23 research outputs found
κ°μΈν μμ±μΈμμ μν DNN κΈ°λ° μν₯ λͺ¨λΈλ§
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Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2019. 2. κΉλ¨μ.λ³Έ λ
Όλ¬Έμμλ κ°μΈν μμ±μΈμμ μν΄μ DNNμ νμ©ν μν₯ λͺ¨λΈλ§ κΈ°λ²λ€μ μ μνλ€. λ³Έ λ
Όλ¬Έμμλ ν¬κ² μΈ κ°μ§μ DNN κΈ°λ° κΈ°λ²μ μ μνλ€. 첫 λ²μ§Έλ DNNμ΄ κ°μ§κ³ μλ μ‘μ νκ²½μ λν κ°μΈν¨μ 보쑰 νΉμ§ 벑ν°λ€μ ν΅νμ¬ μ΅λλ‘ νμ©νλ μν₯ λͺ¨λΈλ§ κΈ°λ²μ΄λ€. μ΄λ¬ν κΈ°λ²μ ν΅νμ¬ DNNμ μ곑λ μμ±, κΉ¨λν μμ±, μ‘μ μΆμ μΉ, κ·Έλ¦¬κ³ μμ νκ²κ³Όμ 볡μ‘ν κ΄κ³λ₯Ό λ³΄λ€ μννκ² νμ΅νκ² λλ€. λ³Έ κΈ°λ²μ Aurora-5 DB μμ κΈ°μ‘΄μ 보쑰 μ‘μ νΉμ§ 벑ν°λ₯Ό νμ©ν λͺ¨λΈ μ μ κΈ°λ²μΈ μ‘μ μΈμ§ νμ΅ (noise-aware training, NAT) κΈ°λ²μ ν¬κ² λ°μ΄λλ μ±λ₯μ 보μλ€.
λ λ²μ§Έλ DNNμ νμ©ν λ€ μ±λ νΉμ§ ν₯μ κΈ°λ²μ΄λ€. κΈ°μ‘΄μ λ€ μ±λ μλ리μ€μμλ μ ν΅μ μΈ μ νΈ μ²λ¦¬ κΈ°λ²μΈ λΉν¬λ° κΈ°λ²μ ν΅νμ¬ ν₯μλ λ¨μΌ μμ€ μμ± μ νΈλ₯Ό μΆμΆνκ³ κ·Έλ₯Ό ν΅νμ¬ μμ±μΈμμ μννλ€. μ°λ¦¬λ κΈ°μ‘΄μ λΉν¬λ° μ€μμ κ°μ₯ κΈ°λ³Έμ κΈ°λ² μ€ νλμΈ delay-and-sum (DS) λΉν¬λ° κΈ°λ²κ³Ό DNNμ κ²°ν©ν λ€ μ±λ νΉμ§ ν₯μ κΈ°λ²μ μ μνλ€. μ μνλ DNNμ μ€κ° λ¨κ³ νΉμ§ 벑ν°λ₯Ό νμ©ν 곡λ νμ΅ κΈ°λ²μ ν΅νμ¬ μ곑λ λ€ μ±λ μ
λ ₯ μμ± μ νΈλ€κ³Ό κΉ¨λν μμ± μ νΈμμ κ΄κ³λ₯Ό ν¨κ³Όμ μΌλ‘ νννλ€. μ μλ κΈ°λ²μ multichannel wall street journal audio visual (MC-WSJAV) corpusμμμ μ€νμ ν΅νμ¬, κΈ°μ‘΄μ λ€μ±λ ν₯μ κΈ°λ²λ€λ³΄λ€ λ°μ΄λ μ±λ₯μ 보μμ νμΈνμλ€.
