23 research outputs found

    κ°•μΈν•œ μŒμ„±μΈμ‹μ„ μœ„ν•œ DNN 기반 음ν–₯ λͺ¨λΈλ§

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ 전기·컴퓨터곡학뢀, 2019. 2. κΉ€λ‚¨μˆ˜.λ³Έ λ…Όλ¬Έμ—μ„œλŠ” κ°•μΈν•œ μŒμ„±μΈμ‹μ„ μœ„ν•΄μ„œ DNN을 ν™œμš©ν•œ 음ν–₯ λͺ¨λΈλ§ 기법듀을 μ œμ•ˆν•œλ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” 크게 μ„Έ κ°€μ§€μ˜ DNN 기반 기법을 μ œμ•ˆν•œλ‹€. 첫 λ²ˆμ§ΈλŠ” DNN이 가지고 μžˆλŠ” 작음 ν™˜κ²½μ— λŒ€ν•œ 강인함을 보쑰 νŠΉμ§• 벑터듀을 ν†΅ν•˜μ—¬ μ΅œλŒ€λ‘œ ν™œμš©ν•˜λŠ” 음ν–₯ λͺ¨λΈλ§ 기법이닀. μ΄λŸ¬ν•œ 기법을 ν†΅ν•˜μ—¬ DNN은 μ™œκ³‘λœ μŒμ„±, κΉ¨λ—ν•œ μŒμ„±, 작음 μΆ”μ •μΉ˜, 그리고 μŒμ†Œ νƒ€κ²Ÿκ³Όμ˜ λ³΅μž‘ν•œ 관계λ₯Ό 보닀 μ›ν™œν•˜κ²Œ ν•™μŠ΅ν•˜κ²Œ λœλ‹€. λ³Έ 기법은 Aurora-5 DB μ—μ„œ 기쑴의 보쑰 작음 νŠΉμ§• 벑터λ₯Ό ν™œμš©ν•œ λͺ¨λΈ 적응 기법인 작음 인지 ν•™μŠ΅ (noise-aware training, NAT) 기법을 크게 λ›°μ–΄λ„˜λŠ” μ„±λŠ₯을 λ³΄μ˜€λ‹€. 두 λ²ˆμ§ΈλŠ” DNN을 ν™œμš©ν•œ λ‹€ 채널 νŠΉμ§• ν–₯상 기법이닀. 기쑴의 λ‹€ 채널 μ‹œλ‚˜λ¦¬μ˜€μ—μ„œλŠ” 전톡적인 μ‹ ν˜Έ 처리 기법인 빔포밍 기법을 ν†΅ν•˜μ—¬ ν–₯μƒλœ 단일 μ†ŒμŠ€ μŒμ„± μ‹ ν˜Έλ₯Ό μΆ”μΆœν•˜κ³  κ·Έλ₯Ό ν†΅ν•˜μ—¬ μŒμ„±μΈμ‹μ„ μˆ˜ν–‰ν•œλ‹€. μš°λ¦¬λŠ” 기쑴의 빔포밍 μ€‘μ—μ„œ κ°€μž₯ 기본적 기법 쀑 ν•˜λ‚˜μΈ delay-and-sum (DS) 빔포밍 기법과 DNN을 κ²°ν•©ν•œ λ‹€ 채널 νŠΉμ§• ν–₯상 기법을 μ œμ•ˆν•œλ‹€. μ œμ•ˆν•˜λŠ” DNN은 쀑간 단계 νŠΉμ§• 벑터λ₯Ό ν™œμš©ν•œ 곡동 ν•™μŠ΅ 기법을 ν†΅ν•˜μ—¬ μ™œκ³‘λœ λ‹€ 채널 μž…λ ₯ μŒμ„± μ‹ ν˜Έλ“€κ³Ό κΉ¨λ—ν•œ μŒμ„± μ‹ ν˜Έμ™€μ˜ 관계λ₯Ό 효과적으둜 ν‘œν˜„ν•œλ‹€. μ œμ•ˆλœ 기법은 multichannel wall street journal audio visual (MC-WSJAV) corpusμ—μ„œμ˜ μ‹€ν—˜μ„ ν†΅ν•˜μ—¬, 기쑴의 닀채널 ν–₯상 기법듀보닀 λ›°μ–΄λ‚œ μ„±λŠ₯을 λ³΄μž„μ„ ν™•μΈν•˜μ˜€λ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ, λΆˆν™•μ •μ„± 인지 ν•™μŠ΅ (Uncertainty-aware training, UAT) 기법이닀. μœ„μ—μ„œ μ†Œκ°œλœ 기법듀을 ν¬ν•¨ν•˜μ—¬ κ°•μΈν•œ μŒμ„±μΈμ‹μ„ μœ„ν•œ 기쑴의 DNN 기반 기법듀은 각각의 λ„€νŠΈμ›Œν¬μ˜ νƒ€κ²Ÿμ„ μΆ”μ •ν•˜λŠ”λ° μžˆμ–΄μ„œ 결정둠적인 μΆ”μ • 방식을 μ‚¬μš©ν•œλ‹€. μ΄λŠ” μΆ”μ •μΉ˜μ˜ λΆˆν™•μ •μ„± 문제 ν˜Ήμ€ 신뒰도 문제λ₯Ό μ•ΌκΈ°ν•œλ‹€. μ΄λŸ¬ν•œ λ¬Έμ œμ μ„ κ·Ήλ³΅ν•˜κΈ° μœ„ν•˜μ—¬ μ œμ•ˆν•˜λŠ” UAT 기법은 ν™•λ₯ λ‘ μ μΈ λ³€ν™” 좔정을 ν•™μŠ΅ν•˜κ³  μˆ˜ν–‰ν•  수 μžˆλŠ” λ‰΄λŸ΄ λ„€νŠΈμ›Œν¬ λͺ¨λΈμΈ λ³€ν™” μ˜€ν† μΈμ½”λ” (variational autoencoder, VAE) λͺ¨λΈμ„ μ‚¬μš©ν•œλ‹€. UATλŠ” μ™œκ³‘λœ μŒμ„± νŠΉμ§• 벑터와 μŒμ†Œ νƒ€κ²Ÿκ³Όμ˜ 관계λ₯Ό λ§€κ°œν•˜λŠ” κ°•μΈν•œ 은닉 λ³€μˆ˜λ₯Ό κΉ¨λ—ν•œ μŒμ„± νŠΉμ§• 벑터 μΆ”μ •μΉ˜μ˜ 뢄포 정보λ₯Ό μ΄μš©ν•˜μ—¬ λͺ¨λΈλ§ν•œλ‹€. UAT의 은닉 λ³€μˆ˜λ“€μ€ λ”₯ λŸ¬λ‹ 기반 음ν–₯ λͺ¨λΈμ— μ΅œμ ν™”λœ uncertainty decoding (UD) ν”„λ ˆμž„μ›Œν¬λ‘œλΆ€ν„° μœ λ„λœ μ΅œλŒ€ μš°λ„ 기쀀에 λ”°λΌμ„œ ν•™μŠ΅λœλ‹€. μ œμ•ˆλœ 기법은 Aurora-4 DB와 CHiME-4 DBμ—μ„œ 기쑴의 DNN 기반 기법듀을 크게 λ›°μ–΄λ„˜λŠ” μ„±λŠ₯을 λ³΄μ˜€λ‹€.In this thesis, we propose three acoustic modeling techniques for robust automatic speech recognition (ASR). Firstly, we propose a DNN-based acoustic modeling technique which makes the best use of the inherent noise-robustness of DNN is proposed. By applying this technique, the DNN can automatically learn the complicated relationship among the noisy, clean speech and noise estimate to phonetic target smoothly. The proposed