν•΄μ„λœλ‹€. ν•œνŽΈ, 1) κ΅¬κ°„μ˜ λ‚œλ₯˜ λ°œμƒμ€ νƒœν’ κ³ κΈ°μ••μ„± 흐름 λ‚΄ μ˜¨λ„ 이λ₯˜ 차이에 μ˜ν•œ 얕은 λŒ€λ₯˜ λΆˆμ•ˆμ •, 3) κ΅¬κ°„μ˜ λ‚œλ₯˜ λ°œμƒμ€ ꢌ운 λ‚΄ λ–¨μ–΄μ§€λŠ” 눈 μž…μžμ˜ μŠΉν™” κ³Όμ •μœΌλ‘œ λ°œμƒν•˜λŠ” λŒ€λ₯˜ λΆˆμ•ˆμ • λ•Œλ¬ΈμΈ κ²ƒμœΌλ‘œ λΆ„μ„λœλ‹€.From 0840 to 0900 UTC 11 October 2019, light-or-moderate turbulence events were observed with in situ eddy dissipation rate data provided by Aircraft Meteorological Data Relay at 11 km within the anticyclonic outflow of tropical cyclone (TC) Hagibis over the northwestern Pacific Ocean. The area of turbulence was farther than 500 km from the central of the TC and showed the low density of cloud. The generation mechanism of near-cloud turbulence (NCT) occurred in the northwestern side of the TC was examined using the Weather Research and Forecasting (WRF) model. Four nested model domains with horizontal grid spacings of 15, 5, 1, and 0.2 km and 112 hybrid layers with vertical grid spacing of about 280 m within z = 8-13 km, near altitudes where the NCT encounters occurred were used. The Mellor-Yamada-Janjic scheme was applied in each domain to parameterize local vertical mixings by computing subgrid-scale turbulent kinetic energy (SGS TKE) in free atmosphere by Mellor-Yamada 2.5-level turbulence closure method. The results showed that there were three distinct areas of simulated turbulence, which occurred in 1) z = 13-15 km, 2) z = 10-12 km, and 3) z = 6-8 km layers showing SGS TKE larger than 0.25 m2s-2. We focused on the 10 to 12 km layer in which turbulence was observed. Richardson (Ri) number smaller than 0.25 was found consistently before the time of the incident, which implies Kelvin-Helmholtz instability occurred due to strong vertical wind shear induced by anticyclonic outflow of the TC at the beneath of the cirrus anvil cloud. From 0800 UTC, static stability started to decrease and convective instability occurred during 0840 to 0900 UTC, which produced light-or-moderate level turbulence. At the same time, intensity of inertial instability at z = 10-12 km layer increased with strengthened upper-level anticyclonic outflow where neutral or weak inertial instability was consistently existed due to anticyclonic outflow of the TC. Consequently, we suggested that inertial instability was responsible for the occurrence of convective instability given that the strengthening period of inertial instability was coincided with the manifestation period of convective instability. SGS TKE simulated at z = 13-15 km was due to convective instability induced by the differential thermal advection within the anticyclonic outflow of the TC. SGS TKE found at z = 6-8 km was also induced by convective instability by sublimation of precipitating snow in the beneath of cirrus anvil cloud.1. μ„œλ‘  1 2. 사둀 μ„ μ • 6 3. μ‹€ν—˜ 섀계 13 4. μ‹€ν—˜ κ²°κ³Ό κ°œμš” 17 5. λ‚œλ₯˜ λ©”μ»€λ‹ˆμ¦˜ 뢄석 24 5.1 횑적 ꢌ운 λ°΄λ“œ λ‚΄ λ‚œλ₯˜ λ°œμƒ 24 5.2 νƒœν’ 상측 κ³ κΈ°μ••μ„± 흐름 λ‚΄ λ‚œλ₯˜ λ°œμƒ 40 5.3 λͺ¨λ£¨μš΄ ν•˜λ‹¨ λ‚΄ λ‚œλ₯˜ λ°œμƒ 61 6. μš”μ•½ 및 κ²°λ‘  66 μ°Έκ³ λ¬Έν—Œ 71 Abstract 78석

