10 research outputs found

    ์‹œ์Šคํ…œ ์•„ํ‚คํ…์ณ ๊ฐ•๊ฑด์„ฑ ๋ฐ ํ˜„์—… ์—”์ง€๋‹ˆ์–ด ์ผ๊ด€์„ฑ ํ‰๊ฐ€๋ฅผ ์œ„ํ•œ ์—”ํŠธ๋กœํ”ผ ๊ธฐ๋ฐ˜ ์ธก์ •๋ฒ•: ๊ณต์‹ํ™” ๋ฐ ์‘์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2019. 2. ์„œ์€์„.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹œ์Šคํ…œ ์•„ํ‚คํ…์ณ ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ ๋‹ค์–‘ํ•œ ์ดํ•ด๊ด€๊ณ„์ž ๊ด€์ ์— ๊ฐ•๊ฑดํ•œ ์•„ํ‚คํ…์ณ๋ฅผ ์‹๋ณ„ํ•˜๋Š” ์ •๋Ÿ‰์  ๋ฉ”ํŠธ๋ฆญ์„ ์—ฐ๊ตฌํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ๋ณต์žก๋„๊ฐ€ ๋†’์€ ์‹œ์Šคํ…œ ์•„ํ‚คํ…์ณ์˜ ๊ฐœ๋ฐœ ๊ณผ์ •์—์„œ๋Š” ๊ณ ๊ฐ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ „์ฒด ์ˆ˜๋ช…์ฃผ๊ธฐ์˜ ์ดํ•ด๊ด€๊ณ„์ž์˜ ์š”๊ตฌ ์‚ฌํ•ญ์„ ๊ฐ์•ˆํ•˜์—ฌ ์„ค๊ณ„ํ•ด์•ผ ํ•œ๋‹ค. ๊ธฐ์ˆ ์˜ ๋ฐœ์ „๊ณผ ์†Œ๋น„์ž์˜ ์š”๊ตฌ ์‚ฌํ•ญ์— ๋”ฐ๋ผ์„œ ๊ฐœ๋ฐœ๋˜๋Š” ์‹œ์Šคํ…œ์˜ ๋ณต์žก๋„์™€ ์ˆ˜๋ช…์ฃผ๊ธฐ๊ฐ€ ์ง€์†์ ์œผ๋กœ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ์„œ ์‹œ์Šคํ…œ์˜ ์ „๋ฐ˜์ ์ธ ๊ฐœ๋ฐœ, ์ƒ์‚ฐ, ์šด์šฉ/์œ ์ง€๋ณด์ˆ˜ ๋“ฑ ์ „ ๋‹จ๊ณ„์˜ ๊ด€์ ์„ ์‹œ์Šคํ…œ ๊ธฐ๋ณธ ์„ค๊ณ„ ๋‹จ๊ณ„์—์„œ ๋ฏธ๋ฆฌ ๋ฐ˜์˜ ๋ฐ ์ˆ˜์šฉ์„ ํ•˜๋Š” ํ•„์š”์„ฑ์ด ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ์กฐ์ง์ , ๊ธฐ์ˆ ์ ์ธ ๋ฌธ์ œ๋ฅผ ํ•ด์†Œํ•˜๋Š” ๊ด€์ ์ด ์–ด๋–ป๊ฒŒ ์ฐจ์ด๊ฐ€ ๋‚˜๋Š”์ง€ ์ •๋Ÿ‰์ ์ธ ํ‰๊ฐ€์˜ ์ค‘์š”์„ฑ์ด ๋ถ€๊ฐ๋œ๋‹ค. ํ†ต๊ณ„์—ญํ•™์˜ ์—”ํŠธ๋กœํ”ผ ๊ธฐ๋ฐ˜ ๋ฉ”ํŠธ๋ฆญ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ์‹œ์Šคํ…œ ๋ถ„ํ•ด ๊ด€์ ์— ๋Œ€ํ•œ ๋น„๊ต๋ฅผ ์ •๋Ÿ‰ํ™”ํ•œ๋‹ค. ๊ฐœ๋ฐœํ•œ ๋ฉ”ํŠธ๋ฆญ์œผ๋กœ ๋‘ ๊ฐ€์ง€ ์‚ฌ๋ก€์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด์„œ ๋‹ค์–‘ํ•œ ๊ด€์ ์— ๊ฐ•๊ฑดํ•œ ์‹œ์Šคํ…œ ์•„ํ‚คํ…์ณ๋ฅผ ํ‰๊ฐ€, ๋ฐ ์ „๋ฌธ๊ฐ€์˜ ์‹œ์Šคํ…œ ๋ถ„ํ•ด์˜ ์ผ๊ด€์„ฑ์„ ํ‰๊ฐ€ํ•˜๋Š” ๊ณผ์ •์„ ๊ฑฐ์ณ์„œ ์ œ์‹œ๋œ ๋ฉ”ํŠธ๋ฆญ์˜ ์‹ค์šฉ์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด์„œ ๊ด€์ ์˜ ์ฐจ์ด๊ฐ€ ์ž‘๊ฒŒ ๋‚˜๋Š” ์•„ํ‚คํ…์ณ์˜ ๊ฐœ๋ฐœ์— ๊ธฐ์—ฌ๋ฅผ ํ•˜๋Š” ๋ฐ์— ๋ชฉ์ ์„ ๋‘”๋‹ค.This thesis proposes an entropy-based metric which quantifies complex system architecture robustness to different decomposition perspectives for resolving varying stakeholders architectural preferences during the critical stages of the system architecting process. The newly developed metric aims to identify architectures that are robust to different decomposition perspectives by quantifying pairwise comparisons between two different architectural decompositions that may arise from the system architecting process. While system architects typically rely on decomposing a system into its constituent functions and subfunctions, the architecture of a complex system may be interpreted differently by various stakeholders throughout the value chain, which can result in several different system decomposition perspectives, including, but not limited to, assembly or maintenance-based decomposition preferences. As such, the various modular configurations should be quantitatively assessed for the development of an architecture that is robust for different perspectives. The newly proposed module diffusion index adapts entropy, a statistical mechanics concept, to quantify the level of re-arrangement that is required for a modules components to be reassigned to another decomposition perspective as a means of assessing an architectures robustness to different stakeholder requirements. Two feasibility studies were conducted to observe how the newly proposed metric evaluates decomposition perspectives of three different architectures to find a perspective-robust architecture, and to assess the consistency at which industry professionals decompose a given architecture to different perspectives. The proposed metric aims to assist system architects as a quantitative evaluation criterion for analyzing different system architecture concepts during the early engineering phases of complex system design.Abstract ii Contents iv List of Tables vi List of Figures vii Chapter 1 Introduction 1 Chapter 2 Literature Review 5 2.1 System Architecture Development and Selection 5 2.2 Decomposition Perspectives of System Architecture 7 2.3 Quantitative System Architecture Assessment 9 2.4 Research Gap Analysis 10 Chapter 3 Entropy-based Metric Development 12 3.1 Metric Development Overview and Background 12 3.2 Module Diffusion Index Formulation 14 Chapter 4 Case Study: System Architecture Robustness Assessment for Different Stakeholder Perspectives 19 4.1 Introduction 19 4.2 Clock Architecture Overview 21 4.3 Stakeholders Decomposition Perspectives 23 4.4 Case Study Results 27 4.5 Case Study Discussion and Summary 32 Chapter 5 Case Study: Expert Evaluation for Decomposition Consistency 35 5.1 Introduction 35 5.2 Case Study Results 38 5.3 Case Study Discussion and Summary 