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    ์•ก์ƒ ์†Œ๊ฒฐ ๋ฐ ํ†ต์ „ ํ™œ์„ฑ ์†Œ๊ฒฐ ๋ฐฉ๋ฒ•์œผ๋กœ ์ œ์ž‘ํ•œ ์ฒ  ๊ณ„ ํ•ฉ๊ธˆ์˜ ๋ฏธ์„ธ ๊ตฌ์กฐ ๊ฐœ์„ ๊ณผ ๊ธฐ๊ณ„์  ํŠน์„ฑ ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2021.8. ํ™์„ฑํ˜„.๋ถ„๋ง์•ผ๊ธˆ๊ธฐ์ˆ ์€ ์›์žฌ๋ฃŒ ๊ธˆ์† ๋ถ„๋ง์„ ์‚ฌ์šฉํ•˜์—ฌ ์†Œ๊ฒฐ ๋ถ€ํ’ˆ์˜ ์ตœ์ข… ํ˜•์ƒ์— ๊ฐ€๊น๊ฒŒ ์ œ์กฐํ•˜์—ฌ ๋†’์€ ์ƒ์‚ฐ์„ฑ ๋ฐ ์žฌ๋ฃŒ ์†์‹ค์ด ์ตœ์†Œํ™”ํ•˜๋Š” ์žฅ์ ์ด ์žˆ๋Š” ๊ณต์ • ๊ธฐ์ˆ ์ด๋‹ค. ๋˜ํ•œ, ์ž๋™์ฐจ ์†Œ์žฌ, ๊ฐ€์ „์ œํ’ˆ, ์ „์ž์ œํ’ˆ ๋“ฑ์˜ ๋ฒ”์šฉ ๊ธฐ๊ณ„ ์†Œ์žฌ์— ์ด๋ฅด๊ธฐ ๊นŒ์ง€ ์ ์šฉ ์žฌ๋ฃŒ์˜ ๋ฒ”์œ„๋„ ๊ด‘๋ฒ”์œ„ํ•˜๋‹ค. ํ•˜์ง€๋งŒ ์ฒ  ๊ณ„ ํ•ฉ๊ธˆ์€ ์†Œ๊ฒฐ ํ›„ 5~15 %์˜ ๊ธฐ๊ณต์ด ์กด์žฌํ•˜์—ฌ ์†Œ๊ฒฐ ๋ถ€ํ’ˆ์œผ๋กœ์˜ ์ ์šฉ์ด ์ œํ•œ์ด ๋œ๋‹ค. ์†Œ๊ฒฐ์ฒด์˜ ๊ณ ๋ฐ€๋„ํ™”๋ฅผ ์œ„ํ•ด์„œ ์•ก์ƒ์†Œ๊ฒฐ๋ฒ•, ํ•ซํ”„๋ ˆ์‹ฑ, ํ†ต์ „ํ™œ์„ฑ์†Œ๊ฒฐ๊ณผ ๊ฐ™์€ ์†Œ๊ฒฐ ๊ธฐ์ˆ ๋“ค์ด ํ˜„์žฌ ๋งŽ์ด ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์–ด์™”๋‹ค. ํ•˜์ง€๋งŒ ์ฒ  ๊ณ„ ํ•ฉ๊ธˆ์€ ๊ฐ๊ฐ์˜ ์†Œ๊ฒฐ ๋ฐฉ๋ฒ•์—์„œ ๋น„๋กฏ๋˜๋Š” ๊ณ ์œ ํ•œ ๋ฏธ์„ธ ๊ตฌ์กฐ๋กœ ์ธํ•ด ๊ธฐ๊ณ„์  ํŠน์„ฑ์˜ ํ–ฅ์ƒ์ด ์ œํ•œ์ ์ด๋‹ค. ๋˜ํ•œ ์—ด์ฒ˜๋ฆฌ ๋„์ค‘ ๋ฏธ์„ธ๊ตฌ์กฐ์™€ ์†Œ๊ฒฐ ๊ฑฐ๋™์— ๋Œ€ํ•œ ์ด๋ก ์ ์ธ ์—ฐ๊ตฌ๊ฐ€ ์ฒ ์ €ํ•˜๊ฒŒ ๋ถ„์„๋˜์ง€ ์•Š์•˜๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์†Œ๊ฒฐ ๋ฐฉ๋ฒ•, ์†Œ๊ฒฐ ์กฐ๊ฑด, ์กฐ์„ฑ, ์—ด ์ฒ˜๋ฆฌ ๋“ฑ์„ ํ†ตํ•œ ๋ฏธ์„ธ๊ตฌ์กฐ๋ฅผ ๊ฐœ์„ ์ด ๊ธฐ๊ณ„์  ํŠน์„ฑ์— ๋ผ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ์ฃผ์ œ๋กœ ์ด ์„ธ ๊ฐ€์ง€์— ๋Œ€ํ•ด ๋…ผํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์ฃผ์ œ๋กœ ๋‹ˆ์ผˆ (Ni)์ด Fe-B-C ํ•ฉ๊ธˆ ์‹œ์Šคํ…œ์— ๋„์ž…๋˜์–ด ๊ณต์œต๋ฐ˜์‘ ์˜จ๋„๋ฅผ ๋‚ฎ์ถฐ ๋ถ•์†Œ ํ•จ์œ  ํ•ฉ๊ธˆ์˜ ์น˜๋ฐ€ํ™”๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๊ณ ์ž ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ, ๋‹ค๋Ÿ‰์˜ ์•ก์ƒ์ด ์ž… ๊ณ„์—์„œ ๊ณต์œต๊ฒฝ์งˆ์ƒ (M23B6, M3B, M2B)์˜ ์—ฐ์† ๋„คํŠธ์›Œํฌ๋ฅผ ์ƒ์„ฑํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์•ก์ƒ์†Œ๊ฒฐ์—์„œ๋Š” ์—ฐ์‹  ํŠน์„ฑ์˜ ํ–ฅ์ƒ์ด ๋งค์šฐ ์ œํ•œ์ ์ด๋‹ค. ์ด๋ฅผ ๊ทน๋ณต ํ•˜๊ธฐ ์œ„ํ•ด, ํ•ฉ๊ธˆ์˜ ์กฐ์„ฑ์„ ์ œ์–ดํ•˜์—ฌ ์‘๊ณ ์ƒ์˜ ๋ถ€ํ”ผ ๋ถ„์œจ์„ ์ตœ์ ํ™”ํ•˜๊ณ  ํŽ„๋ผ์ดํŠธ ๋งคํŠธ๋ฆญ์Šค๋กœ ์‘๊ณ  ๋œ ฮฑ-Fe ์ž…์ž์˜ ์กฐ ๋Œ€ํ™”๋ฅผ ์‚ฌํ›„ ์—ด์ฒ˜๋ฆฌ์— ์˜ํ•ด ๊ณต์œต์ƒ์˜ ์—ฐ์† ๋„คํŠธ์›Œํฌ๋ฅผ ์ค„์ด๊ณ  ์ž…์ž ์—ฐ์†์„ฑ์„ ์ฆ๊ฐ€์‹œํ‚ค๊ณ ์ž ํ•˜์˜€๋‹ค. ์ตœ์ข…์ ์œผ๋กœ ๋ฏธ์„ธ ๊ตฌ์กฐ ๊ฐœ์„  ๊ฒฐ๊ณผ, ํ›„์—ด์ฒ˜๋ฆฌ ๋œ Fe-1Ni-0.4B-0.8C ํ•ฉ๊ธˆ์€ 5.2 %์˜ ๋†’์€ ์—ฐ์‹ ์œจ ๊ฐ’์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ๋‘ ๋ฒˆ์งธ ์ฃผ์ œ๋กœ ๋‘ ๊ฐœ์˜ Fe-Mo-B ๋ฐ Fe-B-C ๊ณต์ • ์ƒ ํ˜•์„ฑ์„ ํ†ตํ•ด ์ž… ๊ณ„ ๋ฏธ์„ธ ๊ตฌ์กฐ๋ฅผ ์ˆ˜์ •ํ•˜๊ธฐ ์œ„ํ•ด Fe-B-C ํ•ฉ๊ธˆ ์‹œ์Šคํ…œ์— ๋ชฐ๋ฆฌ๋ธŒ๋ด (Mo)์ด ๋„์ž…๋˜์—ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด Fe-xMo-0.4B-0.8C (x=1.0~5.0 in wt %) ํ•ฉ๊ธˆ์„ LPS๋กœ ์ œ์กฐํ•˜๊ณ  ๋ฏธ์„ธ ๊ตฌ์กฐ ๋ฐ ๊ธฐ๊ณ„์  ํŠน์„ฑ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. Mo ์ฒจ๊ฐ€๋กœ ๋งคํŠธ๋ฆญ์Šค๋Š” ํŽ„๋ผ์ดํŠธ ๋ฐ ์žฌ ์นจ์ „๋œ ํŽ˜๋ผ์ดํŠธ์—์„œ ํŽ„๋ผ์ดํŠธ (๋˜๋Š” ๋ฒ ์ด๋‚˜์ดํŠธ)๋กœ ๋ณ€๊ฒฝ๋˜๊ณ , ์ž…๊ณ„๋Š” ์—ฐ์† ๋„คํŠธ์›Œํฌ์—์„œ MoFe(C,B)๊ณผ (Fe,Mo)3(C,B) ๋กœ ๊ตฌ์„ฑ๋œ ๋ผ๋ฉœ๋ผ ๊ตฌ์กฐ๋กœ ๋ณ€๊ฒฝ๋˜์—ˆ๋‹ค. ํ•ฉ๊ธˆ ๋‚ด ๋ฏธ์„ธ ๊ตฌ์กฐ์˜ ๊ฐœ์„ ์„ ํ†ตํ•ด ํ•ฉ๊ธˆ์˜ ๊ฒฝ๋„, ์ธ์žฅ ๊ฐ•๋„, ํŒŒ๋‹จ ์—ฐ์‹ ์œจ๊ณผ ๊ฐ™์€ ๊ธฐ๊ณ„์  ํŠน์„ฑ์ด ํฌ๊ฒŒ ํ–ฅ์ƒ๋˜์—ˆ์œผ๋ฉฐ, ํŠนํžˆ Fe-5Mo-0.4B-0.8C ํ•ฉ๊ธˆ์€ 674 MPa์˜ ๋†’์€ ์ธ์žฅ ๊ฐ•๋„์™€ 4.92 %์˜ ๋†’์€ ์—ฐ์‹ ์œจ์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์„ธ ๋ฒˆ์งธ ์ฃผ์ œ๋กœ ์ผ๋ฐ˜์ ์ธ ์†Œ๊ฒฐ๋ฒ•, ํ•ซํ”„๋ ˆ์‹ฑ, ํ†ต์ „ํ™œ์„ฑ์†Œ๊ฒฐ๋กœ Fe-Ni ํ•ฉ๊ธˆ (x=0.0~5.0 in wt %)์„ ์ œ์ž‘ํ•˜์—ฌ ํŠนํžˆ, ํ†ต์ „ ํ™œ์„ฑ ์†Œ๊ฒฐ๋ฒ•์˜ ์ „๋ฅ˜์˜ ํšจ๊ณผ์— ์ง‘์ค‘ํ•˜์—ฌ ์‹œํŽธ์˜ ์น˜๋ฐ€ํ™”, ๋ฏธ์„ธ๊ตฌ์กฐ, ์›์†Œ ๋ถ„ํฌ๋„, ๊ธฐ๊ณ„์  ํŠน์„ฑ ๋“ฑ์„ ๋น„๊ต ์กฐ์‚ฌ ํ•˜์˜€๋‹ค. ๋‹ˆ์ผˆ์˜ ์ฒจ๊ฐ€๋Š” ์ผ๋ฐ˜์ ์ธ ์†Œ๊ฒฐ๋ฒ•๊ณผ ํ†ต์ „ํ™œ์„ฑ๋ฒ• ๋ชจ๋‘์—์„œ ์น˜๋ฐ€ํ™”๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๊ณ  ์ž…์ž ์„ฑ์žฅ์„ ์–ต์ œํ•˜์˜€์œผ๋ฉฐ, ํŠนํžˆ ํ†ต์ „ํ™œ์„ฑ๋ฒ•์œผ๋กœ ์ž‘์€ ๊ฒฐ์ • ํฌ๊ธฐ๋ฅผ ๊ฐ€์ง€๋Š” ๊ณ ๋ฐ€๋„ Fe-Ni ํ•ฉ๊ธˆ์„ ์ œ์ž‘ํ•˜๋Š”๋ฐ ์„ฑ๊ณตํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ํ†ต์ „ํ™œ์„ฑ๋ฒ•์œผ๋กœ ์ œ์ž‘ํ•œ Fe-Ni ํ•ฉ๊ธˆ์€ ๋‹ค๋ฅธ ์†Œ๊ฒฐ ๋ฒ•์œผ๋กœ ์ œ์ž‘ํ•œ ํ•ฉ๊ธˆ์— ๋น„ํ•ด์„œ ๋ฒŒํฌ ๊ฒฝ๋„์—์„œ ํฐ ํ–ฅ์ƒ์„ ๋ณด์˜€๋‹ค. ์›์†Œ ๋ถ„ํฌ๋„ ๋ถ„์„์„ ํ†ตํ•ด ๋‹ˆ์ผˆ์€ ์‹œํŽธ์˜ ๊ฒฐ์ •๋ฆฝ๊ณ„์— ๋‹ค์ˆ˜ ๋ถ„ํฌํ•˜๋Š” ๊ฒƒ์„ ๊ด€์ฐฐํ•˜์˜€์œผ๋ฉฐ, ํ™•์‚ฐ ์‹คํ—˜์„ ํ†ตํ•ด ํ†ต์ „ํ™œ์„ฑ์†Œ๊ฒฐ์ด ๊ฒฐ์ •๋ฆฝ๊ณ„ ํ™•์‚ฐ์„ ํ–ฅ์ƒํ•จ์„ ๋’ท๋ฐ›์นจ ํ•˜์˜€๋‹ค. ์œ„ ๊ฒฐ๊ณผ๋กœ๋ถ€ํ„ฐ ํ†ต์ „ํ™œ์„ฑ์†Œ๊ฒฐ ๋ฒ•์˜ ์••๋ ฅ๊ณผ ๊ฐ€ํ•ด์ฃผ๋Š” ์ „๋ฅ˜๋Š” ๊ฒฐ์ •๋ฆฝ๊ณ„์—์„œ Ni์˜ ํ™•์‚ฐ์„ ์šฉ์ดํ•˜๊ฒŒ ํ•˜์—ฌ ๊ฒฐ๊ณผ์ ์œผ๋กœ ์‹œํŽธ์˜ ์น˜๋ฐ€ํ™”๋ฅผ ํ–ฅ์ƒ์‹œํ‚ค๊ณ  ์ž… ๊ณ„ ์„ฑ์žฅ์„ ์„ฑ๊ณต์ ์œผ๋กœ ์–ต์ œํ•˜์˜€๋‹ค.Powder metallurgy (P/M) is metal consolidation technique for the manufacture of parts from raw starting powders by compaction and heating. P/M technique has been used in various industries and applications, because of its efficiency and net-shaped capability. However, the P/M applications are very limited due to the presence of porosity ranging from 5 to 15 vol. %. To solve this problem, several sintering techniques, such as liquid phase sintering, hot pressing and field assisted sintering have been extensively investigated. However, the improvement of mechanical properties was still limited, and thus microstructure modification is required to enhance the mechanical properties. In addition, the microstructural development and densification mechanism during sintering have not been fully explored. In this thesis, the effects of sintering methods, sintering condition, composition and heat treatment on microstructure were intensively studied, and then the effect of microstructure modification on the mechanical properties of Fe-based alloys was thoroughly investigated. The mentioned above will be discussed in more detail on the three main topics. 