89 research outputs found
BRICS and Neighbors
νμλ
Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : κ΅μ λνμ κ΅μ νκ³Ό(κ΅μ νλ ₯μ 곡), 2021.8. κΉνκ· .The recognition of South Africaβs role on the African continent is decreasing with its stagnating economic status, along with geopolitical issues. While joining the forum of BRICS in 2010 and serving its third two-year term in the UNSC allowed South Africa to take up the symbolic role of the representative of Africa. However, the rising recognition of China in Africa and the fast-growing regional rival Nigeria challenges South Africaβs quest for hegemony on the continent. Furthermore, South Africaβs influence in the nearest countries, such as Namibia and Botswana, has weakened in areas of trade as well as foreign direct investment. This study analyzes South Africaβs status as the representative of the African continent and seeks to explore the question of whether it still holds the position of a hegemon, or at least as a continental representative, by examining South Africaβs role in the BRICS, UNSC, and the AU in terms of both economic and political issues.
The research reveals that South Africa tries to gain back its reputation on the global scale after their successful transition to democracy by precisely structured foreign policies of their presidents. However, they were neither fully successful in becoming recognized as a global leader of the continent, nor were they accepted by the states within the continent due to their fluctuating economic status, hostile neighbors, and continental rival. Furthermore, South Africa was the biggest trade partner within the SADC but with the rising recognition of China and their investment towards overall Africa, South Africa could not position itself as the hegemon of the continent.μν리카 λλ₯μμ λ¨μν리카곡νκ΅μ μν μ΄ μ§μ νμ λ¬Έμ λ° μΉ¨μ²΄λ κ²½μ μ μ§μλ‘ μΈν΄ μΆμλκ³ μλ€. 2010λ
, λ¨μν리카곡νκ΅μ BRICS ν¬λΌμ κ°μ
νκ³ 3λ²μ μ보리 μκΈ°λ₯Ό μννλ©΄μ μν리카λ₯Ό λννλ μμ§μ μν μ 맑μλ€. κ·Έλ¬λ μν리카μμ μ€κ΅μ μν₯λ ₯μ΄ λμμ§κ³ λλ₯ λ΄ κ²½μμμ΄λ©° λΉ λ₯΄κ² μ±μ₯νκ³ μλ λμ΄μ§λ¦¬μλ‘ μΈνμ¬ λ¨μν리카곡νκ΅μ λλ₯ ν¨κΆκ΅μΌλ‘μμ μν μ μ΄ννμ§ λͺ»νκ³ μλ€. λμκ° λλ―ΈλΉμ, λ³΄μΈ μλ λ± μΈμ κ΅μμ λ¨μν리카곡νκ΅μ μν₯λ ₯μ μΈκ΅μΈ μ§μ ν¬μλ λ¬Όλ‘ κ΅μ λΆμΌμμλ μ½ν΄μ‘λ€. λ³Έ μ°κ΅¬λ λ¨μν리카곡νκ΅μ΄ μν리카 λλ₯ λνλ‘μμ μ§μλ₯Ό λΆμνκ³ , κ²½μ Β·μ μΉμ λ¬Έμ μ BRICS, μ보리, AUμμ λ¨μν리카곡νκ΅μ μν μ κ²ν ν΄ ν¨κΆκ΅ μ§μλ₯Ό μ¬μ ν μ μ§νκ³ μλμ§μ λν λ¬Έμ λ₯Ό νꡬνκ³ μ νλ€.
λ¨μν리카곡νκ΅μ μ±κ³΅μ μΈ λ―Όμ£Όμ£Όμ μ¬νλ₯Ό μ΄λ£©ν ν λν΅λ Ήλ€μ μ λ°ν μΈκ΅μ μ±
μ ꡬ쑰ν νμλ€. μ΄κ²μ μΈκ³μ μΈ κ΅κ°λ‘ μ§μ
ν¨κ³Ό λμμ μμ λͺ
μ±μ λμ°ΎκΈ° μν μ μ±
μ νΌ κ²μ΄λΌλ μ°κ΅¬ κ²°κ³Όκ° λμλ€. κ·Έλ¬λ μ΄λ€μ λλ₯μ κΈλ‘λ² λ¦¬λλ‘ μΈμ λ°λλ° μ±κ³΅νμ§ λͺ»νλ€. κ·Έ μ΄μ λ μμ μ μ΄μ§ λͺ»ν κ²½μ μ λ³λ, μ λμ μ΄μ, λλ₯μ λΌμ΄λ²λ‘ μΈν΄ λλ₯ λ΄ κ΅κ°λ€μκ² λ¦¬λλ‘μ μΈμ λ°μ§ λͺ»νκΈ° λλ¬Έμ΄λ€. 무μλ³΄λ€ λ¨μν리카곡νκ΅μ SADC λ΄ μ΅λ κ΅μκ΅μ΄μμ§λ§ μ€κ΅μ΄ μν리카 μ 체μ λν ν¬μλ₯Ό μμνλ©΄μ λλ₯μ ν¨κΆκ΅μΌλ‘ μ리맀κΉν μ μμλ€.1. Introduction 1
1.1 Argument overview 1
1.2 Historical Background and Influence of the Cold War 2
1.3 Literature review 8
1.4 Research Question and Outline of Thesis 11
2. Research Design 12
2.1 Analytical Framework 12
2.2 Methodology 14
3. Status within BRICS 15
3.1 Formation of BRICS 16
3.2 South Africa's decreasing role on the African Continent 20