    μ˜€ν”„λ‘œλ”©μ„ μ΄μš©ν•œ λͺ¨λ°”일 κΈ°κΈ°μ—μ„œμ˜ μ‹€μ‹œκ°„ 이미지 μ΄ˆν•΄μƒλ„ 기술

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ 전기·정보곡학뢀, 2018. 8. μ΅œμ„±ν˜„.The rapid enhancement of camera performances in smartphones has allowed users to take high quality pictures without high-end digital cameras. However, there still remains a large gap between smartphone cameras and digital cameras when in comes to zoom-in functionality. Most smartphones provide only digial zoom-in functionality, where image quality degradation is inevitable when the user enlarges the image. Even the high-end smartphones embedded with optical lens provide limited optical zoom-in capabilities, leaving users with great inconvenience. While users can employ an external optical lens to utilize the optical zoom-in functionality, having to carry around an extra hardware incurs great overhead, not to mention its price. Image Super Resolution (SR) can be a solution to overcome this limitation by recovering the quality degradation caused by digital zoom-in. Image SR, a technique to restore high frequency details from a Low Resolution (LR) image to obtain a High Resolution (HR) image, has been a traditional field of research in computer vision. As deep learning based, especially Convolutional Neural Network (CNN) based, methods have shown to outperform traditional methods, and have been actively researched in recent years. In this paper, we exploit deep learning based image SR to replace the optical zoom-in functionality in smartphones without embedded optical lenses. As there are several resource constraints in smartphones~(e.g., computing power, energy, memory), challenges occur when aiming to provide a real-time performance relying solely based on local execution. To tackle the challenge, we propose a server offloading based approach to provide higher frame rate. Through a prototype implementation on Android and extensive experiments in real world environments, we show that our proposed system can provide at least 10~fps.1 Introduction 1 2 Preliminaries 4 2.1 Image Super Resolution (SR) . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Mobile deep learning framework . . . . . . . . . . . . . . . . . . . . 5 2.2.1 Local execution based framework . . . . . . . . . . . . . . . 5 2.2.2 Server offloading based framework . . . . . . . . . . . . . . 6 2.3 What is different about SR in smartphones . . . . . . . . . . . . . . 6 2.3.1 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3.2 Resource constraints . . . . . . . . . . . . . . . . . . . . . . 7 2.4 Local or server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3 Implementation 8 3.1 SR model implementation . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 Prototype implementation on Android . . . . . . . . . . . . . . . . . 9 4 Evaluation 11 4.1 SR model performance . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Inference time measured on smartphone and server . . . . . . . . . . 12 4.3 Latency analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.3.1 Offloading latency . . . . . . . . . . . . . . . . . . . . . . . 14 4.3.2 Overall latency breakdown . . . . . . . . . . . . . . . . . . . 17 5 Discussion 19 5.1 Perceptual quality of generated images . . . . . . . . . . . . . . . . . 19 5.2 Managing high data rate . . . . . . . . . . . . . . . . . . . . . . . . 20 6 Conclusion 22 Abstract (In Korean) 28 κ°μ‚¬μ˜κΈ€ 31Maste