method outperformed noise-aware training (NAT), i.e., the conventional auxiliary-feature-based model adaptation technique in Aurora-5 DB. The second method is multi-channel feature enhancement technique. In the general multi-channel speech recognition scenario, the enhanced single speech signal source is extracted from the multiple inputs using beamforming, i.e., the conventional signal-processing-based technique and the speech recognition process is performed by feeding that source into the acoustic model. We propose the multi-channel feature enhancement DNN algorithm by properly combining the delay-and-sum (DS) beamformer, which is one of the conventional beamforming techniques and DNN. Through the experiments using multichannel wall street journal audio visual (MC-WSJ-AV) corpus, it has been shown that the proposed method outperformed the conventional multi-channel feature enhancement techniques. Finally, uncertainty-aware training (UAT) technique is proposed. The most of the existing DNN-based techniques including the techniques introduced above, aim to optimize the point estimates of the targets (e.g., clean features, and acoustic model parameters). This tampers with the reliability of the estimates. In order to overcome this issue, UAT employs a modified structure of variational autoencoder (VAE), a neural network model which learns and performs stochastic variational inference (VIF). UAT models the robust latent variables which intervene the mapping between the noisy observed features and the phonetic target using the distributive information of the clean feature estimates. The proposed technique outperforms the conventional DNN-based techniques on Aurora-4 and CHiME-4 databases.Abstract i Contents iv List of Figures ix List of Tables xiii 1 Introduction 1 2 Background 9 2.1 Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Experimental Database . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.1 Aurora-4 DB . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.2 Aurora-5 DB . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.3 MC-WSJ-AV DB . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.4 CHiME-4 DB . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3 Two-stage Noise-aware Training for Environment-robust Speech Recognition 25 iii 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 Noise-aware Training . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3 Two-stage NAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.1 Lower DNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3.2 Upper DNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.3.3 Joint Training . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.4.1 GMM-HMM System . . . . . . . . . . . . . . . . . . . . . . . 37 3.4.2 Training and Structures of DNN-based Techniques . . . . . . 37 3.4.