34 research outputs found
์ ๊ฒฉ์ ๊ณ ๋ คํ ๋ฌด๋ฏธ์ต ์ด์ํ ๋ ๊ฐฏ์ง ๋นํ์ฒด ํตํฉ ์ค๊ณ: ๊ธฐํ๋ถ์ ๋ฐ ์์น ํด์์ ํตํ ์ ๊ทผ
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ํ๋๊ณผ์ ์ฐ์ฃผ์์คํ
์ ๊ณต, 2021. 2. ์ ์์ค.Unlike birds, an insect type tailless flapping wing does not possess tail wings. Therefore, insect type flapping wing may be fabricated in small size and of decreased weight. Because of the taillessness, however, stable flight of an insect type flapping wing depends only on main wings. Thus, a number of researches were conducted regarding its control mechanisms. In this thesis, the trailing edge control, one of the methods developed to produce control moments, is adopted. Such method requires additional shafts that connect the root of the main wing and control mechanism, and the shafts are rotated to deform the wing shape. In this manner, asymmetric aerodynamic forces are produced. The control mechanism uses micro actuators for compact design. However, small size of the micro actuator gearbox causes relatively large backlash and the resulting free play of the main wings that generates undesirable aerodynamic forces.
Under such circumstance, design improvement of the control mechanism is conducted to minimize the effects of the free play. First, geometry analysis is performed to investigate the factors that cause the free play. Control mechanism design for the minimized free play is obtained. Then, three-dimensional computer aided design (CAD) of modified configuration is drawn, and kinematic simulations are conducted by RecurDyn to determine the prevention of interference. Finally, the feasibility of modified design is examined by the numerical simulation. The main wings are modeled by the displacement-based geometrically exact beam model combined with cross-sectional analysis. To mimic the free play appropriately, the spring elements are attached to the joints. At the same time, two-dimensional unsteady aerodynamic model is used for aerodynamic forces. Consequently, the reasonable control moments are gathered in terms of the maneuverability.๊ณค์ถฉ ๋ชจ๋ฐฉํ ๋ ๊ฐฏ์ง ๋นํ์ฒด๋ ๊ผฌ๋ฆฌ๋ ๊ฐ๊ฐ ์๊ธฐ ๋๋ฌธ์ ์ ๋ชจ๋ฐฉํ ๋ ๊ฐฏ์ง ๋นํ์ฒด์ ๋น๊ตํ์ฌ ๊ฐ๋ณ๊ณ ์๊ฒ ์ค๊ณ๋ ์ ์๋ค. ๊ทธ๋ฌ๋, ๊ณค์ถฉ ๋ชจ๋ฐฉํ ๋ ๊ฐฏ์ง ๋นํ์ฒด๋ ๊ผฌ๋ฆฌ๋ ๊ฐ๊ฐ ์๋ค๋ ํน์ง์ผ๋ก ์ธํ์ฌ, ์ค์ง ๋ ๋ ๊ฐ๋ง์ ์ด์ฉํ์ฌ ์กฐ์ข
๋ ฅ์ ๋ฐ์์ํจ๋ค. ๋ฐ๋ผ์, ์ด์ ๋ํ ๋ง์ ์ฐ๊ตฌ๊ฐ ์ํ๋์๊ณ ๊ฐ๋ฐ๋ ์ฌ๋ฌ ์์ธ ์ ์ด ๋ฐฉ๋ฒ ์ค ๋ณธ ํ์ ๋
ผ๋ฌธ์์ ๋ ๊ฐ ๋๋จ ๋นํ๋ฆผ์ ์ด์ฉํ ์์ธ ์ ์ด ์ฅ์น๋ฅผ ๋ค๋ฃฌ๋ค. ํด๋น ๋ฐฉ๋ฒ์ ์ฃผ๋ ๊ฐ์ ๋ฟ๋ฆฌ ๋ถ๋ถ์ ์์ธ ์ ์ด ์ฅ์น์ ์ฐ๊ฒฐํ๊ณ ์ด๋ฅผ ํ์ ์์ผ ๋ ๊ฐ ๋๋จ์ ๋ณํ์ ๋ฐ์์ํจ๋ค. ์์ธ ์ ์ด ์ฅ์น์๋ ๊ฒฝ๋ํ๋ฅผ ์ํ์ฌ ๊ฐ๋ณ๊ณ ์์ ์ฅ๋น๋ค์ด ์ฌ์ฉ๋๋ค. ๊ทธ๋ฌ๋, ์์ธ ์ ์ด ์ฅ์น ์ ์์ ์ฌ์ฉ๋๋ ์ด์ํ ๊ตฌ๋๊ธฐ๋ ์์ ํฌ๊ธฐ๋ก ์ธํ์ฌ ๋ด๋ถ ๊ธฐ์ด์ ๋ฐฑ๋์๋ฅผ ๊ฐ๊ณ ์๋ค. ๋ฐ๋ผ์, ์ด๋ ์ฃผ๋ ๊ฐ์ ๋ถํ์ํ ์ ๊ฒฉ์ ๋ฐ์์ํฌ ์ ์๋ค. ์ด๋ฌํ ์ ๊ฒฉ์ ์ฃผ๋ ๊ฐ์ ์ง๋์ผ๋ก ์ด์ด์ ธ, ๋ถํ์ํ ๋น๋์นญ์ ๊ณต๋ ฅ์ ๋ฐ์์ํฌ ์ ์๋ค.