λ§μ§λ§μΌλ‘, λΆνμ μ± μΈμ§ νμ΅ (Uncertainty-aware training, UAT) κΈ°λ²μ΄λ€. μμμ μκ°λ κΈ°λ²λ€μ ν¬ν¨νμ¬ κ°μΈν μμ±μΈμμ μν κΈ°μ‘΄μ DNN κΈ°λ° κΈ°λ²λ€μ κ°κ°μ λ€νΈμν¬μ νκ²μ μΆμ νλλ° μμ΄μ κ²°μ λ‘ μ μΈ μΆμ λ°©μμ μ¬μ©νλ€. μ΄λ μΆμ μΉμ λΆνμ μ± λ¬Έμ νΉμ μ λ’°λ λ¬Έμ λ₯Ό μΌκΈ°νλ€. μ΄λ¬ν λ¬Έμ μ μ 극볡νκΈ° μνμ¬ μ μνλ UAT κΈ°λ²μ νλ₯ λ‘ μ μΈ λ³ν μΆμ μ νμ΅νκ³ μνν μ μλ λ΄λ΄ λ€νΈμν¬ λͺ¨λΈμΈ λ³ν μ€ν μΈμ½λ (variational autoencoder, VAE) λͺ¨λΈμ μ¬μ©νλ€. UATλ μ곑λ μμ± νΉμ§ 벑ν°μ μμ νκ²κ³Όμ κ΄κ³λ₯Ό 맀κ°νλ κ°μΈν μλ λ³μλ₯Ό κΉ¨λν μμ± νΉμ§ λ²‘ν° μΆμ μΉμ λΆν¬ μ 보λ₯Ό μ΄μ©νμ¬ λͺ¨λΈλ§νλ€. UATμ μλ λ³μλ€μ λ₯ λ¬λ κΈ°λ° μν₯ λͺ¨λΈμ μ΅μ νλ uncertainty decoding (UD) νλ μμν¬λ‘λΆν° μ λλ μ΅λ μ°λ κΈ°μ€μ λ°λΌμ νμ΅λλ€. μ μλ κΈ°λ²μ Aurora-4 DBμ CHiME-4 DBμμ κΈ°μ‘΄μ DNN κΈ°λ° κΈ°λ²λ€μ ν¬κ² λ°μ΄λλ μ±λ₯μ 보μλ€.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
μμ½ 145Docto
μννΈ κ·Έλ¦¬νΌλ₯Ό μν μ κΈ° μ μ μ‘μΈμμ΄ν°μ μ€μ μ ν΄μ
νμλ
Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : 곡과λν κΈ°κ³κ³΅νλΆ, 2023. 2. κΉμ°¬μ€.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΅¬λ ν΄λ¦¬μνΈλ μ£Όλ¨Έλ, ν΄λ¦¬λλ©νΈμ€λ‘μΈμΈ λ°±λ³Έ νλ μ΄νΈ, κ·Έλ¦¬κ³ μ μ μ‘μ²΄λ‘ μλνλ μ κΈ° μ μ μ‘μΈμμ΄ν°μ μλ λ³μμ λν μ±λ₯μ λΆμνκΈ° μν μ΄λ‘ μ λͺ¨λΈμ μ μνλ€. λν λ κ°μ λ§μ£Όλ³΄λ μ κΈ° μ μ μ‘μΈμμ΄ν°λ‘ ꡬμ±λ μννΈ κ·Έλ¦¬νΌλ₯Ό μ€κ³νκ³ , κ·Έ μ±λ₯μ μλ λ²μμ νμ§ κ°λμ λ κ°μ§ μΈ‘λ©΄μμ μΆμ νλ€. μ΅μ’
μ μΌλ‘, λ°±λ³Έ νλ μ΄νΈμ κΈΈμ΄, μ μ μ‘체μ μ, κ·Έλ¦¬κ³ μ μ μ‘체μ μ μ μ¨μ μΈ νλΌλ―Έν°λ‘ μ€μ νκ³ μ΄μ λ°λ₯Έ 그리νΌμ μ±λ₯μ λΆμνλ νλΌλ―Έν° μ€ν°λλ₯Ό μννκ³ κ·Έ κ²°κ³Όλ₯Ό μ μνλ€.Chapter1.Introduction 1