1) Microstructure modification of liquid phase sintered Fe-Ni-B-C alloys for improved mechanical properties, 2) Effects of molybdenum addition on microstructure and mechanical properties of Fe-B-C sintered alloys, and 3) Effect of field assisted sintering on densification, microstructure and mechanical properties of Fe-Ni alloys. First, Ni addition was employed to decrease the eutectic temperature and improve the densification and mechanical properties of Fe-B-C alloys. To solve the formation of continuous network of hard eutectics, the composition was controlled to achieve the system with the optimized hard phase fraction, and heat treatment was performed to induce the coarsening of solidified ฮฑ-Fe particles into the matrix. As a result of microstructure modification, the post annealed Fe-1Ni-0.4B-0.8C alloy resulted in the high elongation to failure of 5.2 %. Second, molybdenum (Mo) was introduced in Fe-B-C alloy system to modify the grain boundary microstructure through the formation of two Fe-Mo-B and Fe-B-C solidified phases. For this, Fe-xMo-0.4B-0.8C (x=1.0~5.0 in wt%) alloys were prepared by LPS and their microstructure and mechanical properties were investigated. With Mo addition, the matrix grain changed from pearlite and re-precipitated ferrite to pearlite (or pearlite/bainite) and the grain boundary changed from a continuous network to a lamella structure composed of MoFe(C,B) (WCoB-type boride) and (Fe,Mo)3(C,B) (Fe3C-type carbide). As a result of microstructure modification, the mechanical properties such as hardness, tensile strength, and elongation to failure were significantly improved. In particular, Fe-5Mo-0.4B-0.8C alloy exhibited a high tensile strength of 674 MPa and a high elongation to failure of 4.92%. Third, Fe-xNi alloys (x=0~5.0 in wt%) were consolidated by conventional sintering (CS), field assisted sintering (FAS), and hot pressing (HP) methods, and their densification, microstructure, mechanical properties were comparatively investigated, particularly focusing on the field or current effects of FAS technique. The Ni addition promoted the densification and suppressed the grain growth in Fe-Ni alloys consolidated by both CS and FAS, and the FAS produced the Fe-Ni alloys with higher apparent density and smaller grain size. Consequently, the Fe-Ni alloys fabricated by FAS exhibited a significant improvement in the bulk hardness compared to those consolidated by CS and HP. The obtained results indicated that the electric current together with mechanical pressure in FAS promoted the Ni diffusion along the grain boundaries, which resulted in the enhanced densification with suppressed grain growth.Chapter 1. General background 1 1.1. Powder metallurgy 1 1.2. Liquid phase sintering 5 1.3. Field assisted sintering 11 Chapter 2. Microstructure modification of liquid phase sintered Fe-Ni-B-C alloys for high mechanical properties 19 2.1. Introduction 19 2.2. Experimental 21 2.3. Results and discussion 23 2.4. Conclusions 28 Chapter 3. Effect of Molybdenum Addition on the Microstructure and Mechanical Properties of Fe-Mo-B-C Sintered Alloys 58 3.1. Introduction 58 3.2. Experimental 60 3.3. Results and discussion 61 3.4. Conclusions 68 Chapter4. Densification Mechanism and Microstructure Development of Fe-Ni Alloys onsolidated by Field Assisted Sintering 105 4.1. Introduction 105 4.2. Experimental 107 4.3. Results and discussion 109 4.4. Conclusions 114 Abstract in Korean 142๋ฐ•