3.3 China's Rising Recognition in Africa 33
4. Case Study 47
4.1 Namibia 50
4.2 Botswana 58
5. Thesis Findings 63
5.1 Conclusion 63
5.2 Limitations and further research reference 66
Reference 68μ
Correlation Network Analysis for Deciphering Rust Disease-Triggered Metabolism Using Mass Spectrometry-based Metabolomics
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : μ½νκ³Ό, 2017. 2. κΆμ±μ.νμ¬κΉμ§μ κ°μΌμ± μ§λ³ μ°κ΅¬λ, λ³μμ± κ· μ μΉ¨μ
λ°©λ² λ° κ· μ΄ μμ±νλ μμ£Όμ λν μκ·Ή λ¬Όμ§μ μμ±μ λν΄ μ£Όλ‘ μ§μ€λμ΄ μλ€. κ·Έλ¬λ, κ· μ μΉ¨μ
μ μν μμ£Όμ κ³Όλ―Όλ°μμΌλ‘λΆν° μ§λ³μ μ¦μκ³Ό κ°μ λ³μμ± ννν λ³νκ° μ λν μ μλ€. μ΄λ₯Ό μ¦λͺ
νκΈ° μνμ¬, μ§λλΆμκΈ° κΈ°λ° λμ¬μ²΄νμ μ μ©νμμΌλ©°, μκ΄κ΄κ³ λ€νΈμν¬ λΆμμ μ΄μ©νμ¬ λμ¬κΈ°μ κ°μ κΈ΄λ°ν κ΄κ³λ₯Ό μΆμ νμλ€. λμ¬κΈ°μ -λ³μμ± μ°κ΄μ± μ°κ΅¬λ₯Ό μν΄ μμ£ΌμΈ 3μ’
μ μ₯λ―Έκ³Ό μλ¬Όκ³Ό μ΄λ€μ κ· μ²΄λ₯Ό νμ±νμ¬ μ΄λ€μ λ©λ§λ₯΄κ² νλ Gymnosporangium asiaticumμ μν λμ¬κΈ°μ λ³νλ₯Ό κ΄μ°°νμλ€. μ§λλΆμκΈ°λ₯Ό μ¬μ©νμ¬ λ€μμ Primary metabolitesλ₯Ό κ²μΆνμμΌλ©°, λ€λ³λν΅κ³λΆμμ ν΅ν΄ μ»μ μ§νλ¬Όμ§λ€μ κΈ°λ°μΌλ‘ νμ¬ Pathway enrichmentλ₯Ό μννμλ€. μ΄λ‘λΆν° μ»μ μκ΄μ± λμ λμ¬κΈ°μ μ¬μ΄μ κ΄κ³λ₯Ό μΆμ νμ¬, Aminosugar and nucleotide sugar metabolism μ κ²°νμ κ΄μ¬λμ΄ μλ λμ¬κΈ°μ μ λ°κ²¬νμλ€. μ΄ λμ¬κΈ°μ μμμ μ£Όλ‘ λΉμμ½μ¬μ μΆμ μ΄ ANMμ Supply metabolitesλ₯Ό κ²°νμν¨λ€λ κ²μ PPMCCμ κΈ°λ°ν μκ΄κ΄κ³ λ€νΈμν¬ λΆμμΌλ‘λΆν° κ²μ¦νμμΌλ©°, κ΅νΈμμ©μ λν κ΄μ°°λ‘μ¨ Supply metabolitesμ κ²°νκ³Ό λΉμμ½μ¬μ μΆμ μ΄ μ°κ΄μ΄ λμ΄ μμμ κ²°λ‘ μ§μλ€. μ΄λ¬ν μμ£Ό μ€μ¬μ λμ¬ λ°μμ μκ΄κ΄κ³ λ€νΈμν¬ λΆμμΌλ‘ κ΄μ°°νλ λ°©λ²μΌλ‘μ¨ λμ¬κΈ°μ μ λ³ν λ° κ·Έλ‘ μΈν κ²°νμ΄ νΉμ μ§λ³μμ΄ λ μ μμμ 보μμΌλ©°, λμ¬μ²΄ν μ°κ΅¬μ μ μ ν λκ΅¬λ‘ μ¬μ©κ°λ₯ν¨μ νμΈνμλ€.I. μλ‘ 1
II. μ¬λ£ λ° λ°©λ² 6
III. κ²°κ³Ό λ° κ³ μ°° 9
IV. κ²°λ‘ 26
V. μ°Έκ³ λ¬Έν 27
VI. λΆλ‘ 30
1. GC-MS spectrum of 34 metabolites 31
2. GC-MS κΈ°λ° λμ¬μ²΄ν 39
3. MZmine 57
4. Multivariate statistical analysis 62
Abstract 73Docto
νλ₯ μ μ§λ°° κ²μ λ°©λ²μ νμ©ν κ΅μ μμ κ°κ²©κ° μμ΅μ± λΉκ΅λΆμ
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : 곡과λν μλμ§μμ€ν
곡νλΆ, 2019. 2. νμλ
.λ³Έ μ°κ΅¬μ λͺ©μ μ WTI μ , Brent μ , Dubai μ κ°κ²©μ μμ΅ λΆν¬ κ° κ΄κ³, μμ μ μ λ¬Όμνκ³Ό νλ¬Όμν κ°κ²©μ μμ΅λΆν¬ κ° κ΄κ³, λ€ μ’
λ₯μ μμ μ ν (νλ°μ , λλ°©μ , κ²½μ , μ νΈ μ°λ£) κ°κ²©μ μμ΅λΆν¬ κ° κ΄κ³λ₯Ό νλ₯ μ μ§λ°°κ΄κ³ μ κ·Όλ² (Stochastic Dominance approach)μ μ μ©νμ¬ λΆμνλ κ²μ΄λ€.
νλ₯ μ μ§λ°° κ΄κ³λ, λ νλ₯ λ³μλ₯Ό λΉκ΅νκ³ μκ³λ₯Ό μ νλ λ° μμ΄ κΈ°μ€μ μ μνλ μ΄λ‘ μ΄λ€. νλ₯ μ μ§λ°° (Stochastic Dominance) κ°λ
μ μ μ©ν μμ΅ λΆν¬ λΉκ΅ λ°©λ²μ νλ₯ μ μ§λ°° κΈ°μ€μ΄ ν¨μ© ν¨μμ λν μ΅μνμ κ°μ λ§μ νμλ‘ νκΈ° λλ¬Έμ νλ₯ λ³μλ€ κ°μ μμμ κ΄ν λͺ
νν ν΄μμ ν μ μλ€λ μ₯μ μ μ§λλ€. μ΄λ¬ν μ΄μ λ‘, κΈμ΅ κ²½μ νμμλ λ€μν ν¬μ λμμ λΆνμ€μ±μ λΉκ΅ν¨μ μμ΄μ νλ₯ μ μ§λ°° κ΄κ³κ° μ€μν κ°λ
μΌλ‘ μ¬μ©λλ€.
μ°μ , λ³Έ μ°κ΅¬λ νλ₯ μ§λ°° κ°λ
μ μ μ©νμ¬ WTI μ , Brent μ , Dubai μ μ μμ΅ λΆν¬ κ° κ΄κ³λ₯Ό λΆμνκΈ° μν΄ 1988λ
12μλΆν° 2018λ
11μκΉμ§μ μκ° νλ¬Ό κ°κ²© μλ£λ₯Ό μ¬μ©νμλ€. λΆμ κ²°κ³Ό, μ 체 λΆμ κΈ°κ° λμ WTI μ , Dubai μ μ μμ΅ λΆν¬κ° Brent μ μ μμ΅ λΆν¬λ₯Ό 2μ°¨ νλ₯ μ§λ°°νλ κ²μΌλ‘ λνλ¬λ€. 2008λ
κΈμ΅μκΈ° μ΄ν κΈ°κ°μλ WTI μ , Brent μ , Dubai μ κ°κ²©μ μμ΅ λΆν¬ κ° μ΄λ ν νλ₯ μ§λ°° κ΄κ³λ μ‘΄μ¬νμ§ μμλ€.
λλ²μ§Έλ‘, λ³Έ μ°κ΅¬λ 1996λ
2μλΆν° 2018λ
11μκΉμ§μ μκ° κ°κ²© μλ£λ₯Ό μ¬μ©νμ¬ μμ μ ν κ°κ²©μ μμ΅ λΆν¬ κ° κ΄κ³λ₯Ό λΆμνμλ€. λΆμ κ²°κ³Ό, μ νΈ μ°λ£, λλ°©μ μ μμ΅ λΆν¬λ νλ°μ μ μμ΅μ 2μ°¨ νλ₯ μ μ§λ°°νμμΌλ©° κ²½μ μ νλ°μ μμ΅ λΆν¬ κ°μλ νλ₯ μ μ§λ°°κ΄κ³κ° μ‘΄μ¬νμλ€.