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 40 3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4 DNN-based Feature Enhancement for Robust Multichannel Speech Recognition 45 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2 Observation Model in Multi-Channel Reverberant Noisy Environment 49 4.3 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.3.1 Lower DNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.3.2 Upper DNN and Joint Training . . . . . . . . . . . . . . . . . 54 4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.4.1 Recognition System and Feature Extraction . . . . . . . . . . 56 4.4.2 Training and Structures of DNN-based Techniques . . . . . . 58 4.4.3 Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.4.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 62 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 iv 5 Uncertainty-aware Training for DNN-HMM System using Varia- tional Inference 67 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 5.2 Uncertainty Decoding for Noise Robustness . . . . . . . . . . . . . . 72 5.3 Variational Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.4 VIF-based uncertainty-aware Training . . . . . . . . . . . . . . . . . 83 5.4.1 Clean Uncertainty Network . . . . . . . . . . . . . . . . . . . 91 5.4.2 Environment Uncertainty Network . . . . . . . . . . . . . . . 93 5.4.3 Prediction Network and Joint Training . . . . . . . . . . . . . 95 5.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 5.5.1 Experimental Setup: Feature Extraction and ASR System . . 96 5.5.2 Network Structures . . . . . . . . . . . . . . . . . . . . . . . . 98 5.5.3 Eects of CUN on the Noise Robustness . . . . . . . . . . . . 104 5.5.4 Uncertainty Representation in Dierent SNR Condition . . . 105 5.5.5 Result of Speech Recognition . . . . . . . . . . . . . . . . . . 112 5.5.6 Result of Speech Recognition with LSTM-HMM . . . . . . . 114 5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 6 Conclusions 127 Bibliography 131 μš”μ•½ 145Docto

    μ†Œν”„νŠΈ 그리퍼λ₯Ό μœ„ν•œ μ „κΈ° μœ μ•• μ•‘μΈ„μ—μ΄ν„°μ˜ 쀀정적 해석

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    ν•™μœ„λ…Όλ¬Έ(석사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ 기계곡학뢀, 2023. 2. 김찬쀑.In this study, we develop an analytical model for an electrohydraulic actuator operating with polyethylene pouches, polydimethylsiloxane backbone plates, and dielectric fluids, to analyze its performance considering the displacement. We assume a soft gripper based on two facing electrohydraulic actuators and theoretically estimate the gripper performance in two respects: the workspace and the grasping force. Finally, we conduct parametric studies by varying three parameters: the length of backbone plate, the amount of dielectric fluid, and the permittivity of the dielectric fluid, to demonstrate the changes in performance according to their values.