์ด๋ฌํ ์ํฉ ๋๋ฌธ์ ์ ๊ฒฉ์ด ์ต์ํ๋ ์์ธ ์ ์ด ์ฅ์น ์ค๊ณ๋ฅผ ์ํํ์๋ค. ์ฒซ์งธ๋ก, ๊ธฐํํ์ ํด์์ ํตํ์ฌ ์ ๊ฒฉ์ ์ํฅ์ ์ฃผ๋ ์์ธ์ ํ์
ํ์๋ค. ์ด๋ฅผ ํตํ์ฌ ์ ๊ฒฉ์ ์ต์ํํ ์ค๊ณ๋ฅผ ๋์ถํ์์ผ๋ฉฐ, 3์ฐจ์ computer aided design (CAD) ํ์๊ณผ RecurDyn์ ์ด์ฉํ์ฌ ๋์ญํ์ ํด์์ ์ํํ์๋ค. ์ด๋ฅผ ํตํ์ฌ ์์ธ ์ ์ด ์ฅ์น์ ๊ตฌ๋ ์ค ๋ฐ์ํ๋ ๊ฐ์ญ์ ํ์ธํ์๋ค. ์ต์ข
์ ์ผ๋ก, ์์น์ ์๋ฎฌ๋ ์ด์
์ ์ด์ฉํ์ฌ ๊ฐ์ ๋ ์์ธ ์ ์ด ์ฅ์น์ ํ๋น์ฑ์ ํ์ธํ์๋ค. ์ด๋, ์ฃผ๋ ๊ฐ๋ ๋ณ์ ๊ธฐ๋ฐ ๊ธฐํํ์ ์ ๋ฐ ๋ณด๋ก ๋ชจ๋ธ๋ง ๋์์ผ๋ฉฐ, 2์ฐจ์ ๋จ๋ฉด ํด์ ๊ฒฐ๊ณผ๋ฅผ ์ฌ์ฉํ์ฌ ํด์์ ์ํํ์๊ณ ๊ณต๋ ฅ ๋ชจ๋ธ์ 2์ฐจ์ ๋น์ ์ ๋ชจ๋ธ์ ์ฌ์ฉํ์๋ค. ๋ํ, ์ ๊ฒฉ์ ๋ชจ์ฌํ๊ธฐ ์ํ์ฌ ์คํ๋ง ์์๋ฅผ ๊ด์ ์ ์ฝ์
ํ์ฌ ํด์์ ์ํํ์๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก, ๋ณธ ์ฐ๊ตฌ์์ ์ค๊ณํ ์์ธ ์ ์ด ์ฅ์น๊ฐ ์ ํจํ ์กฐ์ข
๋ ฅ์ ๋ฐ์์ํค๋ ๊ฒ์ ํ์ธํ์๋ค.Abstract i
Contents iii
List of Tables vi
List of Figures vii
List of Symbols x
Preface xi
Chpater 1 Introduction 1
1.1 Background 1
1.2 Previous Researches 3
1.2.1 Review of Control Mechanism Design Regarding the Insect-Type Flapping Wing 3
1.2.2 Review of Numerical Simulation Studies Regarding the Insect-type Flapping Wing 6
1.3 Research Objectives and Thesis Outline 8
Chpater 2 Control Mechanism Design with Free play 9
2.1 Overview of Control Mechanism Design with Free play 9
2.2 Control Mechanism: Trailing Edge Control 11
2.3 Components of the Control Mechanism 14
2.4 Control Mechanism Design with Minimize free play effect 17
Chpater 3 Numerical Simulations of FWMAV 25
3.1 Overview of Numerical Simulation based on Flexible Multibody Dynamics 25
3.2 Simulation Setup 26
3.2.1 Simulation Methodology 31
3.2.2 Aerodynamics 34