λ§μ§λ§μΌλ‘ λ³Έ μ°κ΅¬λ 1988λ
12μλΆν° 2018λ
11μκΉμ§μ μκ° μλ£λ₯Ό μ¬μ©νμ¬ WTI μ , Brent μ μ μ λ¬Όκ³Ό νλ¬Ό μμ΅ λΆν¬ κ° κ΄κ³λ₯Ό λΆμνμλ€. κ·Έ κ²°κ³Ό, Brentμ μ WTIμ λͺ¨λ μ λ¬Ό κ°κ²© μμ΅ λΆν¬κ° νλ¬Ό κ°κ²© μμ΅ λΆν¬λ₯Ό 2μ°¨ νλ₯ μ μ§λ°°νλ κ²μΌλ‘ λνλ¬λ€. μ΄λ¬ν κ²°κ³Όλ μνννΌ μ±ν₯μ ν¬μμλ€μκ² μμ μ λ¬Όμ΄ μμ νλ¬Όλ³΄λ€ λμ ν¨μ©μ κΈ°λν μ μλ ν¬μμ²κ° λ μ μμμ ν¨μνλ€.Crude oil is historically one of the most representative assets in financial market, as well as it shows tremendous effects on world economy. Among oil prices, three most renowned and frequently traded benchmark assets are WTI(represents North American market), Brent(represents European market), Dubai(represents Asian market). Numerous papers have studied the relationship between three marker crude prices, including famous arguments about regionalization or integration of world crude oil market(Weiner(1991), Gulen(1999), Charles(2009), etc) and finacial studies about making optimal crude oil portfolio(Daniel(2001), Chang(2011), etc). In a financial point of view, however, No researchers have tried to study the relationship between returns of benchmark oil prices. This study compares returns of three marker crude oil assets using stochastic dominance approach, widely used investment-decision-making method as well as keen ordering rule of distributions. The period for the analysis is from January 3, 2000 to July 31, 2018, and daily data is used. The study compares the returns using spot prices for reasons that futures of Dubai do not exist. Result shows that returns of Dubai crude oil 2nd order-stochastically dominates that of Brent, and returns of Brent also 2nd order-stochastically dominates that of WTI in whole period. It implies that Dubai crude is the best invest asset for risk-averse investors, and Brent crude would be the next option for investment. It is quite interesting point since it show that WTI, the most renowned crude among three benchmarks, would the last option and Dubai, comparatively minor benchmark, would be the best option for risk-averse oil market investors.μ 1 μ₯ μ λ‘ 1
μ 1 μ μ°κ΅¬μ λ°°κ²½ λ° λͺ©μ 1
μ 2 μ₯ κ΅μ λ²€μΉλ§ν¬ μμ μ μ£Όμ μμ μ ν 5
μ 1 μ κ΅μ λ²€μΉλ§ν¬ μμ κ°κ²© 5
μ 2 μ μμ μ μμ μ ν 13
μ 3 μ νλ°μ 15
μ 4 μ λμ € μ°λ£ 22
μ 5 μ λλ°©μ 27
μ 3 μ₯ μμ΅κ³Ό μμ΅λ₯ μ μ’
λ₯ 30
μ 1 μ μμ΅κ³Ό μμ΅λ₯ 30
μ 2 μ λ¨μΌ κΈ°κ°κ³Ό μ¬λ¬ κΈ°κ° ν¬μ μμ΅λ₯ 31
μ 2 μ μ¬λ¬ κΈ°κ° ν¬μμ μ°νκ· μμ΅λ₯ 33
μ 4 μ₯ νλ₯ μ μ§λ°°κ΄κ³ κ²μ λ² κ΄λ ¨ μ£Όμ μ°κ΅¬ 37
μ 1 μ νμ κ²½μ νμμμ μ μ© μ¬λ‘ 38
μ 2 μ κΈμ΅ κ²½μ νμμμ μ μ© μ¬λ‘ 40
μ 3 μ μ°μ
μ‘°μ§λ‘ μμμ μ μ© μ¬λ‘ 43
μ 4 μ λ
Έλκ²½μ νμμμ μ μ© μ¬λ‘ 45
μ 5 μ κ΅μ κ²½μ νμμμ μ μ© μ¬λ‘ 46
μ 5 μ₯ λΆμ μλ£ λ° λΆμ λ°©λ²λ‘ 47
μ 1 μ λΆμ μλ£ 47
μ 2 μ νλ₯ μ μ§λ°° μ΄λ‘ λ° κ²μ 49
μ 3 μ νλ₯ μ μ§λ°° κ΄κ³ 53
μ 4 μ κ· λ± νλ₯ λΆν¬μ λν κ·λ¬΄κ°μ€ κ²μ 54
μ 6 μ₯ λΆμ κ²°κ³Ό 56
μ 7 μ₯ κ²°λ‘ 75
μ°Έκ³ λ¬Έν 78
λΆ λ‘ 85
Abstract 94Maste
μ₯κΈ°μμ보νμ΄ μ₯κΈ°μμμλΉμ€μ μλ£μ΄μ©μ λ―ΈμΉλ μν₯: μΉλ§€λ±κΈμ μ€μ¬μΌλ‘
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λ³΄κ±΄λνμ 보건νκ³Ό, 2017. 8. μ΄νμ§.β« μ°κ΅¬λͺ©μ
μΉλ§€ μ΄λ₯΄μ λ€μ΄ μ₯κΈ°μμ보ν μΉλ§€λ±κΈμ λ°μ ν, μ₯κΈ°μμ μλΉμ€ λ° μλ£ μ΄μ©μ΄ μΌλ§λ λ¬λΌμ§λμ§, κ·Έλ¦¬κ³ μ΄μ λ°λΌ μΈμ§κΈ°λ₯κ³Ό κ°μ‘±μ λΆλ΄ μ λλ μΌλ§λ λ³ννμλμ§ μμλ³Έλ€.
β« μ°κ΅¬λ°©λ²
κ΅λ―Όκ±΄κ°λ³΄ν곡λ¨μ 2012λ
-2015λ
λ λ§μΆ€νDBλ₯Ό μλ£μμΌλ‘ μ΄μ©νμλ€. μ΄ μλ£μμλ λμμμ μλ£μ΄μ© λ° μ₯κΈ°μμμλΉμ€ μ΄μ©κ³Ό λ±κΈνμ κ²°κ³Όκ° ν¬ν¨λμ΄ μλ€. λ³Έ μ°κ΅¬μμλ μΉλ§€λ±κΈμ 7,226λͺ
μ ν¬ν¨νμ¬ μ΄ 21,192λͺ
μ λμμΌλ‘ λΆμμ μ§ννμλ€. λΆμμ μ΄μ€μ°¨λΆλ²(Difference-in-Difference)μ νμ©νμ¬, μΈμ§νλν νλ‘κ·Έλ¨μ μ무μ μΌλ‘ μ°Έμ¬ν΄μΌ νλ μΉλ§€λ±κΈ μ΄μ©μλ€μ μΉλ§€λ±κΈ λμ
μ ν 6κ°μμ λ³νλ₯Ό μμ보μλ€. μ΄ λ λ±κΈμΈ νμ μ λ°κ±°λ 4λ±κΈ νμ μ λ°μ μΉλ§€ νμλ€μ λμ‘°κ΅°μΌλ‘ νμ©νμλ€.