λ³Έ μ—°κ΅¬λŠ” ν΄λ¦¬μ—ν‹Έλ Œ μ£Όλ¨Έλ‹ˆ, 폴리디메틸싀둝세인 λ°±λ³Έ ν”Œλ ˆμ΄νŠΈ, 그리고 μœ μ „ μ•‘μ²΄λ‘œ μž‘λ™ν•˜λŠ” μ „κΈ° μœ μ•• μ•‘μΈ„μ—μ΄ν„°μ˜ μž‘λ™ λ³€μœ„μ— λŒ€ν•œ μ„±λŠ₯을 λΆ„μ„ν•˜κΈ° μœ„ν•œ 이둠적 λͺ¨λΈμ„ μ œμ‹œν•œλ‹€. λ˜ν•œ 두 개의 λ§ˆμ£Όλ³΄λŠ” μ „κΈ° μœ μ•• μ•‘μΈ„μ—μ΄ν„°λ‘œ κ΅¬μ„±λœ μ†Œν”„νŠΈ 그리퍼λ₯Ό μ„€κ³„ν•˜κ³ , κ·Έ μ„±λŠ₯을 μž‘λ™ λ²”μœ„μ™€ νŒŒμ§€ κ°•λ„μ˜ 두 가지 μΈ‘λ©΄μ—μ„œ μΆ”μ •ν•œλ‹€. μ΅œμ’…μ μœΌλ‘œ, λ°±λ³Έ ν”Œλ ˆμ΄νŠΈμ˜ 길이, μœ μ „ μ•‘μ²΄μ˜ μ–‘, 그리고 μœ μ „ μ•‘μ²΄μ˜ μœ μ „μœ¨μ„ μ„Έ νŒŒλΌλ―Έν„°λ‘œ μ„€μ •ν•˜κ³  이에 λ”°λ₯Έ 그리퍼의 μ„±λŠ₯을 λΆ„μ„ν•˜λŠ” νŒŒλΌλ―Έν„° μŠ€ν„°λ””λ₯Ό μˆ˜ν–‰ν•˜κ³  κ·Έ κ²°κ³Όλ₯Ό μ œμ‹œν•œλ‹€.Chapter1.Introduction 1 Chapter2.Theoretical model of an electrohydraulic actuator 4 Chapter3.Results and discussion 20 Chapter4.Conclusion 28 Bibliography 31 Abstract in Korean 38석

    (The) morphological study on the proximal part of the humerus in the Korean adults

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    μ˜ν•™κ³Ό/석사[ν•œκΈ€] κ²¬κ΄€μ ˆμ„ μ΄λ£¨λŠ” ꡬ쑰물은 맀우 λ³΅μž‘ν•˜λ©° 이듀이 κ΄€μ ˆμ— λ―ΈμΉ˜λŠ” 생역학적 기전은 아직도 잘 μ„€λͺ…λ˜μ–΄ μžˆμ§€ μ•Šλ‹€. μƒμ™„μ΄λ‘κ·Όμ˜ μž₯건은 ν”νžˆ μžμ—°μ μœΌλ‘œ μ „μœ„λ˜λŠ”λ°, μ΄λ•Œ μƒμ™„κ³¨μ˜ κ²°μ ˆμƒλ¦‰, κ²°μ ˆκ°„κ΅¬μ˜ 깊이 및 ꡬ 내츑벽이 μ΄λ£¨λŠ” 각도 등이 이에 μ€‘μš”ν•œ 역할을 ν•œλ‹€κ³  ν•œλ‹€(Meyer, 1928 : Hitchcock, Bechtol, 1948λ“±). λ˜ν•œ μƒμ™„κ³¨μ²΄μ˜ μž₯좕이 λ‘μ˜ 쀑심좕 및 경뢀와 μ΄λ£¨λŠ” 각도 등은 μž„μƒμ μœΌλ‘œ μ€‘μš”ν•˜λ‹€. λ”°λΌμ„œ μ €μžλŠ” 상완골 κ·Όμœ„λΆ€μ— λŒ€ν•œ ν˜•νƒœν•™ 연ꡬ가 ν•΄λΆ€ν•™μ μœΌλ‘œλ‚˜ μž„μƒμ μœΌλ‘œ 큰 μ˜μ˜κ°€ μžˆμ„ κ²ƒμœΌλ‘œ μƒκ°ν•˜μ—¬ ν•œκ΅­ 인 성인 상완골 105μΈ‘μ—μ„œ 이λ₯Ό μ‘°μ‚¬ν•˜μ—¬ λ‹€μŒκ³Ό 같은 κ²°κ³Όλ₯Ό μ–»μ—ˆλ‹€. 1. κ²°μ ˆμƒλ¦‰μ€ μ™„μ „ν˜•μ΄ 22.9%, λΆ€λΆ„ν˜•μ΄ 53.3%μ΄μ—ˆκ³ , 22.8%μ—μ„œλŠ” κ΄€μ°°ν•˜μ§€ λͺ»ν•˜μ˜€λ‹€. λ˜ν•œ 릉이 μ‘΄μž¬ν•˜μ§€ μ•ŠλŠ” κ²½μš°λŠ” 우츑(30.9%)이 쒌츑(16.0%)μ—μ„œ 보닀 더 λ§Žμ•˜λ‹€. 2. μ†Œκ²°μ ˆμ˜ 극은 κ²°μ ˆμƒλ¦‰μ΄ μ‘΄μž¬ν•˜λŠ” 경우(67.5%)κ°€ μ‘΄μž¬ν•˜μ§€ μ•ŠλŠ” 경우(8%)에 더 많이 κ΄€μ°°λ˜μ—ˆλ‹€. 3. κ²°μ ˆκ°„κ΅¬ λ‚΄μΈ‘λ²½μ˜ κ°λ„λŠ” μƒλ‹Ήνžˆ 변이가 μ‹¬ν•˜μ˜€μœΌλ©° 45Β°μ΄ν•˜κ°€ 13.4%μ΄μ—ˆλ‹€. 4. ν•΄λΆ€κ²½μ˜ μΆ•κ³Ό 상완골 μž₯좕이 μ΄λ£¨λŠ” κ°λ„λŠ” 남성(48.1°± 5.6Β°)이 μ—¬μ„±(45.1°± 7.4Β°)보닀 μ»Έλ‹€(P<0.05). 5. μƒμ™„κ³¨λ‘μ˜ μΆ•κ³Ό 상완골 μž₯좕이 μ΄λ£¨λŠ” κ°λ„λŠ” 남성(138.3Β° Β± 5.5Β°)이 μ—¬μ„±(135.1 Β± 7.4Β°)보닀 μ»Έλ‹€(P<0.05). 6. μƒμ™„κ³¨λ‘μ˜ 직경은 남성(4.4 Β± 0.4cm)이 μ—¬μ„±(4.1 Β± 0.4cm)보닀 μ»Έλ‹€(P<0.005). [영문] The structures forming the shoulder joint are very complex and the biomechanical effects of them on the joint are not clearly understood. Frequently the long head of the biceps brachii muscle is dislocated naturally and it has been said that the supratubercular ridged the depth and angle of medial wall of the intertubercular groove are important (Meyer, 1928: Hitchcock, Bechtol, 1948). And the angles between the Long axis of the humerus and the axis of head and neck are important clinically. So the author studied them in the 1O5 cases of the humerus of the Korean adults and the results are as follows: 1. The complete type of the supratubercular ridge were present in 22.9%, and the partial type were in 53.3%. 