3.3 Numerical Simulation 37
Chpater 4 Conclusions 47
4.1 Contirbutions 47
4.2 Future Works 48
Acknowledgments 50
References 50
๊ตญ๋ฌธ์ด๋ก 55Maste
ํต์ผ์ธ์ ์ ๊ณ ๋ฅผ ์ํ ํต์ผ ์ ๊ด๊ธฐ๊ด๋ค์ ICT ํ์ฉ๋ฐฉ์
ํํํต์ผ์ ๊ตญ๊ฐ์ ์ธ ๊ณผ์ ์ด์ง๋ง ํํํต์ผ์ ๋ํ ๊ตญ๋ฏผ๋ค์ ์ธ์์ ๊ณผ๊ฑฐ์ ๋นํด ์๋นํ ๋ฎ์ ๊ฒ์ผ๋ก ํ์
๋๊ณ ์๋ค. ํนํ ์ ์ ์ธ๋์ ํต์ผ์ธ์์ ์ง์์ ์ผ๋ก ๋ฎ๊ฒ ๋ํ๋๊ณ ์๋ ํ์ค์ด๋ค. ํํธ ์ฐ๋ฆฌ๋๋ผ๋ ICT ๊ฐ๊ตญ์ผ๋ก์ ์ธ๊ณ ์ต๊ณ ์์ค์ ICT ์ธํ๋ผ์ ICT ํ์ฉ ๋ฅ๋ ฅ์ ๋ณด์ ํ๊ณ ์๋ค. ๋ณธ ์ฐ๊ตฌ๋ ์ฐ๋ฆฌ๋๋ผ์ ๊ฐ์ ์ธ ICT๋ฅผ ํ์ฉํ์ฌ ๊ตญ๋ฏผ๋ค์ ํํํต์ผ ์ธ์์ ์ ๊ณ ํ ์ ์๋ ๋ฐฉ์์ ๋ชจ์ํด ๋ณด๋๋ฐ ๊ทธ ๋ชฉ์ ์ด ์๋ค. ์ฐ์ ๊ตญ๋ฏผ๋ค์ ํต์ผ์ธ์ ์ ๊ณ ๋ฅผ ์ํด ํต์ผ ๊ด๋ จ ๊ธฐ๊ด๋ค์ด ์ด๋ ํ ํ๋์ ํ๋์ง ์กฐ์ฌํ๋ ICT์ ํ์ฉ ํํฉ๊ณผ ๋ฌธ์ ์ ์ ์ง์ด ๋ณด์๋ค. ๋ถ์ ๋์์๋ ์ ๋ถ ๋ฐ ๊ณต๊ณต๊ธฐ๊ด์ผ๋ก์๋ ํต์ผ๋ถ์ ํต์ผ์ค๋น์์ํ๊ฐ, ๋ฏผ๊ฐ๊ธฐ๊ด์ผ๋ก์๋ ๋ฏผ์กฑํต์ผ์ค์ํ์ํ, ํต์ผ์ ์๊ฐํ๋ ์ฌ๋๋ค์ ๋ชจ์, ๋จ๋ถ์ฌํํตํฉ์ฐ๊ตฌ์์ด ํฌํจ๋์๋ค. ๋ถ์ ๊ฒฐ๊ณผ์ ์ํ๋ฉด ์ด ๊ธฐ๊ด๋ค์ SNS ๋ฑ ICT์ ํ์ฐ ์ถ์ธ์ ๊ทธ ์ค์์ฑ์ ์ธ์ํ๊ณ ์ด๋ฅผ ํ์ฉํ๋ ค๋ ๋
ธ๋ ฅ์ ํ๊ณ ์์ผ๋ ์ค์ ๋ก๋ ๋ช
๋ชฉ์์ ์ด์์ ๊ทธ์น๊ณ ์์ผ๋ฉฐ ICT์ ํน์ฑ์ ์ด๋ฆฌ๋ ํจ๊ณผ์ ์ธ ํ๋์ ํ์ง ๋ชปํ๊ณ ์์๋ค. ์ด๋ฌํ ๋ถ์ ๊ฒฐ๊ณผ๋ฅผ ๋ฐํ์ผ๋ก ๋ณธ ์ฐ๊ตฌ์์๋ ICT์ ํจ๊ณผ์ ์ธ ํ์ฉ์ ํตํ ๊ตญ๋ฏผ๋ค์ ํํํต์ผ ์ธ์ ์ ๊ณ ๋ฅผ ์ํด์ SNS ํน์ฑ์ ๊ทน๋ํ, ์ ์์ธต์ ๋ํ ํ๊ฒํ
, ๋ธ๋๋ฉ ๋ฐ ์ง์์ ์ปค๋ฎค๋์ผ์ด์
์ด๋ผ๋ ์ธก๋ฉด์์ ๊ตฌ์ฒด์ ์ธ ์ ์ธ์ ์ ์ํ์๋ค. ์ด์ ๋๋ถ์ด ICT ํ์ฉ๋ ํฅ์์ ์ํ ๋ค์ํ ์์ฌ์ ์ ์ ์ํ์๋ค
Influence of ovarian hormones on the exocrine secretion in isolated cat pancreas
์ํ๊ณผ/์์ฌ[ํ๊ธ]
์ทจ์ธ๋ถ๋น๋ ์ฃผ๋ก ์ ๊ฒฝ์ฑ์ธ์ ๋ฐ ์์ฅํ๋ชฌ์ ์ํ์ฌ ์กฐ์ ๋๋ฉฐ, steroidํ๋ชฌ๋ ์ทจ์ธ๋ถ๋น๊ธฐ๋ฅ ์กฐ์ ์ ํ ์์ธ์ผ๋ก ๊ด์ฌํ๋ค๊ณ ์๋ ค์ ธ ์๋ค. ์ฒด๋ด steroidํ๋ชฌ์ ๋ถ์ ๊ณผ ๋์์์ cholesterol๋ก๋ถํฐ ํฉ์ฑ๋๋ฉฐ ์ด๋ค์ ์์ฒด์ ํญ์์ฑ ์ ์ง์ ์ค์ํ ์์ฉ์ ํ๋ค.