β« μ°κ΅¬κ²°κ³Ό
μΉλ§€ λ±κΈ μ΄μ©μλ€μ, λ±κΈμΈμμ λΉνμ¬ μ°κ° μ₯κΈ°μμμλΉμ€ μ΄μ©μ‘μ 512.4λ§μμ΄ μ¦κ°νκ³ μ°κ° μλ£λΉλ 74.8λ§μ λ§μμ΄ λ μ¦κ°νλ κ²μΌλ‘ λνλ¬λ€. μ΄λ€μ΄ κ°μ₯ λ§μ΄ μ΄μ©ν μλΉμ€λ μ£ΌμΌκ°λ³΄νΈμ λ°©λ¬ΈμμμμΌλ©°, μλΉμ€ μ΄μ©μ μΈμ§κΈ°λ₯ κ°μ κ³Ό κ°μ‘±μ λλ΄ λΆλ΄ μνμ κΈμ μ μΈ ν¨κ³Όκ° μλ κ²μΌλ‘ λνλ¬λ€. λ€λ§ μ΄λ¬ν ν¨κ³Όλ 4λ±κΈ μ΄μ©μλ€μ λΉνμ¬ κ·Έ ν¬κΈ°κ° μμ κ²μΌλ‘ λνλ¬λ€.μ 1 μ₯ μ λ‘ 1
μ 1 μ μ°κ΅¬μ λ°°κ²½ 1
μ 2 μ μ°κ΅¬μ λͺ©μ 2
μ 3 μ μΉλ§€λ±κΈ νν© 3
μ 2 μ₯ μ νμ°κ΅¬ κ³ μ°° 7
μ 1 μ μλ£λΆμΌ μ νμ°κ΅¬ 7
μ 2 μ μ μ±
λΆμΌ μ νμ°κ΅¬ 11
μ 3 μ₯ μ°κ΅¬ λ°©λ² 14
μ 1 μ λΆμ λμ 14
μ 2 μ λΆμ μλ£ 15
μ 3 μ λΆμ λͺ¨ν 17
μ 4 μ₯ μ°κ΅¬ κ²°κ³Ό 20
μ 1 μ λΆμ λμμμ μΌλ°μ νΉμ± 20
μ 2 μ μ₯κΈ°μμμλΉμ€ λ° μλ£ μ΄μ©λμ λ³ν 26
μ 3 μ μ₯κΈ°μμμλΉμ€ λ° μλ£ μ΄μ©μ‘μ λ³ν 32
μ 4 μ μΈμ§κΈ°λ₯μ λ³ν 35
μ 5 μ λλ΄λΆλ΄μ λ³ν 38
μ 5 μ₯ κ³ μ°° 41
μ 1 μ μμ½ λ° μ μ±
μ ν¨μ 41
μ 2 μ μ°κ΅¬μ μ νμ 42
μ΄λ‘ 48Maste
Cognitive Dysfunctions in Patients With Severe Obstructive Sleep Apnea Syndrome: Neuropsychological Test and Event-Related Potential Study
Background: Patients with obstructive sleep apnea syndrome (OSAS) show variety of dysfunctions in cognitions including general cognitive function, attention, and frontal lobe and executive function. However, there is no consensus on the main features of the cognitive dysfunction in OSAS patients. So we performed neuropsychological tests and event-related potential (ERP) studies in patients with severe OSAS to evaluate the cognitive dysfunctions and changes of auditory and visual P300.
Methods: Twenty-eight men with severe OSAS (apnea hypopnea index (AHI)=63.1Β±}17.8/hr) and 16 age, sex, educationmatched normal controls (AHI=2.9Β±}1.8/hr) underwent neuropsychological tests and ERP studies.
Results: Patients with severe OSAS showed deficits in corsi block forward and backward test during neuropsychological evaluation, and delayed latency and decreased amplitude of auditory P300. There were significant correlations between auditory P300 amplitudes and digit span forward or corsi block forward test scores, and between visual P300 amplitudes and digit symbol test scores.
Conclusions: These findings suggest that severe OSAS patients may have deficits in attention and short-term memory, and abnormal auditory P300.OAIID:oai:osos.snu.ac.kr:snu2008-01/102/2014017262/5SEQ:5PERF_CD:SNU2008-01EVAL_ITEM_CD:102USER_ID:2014017262ADJUST_YN:NEMP_ID:A079623DEPT_CD:801CITE_RATE:0DEPT_NM:μνκ³ΌSCOPUS_YN:NCONFIRM:
μμ ν¨ν΄λΆμμ ν΅ν μ€νΈλ μΈ μ¨μ΄λΈ κΈ°μ΄ κΈ°λ° λ‘λ΄ ν μ ν©λΆ μ§λ¨ νλ μμν¬
νμλ
Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : 곡과λν κΈ°κ³κ³΅νκ³Ό, 2021.8. μ€λ³λ.μ°μ
μ© λ‘λ΄μ μ€λ§νΈ ν©ν 리μ 보κΈμ λ°λΌ λ§μ μ°μ
λΆμΌμ κ±Έμ³ μ¬μ©λκ³ μμΌλ©°, κ·Έ μμλ μ§μμ μΌλ‘ λμ΄λκ³ μλ μΆμΈμ΄λ€. 볡μ‘ν μλν 곡μ λΌμΈλ€μ μμ‘΄μ±μΌλ‘ μΈν΄ ν λΌμΈμ κ³ μ₯μ 컀λ€λ κ²½μ μ μμ€μ μΌκΈ°ν μ μκ³ , μ΄μ λ°λΌ λ‘λ΄μ 건μ μ± μ§λ¨μ μ€μν μ΄μ λ‘ μ리μ‘μλ€.
μ°μ
μ© λ‘λ΄μ ν¬κ² μΌμλΆ, μ μ΄λΆ, ꡬλλΆλ‘ μ΄λ£¨μ΄μ Έ μμΌλ©°, ꡬλλΆμ ν΅μ¬ λΆνμΈ μ€νΈλ μΈ μ¨μ΄λΈ κ°μκΈ°λ λͺ¨ν°μ λλ ₯μ μλ μ΄νν°λ‘ μ λ¬ν΄μ£Όλ κΈ°λ₯μ νκ² λλ€. νμ§λ§ κ°μκΈ°μ μ£Όμ ꡬμ±μμμΈ νλ μ€νλΌμΈμ νΌλ‘νκ΄΄κ° μ μΌμ΄λλ©°, μ΄μ λ°λΌ λ°±λμ¬ νμμ΄ μΌμ΄λλ λ¬Έμ λ₯Ό 보μΈλ€. κΈ°μ‘΄μ λ‘λ΄μ 건μ μ± μ§λ¨μ μν λ§μ λ°©λ²μ΄ μκ°λμμ§λ§ 1) μνμ λͺ¨λΈμ κ·Όμ¬ν κ³Όμ μμ λΆνμ€μ±μ΄ μ‘΄μ¬νμκ³ , 2) μ§λμ νΈλ μΈλΆ λ
Έμ΄μ¦ μν₯μ λ§μ΄ λ°μμΌλ©°, 3) 물리μ μ€λͺ
λ° λͺ¨μ
μ μ²λ¦¬ κ³Όμ μ΄ λΆμ‘±νλ€λ λ¨μ μ 보μλ€.