2. If the ridge was present the spur of the lesser tubercle was present in 67.5%, and if not present the spur was present in 8%. 3. The angle of the medial wall of the intertubercular groove was variable and Less than 45' was 13.4%. 4. The angles between the long axis of the humerus and the axis of head and neck were more greater in male than female(P<0.05). 5. The diameter of the head of the humerus is 4.4Β±0.4 cm in male and 4.1Β±0.4 in female (P<0.005).restrictio

    Evaluation of cardiac injury in different types of sternal fracture

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    μ˜ν•™κ³Ό/석사[ν•œκΈ€] κ΅ν†΅μˆ˜λ‹¨μ˜ 가속화와 μ‚°μ—…μ˜ λ°œλ‹¬λ‘œ μΈν•˜μ—¬ νƒ€λΆ€μœ„μ˜ 손상과 ν•¨κ»˜ ν‰κ³¨κ³¨μ ˆμ˜ λΉˆλ„κ°€ μ¦κ°€ν•˜κ³  있으며 μ†μƒμ˜ 정도도 μ‹¬ν•΄μ§€λŠ” 좔세이닀. 흉뢀둔상에 μ˜ν•˜μ—¬ 흉골이 골절되면 μ‹¬ν˜ˆκ΄€μ΄ 손상될 수 있으며, 손상이 심할 κ²½μš°μ—λŠ” 생λͺ…이 μœ„ν—˜ν•΄μ§ˆ 수 μžˆλ‹€. λ”°λΌμ„œ ν‰κ³¨κ³¨μ ˆμ΄ λ°œμƒν•  λ•Œ 골절의 μœ ν˜•μ„ μ°Έμ‘°ν•˜μ—¬ μ‹¬μ†μƒμ˜ 정도λ₯Ό μ˜ˆμΈ‘ν•  수 있으면 μ˜λ£Œμ§„μ€ μ‹ μ†νžˆ μ‘κΈ‰μ²˜μΉ˜λ₯Ό μ‹œν–‰ ν•  수 μžˆλ‹€. 이에 μ €μžλŠ” 흉뢀 둔상에 μ˜ν•œ λ‹¨μˆœ ν‰κ³¨κ³¨μ ˆ ν™˜μžμ—μ„œ κ³¨μ ˆλΆ€μœ„μ™€ κ³¨μ ˆμ •λ„μ— λ”°λΌμ„œ 심손상을 뢄석 κ²€ν† ν•˜μ˜€λ‹€. 1993λ…„ 1μ›”λΆ€ν„° 1996λ…„ 2μ›”κΉŒμ§€ μ—°μ„ΈλŒ€ν•™κ΅ μ›μ£Όμ˜κ³ΌλŒ€ν•™ 뢀속 원주기독병원 μ‘κΈ‰μ„Όν„°λ‘œ λ‚΄μ›ν•œ 흉뢀둔상 ν™˜μžμ€‘ λŠ‘κ³¨κ³¨μ ˆμ΄ 없이 λ‹¨μˆœνžˆ ν‰κ³¨λ§Œ 골절된 ν™˜μžμ€‘ κ³Όκ±°λ ₯상 심μž₯μ§ˆν™˜μ΄ μ—†μ—ˆλ˜ ν™˜μž 33예λ₯Ό λŒ€μƒμœΌλ‘œ ν•˜μ˜€λ‹€. 흉골 μΈ‘λ©΄ λ‹¨μˆœ 방사선 κ²€μ‚¬λ‘œ κ³¨μ ˆλΆ€μœ„λ₯Ό 병뢀, 체뢀, κ²€μƒλŒκΈ°λΆ€, λ‹€λ°œμ„±μœΌλ‘œ λ‚˜λˆ„μ—ˆμœΌλ©°, μ΄μ€‘μ—μ„œ ν‰κ³¨μ˜ μ²΄λΆ€κ³¨μ ˆκ³Ό μ²΄λΆ€μ΄μ™Έμ˜ 골절둜 λ‚˜λˆ„μ–΄ λΉ„κ΅ν–ˆλ‹€. 골절의 μ „μœ„μ •λ„λŠ” 흉골츑면 λ‹¨μˆœ 방사선 검사λ₯Ό μ΄μš©ν•˜μ—¬ 골절편의 μ „μœ„ 정도λ₯Ό κ³„μΈ‘ν•˜μ—¬ λ°±λΆ„μœ¨(%)둜 ν‘œμ‹œν•˜μ˜€λ‹€. μ „μœ„ 정도λ₯Ό μ „μœ„κ°€ μ—†λŠ” 것과, 50% 미만의 μ „μœ„, 50%이상 μ „μœ„κ°€ μžˆλŠ” κ²ƒμœΌλ‘œ λΆ„λ₯˜ν•˜κ³  μ „μœ„ 정도에 따라 심손상을 λΉ„κ΅ν•˜μ˜€λ‹€. μ‹¬ν˜ˆκ΄€μ†μƒμ˜ 진단을 μœ„ν•˜μ—¬ 심전도 검사와 μ‹¬μ΄ˆμŒνŒŒ 검사, ν˜ˆμ€‘ CK(creatinine phosphokinase)-MB 및 Troponin-T검사λ₯Ό μ‹œν–‰ν•˜μ˜€λ‹€. 심전도검사, μ‹¬μ΄ˆμŒνŒŒ 검사 및 ν˜ˆμ€‘ Troponin-T검사 쀑 ν•˜λ‚˜ μ΄μƒμ˜ κ²€μ‚¬μ—μ„œ 이상 μ†Œκ²¬μΌ λ•Œλ₯Ό 심손상이라 μ§„λ‹¨ν•˜μ—¬ λ‹€μŒκ³Ό 같은 결둠을 μ–»μ—ˆλ‹€. 1. ν‰κ³¨κ³¨μ ˆμ΄ κ°€μž₯ 많이 λ°œμƒν•˜λŠ” λΆ€μœ„λŠ” μ²΄λΆ€μ˜€μœΌλ©°, μ²΄λΆ€μ€‘μ—μ„œλ„ ν•˜λΆ€ 1/3μ—μ„œ 골절이 κ°€μž₯ λ§Žμ•˜λ‹€. 2. ν‰κ³¨κ³¨μ ˆ ν™˜μžμ—μ„œ μ‹¬μ „λ„μ˜ μ΄μƒμ†Œκ²¬μ΄ κ΄€μ°°λœ κ²½μš°λŠ” 14예(42.4%)μ˜€μœΌλ©°, 그쀑 μš°κ°μ°¨λ‹¨μ΄ κ°€μž₯ λ§Žμ•˜μœΌλ©°, λ™μ„±λΉˆλ§₯, ST-Tλ³€ν™”, λ™μ„±μ„œλ§₯, μ‹¬λ°©μ‘°κΈ°μˆ˜μΆ• μˆœμ΄μ—ˆλ‹€. 심전도상 흉골체뢀 쀑앙 1/3, ν•˜λΆ€ 1/3, λ³‘λΆ€μ˜ κ³¨μ ˆμ—μ„œ μ‹¬μ „λ„μ΄μƒμ†Œκ²¬μ΄ κ°€μž₯ 많이 λ°œμƒν•˜μ˜€κ³  흉골체뢀와 체뢀이외 λΆ€μœ„μ˜ 골절 μ‚¬μ΄μ˜μ‹¬μ „λ„ μ΄μƒμ†Œκ²¬ λ°œμƒλ₯ μ˜ μœ μ˜ν•œ μ°¨μ΄λŠ” μ—†μ—ˆλ‹€(p=0.24). 골절편의 μ „μœ„μ •λ„μ— λ”°λ₯Έ 심전도 μ΄μƒμ†Œκ²¬ λ°œμƒλ₯ μ—λ„ ν†΅κ³„μ μœΌλ‘œ μœ μ˜ν•œ μ°¨μ΄λŠ” μ—†μ—ˆλ‹€(p=0.92). 