    Mobile telemedicine system via PSTN

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    보건정보관리학과/석사[ν•œκΈ€] 보건의료 μ •λ³΄ν™”μ˜ 핡심 λ‚΄μš© 쀑 ν•˜λ‚˜μΈ μ›κ²©μ§„λ£ŒλŠ” ν˜„μž¬ λ‹€κ°λ„λ‘œ μ‹œλ„λ˜κ³  μžˆμœΌλ‚˜ λŒ€λΆ€λΆ„ λ³΄νŽΈν™”λ˜μ§€ μ•Šμ€ 톡신 μˆ˜λ‹¨μ„ μ‚¬μš©ν•˜κΈ° λ•Œλ¬Έμ— μ˜λ£Œμ·¨μ•½μ§€μ—­ 및 κ±°λ™λΆˆλŠ₯ ν™˜μžμ— λŒ€ν•œ μ›κ²©μ§„λ£Œμ˜ μž₯점을 μΆ©λΆ„νžˆ 살리지 λͺ»ν•˜κ³  μžˆλ‹€. 이에 κ³Όμ²œλ³΄κ±΄μ†Œμ˜ λ°©λ¬Έκ°„ν˜Έ ν™˜μžλ“€μ„ λŒ€μƒμœΌλ‘œ λ°©λ¬Έκ°„ν˜Έμ‚¬κ°€ μΌλ°˜κ³΅μ€‘μ „ν™”λ§μ„ μ‚¬μš©ν•œ μ΄λ™ν˜• μ›κ²©μ§„λ£Œ κΈ°κΈ°λ₯Ό μ§€μ°Έν•˜κ³  ν™˜μžμ˜ 거주지λ₯Ό λ°©λ¬Έν•˜μ—¬ λ³΄κ±΄μ†Œμ˜ μ˜μ‚¬μ™€ μ˜μƒκ³Ό μŒμ„±μ„ κ΅ν™˜ν•˜λŠ” μ›κ²©μ§„λ£Œ μ‹œμŠ€ν…œμ„ 8κ°œμ›”κ°„ μš΄μ˜ν•˜μ˜€λ‹€. μš΄μ˜μ„±κ³Όλ₯Ό ν‰κ°€ν•˜κΈ° μœ„ν•΄ ν™˜μžμ˜ λ§Œμ‘±λ„μ™€ μΌλ°˜μ§„λ£Œ λŒ€μ²΄ κ°€λŠ₯μ„±μœΌλ‘œμ„œμ˜ νš¨μš©μ„±μ„ μ‘°μ‚¬ν•œ κ²°κ³Ό 전체 λŒ€μƒν™˜μž 50λͺ… 쀑 38λͺ…(76.0%)이 λ³Έ μ›κ²©μ§„λ£Œμ‹œμŠ€ν…œμ„ 톡해 도움을 λ°›μ•˜λ‹€κ³  μ‘λ‹΅ν•˜μ˜€μœΌλ©° λ°©λ¬Έκ°„ν˜Έλ§Œμ„ μ‹€μ‹œν–ˆμ„ λ•Œ ν™˜μžμ˜ μ˜λ£ŒκΈ°κ΄€μ„ 내원 νšŸμˆ˜κ°€ ν™˜μž 1인당 1달 평균 0.64νšŒμ˜€μœΌλ‚˜ μ›κ²©μ§„λ£Œμ™€ λ°©λ¬Έκ°„ν˜Έλ₯Ό λ™μ‹œμ— μ‹€μ‹œν•œ 이후 0.42회둜 κ°μ†Œν•˜μ˜€λ‹€. λŒ€μƒν™˜μž 쀑 μ–‘λ‘œμ›λ³΄λ‹€ κ°œμΈκ°€μ •μ— κ±°μ£Όν•˜λŠ” ν™˜μžμ˜ λ§Œμ‘±λ„κ°€ λ†’μ•˜μœΌλ©°, ν™”μ§ˆ 및 음질, μ—°κ²°μ‹œκ°„ λ“± μ‹œμŠ€ν…œμ˜ μ„±λŠ₯κ³Ό ν™˜μžμ˜ μ „μ‚°κ²½ν—˜, μž„μƒμƒν™©μ€ λ§Œμ‘±λ„μ™€ μœ μ˜ν•œ 관련성을 λ‚˜νƒ€λ‚΄μ§€ λͺ»ν–ˆλ‹€. λ³Έ μ‹œμŠ€ν…œμ„ 톡해 ν˜„μž¬μ˜ ν†΅μ‹ ν™˜κ²½ λ‚΄μ—μ„œ μ›κ²©μ§„λ£Œκ°€ μΌλ°˜ν™”λ˜μ–΄ 의료 μ·¨μ•½μ§€μ—­μ˜ ν™˜μžλ“€μ΄ λ³΄κ±΄μ˜λ£Œμ„œλΉ„μŠ€λ₯Ό μ†μ‰½κ²Œ 받을 수 있게 됨과 μ•„μšΈλŸ¬ μ›κ²©μ§„λ£Œμ˜ κ²½ν—˜μΆ•μ μ„ 톡해 미래 ν†΅μ‹ ν™˜κ²½μ˜ 보건정보 μ‹œμŠ€ν…œ 정비에 λ§Žμ€ 도움을 받을 수 μžˆμ„ κ²ƒμœΌλ‘œ κΈ°λŒ€λœλ‹€. [영문] There is a need for caring the elderly and disabled people without increasing the cost of on-site expertise at the location where home health nursing services are provided. Recently, telemedicine is changing the traditional form of health care delivery, by providing cost-effective technical solutions to communicate between patients and doctor. Despite of high reliability, ISDN-based telemedicine systems are not widely used in home health care because of high communication cost. In this study, an efficient and inexpensive electronic system to transmit audio and video signal in telemedicine via PSTN was presented. The Mobile Telemedicine System via PSTN was developed and implemented to provide health care for elderly and disabled people at Kwachon as a demonstration project. The telemedicine systems consisted of notebook computer and digital camera interconnected via PSTN, which enables communication between the patient at home and the doctor at health center. After eight months trial of this project, 50 patients are being cared for at the patient's home or the nursing home for the old. The ratio of satisfied patients was 76% and the number of patient's visit to the clinic was reduced. The quality of video and audio did not have an influence on satisfaction. The results suggested that telemedicine was both feasible and acceptable via PSTNrestrictio