λ°λΌμ λ³Έ μ°κ΅¬μμλ λ‘λ΄μ μΆμμ μλμ μΌλ‘ 물리μ μΈ μΈλΆ μν₯μ λ λ°λ μ λ₯μ νΈλ₯Ό μΈ‘μ νμ¬ λ‘λ΄μ 건μ μ±μ λΆμνμλ€. λν 물리μ ν΄μμ κΈ°λ°νμ¬ μ§λ¨ νλ μμν¬λ₯Ό ꡬμ±νμκ³ , κ° λ¨κ³λ 1) λ‘λ΄ λͺ¨μ
λΆλ¦¬, 2) λͺ¨ν° μ λ₯μ μμν¨ν΄ λΆμ (OPAMC), 3) ν΅κ³μ κ³ μ₯ μ§λ¨μΌλ‘ μ€λͺ
λλ€. μ²μμΌλ‘, λͺ¨μ
λΆλ¦¬ λ¨κ³μμλ μΆμ νμ μλμ λΉλ‘νμ¬ μ λ₯μ νΈμ μ£Όνμ λ³μ‘°κ° μΌμ΄λλ μ μ μ°©μν΄ μ νΈμ μμ£Όνμ λΆμμ μ§ννμκ³ , μ κ·ν λͺ¨μ
μ νΈλ₯Ό μ΄μ©ν΄ λ‘λ΄μ μλ ꡬκ°μ λλμλ€. λν λͺ¨μ
λ νΌλ°μ€μμ μ½μ¬μΈ μ μ¬λλ₯Ό μ΄μ©νμ¬ λͺ¨μ
μλ³ κ³Όμ μ μ§ννμκ³ , μ΄ μλ μ¬μ΄ν΄μ κ³μ°νκ³ λͺ¨μ
νμ
μ λΆλ₯νμλ€. λλ²μ§Έ OPAMC λ¨κ³μμλ λ νΌλ°μ€ λͺ¨μ
μ μκ° λκΈ°νλ μ νΈλ₯Ό νλ²νΈ λ³ν λ° ν¬λ½ μ νΈλ₯Ό ꡬνμκ³ , μ΄ν μνμ κΈ°λ°ν κΈ°λ³Έ μ£Όνμλ₯Ό μ΄μ©νμ¬ μ΅μ νλΌλ―Έν°λ₯Ό μ΄μ©νμ¬ μμ ν¨ν΄μ μΆμΆνμλ€. λν νΉμ μΆμ λνμ¬, μ μ μν λ° κ΄μ°° λμμ μμν¨ν΄μ νλ₯ μ§λ λΆν¬λ‘ λνλ΄μμΌλ©° κ·Έ μ°¨μ΄λ₯Ό Jensen-Shannon Divergence (JSD)μΌλ‘ κ³μ°νμ¬ μμν¨ν΄μ λΆν¬κ° μ μ μνμ κΈ°λ°νμ¬ μΌλ§λ λ³ννλμ§ μ λμ μΌλ‘ λνλ΄μλ€. λ§μ§λ§μΌλ‘, νλ₯ μ κ³ μ₯ μ§λ¨ λ¨κ³μμλ μμ κ³μ°λ JSD λΆν¬λ₯Ό μ λνλΌ μ μλ ν΅κ³μ μΆμ μΉλ₯Ό μ΄μ©νμ¬ κ³ μ₯ κ°μκΈ°κ° κ²°ν©λ μΆκ³Ό μ μ μΆμ νΉμ§μΈμ 곡κ°μμ ꡬλ³νμκ³ , μ°¨μ μΆμλ₯Ό μ§ννμ¬ 1μ°¨μ 곡κ°μμ μ λμ μΌλ‘ λνλλ€.
μ€νμ 2κ°μ§ μ΄ν쑰건μ ν΅μ λ₯Ό ν΅ν΄ κ²μ¦λμλλ°, λ‘λ΄ μΆμ μλλ₯Ό 20,50,100%λ‘ μ¦κ°νμκ³ , νμ΄λ‘λλ₯Ό 0, 1, 2kgλ‘ μ¦κ°μν€λ©° κ³ μ₯ λ° μ μμΆμ λΉκ΅ν΄λ³΄μλ€. κ²°κ³Όμ μΌλ‘ μλκ° μ¦κ°ν¨μ λ°λΌ μΆμ κ΄κ³μμ΄ κΈ°λ³Έμ£Όνμκ° λμΌνκ² λΉλ‘νμ¬ μ¦κ°νλ κ²μ κ΄μ°°ν μ μμκ³ , λΆν΄κ²°ν© μ νλ‘ κ³ μ₯ μΆμμ μμν¨ν΄ λΆν¬μ JSDκ° λ νΌμ λ³Ό μ μμλ€. λν λͺ¨λ μλ쑰건μμ μ μμΆ λ° κ³ μ₯μΆμ JSD λΆν¬ μ°¨μ΄κ° νμ°ν ꡬλ³λλ κ²μ λ³Ό μ μμλ€. νμ΄λ‘λμ ν΅μ 쑰건μμλ μλ μ΄νν°μ λΆνκ° μ‘΄μ¬νμ§ μμ λ μ μ λ° κ³ μ₯μΆμ΄ νμ°ν ꡬλ³λλ κ²μ κ΄μ°°ν μ μμλ€. μ΄λ νμ΄λ‘λκ° μμ λ μλμ μΌλ‘ μΆμ 걸리λ ν ν¬μ λ³μ‘°κ° μ»Έκ³ , κ°μκΈ°μ λ°±λμ¬κ° μ νΈμ μμ ν¨ν΄μ λ μ λͺ
ν λλ¬λ¨μ μλ―Ένλ€.
λ³Έ μ°κ΅¬μμλ λͺ¨μ
λΆλ¦¬ λ° μμν¨ν΄ λΆμ κΈ°λ²κ³Ό μ΄μ νμν μ΅μ νλΌλ―Έν°λ₯Ό μ μν¨μΌλ‘μ¨ κ³ μ₯ μ§λ¨ νλ μμν¬μ ꡬμ±νμλ€. μ¬κΈ°μ λΆν¬μ κΈ°λ°ν κ²°ν¨ μ§λ¨μ λ‘λ΄ κ³ μ₯μ νλ¨νλ κΈ°μ€μ΄ λͺ¨νΈνλ€λ μ μ μ°©μνμ λ, μΆ κ° λΉκ΅ λΆμ λ±μ ν΅ν΄ λμ± ν¨κ³Όμ μΌλ‘ μ§λ¨ν μ μμμ μλ―Ένμλ€. λν μ΄λ¬ν μΌλ ¨μ κ³Όμ μ μλ λ° νμ΄λ‘λ λ± λ€μν μ΄ν쑰건μμ κ²μ¦λ¨μΌλ‘μ¨ μ λ’°μ±μ΄ λμ κ²°κ³Όλ₯Ό λμΆνλ€λ μ μμ κ·Έ μμκ° μλ€κ³ λ³Ό μ μλ€.With the fourth industrial revolution, a smart manufacturing system has been adopted in many enterprises and the demand for health assessment of industrial robots has skyrocketed in various industrial sectors. In this context, the conventional study has focused on diagnosing robots based on the physical dynamic model and vibration signal. However, 1) the approximate model had intrinsic modeling uncertainty, 2) the vibration signal could be disturbed by external noise, and 3) the motion preprocessing and physical reasoning of health assessment have not been well organized. In addition, Strain wave gear (SWG) has not been considered of health assessment although it has been used for many years as a speed reducer due to its high performance. Its elastic flexspline has been susceptible to fatigue failure causing gear backlash.