3. μ‹¬μ΄ˆμŒνŒŒμƒ μ΄μƒμ†Œκ²¬μ€ 11예(33.3%)μ˜€μœΌλ©°, μ‹¬μ΄ˆμŒνŒŒμ˜ μ΄μƒμ†Œκ²¬μ€‘ μš°μ‹¬μ‹€λ²½ μš΄λ™ μž₯μ• κ°€ κ°€μž₯ λ§Žμ•˜κ³ , μ‹¬λ‚­μ‚ΌμΆœ, μš°μ‹¬μ‹€λ²½ ν™•μž₯, νŒλ§‰μ†μƒ μˆœμ΄μ—ˆλ‹€. μ‹¬μ΄ˆμŒνŒŒλ‘œ μ§„λ‹¨λœ 심손상은 λͺ¨λ‘ 흉골체뢀 κ³¨μ ˆμ—μ„œ λ°œμƒν•˜μ˜€μœΌλ©°, ν‰κ³¨μ²΄λΆ€μ—μ„œ μ‹¬μ΄ˆμŒνŒŒλ‘œ μ§„λ‹¨λœ 심손상 λ₯ μ΄ μœ μ˜ν•˜κ²Œ λ†’μ•˜λ‹€(p≀0.05). μ‹¬μ΄ˆμŒνŒŒμƒ 진단에 μ˜ν•œ 골절의 μ „μœ„μ •λ„μ— λ”°λ₯Έ μ‹¬μ†μƒμ—λŠ” μœ μ˜ν•œ 차이가 μ—†μ—ˆλ‹€(p=0.56). 4. ν˜ˆμ€‘ Troponin-T 양성을 보인 κ²½μš°κ°€ 10예(30.3%)μ˜€μœΌλ©°, ν˜ˆμ€‘ Troponin-T의 μΈ‘μ •κ°’κ³Ό μ–‘μ„±λ₯ μ€ ν‰κ³¨κ³¨μ ˆμ˜ λΆ€μœ„μ— λ”°λΌμ„œ μœ μ˜ν•œ μ°¨μ΄λŠ” μ—†μ—ˆλ‹€(p>O.05). 골절편의 μ „μœ„μ •λ„μ— λ”°λ₯Έ ν˜ˆμ€‘ Troponin-T 츑정값은 μ „μœ„μ •λ„κ°€ 클수둝 μ¦κ°€λ˜μ—ˆλ‹€(p≀0.05). Troponin-T μ–‘μ„±λ₯ μ€ μ „μœ„ 정도가 클수둝 μ¦κ°€ν•˜λŠ” κ²½ν–₯을 λ³΄μ˜€μœΌλ‚˜ μœ μ˜ν•œ μ°¨μ΄λŠ” μ—†μ—ˆλ‹€(p=0.46). 5. 심전도, μ‹¬μ΄ˆμŒνŒŒ 및 ν˜ˆμ€‘ Troponin-T κ²€μ‚¬λ‘œ μ§„λ‹¨ν•œ 심손상은 33예의 ν‰κ³¨κ³¨μ ˆ ν™˜μžμ€‘ 16예(48.5%)μ—μ„œ 심손상이 μ§„λ‹¨λ˜μ—ˆμœΌλ©°, 골절 λΆ€μœ„λ³„ μ‹¬μ†μƒμ˜ μœ μ˜ν•œ μ°¨μ΄λŠ” μ—†μ—ˆμœΌλ‚˜ μ²΄λΆ€κ³¨μ ˆμ΄ μ²΄λΆ€μ΄μ™Έμ˜ κ³¨μ ˆλ³΄λ‹€ 심손상이 더 λ§Žμ€ κ²½ν–₯을 λ³΄μ˜€λ‹€. μ „μœ„μ •λ„μ— 따라 심손상 μ—­μ‹œ μœ μ˜ν•œ μ°¨μ΄λŠ” μ—†μ—ˆμœΌλ‚˜ 50%μ΄μƒμ˜ μ „μœ„μ—μ„œ 심손상이 더 딸은 κ²½ν–₯을 λ³΄μ˜€λ‹€. μ΄μƒμ˜ 결과둜 ν‰κ³¨κ³¨μ ˆ ν™˜μžμ—μ„œ κ³¨μ ˆλΆ€μœ„μ— λ”°λ₯Έ μ‹¬μ†μƒμ˜ μ°¨μ΄λŠ” μ—†μ—ˆμœΌλ©°, ν‰κ³¨κ³¨μ ˆμ˜ μ „μœ„μ •λ„μ— λ”°λΌμ„œ 심손상은 μœ μ˜ν•œ 연관성이 μ—†μ—ˆλ‹€. κ·ΈλŸ¬λ‚˜ 골절편의 μ „μœ„λ˜λŠ” 정도가 μ¦κ°€ν• μˆ˜λ‘ ν˜ˆμ€‘ Troponin-T 츑정값은 μ¦κ°€ν–ˆμœΌλ―€λ‘œ μ‹¬κ·Όμ˜ μ’Œμƒμ΄ λ°œμƒν•  κ°€λŠ₯성이 클 κ²ƒμœΌλ‘œ μ‚¬λ£Œλœλ‹€. [영문] Sternal fractures are not uncommon injuries. Sternal fractures have been associated with major complications such as cardiac injuries and major thoracic vascular injuries. To identify the incidence and characteristics of cardiac injury according to the different types of sternal fracture, thirty-three patients with an isolated sternal fracture were identified. In an attempt to determine the occurrence of cardiovascular injury in 33 patients with documented sternal fractures, the patients were evaluated by using two dimensional echocardiography, serial ECG, and serial CK(creatinine phosphokinase)-MB and Troponin-T. Sixteen of thirty-three patients had a documented cardiac injury based on at least one positive diagnostic study. The results were as follows: 1. The most common type of sternal fracture was sternal body fracture, especially the lower one third. 2. Fourteen patients(42.4%) were found to have an abnormal ECG. The most common type of abnormal ECG was right bundle branch block, and others were us tachycardia, ST-T wave change, sinus bradycardia, and PVC. There were no differences in cardiovascular injuries with the injury site and displacement of fracture as seen by ECG findings. 3. All positive findings of the two dimensional echocardiography confined in cases of sternal body fracture, and the incidence of cardiac injury increased according to the high degree of displacement in the sternal fracture as founded by echocardiogrphy. 