์ทจ์ธ๋ถ๋น์ ๋ํ ๋ถ์ ํผ์งํ๋ชฌ์ ์ํฅ์ ๊ดํด์๋ ์๋ฐ๋ ๋ณด๊ณ ๊ฐ ๋ง์ผ๋, ๋์ฒด๋ก glucocorticoid๋ ์ทจ์ธ๋ถ๋น๋ฅผ ์ต์ํ๋ฉฐ, mineralocorticoid๋ ์ทจ์ธ๋ถ๋น๋ฅผ ํญ์ง์ํค๋ ๊ฒ์ผ๋ก ๋ฐ์๋ค์ฌ์ง๊ณ ์๋ค.
๋ํ ์ทจ์ ํฌ ์ธํฌ์ง์ estrogen๊ณผ ๊ฒฐํฉํ๋ ๋จ๋ฐฑ์ด ์์์ด ์๋ ค์ ธ ์๊ณ progesterone์ ์ทจ์ข
์ ์ธํฌ์ ์ธํฌ๋ด mRNA ๋ฐ amylase์ ์์ ์ฆ๊ฐ์ํจ๋ค๊ณ ํ์ฌ ๋์ํ๋ชฌ ์ญ์ ์ทจ์ธ๋ถ๋น๊ธฐ๋ฅ๊ณผ ๊ด๋ จ์ฑ์ด ์์์ด ์์ฌ๋๊ณ ์๋ค.
๋ฐ๋ผ์ ๋ณธ ์คํ์์๋ ์ ํด์ง ์ฉ์ก์ ๊ด๋ฅํ๋ ์์ฒด์ธ ์ ์ถ ๊ณ ์์ด ์ทจ์ฅํ๋ณธ์ ์ด์ฉํ์ฌ ์ทจ์ธ๋ถ๋น๊ธฐ๋ฅ์ ๋ํ ๋์ํ๋ชฌ์ ์ง์ ์ ์ธ ์ํฅ์ ๊ฒ์ํ๊ณ ์ ํ์๋ค.
์คํ๋๋ฌผ๋ก๋ 1.5kg๋ด์ธ์ ์ก์ข
๊ณ ์์ด๋ฅผ ์์ ๊ตฌ๋ณ์์ด ์ฌ์ฉํ์์ผ๋ฉฐ secobarbital ๋ง์ทจํ์ ์ทจ์ฅ์ ์ ์ถํ์ฌ Krebs Ringer bicarbonate buffer(KRBB)๋ฅผ ๊ด๋ฅ์์ผฐ๋ค. ๋์ํ๋ชฌ์ ์ํฅ์ ๊ฒ์ํ๊ธฐ ์ํ์ฌ estrogen ๋๋ progesterone์ ๊ฐ๊ฐ 10**-8 โ 10**-6 M๋๊ฒ
KRBB์ ์ฒจ๊ฐํ์ฌ 30๋ถ๊ฐ์ฉ ๊ด๋ฅํ์๋ค. ์ทจ์ก๋ถ๋น๋ secretin์ submaximal dose ์ฃผ์
์ผ๋ก ์ผ์ ํ๊ฒ ์ ์ง์์ผฐ๊ณ ์ทจํจ์๋ถ๋น๋ secretin ์ฃผ์
ํ์ cholecystokinin(CCK) ์๊ทน์ผ๋ก ์ ๋ฐ์์ผฐ๋ค.
์คํ์ฑ์ ์ ์์ฝํ๋ฉด ๋ค์๊ณผ ๊ฐ๋ค.
1. Estrogen๊ด๋ฅ๋ก ์ ์ถ์ทจ์ฅ์ ์ธ๋ถ๋น๊ธฐ๋ฅ์ ํญ์ง๋์์ผ๋ฉฐ estrogen 10**-8 M์ด์์ ๋๋์์ secretin์ ์ํ ์ทจ์ก๋ถ๋น์ CCK์๊ทน์ ์ํ amylase๋ถ๋น๊ฐ ์ฆ๊ฐ๋์๋ค.