In this study, therefore, we measure the motor input current of a robotic arm joint to make it less susceptible to external noise. Also, we propose a diagnosis framework of robots with SWG gearing based on ordinal pattern analysis with optimal parameter selection considering physical interpretation. The framework is composed of the following steps: 1) Motion Segmentation based on Time-Frequency Representation (TFR), 2) Ordinal Pattern Analysis of Motor Current (OPAMC), and 3) Distribution-based fault detection.
At first, in the motion segmentation process, the motor current signal is frequency-demodulated using Temporal Fine Structure (TFS) since the robotβs joint speed has a linear relationship with the current signalβs frequency component. Also, the TFS is analyzed in the time-frequency domain to formulate a normalized motion signal which is used to decompose the current signal into multiple segments. Then, the segments are identified into different types of motions based on the similarity with reference motions. Also, the total number of cycles and motion types are calculated in this process. Secondly, in OPAMC, the segments are time-synchronized with reference motion and Hilbert-transformed to get an envelope for ordinal pattern extraction. Using a fundamental frequency of the current signal, the optimal parameter is calculated, and the ordinal patterns are extracted in the form of probability mass function (PMF). Then, the PMF of an observed state is quantitatively compared with a normal state through Jensen-Shannon divergence (JSD). Finally, each JSD distribution among different joints is represented on 3-dimensional feature space through point estimate of mean and variance and Kolmogorov-Smirnov statistics whose dimension is then reduced to 1 dimension scalar through linear discriminant analysis.
For validation, it is tested on two cases of control condition, increasing velocity 20, 50, 100% and payload 0,1,2 kg. Also, the optimal parameter for extracting ordinal patterns is compared with actual values to demonstrate its feasibility. On velocity control, the JSD shows to be higher on the faulty reducer, and its distribution shows a distinct difference between normal and faulty joint on all speed conditions. However, on the payload control condition, the JSD is only higher with no payload mounted than the payload on the fault SWG. The payload caused less modulation on torque and motor current, and thus fewer backlash characteristics appeared on ordinal patterns.
In this research, a diagnosis framework is proposed with the motion segmentation and optimal parameter in ordinal pattern extraction. Since it is subjective to diagnose the robot with a simple threshold, the comparative analysis among different joints shows to be more efficient in terms of rational health assessment. Also, the framework is validated on various operating conditions such as velocity and payload condition yielding high reliability on the study.Chapter 1. Introduction 1
1.1 Motivation 1
1.2 Scope of research 2
1.3 Thesis layout 3
Chapter 2. Literature review 5
2.1 Robot system and malfunction 5
2.1.1. Closed-loop control 6
2.1.2. Backlash of Strain Wave Gear 7
2.2 Continuous Wavelet Transform 8
2.3 Envelope and Temporal fine structure 11
2.4 Ordinal patterns 14
2.5 Measures of statistic distance 16
2.5.1. Jensen-Shannon Divergence 16
2.5.2. Kolmogorov-Smirnov statistics 17
2.6 Linear Discriminant Analysis 18
Chapter 3. Motion segmentation based on Time-Frequency Representation (TFR) 20
3.1 Fundamental frequency of motor current 20
3.2 Motion segmentation 21
3.3 Identification 23
Chapter 4. Ordinal Pattern Analysis of Motor Current (OPAMC) 25
4.1 Preprocessing 25
4.1.1. Time synchronization 25
4.1.2. Envelope extraction 26
4.2 Ordinal pattern analysis 28
4.2.1. Parameter selection based on TFR 29
4.3 Fault detection 33
4.3.1. Jensen-Shannon Divergence 33
4.3.2. Distribution-based diagnosis 34
Chapter 5. Experiment and Validation. 37
5.1 Experimental setup 37
5.2 Experimental result 39
5.2.1. Case 1: Velocity 20%, 50%, 100% 39
5.2.2. Case 2: Payload 0kg, 1kg, 2kg 49
Chapter 6. Conclusion 54
6.1 Contribution and Future work 55
Bibliography 56
κ΅λ¬Έ μ΄λ‘ 62μ
In-situ μ’ ν©μ μν΄ μ μ‘°λ ν΄λ¦¬νΌλ‘€ν΄λ¦¬λΉλμμΈν μ΄νΈ λΈλ λμ μ κΈ°μ λλ νΉμ±μ κ΄ν μ°κ΅¬
νμλ
Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :νν곡νκ³Ό,1998.Maste
μ¬μ΄λ² μμ κ΄λ ¨ λ²λ₯ μ κ²μ μ΄λ‘ μ μ°κ΅¬
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ νμ λνμ : νμ νκ³Ό, 2013. 8. λ°μμΈ.μ 보νμ κΈμν μ§μ μ λ°λΌ κ΅κ°μ μ£Όμ μμ€ λ° μλΉμ€κ° μ μ°¨ μ 보ν΅μ λ§μΌλ‘ μ°κ³λκ³ , μ¬μ΄λ² κ³΅κ° μμ κ°μΈκ³Ό μ¬ν κ·Έλ¦¬κ³ κ΅κ° κ° μνΈμμ‘΄λλ λμμ Έ κ°κ³ μλ€. μ΄λ ννΈμΌλ‘ μ¬μ΄λ² ν
λ¬λ‘ μΈν νΌν΄κ° κ΅κ° μ λ°μΌλ‘ νκΈλμ΄ λκ·λͺ¨μ νΌν΄, λμκ° κ΅κ° μμ μ λν μνμΌλ‘ μ΄μ΄μ§ κ°λ₯μ±μ΄ λμμ§κ³ μμμ μλ―Ένλ€.
κ·ΈλΌμλ λΆκ΅¬νκ³ μ°λ¦¬λλΌλ μμ§ μ²΄κ³μ μΈ κ΅κ° μ¬μ΄λ² μμ κ΄λ ¨ λ²λ₯ 체κ³λ₯Ό ꡬμΆνκ³ μμ§ λͺ»νλ€. νΉν κ΄λ ¨ μ
λ¬΄κ° μκ΄ λΆμ²λ³λ‘ νμ λ λ²μμμ κ°λ³μ μΌλ‘ μ΄λ£¨μ΄μ§λλ‘ λμ΄ μμ΄ μ¬μ΄λ² ν
λ¬μ μλ°©μ λ¬Όλ‘ , μ¬μ΄λ²μκΈ° μν©μμμ μ μνκ³ μ²΄κ³μ μΈ λμμ΄ μ΄λ €μ΄ μ€μ μ΄λ€.