Chapter2.Theoretical model of an electrohydraulic actuator 4
Chapter3.Results and discussion 20
Chapter4.Conclusion 28
Bibliography 31
Abstract in Korean 38μ
(The) morphological study on the proximal part of the humerus in the Korean adults
μνκ³Ό/μμ¬[νκΈ]
견κ΄μ μ μ΄λ£¨λ ꡬ쑰물μ λ§€μ° λ³΅μ‘νλ©° μ΄λ€μ΄ κ΄μ μ λ―ΈμΉλ μμνμ κΈ°μ μ μμ§λ μ μ€λͺ
λμ΄ μμ§ μλ€. μμμ΄λκ·Όμ μ₯건μ νν μμ°μ μΌλ‘ μ μλλλ°, μ΄λ μμ골μ κ²°μ μλ¦, κ²°μ κ°κ΅¬μ κΉμ΄ λ° κ΅¬ λ΄μΈ‘λ²½μ΄ μ΄λ£¨λ κ°λ λ±μ΄ μ΄μ μ€μν μν μ νλ€κ³ νλ€(Meyer, 1928 : Hitchcock, Bechtol, 1948λ±). λν μμ골체μ μ₯μΆμ΄ λμ μ€μ¬μΆ λ° κ²½λΆμ μ΄λ£¨λ κ°λ λ±μ μμμ μΌλ‘ μ€μνλ€. λ°λΌμ μ μλ μμ골 κ·ΌμλΆμ λν ννν μ°κ΅¬κ° ν΄λΆνμ μΌλ‘λ μμμ μΌλ‘ ν° μμκ° μμ κ²μΌλ‘ μκ°νμ¬ νκ΅
μΈ μ±μΈ μμ골 105μΈ‘μμ μ΄λ₯Ό μ‘°μ¬νμ¬ λ€μκ³Ό κ°μ κ²°κ³Όλ₯Ό μ»μλ€. 1. κ²°μ μλ¦μ μμ νμ΄ 22.9%, λΆλΆνμ΄ 53.3%μ΄μκ³ , 22.8%μμλ κ΄μ°°νμ§ λͺ»νμλ€. λν λ¦μ΄ μ‘΄μ¬νμ§ μλ κ²½μ°λ μ°μΈ‘(30.9%)μ΄ μ’μΈ‘(16.0%)μμ λ³΄λ€ λ λ§μλ€.
2. μκ²°μ μ κ·Ήμ κ²°μ μλ¦μ΄ μ‘΄μ¬νλ κ²½μ°(67.5%)κ° μ‘΄μ¬νμ§ μλ κ²½μ°(8%)μ λ λ§μ΄ κ΄μ°°λμλ€.
3. κ²°μ κ°κ΅¬ λ΄μΈ‘λ²½μ κ°λλ μλΉν λ³μ΄κ° μ¬νμμΌλ©° 45Β°μ΄νκ° 13.4%μ΄μλ€.
4. ν΄λΆκ²½μ μΆκ³Ό μμ골 μ₯μΆμ΄ μ΄λ£¨λ κ°λλ λ¨μ±(48.1°± 5.6Β°)μ΄ μ¬μ±(45.1°± 7.4Β°)λ³΄λ€ μ»Έλ€(P<0.05).
5. μμ골λμ μΆκ³Ό μμ골 μ₯μΆμ΄ μ΄λ£¨λ κ°λλ λ¨μ±(138.3Β° Β± 5.5Β°)μ΄ μ¬μ±(135.1 Β± 7.4Β°)λ³΄λ€ μ»Έλ€(P<0.05).
6. μμ골λμ μ§κ²½μ λ¨μ±(4.4 Β± 0.4cm)μ΄ μ¬μ±(4.1 Β± 0.4cm)λ³΄λ€ μ»Έλ€(P<0.005).