    λͺ¨λ©˜ν…€ μ „λž΅μ— 관성은 μ‘΄μž¬ν•˜λŠ”κ°€? ꡭ제 μ£Όμ‹μ‹œμž₯의 싀증연ꡬλ₯Ό λ°”νƒ•μœΌλ‘œ

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ²½μ˜ν•™κ³Ό, 2015. 2. μ΄κ΄€νœ˜.I examine the claim argued by Novy-Marx (2012) that the intermediate past performance of stock does better job in explaining conventional momentum profit. Using equity level data from 49 countries, I find that there exists a cross-country variation in the superiority of performance of momentum portfolio based on intermediate past return, and such variation can be explained by the individualism index established by Hofstede (2001). Partly confirming the finding by Chui, Titman, and Wei (2010), I argue that the finding by Novy-Marx is a manifestation of cross-country cultural differences that have direct impact on investor behavior, expressed in terms of momentum profit.1. Introduction 1 2. Data Selection and Methodology 4 3. Term Structure of Momentum around the world 5 4. Behavioral explanation – Individualism index 9 5. Conclusion 14 6. Reference 16 7. Tables and Figures 19 8. Appendix 29 ꡭ문초둝 39Maste

    μ„œμšΈμ‹œ λ³΅μ§€μ„œλΉ„μŠ€μ „λ‹¬κΈ°κ΄€ κ°„ ν˜‘λ ₯적 λ„€νŠΈμ›Œν¬ κ΅¬μΆ•λ°©μ•ˆ 연ꡬ(A study on collaborative network in Seoul metropolitan social service delivery system)

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    λ…ΈνŠΈ : 이 λ³΄κ³ μ„œμ˜ λ‚΄μš©μ€ μ—°κ΅¬μ§„μ˜ κ²¬ν•΄λ‘œμ„œ μ„œμšΈνŠΉλ³„μ‹œμ˜ μ •μ±…κ³ΌλŠ” λ‹€λ₯Ό μˆ˜λ„ μžˆμŠ΅λ‹ˆλ‹€

    μ„œμšΈμ‹œ 곡무원 κ²½λ ₯κ°œλ°œμ œλ„ λ„μž…λ°©μ•ˆ(Introduction and improvement plans of the career development program in Seoul metropolitan city)

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    λ…ΈνŠΈ : 이 λ³΄κ³ μ„œμ˜ λ‚΄μš©μ€ μ—°κ΅¬μ§„μ˜ κ²¬ν•΄λ‘œμ„œ μ„œμšΈνŠΉλ³„μ‹œμ˜ μ •μ±…κ³ΌλŠ” λ‹€λ₯Ό μˆ˜λ„ μžˆμŠ΅λ‹ˆλ‹€
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