λ³Έ μ°κ΅¬λ μ΄λ¬ν μΈμμ κΈ°μ΄λ‘ μ¬μ΄λ²μμ κ΄λ ¨ λ²λ₯ μ μ μ μ΄ λ¦μ΄μ§κ³ μλ μ΄μ μ μ΄μ λν ν΄κ²° λ°©μμ λνμ¬ κ³ μ°°νκ³ μ νμλ€. λ²λ₯ μ μ μ μν΄μλ λ¨Όμ ν΄λΉ λ²λ₯ μ λ΄μ©μ΄ ν¨κ³Όμ μΈ μ¬μ΄λ² μμ κ΄λ¦¬λ₯Ό λ·λ°μΉ¨ ν μ μλλ‘ λ§λ€μ΄μ ΈμΌ νλ€λ μ μ΄ μ€μνλ, μ΄λ¬ν λ΄μ©μ λ²μμ΄ κ΅νμ μ¬μλ₯Ό ν΅κ³Όν μ μλμ§λ ν° κ΄κ±΄μ΄λΌκ³ ν μ μλ€. μ΄λ λ λ²μμ΄ μ¬μ΄λ² μμ κ΄λ¦¬ 체κ³λ₯Ό μ΄κ΄ν κΈ°κ΄μ κ΅κ°μ 보μμΌλ‘ κ·μ ν κ°λ₯μ±μ΄ λμ μ΄μ λ°λ₯Έ μ견 λλ¦½μ΄ μμλκΈ° λλ¬Έμ΄λ€. μ€μ λ‘ μ§λ μ 18λ κ΅νμμ λ°μλμλ γκ΅κ°μ¬μ΄λ²μκΈ°κ΄λ¦¬λ²(μ)γμ΄ νκΈ°λκ² λ κ²λ μ£Όλ‘ κ΅κ°μ 보μμ μν μ λν μ¬μΌμ μ견 μ°¨μ΄λ₯Ό μ’νμ§ λͺ»νκΈ° λλ¬Έμ΄λ€.
λ°λΌμ λ³Έ μ°κ΅¬λ μ¬μ΄λ² μμ κ΄λ ¨ λ²λ₯ μ
λ² κ³Όμ μμμ μ¬μΌ μνΈμμ©μ λΆμνμ¬ μ§λ μ
λ² μ€ν¨ μ¬λ‘μ μμΈμ μμλ³΄κ³ , μ΄λ₯Ό ν λλ‘ μμ λ²μμ΄ μ μ λκΈ° μν 쑰건μ λμΆνμλ€. μμμμνμ λ²μ μ¬μ¬ κ³Όμ μ μ¬λΉκ³Ό μΌλΉμ΄ μ κΆμμ μ§μ§λ₯Ό ν보νκΈ° μνμ¬ μ λ΅μ μΈ μ νμ λ°λΌ μνΈμμ©νλ κ²μμΌλ‘ λͺ¨ννν μ μλ€. λ°λΌμ κ²μ μ΄λ‘ μ λΆμμ ν΅ν΄ μ
λ² κ³Όμ μμ μ΄λ ν μ μΉμ μμκ° μ
λ² κ΅μ°©μ μ΄λνλμ§ μΆλ‘ ν μ μμμΌλ©° λ¬Έμ ν΄κ²°μ λν λ€μκ³Ό κ°μ μμ¬μ μ λ°κ²¬ν΄ λΌ μ μμλ€.
첫째, κ΅λ―Όλ€μ΄ λ²μμ νμμ±μ λν΄ κ³΅κ°νλ μ λκ° μ»€μ§λ κ²½μ° λ²μ λ°λμ λ°λ₯Έ νλν°λ μλμ μΌλ‘ 컀μ§λ―λ‘ λ²μ ν΅κ³Ό κ°λ₯μ±μ λμμ§ μ μλ€. κ·Έλ¬λ μ΄λ¬ν κ΅λ―Όμ μΈμμ μΈμμ μΌλ‘ μ ννκΈ° μ΄λ €μ°λ―λ‘ μκ°μ΄ μ€λ 걸릴 λΏλ§ μλλΌ μ€λ μκ°μ΄ μ§λλ μΈμμ΄ ν¬κ² λ³νμ§ μμ κ°λ₯μ±λ ν¬λ€λ λ¬Έμ κ° μλ€.
λμ§Έ, μ μΉκΆμ΄ μ€μ€λ‘ μ₯κΈ°μ μΈ μκ³λ₯Ό ν보νκ³ λ³΄λ€ μ μ±
μ§ν₯μ μΌλ‘ λ°λλ€λ©΄ κ΅κ° μ보μ μ€μν λ²μμ λν΄μ ν©λ¦¬μ μΌλ‘ μ¬μ¬νμ¬ λ²μμ ν΅κ³Όμν¬ μ μμ κ²μ΄λ€. λ€λ§, μ΄λ₯Ό μν΄μλ μμ μ€μ€λ‘μ λ
Έλ ₯λΏλ§ μλλΌ λ³΄μ’μ§μ λ¬Όλ‘ , μμμ λΉμ μ§λλΆκ° ν¨κ» λ
Έλ ₯ν΄μΌ νλ€.
μ
μ§Έ, λ²μμ λν΅λ Ήμ μκΈ° μ΄λ°λ³΄λ€λ νλ°, μ£Όμκ΅μ΄λ μ°λ¦¬λλΌμμ μ¬μ΄λ²ν
λ¬κ° λ°μνμ¬ κ΅λ―Όμ κ΄μ¬μ΄ λμ λμ κ°μ΄ λ²μ ν΅κ³Όμ μ 리ν μκΈ°μ λ°μνλ©΄ λ²μμ΄ ν΅κ³Όλ κ°λ₯μ±μ΄ λμμ§ μ μλ€. λ€λ§ μ¬μ΄λ² ν
λ¬μ μλ°©μ΄ μ¬μ΄λ² μμ κ΄λ ¨ λ²λ₯ μ μ£Όμ μ·¨μ§μ΄λ―λ‘ μ΄λ¬ν λ°©μμ λ²λ₯ μ μ μ μ μμ±μ ν΄μΉ μ μλ€.
λ·μ§Έ, λ²μμμ λ
Όλμ μ¬μ§κ° μλ λΆλΆμ μμ νμ¬ λ°μνλ€λ©΄ λ λ²μμ λ°λν μ΄μ κ° μκ² λλ―λ‘ κ²°κ³Όμ μΌλ‘ λ²μμ΄ ν΅κ³ΌλκΈ° μ¬μμ§λ€. νμ€μ μΌλ‘ κ΅κ°μ 보μ μΈμ μ¬μ΄λ² μμ μ
무λ₯Ό μ΄κ΄ν μ λ¬Έμ± λ° λ
Ένμ°λ₯Ό κ°μ§ λΆμ²λ₯Ό μ°ΎκΈ° νλ€λ€λ μ μμλ κ΅κ°μ 보μμ΄ μ¬μ΄λ² μμ κ΄λ¦¬ μ
무λ₯Ό μ΄κ΄νλ μ΄μ λν μ μ ν μ¬μ Β·μ¬νμ ν΅μ κ° κ°λ₯νλλ‘ μΌλΆ μ‘°λ¬Έμ μμ νκ±°λ μ μ€νλ λ°©μμ΄ λ°λμ§ν κ²μ΄λ€.