[μλ¬Έ]
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
μνκ³Ό/μμ¬[νκΈ]
κ΅ν΅μλ¨μ κ°μνμ μ°μ
μ λ°λ¬λ‘ μΈνμ¬ νλΆμμ μμκ³Ό ν¨κ» ν골골μ μ λΉλκ° μ¦κ°νκ³ μμΌλ©° μμμ μ λλ μ¬ν΄μ§λ μΆμΈμ΄λ€. νλΆλμμ μνμ¬ νκ³¨μ΄ κ³¨μ λλ©΄ μ¬νκ΄μ΄ μμλ μ μμΌλ©°, μμμ΄ μ¬ν κ²½μ°μλ μλͺ
μ΄ μνν΄μ§ μ μλ€. λ°λΌμ ν골골μ μ΄ λ°μν λ 골μ μ μ νμ μ°Έμ‘°νμ¬ μ¬μμμ μ λλ₯Ό μμΈ‘ν μ μμΌλ©΄ μλ£μ§μ μ μν μκΈμ²μΉλ₯Ό μν ν μ μλ€. μ΄μ μ μλ νλΆ λμμ μν λ¨μ ν골골μ νμμμ 골μ λΆμμ 골μ μ λμ λ°λΌμ μ¬μμμ λΆμ κ²ν νμλ€.
1993λ
1μλΆν° 1996λ
2μκΉμ§ μ°μΈλνκ΅ μμ£Όμκ³Όλν λΆμ μμ£ΌκΈ°λ
λ³μ μκΈμΌν°λ‘ λ΄μν νλΆλμ νμμ€ λ골골μ μ΄ μμ΄ λ¨μν νκ³¨λ§ κ³¨μ λ νμμ€ κ³Όκ±°λ ₯μ μ¬μ₯μ§νμ΄ μμλ νμ 33μλ₯Ό λμμΌλ‘ νμλ€. ν골 μΈ‘λ©΄ λ¨μ λ°©μ¬μ κ²μ¬λ‘ 골μ λΆμλ₯Ό
λ³λΆ, 체λΆ, κ²μλκΈ°λΆ, λ€λ°μ±μΌλ‘ λλμμΌλ©°, μ΄μ€μμ ν골μ 체λΆκ³¨μ κ³Ό 체λΆμ΄μΈμ 골μ λ‘ λλμ΄ λΉκ΅νλ€. 골μ μ μ μμ λλ ν골츑면 λ¨μ λ°©μ¬μ κ²μ¬λ₯Ό μ΄μ©νμ¬ κ³¨μ νΈμ μ μ μ λλ₯Ό κ³μΈ‘νμ¬ λ°±λΆμ¨(%)λ‘ νμνμλ€. μ μ μ λλ₯Ό μ μκ° μλ κ²κ³Ό,
50% λ―Έλ§μ μ μ, 50%μ΄μ μ μκ° μλ κ²μΌλ‘ λΆλ₯νκ³ μ μ μ λμ λ°λΌ μ¬μμμ λΉκ΅νμλ€.
μ¬νκ΄μμμ μ§λ¨μ μνμ¬ μ¬μ λ κ²μ¬μ μ¬μ΄μν κ²μ¬, νμ€ CK(creatinine phosphokinase)-MB λ° Troponin-Tκ²μ¬λ₯Ό μννμλ€. μ¬μ λκ²μ¬, μ¬μ΄μν κ²μ¬ λ° νμ€ Troponin-Tκ²μ¬ μ€ νλ μ΄μμ κ²μ¬μμ μ΄μ μκ²¬μΌ λλ₯Ό μ¬μμμ΄λΌ μ§λ¨νμ¬ λ€μκ³Ό κ°μ κ²°λ‘ μ μ»μλ€.