40 Chapter 6 Conclusions and Directions for Future Work 43 6.1 Conclusions 43 6.2 Directions for Future Work 44 Bibliography 47 Appendix A: Bill of Materials for VFEC Architecture 53 Appendix B: Bill of Materials for FPC Architecture 58 Appendix C: Bill of Materials for CSC Architecture 62 Appendix D: DSM for for CSC Architecture 67 Appendix E: DSM for CSC Architecture 68 Appendix F: DSM for CSC Architecture 69 ๊ตญ๋ฌธ์ดˆ๋ก 70Maste

    Sensor Calibration and Single-axis HIL Verification of ADCS for Low Earth Orbit Cube-satellite

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2017. 8. ๊ธฐ์ฐฝ๋ˆ.์„œ์šธ๋Œ€ํ•™๊ต GNSS ์—ฐ๊ตฌ์‹ค์—์„œ ๊ฐœ๋ฐœํ•œ 2U ํฌ๊ธฐ์˜ ํ๋ธŒ์œ„์„ฑ์ธ SNUGLITE๋Š” ์ด์ค‘์ฃผํŒŒ์ˆ˜ GPS์ˆ˜์‹ ๊ธฐ๋ฅผ ํƒ‘์žฌํ•˜์—ฌ ์‹ค์ œ ์šด์šฉ์„ ์ฃผ์š” ์ž„๋ฌด๋กœ ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ณผํ•™์  ์ž„๋ฌด๋กœ๋Š” ์šฐ์ฃผํ™˜๊ฒฝ ๊ด€์ธก ๋ฐ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์„ ๋ชฉํ‘œ๋กœ ํ•˜๊ณ  ์žˆ๋‹ค. ํ†ต์‹ ์€ UHF์™€ ์ง€ํ–ฅ์„ฑ์ด ์žˆ๋Š” S-band ์•ˆํ…Œ๋‚˜๋ฅผ ํ†ตํ•ด ์ง€์ƒ๊ตญ๊ณผ ์ž„๋ฌด๋ฐ์ดํ„ฐ๋ฅผ ์†ก์ˆ˜์‹ ํ•œ๋‹ค. ์ด๋•Œ, ์•ˆ์ •์ ์ด๊ณ  ์„ฑ๊ณต์ ์ธ ๋ฐ์ดํ„ฐ ์†ก์ˆ˜์‹ ์„ ์œ„ํ•˜์—ฌ ํ๋ธŒ์œ„์„ฑ์˜ ์ง€๊ตฌ์ง€ํ–ฅ์ž์„ธ ์ œ์–ด๊ฐ€ ํ•„์ˆ˜์ ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด์„œ๋Š” ์ž์„ธ ์ถ”์ • ๋ฐ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์ธ ADCS(Attitude Determination and Control System)์ด On Board Computer(OBC) Processor์—์„œ real-time์œผ๋กœ ๊ตฌํ˜„์ด ๋˜์–ด์•ผ ํ•œ๋‹ค. ์ž์„ธ ์ถ”์ •์•Œ๊ณ ๋ฆฌ์ฆ˜์œผ๋กœ๋Š” EKF(Extended Kalman Filter)๋ฅผ ์‚ฌ์šฉํ•˜๋ฉฐ. ์ž์„ธ์ œ์–ด ๊ธฐ๋ฒ•์œผ๋กœ๋Š” LQG(Linear Quadratic Gaussian) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜๊ณ ` ์žˆ๋‹ค. ํ๋ธŒ์œ„์„ฑ ADCS ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฐœ๋ฐœ๋‹จ๊ณ„๋กœ, ์ฒซ์งธ, ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ S/W Simulation ์ธก๋ฉด์—์„œ ๊ฒ€์ฆํ•˜๋Š” SILS(Software In the Loop Simulation) [์ดํ•˜ SILS]๋ฅผ ํ†ตํ•˜์—ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋Œ€ํ•œ ์„ค๊ณ„๋ฅผ ํ•œ ํ›„, ๋‹ค์Œ ๋‹จ๊ณ„๋กœ PILS(Processor In the Loop Simulation) [์ดํ•˜ PILS]๋ฅผ ํ†ตํ•˜์—ฌ ์‹ค์ œ OBC(On Board Computer)์— ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํƒ‘์žฌํ•˜์—ฌ ๊ฒ€์ฆํ•˜๋Š” ๋‹จ๊ณ„๋ฅผ ๊ฑฐ์น˜๋ฉฐ, ์ตœ์ข…๋‹จ๊ณ„๋กœ ADCS์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‹คํ—˜์ ์œผ๋กœ ๊ฒ€์ฆํ•˜๊ณ ์ž, ์šฐ์ฃผํ™˜๊ฒฝ์„ ๋ชจ์‚ฌํ•˜์—ฌ ์ž์„ธ ์ถ”์ • ๋ฐ ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฒ€์ฆํ•˜๋Š” ๋‹จ๊ณ„์ธ HILS(Hardware In the Loop Simulation) [์ดํ•˜ HILS]๊ฒ€์ฆ๊ณผ์ •์ด ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ผ๋ จ์˜ ๊ฒ€์ฆ๊ณผ์ •์„ ํ†ตํ•˜์—ฌ ํ๋ธŒ์œ„์„ฑ์˜ ADCS ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์„ค๊ณ„ํ•˜๊ฒŒ ๋œ๋‹ค. ์ด์ „ ์—ฐ๊ตฌ๋กœ์„œ๋Š” ADCS์— ๋Œ€ํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋Œ€ํ•œ ๊ฒ€์ฆ๋‚ด์šฉ์œผ๋กœ SILS ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋œ ๋ฐ” ์žˆ๋‹ค. [1], [2] ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹ค์ œ OBC์— ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•œ PILS ์— ๊ด€ํ•œ๋‚ด์šฉ๊ณผ ์‹ค์ œ ํ•˜๋“œ์›จ์–ด์— ์ ์šฉํ•˜์—ฌ ์‹คํ—˜์ ์œผ๋กœ ๊ฒ€์ฆํ•œ ๋‚ด์šฉ์ธ ๋‹จ์ผ ์ถ• HIL ์‹คํ—˜ ๊ฒ€์ฆ์— ๋Œ€ํ•œ ๋‚ด์šฉ์„ ๋‹ค๋ฃจ์—ˆ๋‹ค. ํŠนํžˆ, ์‹ค์ œ ํƒ‘์žฌ์„ผ์„œ๋ฅผ ์‚ฌ์šฉํ•ด์•ผ ํ•˜๋ฏ€๋กœ ์ด๋Ÿฌํ•œ ์„ผ์„œ ์˜ค์ฐจ ๋ชจ๋ธ๋ง๊ณผ ๋ณด์ •์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋ฅผ ํ•จ๊ป˜ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. PILS ์ˆ˜ํ–‰ ๋‚ด์šฉ์œผ๋กœ์„œ๋Š” MATLAB ๊ธฐ๋ฐ˜ SILS ์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๋ชจ๋‘ C์–ธ์–ด๋กœ ๋ณ€ํ™˜ํ•˜์˜€์œผ๋ฉฐ, ์ด๋•Œ, OBC์˜ ๊ณ„์‚ฐ์ˆ˜ํ–‰ ๋Šฅ๋ ฅ์— ๋งž๋„๋ก, EKF์˜ Time Update๋ถ€๋ถ„์—์„œ Van-Loan ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ, State-transition matrix๋ฅผ ๊ตฌํ•  ๋•Œ, 1์ฐจ๋กœ ๊ฐ„๋žตํ™” ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ํ๋ธŒ์œ„์„ฑ์ด ํƒ‘์žฌํ•˜๊ณ  ์žˆ๋Š” Actuator ํŠน์„ฑ์ƒ ์ง€๊ตฌ ์ž๊ธฐ์žฅ์„ ์ด์šฉํ•œ ์ œ์–ด๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์ œ์–ด๋ฅผ ์œ„ํ•œ LQR-gain์„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๊ตฌํ•ด์ฃผ์–ด์•ผ ํ•˜๋ฏ€๋กœ, ์‹œ์Šคํ…œ์˜ Eigenvalue/Eigenvector ๋ฌธ์ œ ๊ตฌํ•˜๋Š” ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํƒ‘์žฌํ•˜์—ฌ Potters method๋ฅผ ์ด์šฉํ•˜์—ฌ LQR-gain์„ ๊ตฌํ•ด์ฃผ๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ, ์ž์„ธ ๊ฒฐ์ •์„ผ์„œ๋กœ 5๋ฉด์— ๋ถ€์ฐฉ๋œ Coarse sun sensor(ํƒœ์–‘์„ผ์„œ)์™€ OBC์— ํƒ‘์žฌ๋œ Gyroscope (๊ฐ์†๋„๊ณ„)์™€ Magnetometer(์ง€์ž๊ณ„)๋ฅผ ์ด์šฉํ•˜์—ฌ ์ž์„ธ๊ฒฐ์ •์„ ์ˆ˜ํ–‰ํ•˜๋ฉฐ, ์ด ์„ผ์„œ๋“ค์˜ ์„ฑ๋Šฅ์„ ์˜ฌ๋ฐ”๋ฅด๊ฒŒ ์‚ฌ์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์„ผ์„œ ์˜ค์ฐจ ๋ชจ๋ธ๋ง ๋ฐ ๋ณด์ •์ด ํ•„์ˆ˜์ ์ด๋‹ค. Coarse sun sensor์˜ ๊ฒฝ์šฐ, Photodiode๋ฅผ ์ด์šฉํ•œ ์ €๊ฐ€ํ˜• ์„ผ์„œ๋กœ์„œ ํƒœ์–‘๋ฒกํ„ฐ๊ฐ€ 