2. Progesterone๊ด๋ฅ๋ก ์ทจ์ธ๋ถ๋น๋ ์ฆ๊ฐํ์ฌ secretin์๊ทน์ ์ํ ์ทจ์ก๋ถ๋น๋ 10**-6 M๊ด๋ฅ์ ์ฆ๊ฐํ์๊ณ , amylase๋ถ๋น๋ CCK์๊ทน์ ์ํ progesterone 10**-7 M์ฒจ๊ฐ๋ก ์ฆ๊ฐํ์๋ค.
3. Estrogen 10**-7 M ์กด์ฌํ์ progesterone์ ๊ด๋ฅํจ์ผ๋ก์จ amylase ๋ถ๋น ๋ณ๋์ ๊ด์ฐฐ๋์ง ์์์ผ๋ฉฐ, secretin์๊ทน์ ์ํ ์ทจ์ก์ ์ถ์ ๋ณ๋์ด ์์๋ค.
์ด์์ ๊ฒฐ๊ณผ๋ก ๋ฏธ๋ฃจ์ด ๋ณด์ estrogen ๋ฐ progesterone๋ฑ ๋์ ํ๋ชฌ์ ์ทจ์ ํฌ์ ์ง์ ์์ฉํ์ฌ, ์ทจ์ก ๋ฐ ์ทจํจ์๋ถ๋น๋ฅผ ํญ์ง์ํค๋ฉฐ ์ทจ์ธ๋ถ๋น์ ๋ํ progesterone์ ํจ๊ณผ๋ estrogen๊ณผ ์๊ด์์ด ๋ํ๋๋ค๊ณ ์๊ฐํ๋ค.
Influence of Ovarian Hormones on the Exocrine Secretion in Isolated Cat Pancreas
Jae Won choi
Department of Medical Science, The Graduate School, Yonsei University
(Directed by Professor Kyung Hwan Kim)
Regulation of pancreatic exocrine secretion is comprised of a complex interplay
between hormonal and nervous systems. It has been claimed that steroid hormones may
participate in maintaining the exocrine function of the pancreas. Corticosteroids
and ovarian hormones are synthesized from the same precursor, cholesterol, in vivo
and they are important regulators of homeostasis.
Though the effects of corticosteroids on pancreatic function are not consistant,
it is widely accepted that the glucocorticoid inhibits the secretory function
whereas mineralocorticoid stimulates.
Several studies have suggested that ovarian hormones exert significant influence
on the exocrine pancreas. And pancreatic function of exocrine cells may be
regulated by estrogen and estrogen-binding protein. Also, it was reported that
protesterone increased both the cellular content of amylase and cytoplasmic mRNA of
rat tumor cell line AR 42J.
In the present study, attempt was made to examine the direct effects of estrogen
and progesterone on exocrine function employing saline perfused isolated cat
pancreas.
Mongrel cats, of both sexes weighing about 1.5Kg , were anesthetized with
secobarbital (30mg/kg, ip), then the pancreas was isolated and perfused with Krebs
Ringer bicarbonate buffer(KRBB) gassed with 95% 0^^2-5% CO^^2 at the rate of 6
ml/min. Ovarian hormone, estrogen or progesterone was added to the perfusate for 30
minutes. Submaximal dose of secretin was infused to maintain pancreatic flow and
cholecystokinin was injected to evoke the enzyme secretion in the presence or
absence of ovarian hormones.
The results are summarized as fellows.
1. Perfusion of estrogen increased the pancreatic flow as well as CCK-stimulated
amylase secretion.
2. CCK-stimulated secretion of amylase was increased by perfusion with
progesterone and over 10**-6 M concentration secretin-stimulated pancreatic flow
was also increased.
3. The effect of progesterone on the exocrine secretory function was not enhanced
or inhibited by background perfusion of estrogen in isolated perfused pancreas.
These results suggest that ovarian hormones exert significant influence on the
exocrine secretory function, both enzyme and waters of the pancreas and this effect
might be exerted by direct effect on the pancreas.
[์๋ฌธ]
Regulation of pancreatic exocrine secretion is comprised of a complex interplay between hormonal and nervous systems. It has been claimed that steroid hormones may participate in maintaining the exocrine function of the pancreas. Corticosteroids and ovarian hormones are synthesized from the same precursor, cholesterol, in vivo and they are important regulators of homeostasis.
Though the effects of corticosteroids on pancreatic function are not consistant, it is widely accepted that the glucocorticoid inhibits the secretory function whereas mineralocorticoid stimulates.