1. ν골골μ μ΄ κ°μ₯ λ§μ΄ λ°μνλ λΆμλ 체λΆμμΌλ©°, 체λΆμ€μμλ νλΆ 1/3μμ 골μ μ΄ κ°μ₯ λ§μλ€.
2. ν골골μ νμμμ μ¬μ λμ μ΄μμκ²¬μ΄ κ΄μ°°λ κ²½μ°λ 14μ(42.4%)μμΌλ©°, κ·Έμ€ μ°κ°μ°¨λ¨μ΄ κ°μ₯ λ§μμΌλ©°, λμ±λΉλ§₯, ST-Tλ³ν, λμ±μλ§₯, μ¬λ°©μ‘°κΈ°μμΆ μμ΄μλ€. μ¬μ λμ νκ³¨μ²΄λΆ μ€μ 1/3, νλΆ 1/3, λ³λΆμ 골μ μμ μ¬μ λμ΄μμκ²¬μ΄ κ°μ₯ λ§μ΄ λ°μνμκ³ ν골체λΆμ 체λΆμ΄μΈ λΆμμ 골μ μ¬μ΄μμ¬μ λ μ΄μμ견 λ°μλ₯ μ μ μν μ°¨μ΄λ μμλ€(p=0.24). 골μ νΈμ μ μμ λμ λ°λ₯Έ μ¬μ λ μ΄μμ견 λ°μλ₯ μλ ν΅κ³μ μΌλ‘ μ μν μ°¨μ΄λ μμλ€(p=0.92).
3. μ¬μ΄μνμ μ΄μμ견μ 11μ(33.3%)μμΌλ©°, μ¬μ΄μνμ μ΄μμκ²¬μ€ μ°μ¬μ€λ²½ μ΄λ μ₯μ κ° κ°μ₯ λ§μκ³ , μ¬λμΌμΆ, μ°μ¬μ€λ²½ νμ₯, νλ§μμ μμ΄μλ€. μ¬μ΄μνλ‘ μ§λ¨λ μ¬μμμ λͺ¨λ νκ³¨μ²΄λΆ κ³¨μ μμ λ°μνμμΌλ©°, ν골체λΆμμ μ¬μ΄μνλ‘ μ§λ¨λ μ¬μμ
λ₯ μ΄ μ μνκ² λμλ€(pβ€0.05). μ¬μ΄μνμ μ§λ¨μ μν 골μ μ μ μμ λμ λ°λ₯Έ μ¬μμμλ μ μν μ°¨μ΄κ° μμλ€(p=0.56).
4. νμ€ Troponin-T μμ±μ λ³΄μΈ κ²½μ°κ° 10μ(30.3%)μμΌλ©°, νμ€ Troponin-Tμ μΈ‘μ κ°κ³Ό μμ±λ₯ μ ν골골μ μ λΆμμ λ°λΌμ μ μν μ°¨μ΄λ μμλ€(p>O.05). 골μ νΈμ μ μμ λμ λ°λ₯Έ νμ€ Troponin-T μΈ‘μ κ°μ μ μμ λκ° ν΄μλ‘ μ¦κ°λμλ€(pβ€0.05). Troponin-T μμ±λ₯ μ μ μ μ λκ° ν΄μλ‘ μ¦κ°νλ κ²½ν₯μ 보μμΌλ μ μν μ°¨μ΄λ μμλ€(p=0.46).
5. μ¬μ λ, μ¬μ΄μν λ° νμ€ Troponin-T κ²μ¬λ‘ μ§λ¨ν μ¬μμμ 33μμ ν골골μ νμμ€ 16μ(48.5%)μμ μ¬μμμ΄ μ§λ¨λμμΌλ©°, 골μ λΆμλ³ μ¬μμμ μ μν μ°¨μ΄λ μμμΌλ 체λΆκ³¨μ μ΄ μ²΄λΆμ΄μΈμ 골μ λ³΄λ€ μ¬μμμ΄ λ λ§μ κ²½ν₯μ 보μλ€. μ μμ λμ λ°λΌ μ¬μμ μμ μ μν μ°¨μ΄λ μμμΌλ 50%μ΄μμ μ μμμ μ¬μμμ΄ λ λΈμ κ²½ν₯μ 보μλ€.