    ๊ต์ฐจ๋กœ์—์„œ ์ž์œจ์ฃผํ–‰ ์ฐจ๋Ÿ‰์˜ ์ œํ•œ๋œ ๊ฐ€์‹œ์„ฑ๊ณผ ๋ถˆํ™•์‹ค์„ฑ์„ ๊ณ ๋ คํ•œ ์ข…๋ฐฉํ–ฅ ๊ฑฐ๋™๊ณ„ํš

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„๊ณตํ•™๋ถ€, 2023. 2. ์ด๊ฒฝ์ˆ˜.This dissertation presents a novel longitudinal motion planning of autonomous vehicle at urban intersection to overcome the limited visibility due to complicated road structures and sensor specification, guaranteeing the safety from the potential collision with vehicles appearing from the occluded region. The intersection autonomous driving requires high level of safety due to congested traffics and environmental complexities. Due to complicated road structures and the detection range of perception sensors, the occluded region is generated in urban autonomous driving. The virtual target is one of the motion planning methods to react the sudden appearance of vehicles from the blind spot. The Gaussian Process Regression (GPR) is implemented to train the virtual target model to generate various future driving trajectories interacting with the motion of the ego vehicle. The GPR model provides not only the predicted trajectories of the virtual target but also the uncertainty of the future motion. Therefore, prediction results from GPR can be utilized to a position constraint for the Model Predictive Control (MPC), and the uncertainties are taken into account as a chance constraint in the MPC. In order to comprehend the surrounding environment including dynamic objects, a region of interest (ROI) is defined to determine targets of the interest. With the pre-determined driving route of the ego vehicle and the route information of the intersection, driving lanes intersecting with the ego driving lane can be determined, and the intersecting lanes are defined as ROI, reducing the computational load by eliminating targets of disinterest. Then the future motion of the selected target is predicted by a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN). Driving data for training are directly obtained with two different autonomous vehicles, providing their odometry information regardless to the limited field of view (FOV). For a widely known autonomous driving datasets such as Waymo and nuScenes, the vehicle odometry information are collected from the perceptive sensors mounted on the test vehicle. Thus, information of target that are out of the FOV of the test vehicle cant be obtained. The obtained training data are organized in the target centered coordinates for better input-domain adaptation and generalization. The mean squared error and the negative log likelihood loss functions are adapted to train and provide the uncertainty information of the target vehicle for the motion planning of the autonomous vehicle. The MPC with a chance constraint is formulated to optimize the longitudinal motion of the autonomous vehicle. The dynamic and actuator constraints are designed to provide ride comfort and safety to drivers. The position constraint with the chance constraint guarantees the safety and prevent the potential collision with target vehicles. The position constraint for the travel distance over the prediction horizon time is determined based on the clearance between the predicted trajectories of the target and ego vehicle at every prediction sample time. The performance and feasibility of the proposed algorithm are evaluated via computer simulation and test-data based simulation. The offline simulation validates the safety of the proposed algorithm, and the suggested motion planner has been implemented on an autonomous driving vehicle and tested in a real road. Through the implementation of the algorithm to an actual vehicle, the suggested algorithm is confirmed to be applicable in real life autonomous driving.๋ณธ ๋…ผ๋ฌธ์€ ๋ณต์žกํ•œ ๋„๋กœ ๊ตฌ์กฐ์™€ ์„ผ์„œ ์‚ฌ์–‘์œผ๋กœ ์ธํ•œ ์‹œ์•ผ ์ œํ•œ์„ ๊ทน๋ณตํ•˜๋ฉฐ ์‚ฌ๊ฐ์ง€๋Œ€์—์„œ ๋“ฑ์žฅํ•˜๋Š” ์ฐจ๋Ÿ‰๊ณผ์˜ ์ž ์žฌ์ ์ธ ์ถฉ๋Œ๋กœ๋ถ€ํ„ฐ ์•ˆ์ „์„ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•œ ๋„์‹ฌ ๊ต์ฐจ๋กœ์—์„œ์˜ ์ž์œจ์ฃผํ–‰์ฐจ์˜ ์ƒˆ๋กœ์šด ์ข…๋ฐฉํ–ฅ ๊ฑฐ๋™ ๊ณ„ํš์„ ์ œ์‹œํ•œ๋‹ค. ๋„์‹ฌ ์ž์œจ์ฃผํ–‰์€ ๊ตํ†ต์ฒด์ฆ๊ณผ ํ™˜๊ฒฝ์˜ ๋ณต์žก์„ฑ์œผ๋กœ ์ธํ•ด ๋†’์€ ์ˆ˜์ค€์˜ ์•ˆ์ „์„ฑ์ด ์š”๊ตฌ๋ฉ๋‹ˆ๋‹ค. ๋ณต์žกํ•œ ๋„๋กœ ๊ตฌ์กฐ์™€ ์ธ์ง€ ์„ผ์„œ์˜ ์ธ์ง€ ๋ฒ”์œ„๋กœ ์ธํ•ด ๋„์‹ฌ ์ž์œจ์ฃผํ–‰์—์„œ๋Š” ์‚ฌ๊ฐ์ง€๋Œ€๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ๊ฐ€์ƒ ํƒ€๊ฒŸ์€ ์‚ฌ๊ฐ์ง€๋Œ€์—์„œ ์ฐจ๋Ÿ‰์˜ ๊ฐ‘์ž‘์Šค๋Ÿฌ์šด ์ถœํ˜„์— ๋Œ€์‘ํ•˜๊ธฐ ์œ„ํ•œ ๊ฑฐ๋™ ๊ณ„ํš ๋ฐฉ๋ฒ• ์ค‘ ํ•˜๋‚˜์ž…๋‹ˆ๋‹ค. ์ž์ฐจ๋Ÿ‰์˜ ๊ฑฐ๋™๊ณผ ์ƒํ˜ธ์ž‘์šฉํ•˜๋Š” ๋‹ค์–‘ํ•œ ๋ฏธ๋ž˜ ์ฃผํ–‰ ๊ถค์ ์„ ์ƒ์„ฑํ•˜๋Š” ๊ฐ€์ƒ ํƒ€๊ฒŸ ๋ชจ๋ธ์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ Gaussian Process Regression (GPR) ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•ฉ๋‹ˆ๋‹ค. GPR ๋ชจ๋ธ์€ ๊ฐ€์ƒ ํ‘œ์ ์˜ ์˜ˆ์ธก๋œ ๊ถค์ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋ฏธ๋ž˜ ๊ถค์ ์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ๋„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ GPR์˜ ์˜ˆ์ธก ๊ฒฐ๊ณผ๋Š” Model Predictive Control (MPC)์— ๋Œ€ํ•œ ์œ„์น˜ ์ œ์•ฝ ์กฐ๊ฑด์œผ๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ ๋ถˆํ™•์‹ค์„ฑ์€ MPC์—์„œ ๊ธฐํšŒ ์ œ์•ฝ ์กฐ๊ฑด์œผ๋กœ ๊ณ ๋ ค๋ฉ๋‹ˆ๋‹ค. ๋™์  ๊ฐ์ฒด๋ฅผ ํฌํ•จํ•œ ์ฃผ๋ณ€ ํ™˜๊ฒฝ์„ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด ๊ด€์‹ฌ์˜์—ญ์„ ์ •์˜ํ•˜์—ฌ ๋ชฉํ‘œ ๋Œ€์ƒ์„ ๊ฒฐ์ •ํ•ฉ๋‹ˆ๋‹ค. ๋ฏธ๋ฆฌ ๊ฒฐ์ •๋œ ์ž์ฐจ๋Ÿ‰์˜ ์ฃผํ–‰๊ฒฝ๋กœ์™€ ๊ต์ฐจ๋กœ์˜ ๊ฒฝ๋กœ์ •๋ณด๋ฅผ ํ†ตํ•˜์—ฌ ์ž์ฐจ๋Ÿ‰์˜ ์ฃผํ–‰์ฐจ๋กœ์™€ ๊ต์ฐจํ•˜๋Š” ๋‹ค๋ฅธ ์ฐจ์„ ์„ ํŒ๋‹จํ•˜์—ฌ ๊ด€์‹ฌ์˜์—ญ์œผ๋กœ ์ •์˜ํ•จ์œผ๋กœ์จ ๊ด€์‹ฌ์˜์—ญ ๋ฐ–์˜ ์ฐจ๋Ÿ‰์„ ์ œ์™ธํ•˜์—ฌ ์—ฐ์‚ฐ๋Ÿ‰์„ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๋‹ค์Œ์œผ๋กœ ์ธ์ง€๋œ ์ฐจ๋Ÿ‰์˜ ๋ฏธ๋ž˜ ์ด๋™ ๊ถค์ ์€ LSTM-RNN (Long Short-Term Memory Recurrent Neural Network)์— ์˜ํ•ด ์˜ˆ์ธก๋ฉ๋‹ˆ๋‹ค. ํ›ˆ๋ จ์„ ์œ„ํ•œ ์ฃผํ–‰ ๋ฐ์ดํ„ฐ๋Š” ๋‘ ๋Œ€์˜ ์ž์œจ์ฃผํ–‰ ์ฐจ๋Ÿ‰์—์„œ ์ง์ ‘ ํš๋“ํ•˜์—ฌ ์ œํ•œ๋œ ์‹œ์•ผ์— ๊ด€๊ณ„์—†์ด ์ฐจ๋Ÿ‰์˜ ์ƒํƒœ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ๊ตฌ๊ธ€ Waymo ๋ฐ nuScenes์™€ ๊ฐ™์ด ๋„๋ฆฌ ์•Œ๋ ค์ง„ ์ž์œจ์ฃผํ–‰ ๋ฐ์ดํ„ฐ์˜ ๊ฒฝ์šฐ ์ฐจ๋Ÿ‰ ์ƒํƒœ ์ •๋ณด๋Š” ํ…Œ์ŠคํŠธ ์ฐจ๋Ÿ‰์— ์žฅ์ฐฉ๋œ ์ธ์ง€ ์„ผ์„œ์—์„œ ์ˆ˜์ง‘๋ฉ๋‹ˆ๋‹ค. ๋”ฐ๋ผ์„œ ํ…Œ์ŠคํŠธ ์ฐจ๋Ÿ‰์˜ ์‹œ์•ผ์—์„œ ๋ฒ—์–ด๋‚˜ ์žˆ๋Š” ์ฐจ๋Ÿ‰ ์ •๋ณด๋Š” ์–ป์„ ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค. ์ทจ๋“ํ•œ ์ฃผํ–‰ ๋ฐ์ดํ„ฐ๋Š” ๋” ๋‚˜์€ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ ์ ์‘ ๋ฐ ์ผ๋ฐ˜ํ™”๋ฅผ ์œ„ํ•ด ์ž์ฐจ๊ฐ€ ์•„๋‹Œ ํƒ€๊ฒŸ์ฐจ๋Ÿ‰ ์ค‘์‹ฌ ์ขŒํ‘œ๋กœ ๊ตฌ์„ฑ๋ฉ๋‹ˆ๋‹ค. ์†์‹คํ•จ์ˆ˜๋กœ ํ‰๊ท  ์ œ๊ณฑ ์˜ค์ฐจ ๋ฐ ์Œ์˜ ๋กœ๊ทธ ์šฐ๋„ํ•จ์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜์˜€๊ณ  ์Œ์˜ ๋กœ๊ทธ ์šฐ๋„ํ•จ์ˆ˜๋Š” ์ž์œจ์ฃผํ–‰ ์ฐจ๋Ÿ‰์˜ ๊ฑฐ๋™๊ณ„ํš์— ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๊ฒŒ ํƒ€๊ฒŸ์ฐจ๋Ÿ‰์˜ ๋ฏธ๋ž˜ ๊ถค์ ์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๊ธฐํšŒ ์ œ์•ฝ ์กฐ๊ฑด์ด ์žˆ๋Š” MPC๋Š” ์ž์œจ์ฐจ๋Ÿ‰์˜ ์ข…๋ฐฉํ–ฅ ๊ฑฐ๋™์„ ์ตœ์ ํ™”ํ•˜๋„๋ก ๊ตฌํ˜„๋ฉ๋‹ˆ๋‹ค. ๋™์  ์ œ์•ฝ ์กฐ๊ฑด ๋ฐ ๊ตฌ๋™๊ธฐ ์ œ์•ฝ ์กฐ๊ฑด์€ ์šด์ „์ž์—๊ฒŒ ์Šน์ฐจ๊ฐ๊ณผ ์•ˆ์ „์„ ์ œ๊ณตํ•˜๋„๋ก ์„ค๊ณ„๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ๊ธฐํšŒ ์ œ์•ฝ ์กฐ๊ฑด์€ ์œ„์น˜ ์ œ์•ฝ ์กฐ๊ฑด์„ ๊ฐ•๊ฑดํ•˜๊ฒŒ ํ•˜์—ฌ ์•ˆ์ „์„ ๋ณด์žฅํ•˜๊ณ  ๋Œ€์ƒ ์ฐจ๋Ÿ‰๊ณผ์˜ ์ž ์žฌ์ ์ธ ์ถฉ๋Œ์„ ๋ฐฉ์ง€ํ•ฉ๋‹ˆ๋‹ค. ์˜ˆ์ธก ์‹œ๊ฐ„๋™์•ˆ ์ด๋™ ๊ฑฐ๋ฆฌ์— ๋Œ€ํ•œ ์œ„์น˜ ์ œ์•ฝ ์กฐ๊ฑด์€ ๊ฐ ์˜ˆ์ธก์‹œ๊ฐ„์˜ ํƒ€๊ฒŸ๊ณผ ์ž์ฐจ๋Ÿ‰์˜ ์˜ˆ์ธก๋œ ๊ถค์  ๊ฐ„์˜ ๊ฑฐ๋ฆฌ ์ฐจ์ด์— ์˜ํ•ด ๊ฒฐ์ •๋œ๋‹ค. ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ๊ณผ ํƒ€๋‹น์„ฑ์€ ์ปดํ“จํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜๊ณผ ํ…Œ์ŠคํŠธ ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ํ‰๊ฐ€๋œ๋‹ค. ์˜คํ”„๋ผ์ธ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ํ†ตํ•ด ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์•ˆ์ „์„ฑ์„ ๊ฒ€์ฆํ•˜์˜€์œผ๋ฉฐ ์ œ์•ˆํ•œ ๊ฑฐ๋™๊ณ„ํš ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ž์œจ์ฃผํ–‰์ฐจ์— ๊ตฌํ˜„ํ•˜์—ฌ ์‹ค์ œ ๋„๋กœ์—์„œ ํ…Œ์ŠคํŠธํ•˜์˜€๋‹ค. ์ œ์•ˆํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์‹ค์ œ ์ฐจ๋Ÿ‰์— ๊ตฌํ˜„ํ•˜์—ฌ ์‹ค์ œ ์ž์œจ์ฃผํ–‰์— ์ ์šฉํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.Chapter 1. Introduction 1 1.1. Research Background and Motivation of Intersection Autonomous Driving 1 1.2. Previous Researches on Intersection Autonomous Driving 9 1.2.1. Research on Trajectory Prediction and Intention Inference at Urban Intersection 10 1.2.2. Research on Intersection Motion Planning 11 1.3. Thesis Objectives 18 1.4. Thesis Outline 19 Chapter 2. Overall Architecture of Intersection Autonomous Driving System 22 2.1. Software Configuration of Intersection Autonomous Driving 22 2.2. Hardware Configuration of Autonomous Driving and Test Vehicle 24 2.3. Vehicle Test Environment for Intersection Autonomous Driving 25 Chapter 3. Virtual Target Modelling for Intersection Motion Planning 27 3.1. Limitation of Conventional Virtual Target Model for Intersection 27 3.2. Virtual Target Generation for Intersection Occlusion 31 3.3. Intersection Virtual Target Modeling 34 3.3.1. Gaussian Process Regression based Virtual Target Model at Intersection 35 3.3.2. Data Processing for Gaussian Process Regression based Virtual Target Model 38 3.3.3. Definition of Visibility Index of Virtual Target at Intersection 45 3.3.4. Long Short-Term Memory based Virtual Target Model at Intersection 51 Chapter 4. Surrounding Vehicle Motion Prediction at Intersection 54 4.1. Intersection Surrounding Vehicle Classification 54 4.2. Data-driven Vehicle State based Motion Prediction at Intersection 58 4.2.1. Network Architecture of Motion Predictor 58 4.2.2. Dataset Processing of the Network 65 Chapter 5. Intersection Longitudinal Motion Planning 68 5.1. Outlines of Longitudinal Motion Planning with Model Predictive Control 68 5.2. Stochastic Model Predictive Control of Intersection Motion Planner 69 5.2.1. Definition of System Dynamics Model 69 5.2.2. Ego Vehicle Prediction and Reference States Definition 70 5.2.3. Safety Clearance Decision for Intersection Collision Avoidance 71 5.2.4. Driving Mode Decision of Intersection Motion Planning 79 5.2.5. Formulation of Model Predictive Control with the Chance Constraint 83 Chapter 6. Performance Evaluation of Intersection Longitudinal Motion Planning 86 6.1. Performance Evaluation of Virtual Target Prediction at Intersection 86 6.1.1. GPR based Virtual Target Model Prediction Results 86 6.1.2. Intersection Autonomous Driving Computer Simulation Environment 90 6.1.2.1. Simulation Result of Effect of Virtual Target in Intersection Autonomous Driving 92 6.1.2.2. Virtual Target Simulation Result of the Right Turn Across Path Scenario in the Intersection 96 6.1.2.3. Virtual Target Simulation Result of the Straight Across Path Scenario in the Intersection 102 6.1.2.4. Virtual Target Simulation Result of the Left Turn Across Path Scenario in the Intersection 108 6.1.2.5. Virtual Target Simulation Result of Crooked T-shaped Intersection 113 6.2. Performance Evaluation of Data-driven Vehicle State based Motion Prediction at Intersection 124 6.2.1. Data-driven Motion Prediction Accuracy Analysis 124 6.2.2. Prediction Trajectory Accuracy Analysis 134 6.3. Vehicle Test for Intersection Autonomous Driving 146 6.3.1. Test Vehicle Configuration for Intersection Autonomous Driving 146 6.3.2. Software Configuration for Autonomous Vehicle Operation 147 6.3.3. Vehicle Test Environment for Intersection Autonomous Driving 148 6.3.4. Vehicle Test Result of Intersection Autonomous Driving 151 Chapter 7. Conclusion and Future Work 161 7.1. Conclusion 161 7.2. Future Work 164 Bibliography 166 Abstract in Korean 172๋ฐ•

    Target depth selection for minimization of detection probability

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    In the detection of underwater target, there exists an optimal depth for the sonar systems where the detection probability is maximized. In contrast, for the targets, there is a target depth where the detection probability by the sonar system is minimized. In this paper, we address this question from the view point of the target, that is to find the target depth where the detection probability of the target by sonar systems is minimized. The detection probability of the target is dependent on the depths of target and sonar, the submarine topography, the sound speed profile and the generated frequency of the target. In this paper, the detection probability of the target is calculated by the depth for the slope direction of the submarine slope based on the information about the sound speed profile and the submarine topography in the ocean.Abstract = I ๋ชฉ ์ฐจ = II ๊ทธ๋ฆผ๋ชฉ์ฐจ = IV 1. ์„œ ๋ก  = 1 1.1 ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ ๋ฐ ๋ชฉ์  = 1 1.2 ์—ฐ๊ตฌ์˜ ๋™ํ–ฅ = 1 1.3 ์—ฐ๊ตฌ๋‚ด์šฉ = 2 1.4 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ = 3 2. ์†Œ๋‚˜๋ฐฉ์ •์‹ = 4 2.1 ์Œ์›์ค€์œ„(Source Level, SL) = 4 2.2 ์ „๋‹ฌ์†์‹ค(Transmission Loss, TL) = 4 2.2.1 ํŒŒ๋™๋ฐฉ์ •์‹ = 5 2.2.2 ํฌ๋ฌผ์„  ๋ฐฉ์ •์‹ = 6 2.2.3 ๊ฑฐ๋ฆฌ์ข…์† ์Œํ–ฅ๋ชจ๋ธ = 14 2.2.4 ์ˆ˜์น˜ํ•ด์„ ๊ธฐ๋ฒ• = 16 2.3 ์†Œ์Œ์ค€์œ„(Noise Level, NL) = 18 2.4 ์ง€ํ–ฅ์ง€์ˆ˜(Directivity Index, DI) = 20 2.5 ํƒ์ง€๋ฌธํ„ฑ(Detection Threshold, DT) = 22 3. ํ”ผํƒ์ง€๊ฑฐ๋ฆฌ ๊ณ„์‚ฐ์„ ์œ„ํ•œ ์•Œ๊ณ ๋ฆฌ๋“ฌ = 24 4. ์ˆ˜์น˜์‹คํ—˜ = 26 4.1 ์„ผ์„œ์˜ ๊นŠ์ด๊ฐ€ ์•Œ๋ ค์ง„ ๊ฒฝ์šฐ = 26 4.1.1 Down slope ํƒ์ง€ํ™˜๊ฒฝ = 26 4.1.2 Up slope ํ™˜๊ฒฝ = 40 4.2 ์„ผ์„œ์˜ ๊นŠ์ด๋ฅผ ๋ชจ๋ฅผ ๊ฒฝ์šฐ = 50 5. ๊ฒฐ ๋ก  = 53 ์ฐธ๊ณ ๋ฌธํ—Œ = 5

    Synthesis of Ta-doped TiO2 Nanorods and Ta:TiO2/Fe2O3 Heterostructure for PEC Application

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2014. 8. ํ™์„ฑํ˜„.์ตœ๊ทผ ํ™”์„์—ฐ๋ฃŒ์˜ ๊ณ ๊ฐˆ๊ณผ ํ™˜๊ฒฝ์˜ค์—ผ์˜ ๋ฌธ์ œ๊ฐ€ ๋Œ€๋‘๋จ์— ๋”ฐ๋ผ ์นœํ™˜๊ฒฝ์ ์ธ ์—๋„ˆ์ง€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์ด ์ œ๊ธฐ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ ์ค‘์—์„œ๋„ ํƒœ์–‘์—๋„ˆ์ง€๋ฅผ ์ˆ˜์†Œ์—๋„ˆ์ง€๋กœ ์ „ํ™˜ํ•  ์ˆ˜ ์žˆ๋Š” ๊ด‘ ์ด‰๋งค๋ฅผ ์ด์šฉํ•œ ๊ธฐ์ˆ ์€ ์•ˆ์ „ํ•˜๋ฉฐ ๊ฐ„๋‹จํ•˜๊ณ  ๋ถ€์‚ฐ๋ฌผ์ด ์—†๋‹ค๋Š” ์ ์—์„œ ์žฌ์ƒ์—๋„ˆ์ง€๋กœ์จ ๋งค์šฐ ํšจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ•์ด๋‹ค. [1] ํ™œ๋ฐœํ•˜๊ฒŒ ์—ฐ๊ตฌ๋˜๋Š” ๊ด‘ ์ด‰๋งค ๋ฌผ์งˆ์€Fe2O3[2]ยฌ, WO3[3], CdS[4] ๋“ฑ ์ด ์žˆ๋Š”๋ฐ ๊ทธ ์ค‘์—์„œ๋„ TiO2๋Š” 1972๋…„ ๋‹จ ๊ฒฐ์ • ์ „๊ทน์— ๋น›์„ ๋น„์ถ”๋ฉด ๋ฌผ์ด ๋ถ„ํ•ด๋œ๋‹ค๋Š” ๋ณด๊ณ ๊ฐ€ ๊ด‘ ์ด‰๋งค ์—ฐ๊ตฌ์˜ ์‹œ์ž‘์ด ๋˜์—ˆ์œผ๋ฉฐ์•„์ง๊นŒ์ง€๋„ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฉด์œผ๋กœ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค.[5, 6] TiO2 ์˜ ๊ฒฝ์šฐ morphology๋ฅผ ์กฐ์ ˆํ•˜๊ธฐ ์‰ฝ๊ณ  electron๊ณผ hole diffusion length๊ฐ€ ๋‹ค๋ฅธ ๋ฌผ์งˆ์— ๋น„ํ•ด ๊ธด ํŽธ์ด๋ผ recombination์„ ์–ต์ œํ•˜๋Š” ๋ฐฉ๋ฉด์—์„œ ํฐ ์žฅ์ ์ด ์žˆ๋‹ค. ํ•˜์ง€๋งŒ TiO2 ๋Š” ์ €ํ•ญ์ด ๋งค์šฐ ์ปค์„œ ์ƒ์„ฑ๋œ electron์ด FTO๊ธฐํŒ์—์„œ ๋น ์ ธ๋‚˜๊ฐ€๊ธฐ ํž˜๋“ค์–ด recombination ์ด ์ผ์–ด๋‚˜ photo-catalytic efficiency์„ ์ƒ๋‹นํžˆ ๋–จ์–ด๋œจ๋ฆฌ๋Š” ๋‹จ์ ์ด ์žˆ๊ธฐ ๋•Œ๋ฌธ์— Ta์ด๋‚˜ Nb ๋“ฑ ์—ฌ๋Ÿฌ atom์„ TiO2 ์— doped์„ ํ•˜์—ฌ ์ €ํ•ญ์„ ๋‚ฎ์ถ”๋Š” ๋“ฑ์˜ ์ด๋ฅผ ๊ทน๋ณตํ•˜๊ณ ์ž ํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•œ ์‹œ์ ์ด๋‹ค.[7, 8] ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” FTO ๊ธฐํŒ ์œ„์— TiO2๋ฅผ hydrothermal ๋ฐฉ์‹์œผ๋กœ ์„ฑ์žฅํ•˜์˜€๊ณ , ํ‘œ๋ฉด์ ์„ ๋„“ํž ์ˆ˜ ์žˆ๋Š” 1D structure์˜ ๊ตฌ์กฐ๋กœ ํ•ฉ์„ฑํ•˜์—ฌ ๋ฌผ๊ณผ ