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    ์ตœ์‹  ECU๋ณด๋“œ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์†Œํ”„ํŠธ์—๋Ÿฌ๋“ค์„ ์‹ค์‹œ๊ฐ„ ๋ณต๊ตฌํ•˜๋Š” ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2020. 8. ์ด์ฐฝ๊ฑด.This dissertation presents the fault-tolerant real-time scheduling using dynamic mode switch support of modern ECU hardware. This dissertation first describes the optimal capacity of the Periodic Resource which contains harmonic periodic task set using the exact time supply function.We show that the optimal capacity can be represented as sum of the each individual utilization of the task in the harmonic periodic task set for both normal state(i.e. no faults) and faulty state. Then, this dissertation proposes non-critical task overlapping technique by only using the idle time intervals of the Periodic Resource in order to overlap the non-critical tasks which ensures no additional capacity increase. Finally, this dissertation proposes the basic form of the Periodic Resources in order to efficiently use the dynamic mode switch support. Next, we also proposes the bin-packing heuristic algorithm that considers both making sub-taskset as a one Periodic Resource and Periodic Resource wide bin-packing which has the pseudo-polynomial time complexity. Experimental results show that the proposed algorithm performs better than the traditional partitioned fixed-priority scheduling approach and partitioned mixed-criticality scheduling approach. Also, the achievement is made up to 18% in terms of the total needed cores compared to traditional partitioned fixed-priority approach for making the given input task set schedulable.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํšจ์œจ์ ์ธ ์žฌ๊ตฌ์„ฑ๊ฐ€๋Šฅ ์‹œ์Šคํ…œ ์‚ฌ์šฉ์„ ์œ„ํ•œ ๊ณ„์ธต๊ธฐ๋ฐ˜ ์‹ค์‹œ๊ฐ„ ๊ฒฐํ•จ ๊ฐ๋‚ด ์Šค์ผ€์ค„๋ง ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฃผ๊ธฐ ์ž์› ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ, ์ตœ์  ์ฃผ๊ธฐ ์ž์› ์„œ๋ฒ„์˜ ์šฉ๋Ÿ‰์„ ์ฃผ๊ธฐ ์ž์› ๋ชจ๋ธ์ด ๊ฐ€์ง€๋Š” ์‹ค์‹œ๊ฐ„ ์ฃผ๊ธฐ ํƒœ์Šคํฌ ์…‹์˜ ์œ ํ‹ธ๋ผ์ด์ œ์ด์…˜์˜ ํ•ฉ์œผ๋กœ ์ œ์‹œํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ํ•ด๋‹น ์ตœ์  ์„œ๋ฒ„ ์šฉ๋Ÿ‰์„ ์‹œ์Šคํ…œ์ด ์ •์ƒ ๋™์ž‘ํ• ๋•Œ์™€ ์˜ค๋™์ž‘ ํ• ๋•Œ ๋ชจ๋‘์— ๋Œ€ํ•ด์„œ ์ œ์‹œํ•œ๋‹ค. ๋‹ค์Œ์œผ๋กœ, ๋น„์ค‘์š” ํƒœ์Šคํฌ ์…‹๋“ค์„ ์ค‘์š” ์ฃผ๊ธฐ ์ž์› ์„œ๋ฒ„์˜ ์—ฌ๋ถ„ ๊ณต๋ฐฑ ์‹œ๊ฐ„์„ ํ™œ์šฉํ•ด ์„œ๋ฒ„ ์šฉ๋Ÿ‰์˜ ์ฆ๊ฐ€ ์—†์ด ๋น„์ค‘์š” ํƒœ์Šคํฌ๋ฅผ ์ค‘์š” ์ฃผ๊ธฐ ์ž์› ์„œ๋ฒ„์— ํ• ๋‹นํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๋ณธ ๋…ผ๋ฌธ์€ ์ฃผ๊ธฐ ์ž์› ์„œ๋ฒ„ ๋‹จ์œ„์˜ ํŒŒํ‹ฐ์…˜ ๊ธฐ๋ฒ•๊ณผ ์ฃผ๊ธฐ ํƒœ์Šคํฌ๋ฅผ ํ•˜๋‚˜์˜ ์ฃผ๊ธฐ ์ž์› ์„œ๋ฒ„๋กœ ๋งŒ๋“œ๋Š” ๋นˆํŒจํ‚น ํœด๋ฆฌ์Šคํ‹ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์‹œํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ, ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ธฐ์กด์— ์‚ฌ์šฉ๋˜์—ˆ๋˜ ํŒŒํ‹ฐ์…˜ ๊ธฐ๋ฐ˜ ์šฐ์„ ์ˆœ์œ„ ์Šค์ผ€์ค„๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ํŒŒํ‹ฐ์…˜ ๊ธฐ๋ฐ˜ ์šฐ์„ ์ˆœ์œ„ ํ˜ผ์žก ์ค‘์š”๋„ ์•Œ๊ณ ๋ฆฌ์ฆ˜๋ณด๋‹ค ๋” ์ž‘์€ ์ˆ˜์˜ ์ฝ”์–ด์˜ ๊ฐœ์ˆ˜๋ฅผ ๋„์ถœ ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ธ๋‹ค. ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์žฌ๊ตฌ์„ฑ๊ฐ€๋Šฅ ์‹œ์Šคํ…œ์— ํ™œ์šฉํ•œ๋‹ค๋ฉด ๊ธฐ์กด ๋ฐฉ๋ฒ• ๋Œ€๋น„ ์ตœ๋Œ€ 18%์˜ ์ฝ”์–ด์ ˆ๊ฐํšจ๊ณผ๋ฅผ ๊ธฐ๋Œ€ํ• ์ˆ˜ ์žˆ๋‹ค.1 Introduction 1 1.1 Motivation and Objective 1 1.2 Approach 2 1.3 Organization 6 2 System Model 7 3 Schedulability Analysis 10 3.1 Background 10 3.2 Optimal Capacity Analysis During Normal State 14 3.3 Optimal Capacity Analysis During Fault State 16 3.4 Periodic Resource Wide Schedulability Test 20 3.5 Non-Critical Task Overlapping 24 4 Proposed Approach 26 4.1 Minimum Harmonic Partitions of the Task Set 26 4.2 Proposed Heuristic Algorithm 28 4.2.1 Choosing Detection method 28 4.2.2 Packing Minimum Harmonic Partitions 29 4.2.3 Packing Free Tasks 30 4.2.4 Packing Non-Critical Tasks 31 4.3 Algorithm Description 32 5 Evaluation 35 5.1 Experimental Setup 35 5.2 Simulation Results 36 5.2.1 Free Task Bin-Packing 38 5.2.2 Minimum Harmonic Partitions Bin-Packing 40 5.2.3 Effect of Non-Critical Task Overlapping 43 5.2.4 Effect of State-Wise Computation 45 6 Related Works 46 6.1 Hierarchical Fault-Tolerant Real-Time Scheduling 46 6.2 Error Detection Method 46 7 Conclusion 48 References 50Maste

    ๋ธŒ๋ผ์šด ๋™๋ ฅํ•™ ์ „์‚ฐ๋ชจ์‚ฌ๋ฅผ ์ด์šฉํ•œ ์ž…์ž๊ณ„ ํ•„๋ฆ„์˜ ๊ฑด์กฐ ๊ณผ์ •์—์„œ์˜ ๋ถˆ๊ท ์ผ ๊ตฌ์กฐ ํ˜•์„ฑ๊ณผ์ • ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2022.2. ์•ˆ๊ฒฝํ˜„.