4. The level of serum Troponin-T was not different according to the site of the sternal fracture(p>0.05), but the level of Troponin-T was higher with high degree of displacement in sternal fractures(p≀0.05). 5. Sixteen(48.5%) of thirty-three patients had positive criteria for cardiac injury. There were no differences in cardiac injuries according to the injury site or the degree of fracture displacement.restrictio

    신경손상이 흰μ₯ κ³¨κ²©κ·Όμ„¬μœ μ˜ 미세ꡬ쑰와 근세사성뢄에 λ―ΈμΉ˜λŠ” 영ν–₯

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    μ˜ν•™κ³Ό/박사[영문] [ν•œκΈ€] 신경손상에 μ˜ν•΄ κ³¨κ²©κ·Όμ„¬μœ μ—μ„œ μ†μƒμ΄ˆκΈ°μ— μΌμ–΄λ‚˜λŠ” λ―Έμ„Έκ΅¬μ‘°μ˜ 변화와 μ‹ κ²½μž¬λΆ„ν¬ ν›„ λ‚˜νƒ€λ‚˜λŠ” 각 κ·Όμ„¬μœ ν˜•μ΄ 손상 μ΄μ „μ˜ κ·Όμ„¬μœ ν˜•κ³Ό 미세ꡬ쑰 및 κ·Όμˆ˜μΆ•λ‹¨λ°±μ§ˆμ˜ ꡬ성에 μ„œ 차이가 μžˆλŠ”μ§€λ₯Ό ꡬλͺ…ν•˜κΈ° μœ„ν•˜μ—¬ 흰μ₯ μ’Œκ³¨μ‹ κ²½μ— 좩격손상을 μ€€ ν›„ 5, 10, 30, 60, 90및 180일에 μž₯딴지근과 κ°€μžλ―Έκ·Όμ—μ„œ Aν˜•, Bν˜• 및 Cν˜•κ·Όμ„¬μœ λ₯Ό λΆ„λ¦¬ν•œ λ‹€μŒ μ „μžν˜„λ―Έκ²½ κ΄€μ°°κ³Ό μ „κΈ°μ˜λ™λ²•μ„ μ‹œν–‰ν•˜μ—¬ λ‹€μŒκ³Ό 같은 κ²°κ³Όλ₯Ό μ–»μ—ˆλ‹€. 1. λͺ¨λ“  κ·Όμ„¬μœ ν˜•μ—μ„œλŠ” 신경손상에 μ˜ν•΄ κ³΅ν†΅μ μœΌλ‘œ κ·Όμ„Έμ‚¬μ˜ μ†Œμ‹€μ΄ μΌμ–΄λ‚¬μœΌλ©°, Aν˜• μ—μ„œλŠ” κ·Όν˜•μ§ˆμ„Έλ§μ˜ λ³€ν™”, Bν˜• 및 Cν˜•μ—μ„œλŠ” Z-μ„ μ˜ λΆ„μ ˆ ν˜„μƒ 및 λ―Έν† μ½˜λ“œλ¦¬μ•„μ˜ 재배 μ—΄ 등이 νŠΉμ§•μ μœΌλ‘œ κ΄€μ°°λ˜μ—ˆλ‹€. 2. 같은 Cν˜•κ·Όμ„¬μœ λΌλ„ 신경손상에 μ˜ν•œ λ―Έμ„Έκ΅¬μ‘°μ˜ λ³€ν™”λŠ” κ°€μžλ―Έκ·Όμ΄ μž₯딴지근보닀 μ‹¬ν•˜κ²Œ 일어났닀. 3. 신경손상 ν›„ 70일이 μ§€λ‚˜λ©΄μ„œ λͺ¨λ“  κ·Όμ„¬μœ ν˜•μ—μ„œ κ·Όμ›μ„¬μœ μ˜ κ°€λ‘œλ¬΄λŠ¬λŠ” 거의 정상 으둜 λ‚˜νƒ€λ‚¬μ§€λ§Œ κ·Όμ›μ„¬μœ μ˜ 배열양상은 정상과 차이λ₯Ό λ³΄μ˜€κ³  μ΄λŠ” 180μΌκΉŒμ§€ μ§€μ†λ˜μ—ˆ λ‹€. 4. 신경손상 180일 ν›„ A띠의 κΈΈμ΄λŠ” μž₯λ”΄μ§€κ·Όμ˜ Aν˜•κ³Ό Cν˜•κ·Όμ„¬μœ μ—μ„œλŠ” κ°μ†Œν•˜μ˜€μœΌλ‚˜ κ°€μžλ―Έκ·Όμ˜ Bν˜•κ³Ό Cν˜•μ—μ„œλŠ” μ¦κ°€ν•˜μ˜€κ³  Zμ„ μ˜ λ‘κ»˜λŠ” Aν˜•μ„ μ œμ™Έν•œ λ‹€λ₯Έ ν˜•μ—μ„œλŠ” λͺ¨λ‘ 정상보닀 μ¦κ°€ν•˜μ˜€λ‹€. 5. 신경손상 ν›„ 180μΌκ΅°μ—μ„œ myosin light chain의 isoformκ°„μ˜ μƒλŒ€μ  λΉ„μœ¨μ€ μž₯딴지 κ·Όμ—μ„œλŠ”μ •μƒμ μœΌλ‘œ νšŒλ³΅λ˜μ—ˆμœΌλ‚˜ κ°€μžλ―Έκ·Όμ—μ„œλŠ” 정상과 큰 차이λ₯Ό λ‚˜νƒ€λ‚΄μ—†λ‹€. μ΄μƒμ˜ κ²°κ³Όλ₯Ό μ’…ν•©ν•˜λ©΄ μ‹ κ²½μ˜ 손상에 μ˜ν•œ κ·Όμ„¬μœ μ˜ 미세ꡬ쑰 λ³€ν™”λŠ” κ·Όμ„¬μœ ν˜•κ³Ό κ·Ό 윑의 μ’…λ₯˜μ— 따라 λ‹€λ₯΄κ²Œ μΌμ–΄λ‚˜λ©° μ‹ κ²½μž¬λΆ„ν¬ ν›„ ν˜•μ„±λœ 각 κ·Όμ„¬μœ ν˜•μ˜ 미세ꡬ쑰와 κ·Όμ„Έ μ‚¬λ‹¨λ°±μ§ˆμ˜ ꡬ성은 신경손상 μ΄μ „μ˜ 각 κ·Όμ„¬μœ ν˜•κ³Ό μ„œλ‘œ 차이가 μžˆλŠ” κ²ƒμœΌλ‘œ μƒκ°λœλ‹€. Studies on the Changes of Ultrastructure and Myofibrillar Proteins of Muscle Fiber Types After Nerve Injury Kang Hyun Lee Department of Medical Science The Graduate School, Yonsei University (Directed by Professor In Hyuk Chung, M.D.) This experiment was attempted to observe the morphological changes in skeletal muscle fibers during the early stage of the nerve injury and also to detennine the differences in ultrastructure and myofibrillar proteins between the muscle fiber types from the normal and those from the one fellowing reinnevation. The sciatic nerve of rats was surgically crushed and A, B and C type muscle fibers were isolated from the m. soleus and the m. gastrocnemius at 5, 10, 30, 60, 90 and 180 days. The isolated muscle fibers were processed for electron microscopic observation and SDS-polyacrylamide gel electrophoresis. The results were as follows. 