μ΄μμ κ²°κ³Όλ‘ ν골골μ νμμμ 골μ λΆμμ λ°λ₯Έ μ¬μμμ μ°¨μ΄λ μμμΌλ©°, ν골골μ μ μ μμ λμ λ°λΌμ μ¬μμμ μ μν μ°κ΄μ±μ΄ μμλ€. κ·Έλ¬λ 골μ νΈμ μ μλλ μ λκ° μ¦κ°ν μλ‘ νμ€ Troponin-T μΈ‘μ κ°μ μ¦κ°νμΌλ―λ‘ μ¬κ·Όμ μ’μμ΄ λ°μν κ°λ₯μ±μ΄ ν΄ κ²μΌλ‘ μ¬λ£λλ€.
[μλ¬Έ]
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(pβ€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
μ κ²½μμμ΄ ν°μ₯ 골격근μ¬μ μ λ―ΈμΈκ΅¬μ‘°μ κ·ΌμΈμ¬μ±λΆμ λ―ΈμΉλ μν₯
μνκ³Ό/λ°μ¬[μλ¬Έ]
[νκΈ]
μ κ²½μμμ μν΄ κ³¨κ²©κ·Όμ¬μ μμ μμμ΄κΈ°μ μΌμ΄λλ λ―ΈμΈκ΅¬μ‘°μ λ³νμ μ κ²½μ¬λΆν¬
ν λνλλ κ° κ·Όμ¬μ νμ΄ μμ μ΄μ μ κ·Όμ¬μ νκ³Ό λ―ΈμΈκ΅¬μ‘° λ° κ·ΌμμΆλ¨λ°±μ§μ ꡬμ±μ
μ μ°¨μ΄κ° μλμ§λ₯Ό ꡬλͺ
νκΈ° μνμ¬ ν°μ₯ μ’골μ κ²½μ 좩격μμμ μ€ ν 5, 10, 30, 60,
90λ° 180μΌμ μ₯λ΄μ§κ·Όκ³Ό κ°μλ―Έκ·Όμμ Aν, Bν λ° Cνκ·Όμ¬μ λ₯Ό λΆλ¦¬ν λ€μ μ μνλ―Έκ²½
κ΄μ°°κ³Ό μ κΈ°μλλ²μ μννμ¬ λ€μκ³Ό κ°μ κ²°κ³Όλ₯Ό μ»μλ€.
1. λͺ¨λ κ·Όμ¬μ νμμλ μ κ²½μμμ μν΄ κ³΅ν΅μ μΌλ‘ κ·ΌμΈμ¬μ μμ€μ΄ μΌμ΄λ¬μΌλ©°, Aν
μμλ κ·Όνμ§μΈλ§μ λ³ν, Bν λ° Cνμμλ Z-μ μ λΆμ νμ λ° λ―Έν μ½λ리μμ μ¬λ°°
μ΄ λ±μ΄ νΉμ§μ μΌλ‘ κ΄μ°°λμλ€.
2. κ°μ Cνκ·Όμ¬μ λΌλ μ κ²½μμμ μν λ―ΈμΈκ΅¬μ‘°μ λ³νλ κ°μλ―Έκ·Όμ΄ μ₯λ΄μ§κ·Όλ³΄λ€
μ¬νκ² μΌμ΄λ¬λ€.
3. μ κ²½μμ ν 70μΌμ΄ μ§λλ©΄μ λͺ¨λ κ·Όμ¬μ νμμ κ·Όμμ¬μ μ κ°λ‘무λ¬λ κ±°μ μ μ
μΌλ‘ λνλ¬μ§λ§ κ·Όμμ¬μ μ λ°°μ΄μμμ μ μκ³Ό μ°¨μ΄λ₯Ό 보μκ³ μ΄λ 180μΌκΉμ§ μ§μλμ
λ€.