Several studies have suggested that ovarian hormones exert significant influence on the exocrine pancreas. And pancreatic function of exocrine cells may be regulated by estrogen and estrogen-binding protein. Also, it was reported that
protesterone increased both the cellular content of amylase and cytoplasmic mRNA of rat tumor cell line AR 42J.
In the present study, attempt was made to examine the direct effects of estrogen and progesterone on exocrine function employing saline perfused isolated cat pancreas.
Mongrel cats, of both sexes weighing about 1.5Kg , were anesthetized with secobarbital (30mg/kg, ip), then the pancreas was isolated and perfused with Krebs Ringer bicarbonate buffer(KRBB) gassed with 95% 0^^2-5% CO^^2 at the rate of 6
ml/min. Ovarian hormone, estrogen or progesterone was added to the perfusate for 30 minutes. Submaximal dose of secretin was infused to maintain pancreatic flow and cholecystokinin was injected to evoke the enzyme secretion in the presence or
absence of ovarian hormones.
The results are summarized as fellows.
1. Perfusion of estrogen increased the pancreatic flow as well as CCK-stimulated amylase secretion.
2. CCK-stimulated secretion of amylase was increased by perfusion with progesterone and over 10**-6 M concentration secretin-stimulated pancreatic flow was also increased.
3. The effect of progesterone on the exocrine secretory function was not enhanced or inhibited by background perfusion of estrogen in isolated perfused pancreas.
These results suggest that ovarian hormones exert significant influence on the exocrine secretory function, both enzyme and waters of the pancreas and this effect might be exerted by direct effect on the pancreas.restrictio
๋ณด์กฐํ์ ๋ง์คํฌ๋ฅผ ์ด์ฉํ ๊ด๋ฆฌ์๊ทธ๋ผํผ์์ ์๋ณํ์ ๊ดํ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณผํ๊ต์ก๊ณผ ๋ฌผ๋ฆฌ์ ๊ณต,1995.Maste
Indoor positioning using received signal strength and reference points in wireless LAN
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ,2006.Docto
์๋ณ ๊ตํต๋์ ๊ณ ๋ คํ ์ฐจ๋ ๊ฒฝ๋ก๊ณํ ์์คํ ์ ๊ตฌํ
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์ ๊ธฐ๊ณตํ๋ถ,2000.Maste
CPF์ฉ ์ฐํ์ด๋งค PtAl2O3์์ Ru ์ฒจ๊ฐ ํจ๊ณผ
Thesis(masters) --์์ธ๋ํ๊ต ๋ํ์ :ํํ์๋ฌผ๊ณตํ๋ถ, 2009.2.Maste
๋ถํ์ค ์ ๋ณดํ์์์ ๊ณต๊ณต์ฌ ๋ฐฐ๋ถ : ๋น๋ชจ์์ ์์๋ฐฐ๋ถ๊ธฐ๊ตฌ๋ฅผ ์ค์ฌ์ผ๋ก
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธๅคงๅญธๆ ก ๅคงๅญธ้ข :ๅ้็ถๆฟๅญธ็ง ๅ้็ถๆฟๅญธๅฐๆป,1996.Maste
๋ฅ๋ฌ๋์ ํ์ฉํ ๋จ์ ๋ฐฉ์ฌ์ ์์์์์ ์์ ๋๊ฐ๊ณจ ๊ณจ์ ์ ์ง๋จ
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ์๊ณผ๋ํ ์ํ๊ณผ, 2021. 2. ๊น์ฐ์ .Objectives: This study aimed to develop and evaluate a deep learning model that detects pediatric skull fractures on plain radiographs.
Materials and Methods: This retrospective multi-center study consisted of development data set acquired from two hospitals (n = 157 and 264) and an external test set (n = 95) from a third hospital. Data sets included pediatric patients who presented for head trauma at different ranges of dates and underwent skull radiography. The development data set was split into training, validation, and internal test sets at a 6:1:2 ratio. The reference standard for the development data set was radiographic findings, while it was cranial CT findings for the external test set. We used a YOLOv3 architecture that predicts bounding boxes and scores for candidate lesions. We conducted a two-session observer study on the external test set that radiologists participated without and with the assistance of the model in each session. To evaluate the diagnostic performance of the model and radiologists, we calculated the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as their 95% confidence intervals (CI).
Results: Our trained model showed an AUC of 0.984 (95% CI, 0.929โ0.999) in the internal test set and 0.791 (95% CI, 0.695โ0.868) in the external test set. The model had a sensitivity of 90.6% (95% CI, 0.750โ0.980), a specificity of 94.3% (95% CI, 0.843โ0.988), a PPV of 90.6% (95% CI, 0.762โ0.967), and an NPV of 94.3% (95% CI, 0.850โ0.980) for the internal test set. For the external test set, the model had a sensitivity of 68.4% (95% CI, 0.435โ0.874), a specificity of 88.2% (95% CI, 0.787โ0.944), a PPV of 59.1% (95% CI, 0.421โ0.741), and an NPV of 91.8% (95% CI, 0.851โ0.956). The AUCs of the radiologists were 0.765 (95% CI, 0.667โ0.846) and 0.908 (95% CI, 0.831โ0.958) in the first sessions and 0.812 (95% CI, 0.718โ0.885) and 0.925 (95% CI, 0.852โ0.969), respectively, in the second sessions, but the differences were not statistically significant (Pโs > 0.05). The less-experienced radiologist showed a higher specificity (71.1% to 93.4%, P 0.05).
Conclusions: Our study demonstrated a deep learning model for the diagnosis of pediatric skull fracture on plain radiographs, which is the first to date that has succeeded in such a task and may improve the diagnostic performance of inexperienced radiologists.๋ชฉ์ : ๋จ์ ๋ฐฉ์ฌ์ ์์์์ ์์ ๋๊ฐ๊ณจ ๊ณจ์ ์ ์ง๋จํ๋ ๋ฅ๋ฌ๋ ๊ธฐ๋ฐ ๋ชจ๋ธ์ ๊ฐ๋ฐํ๊ณ ํ๊ฐํ๊ณ ์ ํ๋ค.
๋์ ๋ฐ ๋ฐฉ๋ฒ: ๋ณธ ํํฅ์ ๋ค๊ธฐ๊ด ์ฐ๊ตฌ๋ ๋ ๋ณ์์ผ๋ก๋ถํฐ ๊ตฌ์ฑ๋ ๊ฐ๋ฐ์ฉ ๋ฐ์ดํฐ๊ตฐ๊ณผ ์ 3๋ณ์์ผ๋ก๋ถํฐ ๊ตฌ์ฑ๋ ์ธ๋ถ ๊ฒ์ ๋ฐ์ดํฐ๊ตฐ์ผ๋ก ์ด๋ฃจ์ด์ก๋ค. ๋๋ถ ์ธ์์ผ๋ก ๋ด์ํ์๊ณ ๋๊ฐ๊ณจ ๋จ์ ๋ฐฉ์ฌ์ ์ดฌ์์ ์ํํ ์์ ํ์๋ค์ด ํฌํจ๋์์ผ๋ฉฐ, ๊ฐ ๋ณ์ ๋ณ ๋ด์ ๋ ์ง ๊ธฐ์ค์ ์์ดํ์๋ค. ๊ฐ๋ฐ์ฉ ๋ฐ์ดํฐ๊ตฐ์์๋ ๋จ์ ๋ฐฉ์ฌ์ ์๊ฒฌ์ ๊ธฐ์ค์ผ๋ก ๊ณจ์ ์ฌ๋ถ๋ฅผ ๋ถ๋ฅํ์๊ณ , ์ธ๋ถ ๊ฒ์ ๋ฐ์ดํฐ๊ตฐ์์๋ ์ ์ฐํ๋จ์ธต์ดฌ์ ์๊ฒฌ์ ๊ธฐ์ค์ผ๋ก ํ์๋ค. ๊ฐ๋ฐ์ฉ ๋ฐ์ดํฐ๊ตฐ์ 6:1:2 ๋น์จ๋ก ํ๋ จ, ๊ฒ์ฆ, ๋ด๋ถ ๊ฒ์ ๊ตฐ์ผ๋ก ๋๋์ด์ก๋ค. ๋ฅ๋ฌ๋ ๋ชจ๋ธ๋ก ์์ฌ ๋ณ๋ณ์ ๋ํ ๊ฒฝ๊ณ ์์์ ๊ทธ ์ ์๋ฅผ ์ถ๋ ฅํ๋ YOLOv3 ๊ตฌ์กฐ๋ฅผ ์ฌ์ฉํ์๋ค. ์ธ๋ถ ๊ฒ์ ๊ตฐ์ ๋์์ผ๋ก ์์์ํ๊ณผ ์์ฌ๊ฐ ๋๊ฐ๊ณจ ๊ณจ์ ์ ํ๋
ํ์๋๋ฐ ๋จ๋
์ผ๋ก ํ๋
ํ๋ ์ฒซ๋ฒ์งธ ์ธ์
, ๋ชจ๋ธ์ ์์ธก ๊ฒฐ๊ณผ์ ๋์์ ๋ฐ์ผ๋ฉฐ ํ๋
ํ๋ ๋๋ฒ์งธ ์ธ์
์ผ๋ก ๊ตฌ์ฑ๋์๋ค. ๋ชจ๋ธ ๋ฐ ์์์ํ๊ณผ ์์ฌ์ ์ง๋จ ์ฑ๋ฅ์ ํ๊ฐํ๊ธฐ ์ํด ์์ ์ ์กฐ์ ํน์ฑ ๊ณก์ ํ ๋ฉด์ (AUC), ๋ฏผ๊ฐ๋, ํน์ด๋, ์์ฑ์์ธก๋ ๋ฐ ์์ฑ ์์ธก๋์ ๋์๋๋ 95% ์ ๋ขฐ๊ตฌ๊ฐ (CI)๋ฅผ ๊ณ์ฐํ์๋ค.