1. Loss of myofilaments were common in all fiber types after the nerve injury. Sarcoplasmic reticulum changes in A fiber, and segmentation of Z-line and rearrangement of mitochondria in B and C fibers were observed specifically. 2. Despite the same fiber type, the early ultrastructural changes of the C fiber in the m. soleus were more profound than that in the m. gastrocnemius. 3. Following nerve injury all muscle fiber types showed normal striations after 30 days. However, the arrangements of myofibrils were different from that of the control, which persisted until 180 days. 4. At the end of 180 days after the nerve injury, the length of A band decreased in A and C fibers from the m. gastrocnemius, but that in B and C fibers from the m. soleus was increased. The thickness of Z-line of all muscle fiber types except A fiber was increased. 5. Relative proportions of isotypes of myosin light chains in m. gastrocnemius were recovered to normal, but that in m. soleus showed great differences compared to the control at 180 days after the nerve injury. In conclusion, ultrastructural changes of muscle fiber following nerve injury were specific accordingly to muscles and the muscle fiber types. In addition the ultrastructure and the composition of the myofibrillar proteins following reinnervation were different from those of normal, which suggests that muslce fiber types following reinnervation are not normal units 180 days after nerve injury.restrictio

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    6μ°¨μ‚°μ—…μ •μ±…ν¬λŸΌ : 6μ°¨μ‚°μ—… 경영체의 μ—­λŸ‰κ°•ν™” λ°©μ•ˆ (강경심, μ΄κ°•ν˜„, 졜λͺ…μ„ )

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    농업인은 κ³Όκ±° 주둜 원물을 μƒμ‚°ν•΄μ„œ νŒλ§€ν•˜λŠ” 역할을 λ‹΄λ‹Ήν•˜μ˜€λ‹€. 졜근 6μ°¨μ‚°μ—… 정책이 μΆ”μ§„λ˜λ©΄μ„œ 농업인도 λŒ€κΈ°μ—…κ³Ό 같은 μƒν’ˆμ„ κΈ°νšν•˜κ³  μ œμ‘°ν•΄μ„œ νŒλ§€ν•˜μ—¬ 원물보닀 λ§Žμ€ μˆ˜μ΅μ„ μ˜¬λ¦¬λ €λŠ” μ‹œλ„κ°€ 행해지고 μžˆμ§€λ§Œ, μ‹œμž₯μ—μ„œ λ‹€λ₯Έ 여타 μƒν’ˆλ“€κ³Ό κ²½μŸν•΄μ•Όν•˜κ³ , μ†ŒλΉ„μžμ˜ μš•κ΅¬(needs)λ₯Ό νŒŒμ•…ν•˜μ—¬ μƒν’ˆμ„ κΈ°νšν•˜κ³  μ„œλΉ„μŠ€ν•˜κΈ°κ°€ μ—¬κ°„ 쉽지 μ•Šλ‹€. λ³Έ ν¬λŸΌμ„ ν†΅ν•΄μ„œ μž‘μ€ μ‹œκ°„μ΄λ‚˜λ§ˆ 6μ°¨μ‚°μ—… 경영체의 경쟁λ ₯ ν–₯상을 μœ„ν•œ 방법을 μ—¬λŸ¬ 전문가와, 6차산업인, 농업인듀과 ν•¨κ»˜ λͺ¨μƒ‰ν•΄ 보고자 ν•œλ‹€. - 이후 μƒλž΅β—‹ μ£Όμ œλ°œν‘œ - 6μ°¨μ‚°μ—… 경영체의 μ—­λŸ‰κ°•ν™” λ°©μ•ˆ/κ³΅μ£ΌλŒ€ 강경심 ꡐ수 - 6μ°¨μ‚°μ—… 경영체의 μƒν’ˆ 경쟁λ ₯ κ°•ν™” λ°©μ•ˆ/κ³΅μ˜ν™ˆμ‡Όν•‘ μ΄κ°•ν˜„ νŒ€μž₯ β—‹ μ‚¬λ‘€λ°œν‘œ - 된μž₯의 진화 : 된μž₯μ†ŒμŠ€λ‘œ κ°€μΉ˜λ₯Ό μ˜¬λ¦¬λ‹€ : 좩남 λ…Όμ‚° κΆκ΅΄μ‹ν’ˆ μ˜λ†μ‘°ν•© 법인 졜λͺ…
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