4. μ κ²½μμ 180μΌ ν Aλ μ κΈΈμ΄λ μ₯λ΄μ§κ·Όμ Aνκ³Ό Cνκ·Όμ¬μ μμλ κ°μνμμΌλ
κ°μλ―Έκ·Όμ Bνκ³Ό Cνμμλ μ¦κ°νμκ³ Zμ μ λκ»λ Aνμ μ μΈν λ€λ₯Έ νμμλ λͺ¨λ
μ μλ³΄λ€ μ¦κ°νμλ€.
5. μ κ²½μμ ν 180μΌκ΅°μμ myosin light chainμ isoformκ°μ μλμ λΉμ¨μ μ₯λ΄μ§
κ·Όμμλμ μμ μΌλ‘ ν볡λμμΌλ κ°μλ―Έκ·Όμμλ μ μκ³Ό ν° μ°¨μ΄λ₯Ό λνλ΄μλ€.
μ΄μμ κ²°κ³Όλ₯Ό μ’
ν©νλ©΄ μ κ²½μ μμμ μν κ·Όμ¬μ μ λ―ΈμΈκ΅¬μ‘° λ³νλ κ·Όμ¬μ νκ³Ό κ·Ό
μ‘μ μ’
λ₯μ λ°λΌ λ€λ₯΄κ² μΌμ΄λλ©° μ κ²½μ¬λΆν¬ ν νμ±λ κ° κ·Όμ¬μ νμ λ―ΈμΈκ΅¬μ‘°μ κ·ΌμΈ
μ¬λ¨λ°±μ§μ ꡬμ±μ μ κ²½μμ μ΄μ μ κ° κ·Όμ¬μ νκ³Ό μλ‘ μ°¨μ΄κ° μλ κ²μΌλ‘ μκ°λλ€.
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
Roles of Hsp90 and E3 ubiquitin ligases in folding or degradation of pendrin
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Thesis(doctors) --μμΈλνκ΅ λνμ :ν΅κ³νκ³Ό,2010.2.Docto
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λμ
μΈμ κ³Όκ±° μ£Όλ‘ μλ¬Όμ μμ°ν΄μ ν맀νλ μν μ λ΄λΉνμλ€. μ΅κ·Ό 6μ°¨μ°μ
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μ΄ μΆμ§λλ©΄μ λμ
μΈλ λκΈ°μ
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νμ¬ μνμ κΈ°ννκ³ μλΉμ€νκΈ°κ° μ¬κ° μ½μ§ μλ€. λ³Έ ν¬λΌμ ν΅ν΄μ μμ μκ°μ΄λλ§ 6μ°¨μ°μ
κ²½μ체μ κ²½μλ ₯ ν₯μμ μν λ°©λ²μ μ¬λ¬ μ λ¬Έκ°μ, 6μ°¨μ°μ
μΈ, λμ
μΈλ€κ³Ό ν¨κ» λͺ¨μν΄ λ³΄κ³ μ νλ€.
- μ΄ν μλ΅β μ£Όμ λ°ν
- 6μ°¨μ°μ
κ²½μ체μ μλκ°ν λ°©μ/곡주λ κ°κ²½μ¬ κ΅μ
- 6μ°¨μ°μ
κ²½μ체μ μν κ²½μλ ₯ κ°ν λ°©μ/곡μνμΌν μ΄κ°ν νμ₯
β μ¬λ‘λ°ν
- λμ₯μ μ§ν : λμ₯μμ€λ‘ κ°μΉλ₯Ό μ¬λ¦¬λ€ : μΆ©λ¨ λ
Όμ° κΆκ΅΄μν μλμ‘°ν© λ²μΈ μ΅λͺ