3์ถ•์œผ๋กœ ๋“ค์–ด์˜จ๋‹ค๋ฉด, Trigonometric method๋ฅผ ์ด์šฉํ•˜๊ณ , 2์ถ•์œผ๋กœ ๋“ค์–ด์˜ฌ ๊ฒฝ์šฐ, Conical shell model์„ ์ด์šฉํ•˜์—ฌ, ๊ฐ ์„ผ์„œ์˜ ์ƒ๋Œ€์ ์ธ ๋น›์˜ ํฌ๊ธฐ(Intensity)๋ฅผ normalizedํ•˜์—ฌ, Sun ๋ฒกํ„ฐ๋ฅผ ๊ตฌํ•˜๊ฒŒ ๋˜๋ฉฐ, ์ด๋•Œ, ์„ผ์„œ ์˜ค์ฐจ ๋ชจ๋ธ๋ง์„ ์ˆ˜ํ–‰ํ•˜์—ฌ Sine ํ•จ์ˆ˜๋กœ output์„ ๊ฐ–๋„๋ก ๋ณด์ •์„ ํ•ด ์ฃผ์–ด์•ผ ํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, Magnetometer๋Š” ์ง€๊ตฌ ์ ˆ๋Œ€์ž๊ธฐ์žฅ ๋Œ€๋น„ ์‹ค์ œ ์ธก์ •๋˜๋Š” ์ž๊ธฐ์žฅ์˜ ํฌ๊ธฐ๋ฅผ scale factor๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ณด์ •ํ•˜๋ฉฐ, 3์ถ•์˜ ๊ฒฝ์šฐ Hard iron๊ณผ Soft iron compensation์„ ํ†ตํ•˜์—ฌ ๋ณด์ •์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. Gyroscope์˜ ๊ฒฝ์šฐ์—๋Š” Scale factor๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋…ธ์ด์ฆˆ ๋ถ„์„์„ ์œ„ํ•œ Allan Variance ๋ถ„์„์„ ํ†ตํ•˜์—ฌ bias ๋ชจ๋ธ์„ 1st order Gauss Markov Process๋กœ ์ ์šฉํ•˜์˜€๋‹ค. ADCS ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ง€์ƒ์—์„œ ๊ฒ€์ฆ์„ ์œ„ํ•˜์—ฌ HILS ๊ฒ€์ฆ์€ ๋ณดํ†ต 3์ถ• ์ž์„ธ ๊ฒฐ์ •, ์ œ์–ด๋ฅผ ์œ„ํ•œ Air-bearing ๊ธฐ๋ฐ˜์˜ HILS ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฒ€์ฆํ•˜๋Š” ์‹คํ—˜๊ณผ์ •์„ ๊ฑฐ์นœ๋‹ค ํ•˜์ง€๋งŒ, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋‹จ์ผ ์ถ• HIL ์‹คํ—˜๊ฒ€์ฆ์— ๋Œ€ํ•œ ๋‚ด์šฉ์œผ๋กœ ๊ฒ€์ฆ์„ ์œ„ํ•œ ๊ธฐ๊ตฌ ์„ค๊ณ„๊ฐ€ ๋ณต์žกํ•˜๊ณ  ๋น„์šฉ์ธก๋ฉด์—์„œ ๋ถˆ๋ฆฌํ•œ 3์ถ• HILS ๊ฒ€์ฆ๋Œ€์‹ ์— ๊ฐ„๋‹จํ•˜๊ณ  ๋น„์šฉ์ ์ธ ์ธก๋ฉด์—์„œ ์œ ๋ฆฌํ•œ ๋‹จ์ผ ์ถ• HIL ์‹คํ—˜๊ฒ€์ฆ์„ ์ฑ„ํƒํ•˜์—ฌ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€์œผ๋ฉฐ ์‹คํ—˜์ ์œผ๋กœ ์ž์„ธ ์ถ”์ • ๋ฐ ์ œ์–ด๊ฒฐ๊ณผ๋ฅผ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์ด๋ฅผ ์ž์„ธ ์ถ”์ • ๋ฐ ์ œ์–ด ์š”๊ตฌ์กฐ๊ฑด๊ณผ ํ™•์ธํ•˜์˜€๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ ๋™๊ธฐ ๋ฐ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ ๋™ํ–ฅ 4 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• 5 ์ œ 4 ์ ˆ ์—ฐ๊ตฌ์˜ ๊ธฐ์—ฌ๋„ 8 ์ œ 2 ์žฅ ADCS ์•Œ๊ณ ๋ฆฌ์ฆ˜ 10 ์ œ 1 ์ ˆ ์ขŒํ‘œ๊ณ„์˜ ์ •์˜ 10 ์ œ 2 ์ ˆ ์ž์„ธ์ถ”์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜ 13 1. ํ•ญ๋ฒ•ํ•ด๋ฅผ ์ด์šฉํ•œ Local Frame ์ขŒํ‘œ๋ณ€ํ™˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 13 2. GPSํ•ญ๋ฒ•ํ•ด๋ฅผ ์ด์šฉํ•œ ์ž๊ธฐ์žฅ, ํƒœ์–‘ ๋ชจ๋ธ๋ฒกํ„ฐ ์ƒ์„ฑ 16 3. TRIAD Method 18 4. EKF(Extended Kalman Filter) 18 ์ œ 3 ์ ˆ ์ž์„ธ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜ 28 1. LQG(Linear Quadratic Gaussian) 29 2. MATLAB ์˜ LQR์•Œ๊ณ ๋ฆฌ์ฆ˜ C๋ณ€ํ™˜๊ณผ์ • 34 ์ œ 3 ์žฅ Processor in the Loop Simulation 38 ์ œ 1 ์ ˆ ์„ผ์„œ ์˜ค์ฐจ ๋ชจ๋ธ๋ง ๋ฐ ๋ณด์ • 39 1. ์ง€์ž๊ณ„(Magnetoemter) ๋ณด์ • 39 2. ๋น„์ •๋ฐ€ ํƒœ์–‘์„ผ์„œ(Coarse Sun Sensor) ์˜ค์ฐจ๋ณด์ • 45 3. ๊ฐ์†๋„๊ณ„(Gyroscope) bias ๋ชจ๋ธ 53 ์ œ 4 ์žฅ ๋‹จ์ผ ์ถ• HIL ๊ฒ€์ฆ ์‹คํ—˜ 58 ์ œ 1 ์ ˆ Engineering Model ํ๋ธŒ์œ„์„ฑ ์ œ์ž‘ 60 1. Magnetorquer ์„ค๊ณ„(Actuator) 62 2. ํƒœ์–‘์„ผ์„œ ์„ค๊ณ„ 64 ์ œ 2 ์ ˆ ๋‹จ์ผ ์ถ• HILS ๊ฒ€์ฆ ์‹คํ—˜ ํ™˜๊ฒฝ 65 ์ œ 3 ์ ˆ MATLAB ๊ธฐ๋ฐ˜ ์‹ค์‹œ๊ฐ„ ํ†ต์‹  ํ”„๋กœ๊ทธ๋žจ 67 ์ œ 4 ์ ˆ ๋‹จ์ผ ์ถ• HIL ๊ฒ€์ฆ ์‹คํ—˜ ๋ฐ ๋ถ„์„ 68 1. ์ž์„ธ์ถ”์ •๊ฒฐ๊ณผ, Attitude Estimation Result 69 2. ์ž์„ธ์ œ์–ด๊ฒฐ๊ณผ, Attitude Estimation and Control result 71 3. ์ธก์ •์น˜ ์˜ค์ฐจ๋ถ„์„ 75 ์ œ 5 ์žฅ ๊ฒฐ ๋ก  76 ์ฐธ๊ณ  ๋ฌธํ—Œ 77Maste

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    (A) study on food preference through observation for the leaved food after meals

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    ๋ณด๊ฑดํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] 1981๋…„ 4์›” 1์ผ๋ถ€ํ„ฐ 4์›” 30์ผ๊นŒ์ง€ 1๊ฐœ์›”๋™์•ˆ ์œก๊ตฐ โ—‹โ—‹๋ณ‘์›์— ์ž…์›๋œ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์‹ํ’ˆ์˜ ์„ ํ˜ธ์„ฑํ–ฅ์— ๋”ฐ๋ฅธ ์ž”์‹๋Ÿ‰์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์„ ํ˜ธ๋„๊ฐ€ ์ž”์‹๋Ÿ‰์— ์ „์ฒด์ ์œผ๋กœ ๋ฏธ์น˜๋Š” ์–‘์ƒ์„ ์•Œ์•„๋ณด๊ณ ์žํ•จ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. ๋ชจ๋“  ์‹ํ’ˆ์˜ ์ข…๋ฅ˜ ๋ฐ ๊ธฐ์ค€๋Ÿ‰์˜ ์ž๋ฃŒ๋Š” ๊ด€๊ณ„๊ธฐ๊ด€์œผ๋กœ๋ถ€ํ„ฐ ์–ป์–ด์กŒ๊ณ  ์„ ํ˜ธ๋„์˜ ํŒŒ์•…์€ 1๋…„๋™์•ˆ ๊ตฐ์—์„œ ๊ธ‰์‹ํ•œ ์‹ํ’ˆ์ค‘ ๋ฏธ๋ฆฌ ์ค€๋น„๋œ ์กฐ์‚ฌํ‘œ์— ์˜ํ•ด ์‹œํ–‰๋˜์—ˆ์œผ๋ฉฐ ์ž”์‹๋Ÿ‰์€ ์ฃผ์‹๊ณผ ๋ถ€์‹์„ ์กฐ๋ฆฌ์ „๊ณผ ์กฐ๋ฆฌํ›„ ๊ฐ๊ฐ ํ‰๋Ÿ‰ํ•˜์—ฌ ๋ฐฐ์„ ํ•œ ์‹ค์ œ์˜ ๋Ÿ‰์„ ๊ธฐ์ค€์œผ๋กœ ํ•˜์—ฌ ์ž”์‹๋Ÿ‰์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ด๋“ค ์ž๋ฃŒ๋Š” ํ†ต๊ณ„์ฒ˜๋ฆฌ์— ์˜ํ•ด ์–ป์–ด์ง„ ๊ฒฐ๊ณผ์— ๋”ฐ๋ผ์„œ ์„ ํ˜ธ๋„์™€ ์ž”์‹๋Ÿ‰๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ๋น„๊ตํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๋ก ์„ ์–ป์—ˆ๋‹ค. ์ฃผ์‹์˜ ์„ ํ˜ธ๋„๋Š” ์Œ€๋ฐฅ์ด 55.0%๋กœ์จ ๊ฐ€์žฅ ๋†’์•˜๊ณ  ๋ถ€์‹์˜ ์„ ํ˜ธ๋„๋Š” ์œก๋ฅ˜ 57%, ์†Œ์ฑ„๋ฅ˜ 25%, ์ƒ์„ ๋ฅ˜ 11%, ๋ž€๋ฅ˜ 7%์˜ ์ˆœ์œ„์˜€๋‹ค. ๋ณด๋ฆฌ๋ฐฉ๋ฒ•์— ๋”ฐ๋ผ ์„ ํ˜ธ๋„์˜ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ ๊ธ‰์‹ํšŒ์ˆ˜์™€ ์„ ํ˜ธ๋„๊ฐ€ ์ผ์น˜ํ•˜๋Š” ์š”๋ฆฌ๋Š” ์•„์ฃผ ์ ์—ˆ๋‹ค. ๋Œ€์ƒํ™˜์ž ์ค‘ ์ž”์‹์œจ์€ 24%์ด์—ˆ์œผ๋ฉฐ ์š”๋ฆฌ์˜ ์„ ํ˜ธ๋„์™€ ์ž”์‹์œจ๊ณผ๋Š” ๋ฐ˜๋น„๋ก€๊ด€๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋์œผ๋กœ ๋Œ€์ƒํ™˜์ž์˜1์ผ ํ‰๊ท  ์„ญ์ทจ์—ด๋Ÿ‰์€ 3,392cal์ด์—ˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ์กฐ์‚ฌ๊ฒฐ๊ณผ๋ฅผ ๊ณ ๋ คํ•  ๋•Œ ์•ž์œผ๋กœ ๊ตฐ ๋ณ‘์›์—์„œ ์„ ํ˜ธ๋„์— ๋”ฐ๋ฅธ ์‹๋‹จ์ž‘์„ฑ์„ ์œ„ํ•ด ํ–‰์ •์ ์ธ ๋’ท๋ฐ›์นจ์ด ๊ฐ•๋ ฅํžˆ ์š”์ฒญ๋œ๋‹ค. A Study on Food Preference through Obsevation for the Leaved Food after Meals Choi, Min Kyu Department of Public Health Graduate School of Health Science and Management (Directed by Prof. Kim, Myung Ho) Aiming to observe any relationship between food preference and the leaved food after meals, the study was conducted during April 1-30, 1981 (one month) for 202 in-patients at x x Army Hospital. The study results showed as follow : 1. The preference ration for the rice food was 55.0% which was higher than any other that of grain food. 2. Each of the preference ratio for the material of food was 57% for fresh meat, 25% for vegetable, 11% for fish and 7% egg. 3. It was clearly found that the food preference was influenced by the cooking method. 4. The frequency of food intake was not related to the food preference. 5. The amount of the leveled food after melas was 24% in average. 6. The ratio of the leved food was different depending on ward and date of meals. 7. The ratio of the leaved food was proportionally changed by the food selsectivity. 8. Basal food composition should be balanced in quantity and quality. 9. The Calory intake by in-patients showed 3,392 Cal a day as mean. 10. It was suggested through the study that food preference should be considered in preparation of menu as one of the important factors. [์˜๋ฌธ] Aiming to observe any relationship between food preference and the leaved food after meals, the study was conducted during April 1-30, 1981 (one month) for 202 in-patients at x x Army Hospital. The study results showed as follow : 1. The preference ration for the rice food was 55.0% which was higher than any other that of grain food. 2. Each of the preference ratio for the material of food was 57% for fresh meat, 25% for vegetable, 11% for fish and 7% egg. 3. It was clearly found that the food preference was influenced by the cooking method. 4. The frequency of food intake was not related to the food preference. 5. The amount of the leveled food after melas was 24% in average. 6. The ratio of the leved food was different depending on ward and date of meals. 7. The ratio of the leaved food was proportionally changed by the food selsectivity. 8. Basal food composition should be balanced in quantity and quality. 9. The Calory intake by in-patients showed 3,392 Cal a day as mean. 10. It was suggested through the study that food preference should be considered in preparation of menu as one of the important factors.restrictio

    ์ทŒ์‹ญ์ด์ง€์žฅ ์ ˆ์ œ์ˆ  ํ›„ ๊ธฐ๋Šฅ์  ์ธก๋ฉด์˜ ์žฅ๊ธฐ์  ๊ฒฐ๊ณผ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ์™ธ๊ณผํ•™์ „๊ณต,2004.Maste

    Development of Clinical Pathway Analysis Program based on OMOP CDM (Observational Medical Outcomes Partnership Common Data Model)

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    MasterHospital information systems (HISs) storing a large amount of clinical data trigger the appearance of data-driven clinical research such as improving treatment effectiveness, healthcare management, and CRM (Customer Relationship Management) and detecting fraud and abuse. Deriving CP (Clinical Pathway) from clinical data is also one of those data-driven studies. The current research works try to create and improve methodologies for deriving CP from personal health records. However, most of the studies related to CP strongly depend on the data they exploit. Since each HISs stores data with its own data schema, it is difficult to apply the research from one data source to another clinical data sources. Thus, in this thesis, we propose a common data model (CDM) based CP analysis method and a software program for solving those difficulties. More in detail, we define data and functional requirement for CP Analysis, and construct program modules satisfying those requirements. Furthermore, we construct CP Mining Modules which consist of functional units representing methodologies deriving CPs from clinical data. By reproducing previous CP research related to inventing methodologies deriving CP, we verify proposed methods and programโ€™s applicable any other OMOP CDM based CP Research
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