ννΈ, μ
λ² κ³Όμ μ νκ²½μΌλ‘ μμ©νλ μ μΉ κ΅¬μ‘°Β·λ¬Έν λ± κ±°μμ μμΈμ΄ λ²μμ ν΅κ³Ό κ°λ₯μ±μ λ―ΈμΉλ μν₯λ ν¬λ€. λ°λΌμ μ λΉμ κΈ°μμΌλ‘λΆν° μμλ€μ μμ¨μ±κ³Ό λ
립μ±μ ν보νμ¬ μμ¬μ λ°λΌ μ 체 κ΅λ―Όκ³Ό κ΅κ° μ΄μ΅μ μν κ΅μ°¨ ν¬νκ° κ°λ₯νλλ‘ νλ λ± κ΅ν λ΄μ 건μ ν μ μΉμ κ²μμ κ·μΉμ ν보νλλ‘ νμ¬μΌ νλ€.μ 1μ₯ μλ‘ 1
μ 1μ μ°κ΅¬μ μμ λ° λͺ©μ 1
1. μ°κ΅¬μ λ°°κ²½κ³Ό μμ 1
2. μ°κ΅¬μ λͺ©μ 3
μ 2μ μ°κ΅¬μ λ°©λ² 4
1. μ°κ΅¬ λ¬Έμ 4
2. μ°κ΅¬μ λ°©λ² 5
μ 2μ₯ μ΄λ‘ μ λ°°κ²½ λ° μ ν μ°κ΅¬μ κ²ν 8
μ 1μ μ°κ΅¬μ μ΄λ‘ μ λ°°κ²½ 8
1. μ
λ² κ³Όμ μ μ μΉμ±μ κ΄ν μ΄λ‘ 8
2. μ¬μ΄λ² ν
λ¬ λ±μ λμμ κ΄ν μ΄λ‘ 11
μ 2μ μ ν μ°κ΅¬μ κ²ν 14
1. μ
λ² κ³Όμ μ μ μΉμ±μ κ΄ν μ°κ΅¬ 14
2. μ¬μ΄λ² ν
λ¬ λ±μ λμμ κ΄ν μ°κ΅¬ 16
3. μ ν μ°κ΅¬μ νκ³ λ° λ³Έ μ°κ΅¬μ μμ 18
μ 3μ₯ λ² μ μ μ νμμ± λ° μ μ λ°©μ 20
μ 1μ μ¬μ΄λ² μμ κ΄λ ¨ λ²λ₯ μ μ μ μΆμ§ λ°°κ²½ 20
1. λ² μ μ μ λ°°κ²½ : μ¬μ΄λ² ν
λ¬μ κ°λ
λ° νν© 20
2. λ² μ μ μ νμμ± 24
μ 2μ μ£Όμκ΅μ μ¬μ΄λ² μμ μ²΄κ³ λ° κ΄λ ¨ λ²λ Ή 28
1. λ―Έκ΅ 28
2. μκ΅ 32
3. λ
μΌ 33
4. νλμ€ 34
5. μΌλ³Έ 35
6. μμ¬μ 36
μ 3μ μ°λ¦¬λλΌμ μ¬μ΄λ² μμ 체κ³Β·κ΄λ ¨ λ²λ Ή λ° λ¬Έμ μ 38
1. μ°λ¦¬λλΌ μ¬μ΄λ² μΉ¨ν΄ λμ μ²΄κ³ 38
2. κ΄λ ¨ λ²λ Ή νν© 40
3. ν 체κ³μ λ¬Έμ μ 43
μ 4μ μ¬μ΄λ² μμ κ΄λ ¨ λ²λ₯ μ μ μ λ°©ν₯ 45
1. κ΄λ ¨ μ
λ² λ
Έλ ₯ 45
2. μ μ λ²λ₯ μμ κ°μ λ°©ν₯ 50
μ 4μ₯ λ²λ₯ μ μ μ μ κ²μ μ΄λ‘ μ κ³ μ°° 56
μ 1μ μ μ λ° λͺ¨νμ μ€μ 57
1. κ²μ μ΄λ‘ μ λΆμμ μμ 57
2. μ μ 58
μ 2μ μ
λ² μ€ν¨ μ¬λ‘μ λΆμ 62
1. 2008λ
λ°μ λ²μμ μ
λ² μ€ν¨ μ¬λ‘ 62
2. 2006λ
λ°μ λ²μμ μ
λ² μ€ν¨ μ¬λ‘ 68
3. μ
λ² μ€ν¨ μ¬λ‘μ μμ¬μ 73
μ 3μ λ²λ₯ μ μ λ°©μμ κ²μ μ΄λ‘ μ λμΆ 74
1. λͺ¨νμ μ€μ 74
2. κ²μμ ν΄ 84
3. κ²°κ³Όμ ν΄μ 95
3. μμ¬μ λ° ν΄κ²° λ°©μ 108
μ 5μ₯ κ²°λ‘ 114
μ°Έκ³ λ¬Έν 118
λΆλ‘ 123
ABSTRACT 135Maste
Accompanying lesions and clinical results in the greater tuberosity fracture of the humerus with anterior shoulder dislocation under the age of forty
μνκ³Ό/μμ¬λͺ©μ : μμ골 λκ²°μ 골μ μμ 견κ΄μ νκ΅¬κ° λλ°λ κ²½μ° λλ° λ³λ³μ μ‘°μ¬νκ³ μμμ κ²°κ³Όλ₯Ό μμλ³΄κ³ μ νλ€.λμ λ° λ°©λ²: 2005λ
5μλΆν° 2008λ
11μκΉμ§ 견κ΄μ νκ΅¬κ° λλ°λ μμ골 λκ²°μ 골μ νμμ€ 40μΈ μ΄νμ 30λͺ
μ λμμΌλ‘ κ΄μ κ²½μ μννμλ€. λλ° λ³λ³μ νμΈν ν λκ΄λμ¬ κ³ μ λλ λ΄ν©λμ¬λ₯Ό μ΄μ©νμ¬ κ³ μ μ μννμλ€. Constant μ μμ Rowe μ μλ₯Ό μ‘°μ¬νμλ€. κ²°κ³Ό: Bankart λ³λ³ 2μ, κ³¨μ± Bankart λ³λ³ 4μ, Perthes λ³λ³ 4μ, free ALPSA λ³λ³ 2μ, GLAD λ³λ³ 3μ, κ΄μ λ νμ΄ 6μ κ΄μ°°λμκ³ , νμ κ·Ό κ° λΆλΆ νμ΄ 5μ(16.7%), SLAP type I 2μ(6.7%), SLAP type II λ³λ³ 1μ(3.3%), μ΄λμ₯건 νμ΄ 1μ(3.3%)κ° κ΄μ°°λμλ€. Constant μ μλ μ μ νκ· 56.30Β±11.83 μ μμ μ ν νκ· 94.43Β±7.82 μ μΌλ‘ μ¦κ°νμκ³ (p=0.034), Rowe μ μλ 52.56Β±9.96 μ μμ 91.76Β±9.56 μ μΌλ‘ μ¦κ°νμλ€(p=0.026)κ²°λ‘ : 견κ΄μ μ λ°© νꡬλ₯Ό λλ°ν λκ²°μ 골μ μμ κ΄μ κ²½μ μ΄μ©ν λλ° λ³λ³ νμΈκ³Ό 골μ μΉλ£λ‘ μ°μν μμ κ²°κ³Όμ 골μ ν©μ 보μλ€. 2μ°¨ κ΄μ κ²½ κ²μ¬μ Perthes λ³λ³, Free ALPSA λ³λ³, GLAD λ³λ³ λ° κ΄μ λ νμ΄μ λ΄ν©μ μ μννμ§ μμμ§λ§ μμ° μΉμ λκ±°λ λ μ΄μ μμμ λ²μκ° μ¦κ°λμ§ μμκ³ μ ν μ΄μ°¨μ μΈ λΆμμ μ±μ μ λ°νμ§ μμλ€.ope
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