์ž…์ž๊ณ„ ํ•„๋ฆ„์€ ํฌ๊ธฐ๊ฐ€ ์„œ๋กœ ๋‹ค๋ฅธ ์ž…์ž, ๋ฐ”์ธ๋”, ์šฉ๋งค ๋“ฑ์˜ ํ˜ผํ•ฉ๋ฌผ์ด๊ธฐ ๋•Œ๋ฌธ์— ๊ฑด์กฐ ๊ณผ์ • ์ค‘ ์ด๋“ค์˜ ๋ถ„ํฌ๋ฅผ ์ œ์–ดํ•˜๋Š” ๊ฒƒ์ด ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์ž˜ ๋ถ„์‚ฐ๋œ ์ž…์ž๊ณ„ ํ•„๋ฆ„์—์„œ๋„ ๊ฑด์กฐ ๊ณผ์ • ์ค‘ ๋ถˆ๊ท ์ผ ๊ตฌ์กฐ๊ฐ€ ํ˜•์„ฑ๋œ๋‹ค๋Š” ๊ฒƒ์ด ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๊ฑด์กฐ ๊ณผ์ • ์ค‘ ํ˜•์„ฑ๋˜๋Š” ๋ถˆ๊ท ์ผ ๊ตฌ์กฐ์— ๋Œ€ํ•ด ๋ช‡๋ช‡ ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜์—ˆ์ง€๋งŒ, ์—ฐ๊ตฌ๋“ค์˜ ๋Œ€๋ถ€๋ถ„์€ ๋ถˆ๊ท ์ผ ๊ตฌ์กฐ๊ฐ€ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฑด์กฐ ์กฐ๊ฑด์„ ์ฐพ๋Š” ๋ฐ ์ดˆ์ ์ด ๋งž์ถฐ์ ธ ์žˆ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š”, ๊ฑด์กฐ ๊ณผ์ • ์ค‘ ์‘๋ ฅ ๋ฐ ๋ฏธ์„ธ ๊ตฌ์กฐ ๋ถ„์„์„ ํ†ตํ•ด ๋ถˆ๊ท ์ผ ๊ตฌ์กฐ๊ฐ€ ํ˜•์„ฑ๋˜๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์„ค๋ช…ํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณต์žกํ•œ ์ž…์ž๊ณ„ ํ•„๋ฆ„์˜ ๋ชจ๋ธ ์‹œ์Šคํ…œ์œผ๋กœ์„œ, ๋‹จ๋ถ„์‚ฐ ๋ฐ ์ด๋ถ„์‚ฐ ์ž…์ž๊ณ„ ํ•„๋ฆ„์— ๋Œ€ํ•ด์„œ ์ „์‚ฐ ๋ชจ์‚ฌ ๋ฐฉ๋ฒ•๋ก ์„ ์‚ฌ์šฉํ•˜์—ฌ ํƒ๊ตฌํ•˜์˜€๋‹ค. ์ฒซ์งธ, ๋‹จ๋ถ„์‚ฐ ์ž…์ž๊ณ„ ํ•„๋ฆ„์—์„œ๋Š”, Brownian dynamics simulation ์„ ์ด์šฉํ•ด ํ•„๋ฆ„ ๋‘๊ป˜ ๋ฐฉํ–ฅ์œผ๋กœ์˜ ๋ถˆ๊ท ์ผ ๊ตฌ์กฐ๊ฐ€ ํ˜•์„ฑ๋˜๋Š” ๊ณผ์ •์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฑด์กฐ ์†๋„์™€ ์ž…์ž์˜ ํ™•์‚ฐ ์†๋„์˜ ๋น„๊ฐ€ ๋ถˆ๊ท ์ผ ๊ตฌ์กฐ๋ฅผ ํ˜•์„ฑํ•˜๋Š” ์ฃผ์š” ์ธ์ž๋กœ ์ž‘์šฉํ•˜์˜€๋‹ค. ๊ฑด์กฐ ์†๋„๊ฐ€ ํ™•์‚ฐ ์†๋„๋ณด๋‹ค ์šฐ์„ธํ•œ ์กฐ๊ฑด์—์„œ, ํ•˜๊ฐ•ํ•˜๋Š” ๊ณ„๋ฉด์— ์ž…์ž๋“ค์ด ์ถ•์ ๋˜์–ด ์‹œ๊ฐ„์ด ์ง€๋‚ ์ˆ˜๋ก ์ถ•์  ์˜์—ญ์˜ ๋‘๊ป˜๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์ž…์ž์˜ ์ถ•์ ์€ ์ˆ˜์ง ์‘๋ ฅ์˜ ๊ตญ๋ถ€ํ™”๋ฅผ ์œ ๋ฐœ์‹œ์ผœ ๊ณ„๋ฉด์—์„œ์˜ ์ˆ˜์ง ์‘๋ ฅ์€ ๊ฑด์กฐ ์ดˆ๊ธฐ๋ถ€ํ„ฐ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ํ•„๋ฆ„ ๋‚ด๋ถ€์— ํ˜•์„ฑ๋œ ์ˆ˜์ง ์‘๋ ฅ ๊ธฐ์šธ๊ธฐ๊ฐ€ ์ž…์ž์˜ ์•Œ์งœ ์›€์ง์ž„์„ ์œ ๋ฐœํ•˜๋Š” ๋ฌผ๋ฆฌ์ ์ธ ์›์ธ์ž„์„ ๊ทœ๋ช…ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ˆ˜์ง ์‘๋ ฅ ๊ธฐ์šธ๊ธฐ์™€ ๋ฏธ์„ธ ๊ตฌ์กฐ์˜ ๋ฐœ๋‹ฌ ๊ณผ์ •์„ ์—ฐ๊ด€ ์ง€์Œ์œผ๋กœ์จ ๋ถˆ๊ท ์ผ ๊ตฌ์กฐ์˜ ํ˜•์„ฑ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์ž์„ธํ•˜๊ฒŒ ์ดํ•ดํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‘˜์งธ, ์ด๋ถ„์‚ฐ ์ž…์ž๊ณ„ ํ•„๋ฆ„์—์„œ๋Š”, Brownian dynamics simulation ์„ ์ด์šฉํ•˜์—ฌ ํฌ๊ธฐ๊ฐ€ ์ž‘์€ ์ž…์ž๋งŒ์œผ๋กœ ์ด๋ฃจ์–ด์ง„ stratified layer ๊ฐ€ ํ˜•์„ฑ๋˜๋Š” ๊ณผ์ •์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๊ฑด์กฐ ์†๋„๊ฐ€ ์ž…์ž์˜ ํ™•์‚ฐ ์†๋„๋ณด๋‹ค ์šฐ์„ธํ•œ ์กฐ๊ฑด์—์„œ, ํ•˜๊ฐ•ํ•˜๋Š” ๊ณ„๋ฉด์— ํฌ๊ธฐ๊ฐ€ ์ž‘์€ ์ž…์ž๋“ค์ด ์ถ•์ ๋˜๊ณ  stratified layer ๋ฅผ ํ˜•์„ฑํ•˜์˜€๋‹ค. ๊ณ„๋ฉด ๋ถ€๊ทผ์— ์กด์žฌํ•˜๋˜ ํฌ๊ธฐ๊ฐ€ ํฐ ์ž…์ž๋“ค์€ ๊ธฐ์ €์ธต ๋ฐฉํ–ฅ์œผ๋กœ ๋ฐ€๋ ค๋‚˜๊ณ , stratified layer ์•„๋ž˜์— ์ถ•์  ์˜์—ญ์„ ํ˜•์„ฑํ•˜์˜€๋‹ค. ๊ณ„๋ฉด ์•„๋ž˜์— ์ถ•์ ๋œ ์ž…์ž๋“ค์€ ์ˆ˜์ง ์‘๋ ฅ์˜ ๊ตญ๋ถ€ํ™”๋ฅผ ์œ ๋ฐœํ•˜์—ฌ ๊ณ„๋ฉด์—์„œ์˜ ์ˆ˜์ง ์‘๋ ฅ์€ ๊ฑด์กฐ ์ดˆ๊ธฐ๋ถ€ํ„ฐ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ํ•„๋ฆ„ ๋‚ด๋ถ€์— ํ˜•์„ฑ๋œ ์ˆ˜์ง ์‘๋ ฅ ๊ธฐ์šธ๊ธฐ๊ฐ€ ํฐ ์ž…์ž๋ฅผ ๊ณ„๋ฉด์—์„œ๋ถ€ํ„ฐ ๋ฉ€์–ด์ง€๊ฒŒ ๋งŒ๋“œ๋Š” ํž˜์˜ ๋ฌผ๋ฆฌ์ ์ธ ์›์ธ์ž„์„ ๋ฐํ˜€๋‚ผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ๋ฏธ์„ธ ๊ตฌ์กฐ ๋ถ„์„๊ณผ ์ˆ˜์ง ์‘๋ ฅ ๋ถ„์„์„ ์—ฐ๊ด€ ์ง€์Œ์œผ๋กœ์จ, ํฐ ์ž…์ž์™€ ์ ‘์ด‰ํ•˜๋Š” ์ž‘์€ ์ž…์ž ์ˆ˜์˜ ํ˜„์ €ํ•œ ์ฆ๊ฐ€๊ฐ€ ํฐ ์ž…์ž๋ฅผ ๋ฐ€์–ด๋‚ด๋Š” ํž˜์„ ์œ ๋ฐœํ•จ์„ ์•Œ์•„๋‚ผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ข…ํ•ฉํ•˜๋ฉด, ์ˆ˜์ง ์‘๋ ฅ๊ณผ ๋ฏธ์„ธ ๊ตฌ์กฐ ๋ฐœ๋‹ฌ์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•จ์œผ๋กœ์จ, ๊ฑด์กฐ ๊ณผ์ • ์ค‘ ๋ถˆ๊ท ์ผ ๊ตฌ์กฐ๊ฐ€ ํ˜•์„ฑ๋˜๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ํ•ด์„ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๊ฑด์กฐ ๊ณผ์ •์„ ํ†ตํ•ด ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ์ œํ’ˆ์„ ํ˜•์„ฑํ•˜๋Š” ๋‹ค์–‘ํ•œ ๊ธฐ์ˆ  ๋ถ„์•ผ์—์„œ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Colloidal films are composed of complex mixtures of particles with different sizes, binders, solvents, and additives. The control of their particle size distribution is a critical part of applications. Although the colloidal system is well-dispersed before drying, colloidal films could exhibit structural heterogeneity during the drying process. Several studies have been performed to figure out the heterogeneity in drying films, however, they were mainly focused on the drying conditions where the heterogeneity was observed. In this thesis, stress and microstructural developments during the drying process were investigated to understand the formation mechanism of heterogeneous structure. As a model system for complex colloidal films, mono- and bi-disperse colloidal films were studied by using the numerical approach. First, in the monodisperse colloidal films, the formation of vertical structural heterogeneity was examined by using the Brownian dynamics simulation. The time scale ratio between the evaporation rate and the particle diffusion rate was the key factor in inducing structural heterogeneity. When the evaporation rate was dominant, the particles were accumulated near the evaporating interface, causing the accumulation region to grow. Accumulated particles contributed to the