๊ฒฐ๊ณผ: ๋ชจ๋ธ์ ์ง๋จ๋ฅ ๋ถ์ ๊ฒฐ๊ณผ, ๋ด๋ถ ๊ฒ์ ๊ตฐ์์ AUC 0.984 (95% CI, 0.929โ0.999), ๊ทธ๋ฆฌ๊ณ ์ธ๋ถ ๊ฒ์ ๊ตฐ์์ 0.791 (95% CI, 0.695โ0.868)์ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ฌ์ฃผ์๋ค. ๋ชจ๋ธ์ ๋ฏผ๊ฐ๋๋ ๋ด๋ถ ๊ฒ์ ๊ตฐ์์ 90.6% (95% CI, 0.750โ0.980), ํน์ด๋๋ 94.3% (95% CI, 0.843โ0.988), ์์ฑ์์ธก๋๋ 90.6% (95% CI, 0.762โ0.967), ์์ฑ์์ธก๋๋ 94.3% (95% CI, 0.850โ0.980)์ผ๋ก ๊ณ์ฐ๋์๋ค. ์ธ๋ถ ๊ฒ์ ๊ตฐ์์์ ๋ชจ๋ธ์ ๋ถ์ ๊ฒฐ๊ณผ, ๋ฏผ๊ฐ๋ 68.4% (95% CI, 0.435โ0.874), ํน์ด๋ 88.2% (95% CI, 0.787โ0.944), ์์ฑ์์ธก๋ 59.1% (95% CI, 0.421โ0.741), ์์ฑ์์ธก๋ 91.8% (95% CI, 0.851โ0.956)์ ์ง๋จ๋ฅ์ ๋ณด์๋ค. ์์์ํ๊ณผ ์์ฌ์ AUC๊ฐ์ ์ฒซ๋ฒ์งธ ์ธ์
์์๋ 0.765 (95% CI, 0.667โ0.846), 0.908 (95% CI, 0.831โ0.958)์ด์๊ณ , ๋๋ฒ์งธ ์ธ์
์์๋ ๊ฐ๊ฐ 0.812 (95% CI, 0.718โ0.885), 0.925 (95% CI, 0.852โ0.969)์ด์์ผ๋ ๊ทธ ์ฐจ์ด๋ค์ ๋ชจ๋ ํต๊ณ์ ์ผ๋ก ์ ์๋ฏธํ์ง ์์๋ค (Pโs > 0.05). ๊ฒฝํ์ด ์ ์ ์์์ํ๊ณผ ์์ฌ๋ ๋ชจ๋ธ์ ๋์์ ๋ฐ์์ ๋ ํน์ด๋๊ฐ ์ ์๋ฏธํ๊ฒ ์ฆ๊ฐํ์๊ณ (71.1%์์ 93.4%, P 0.05).
๊ฒฐ๋ก : ๋ณธ ์ฐ๊ตฌ์์๋ ๋จ์ ๋ฐฉ์ฌ์ ์์์์์ ์์ ๋๊ฐ๊ณจ ๊ณจ์ ์ง๋จ์ ์ํ ๋ฅ๋ฌ๋ ๋ชจ๋ธ์ ๊ฐ๋ฐ ๋ฐ ํ๊ฐํ๋ค. ํ์ฌ๊น์ง ๋ฐํ๋ ๊ฐ์ ์ฃผ์ ๋ฅผ ๋ค๋ฃฌ ๋ฅ๋ฌ๋ ๊ด๋ จ ์ฐ๊ตฌ๊ฐ ์์ด, ์ต์ด์ ์ฐ๊ตฌ์์ ์์๊ฐ ์์ผ๋ฉฐ, ๊ฒฝํ์ด ์ ์ ์์์ํ๊ณผ ์์ฌ์ ์ง๋จ๋ฅ์ ํฅ์์ํฌ ๊ฐ๋ฅ์ฑ์ด ์๋ค.Table of Contents
Introduction 1
Materials and Methods 2
Results 8
Discussion 13
References 19
Abstract in Korean 36Maste
์ถฉ๋ถ ๋จ์ ์ง์ญ์ ๋ฐ๋ฌํ๋ ์ฃฝ๋ น ๋จ์ธต๋์ ํน์ฑ
Thesis (master`s)--์์ธ๋ํ๊ต ๋ํ์ :์ง๊ตฌํ๊ฒฝ๊ณผํ๋ถ,2001.Maste