localization of the normal stress, inducing a continuous increase of the normal stress at the interface. The normal stress difference formed across the film was found to be the driving force of the net motion of the particles. The normal stress difference was also correlated with microstructural development, which provided a full understanding of the heterogeneity formation mechanism. Second, in the bi-disperse colloidal films, the formation of a stratified layer consisting of only small particles was studied by using the Brownian dynamics simulation. When the evaporation rate was more dominant than the particle diffusion rate, the small particles were accumulated near the interface, forming the stratified layer. The large particles were depleted near the interface, forming an accumulation region below the stratified layer. These accumulated particles induced localization of the normal stress, so that the normal stress at the interface increased from the beginning of drying. The normal stress difference formed across the film was found to be the physical origin of the force pushing the large particles away from the interface. Associating the microstructural development with the normal stress response, the force on the large particles was explained by the significant increase in the average number of small particles in contact with large ones. The correlation between the normal stress and microstructural development provides systematic insight into the formation mechanism of heterogeneous structure in drying films. Thus the results of this thesis are expected to be beneficial in various technological fields to form the desired products through the drying process.Abstract .i List of Figures .vi Chapter 1. Introduction . 1 Chapter 2. Drying mechanism of monodisperse colloidal film . 6 2.1. Introduction . 7 2.2. Simulation methods . 9 2.2.1. Model system . 9 2.2.2. Brownian Dynamics (BD) simulation . 12 2.2.3. Interaction potential . 14 2.2.4. Normal stress calculation . 16 2.3. Results and discussion . 18 2.3.1 Particle distribution in drying film . 18 2.3.2. Normal stress localization in drying film . 26 2.3.3 Microstructural development . 35 2.4. Summary . 42 Chapter 3 Drying mechanism of bi-disperse colloidal film . 44 3.1. Introduction . 45 3.2. Simulation methods . 48 3.2.1. Model system . 48 3.2.2. Brownian Dynamics (BD) simulation . 51 3.2.3. Interaction potential . 52 3.2.4. Normal stress calculation . 54 3.3. Results and discussion . 55 3.3.1 Particle distribution in drying film . 55 3.3.2. Normal stress localization in drying film . 64 3.3.3 Microstructural development . 75 3.4. Summary . 87 Chapter 4 Stratification mechanism on the local length scale . 89 4.1. Introduction . 90 4.2. Derivation of the local force field . 91 4.3. Results and discussion . 95 4.3.1 Evolution of local force field . 95 4.3.2. Local force field and local volume fraction profile . 103 4.4. Summary . 112 Chapter 5 Concluding remark . 113 References . 119 ๊ตญ๋ฌธ์ดˆ๋ก . 131 Curriculum Vitae . 133๋ฐ•

    A Study on Applications of Inductive Power Line Communications

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    ๋ณธ ๋…ผ๋ฌธ์€ ์ตœ๊ทผ ๋‚ด์—ฐ๊ธฐ๊ด€ ์ž๋™์ฐจ๋ฅผ ๋Œ€์ฒดํ•  ๋Œ€ํ‘œ ์ฐจ์ข…์œผ๋กœ ๋ถ€๊ฐ๋˜๊ณ  ์žˆ๋Š” ์ „๊ธฐ์ž๋™์ฐจ์˜ ๊ณ ์ „๋ฅ˜ ์ผ€์ด๋ธ”์— ์œ ๋„ํ˜• ์ „๋ ฅ์„  ํ†ต์‹  ์ ์šฉ์„ ์ œ์•ˆํ•œ๋‹ค. ์ „๊ธฐ์ž๋™์ฐจ์˜ ๋ฐฐํ„ฐ๋ฆฌ๋Š” ํ•œ ๋ฒˆ์˜ ์ถฉ์ „์œผ๋กœ ์ถฉ๋ถ„ํžˆ ์ฃผํ–‰๊ฐ€๋Šฅํ•˜๊ธฐ ์œ„ํ•ด ๋†’์€ ์—๋„ˆ์ง€ ๋ฐ€๋„, ๊ฐ€๋ฒผ์šด ์ค‘๋Ÿ‰ ๊ทธ๋ฆฌ๊ณ  ๋Œ€์šฉ๋Ÿ‰์˜ ๋ฌด๊ฑฐ์šด ๋ฐฐํ„ฐ๋ฆฌ๋ฅผ ํ•„์š”๋กœ ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ตœ๊ทผ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์ „๊ธฐ์ž๋™์ฐจ์— ์ ์šฉ๋˜๋Š” ๋‹ค์–‘ํ•œ ์‹œ์Šคํ…œ๋“ค์ด ์ œ์•ˆ๋˜๊ณ  ์žˆ๊ณ  ์ด์— ๋”ฐ๋ผ ๋งŽ์€ ์–‘์˜ ์ „๋ ฅ์„ ์ด ์ถ”๊ฐ€๋˜๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์ด๋‹ค. ์ถ”๊ฐ€์ ์ธ ๊ธฐ๋Šฅ์„ ์œ„ํ•ด ๊ณ„์†ํ•ด์„œ ์ „๋ ฅ์„ ์„ ์ถ”๊ฐ€ํ•˜๊ฒŒ ๋˜๋ฉด ์ฐจ๋Ÿ‰์˜ ์ค‘๋Ÿ‰์ด ์ฆ๊ฐ€ํ•˜๊ฒŒ ๋˜์–ด ์ฐจ๋Ÿ‰์˜ ํšจ์œจ์ด ๊ฐ์†Œํ•˜๊ฒŒ ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ค‘๋Ÿ‰ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๊ธฐ์กด์˜ ์ „๋ ฅ์„ ์„ ์‚ฌ์šฉํ•˜์—ฌ ํ†ต์‹ ์ด ๊ฐ€๋Šฅํ•œ ์ „๋ ฅ์„  ํ†ต์‹ ์ด ์ข‹์€ ๋ฐฉ์•ˆ์ด ๋  ๊ฒƒ์ด๋‹ค. ์‹คํ—˜์—๋Š” ์œ ๋„ํ˜• ์ „๋ ฅ์„  ํ†ต์‹ ์„ ์ ์šฉํ•˜๊ณ  ์ด์— ์‚ฌ์šฉ๋˜๋Š” ์œ ๋„ํ˜• ๊ฒฐํ•ฉ๊ธฐ์˜ ๊ฒฝ์šฐ ํŽ˜๋ผ์ดํŠธ ๊ฒฐํ•ฉ๊ธฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋†’์€ ์ „์••์—์„œ์˜ ์ปคํ”Œ๋ง ํšจ๊ณผ๋ฅผ ์ฆ์ง„์‹œํ‚ค๊ณ  ๋” ๋‚ฎ์€ ์†์‹ค๋กœ ์‹คํ—˜์„ ์ง„ํ–‰ํ•œ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ „๊ธฐ์ž๋™์ฐจ ๊ณ ์ „๋ฅ˜ ์ผ€์ด๋ธ”์˜ ์ฑ„๋„ ๋ถ„์„ ๋ฐ ํ†ต์‹ ๋Œ€์—ญํญ์„ ์ธก์ •ํ•˜์—ฌ ์ ์šฉ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด ์ œ์•ˆํ•˜๊ณ  ์‹ค์‹œ๊ฐ„ ์˜์ƒ ๋ฐ์ดํ„ฐ ์ „์†ก ๋ฐ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ๊ฐ์ฒด ์ธ์‹ ์‹คํ—˜์„ ํ†ตํ•ด ์ „๋ ฅ์„  ํ†ต์‹ ์ด ๊ฐ€๋Šฅํ•จ์„ ์ž…์ฆํ•˜๊ณ ์ž ํ•œ๋‹ค. |In this thesis, I propose the application of non-contact type inductive power line communication to the high voltage cable of electric vehicle, which is emerging as a representative vehicle to replace internal combustion engine cars. Electric vehicle batteries require high energy density, light weight and high capacity batteries to be sufficiently driveable on a single charge. And recently, various systems for deep learning-based electric vehicles have been proposed and a large amount of power lines have been added. Continuing to add power lines for additional functions will increase the weight of the vehicle, thereby reducing the efficiency of the vehicle. To address this weight problem, power line communication that can be communicated using existing power lines will be a good solution. In the experiment, non-contact type inductive power line communication method was applied and ferrite coupler was used for the inductive coupler used for it. The ferrite coupler has the characteristics of increasing coupling effects at higher voltages and having lower losses. In addition, the channel analysis and communication bandwidth of electric vehicle high voltage cables are measured to propose the applicability of power line communication and prove that power line communication-based data communication is possible through real-time image data transmission and deep learning-based object recognition experiments.1. ์„œ ๋ก  1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ์—ฐ๊ตฌ ๋‚ด์šฉ 2 1.2 ๋…ผ๋ฌธ ๊ตฌ์„ฑ 4 2. ์œ ๋„ํ˜• ์ „๋ ฅ์„  ํ†ต์‹  2.1 ์ „๋ ฅ์„  ํ†ต์‹  ๊ฐœ์š” 5 2.2 ์ž์„ฑ์ฒด 13 2.3 ์œ ๋„ํ˜• ๊ฒฐํ•ฉ๊ธฐ ํ•ด์„ 15 2.4 ํ† ๋กœ์ด๋‹ฌ ๊ฒฐํ•ฉ๊ธฐ์™€ ์ปท ๊ฒฐํ•ฉ๊ธฐ์˜ ์„ฑ๋Šฅ์‹œํ—˜ 20 3. ์œ ๋„ํ˜• ์ „๋ ฅ์„  ํ†ต์‹ ์˜ ์ „๊ธฐ์ž๋™์ฐจ ์ ์šฉ ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 3.1 ์œ ๋„ํ˜• ํŽ˜๋ผ์ดํŠธ ๊ฒฐํ•ฉ๊ธฐ ์‚ฝ์ž…์†์‹ค ์ธก์ • 24 3.2 ์ „๊ธฐ์ž๋™์ฐจ ์ „๋ ฅ์„  ํŠน์„ฑ๋ถ„์„ 26 3.3 ์ „๋ ฅ์„ ์„ ์ด์šฉํ•œ ํ†ต์‹  ์‹คํ—˜ 31 4. ์ „๊ธฐ์ž๋™์ฐจ์˜ ์ „๋ ฅ์„  ๊ธฐ๋ฐ˜ ๊ฐ์ฒด๊ฒ€์ถœ ๊ธฐ์ˆ ์ ์šฉ ์‹คํ—˜ 4.1 ์ „๊ธฐ์ž๋™์ฐจ์— ์ ์šฉ๋˜๋Š” ๋”ฅ๋Ÿฌ๋‹ ๋™ํ–ฅ 32 4.2 ๊ฐ์ฒด ๊ฒ€์ถœ(Object Detection) 33 4.3 ์œ ๋„ํ˜• ์ „๋ ฅ์„  ํ†ต์‹  ๊ธฐ๋ฐ˜ ๊ฐ์ฒด๊ฒ€์ถœ๊ธฐ์ˆ  ์ ์šฉ ์‹คํ—˜ ๋ฐ ๊ฒฐ๊ณผ 35 5. ๊ฒฐ๋ก  37 ์ฐธ๊ณ ๋ฌธํ—Œ 38Maste

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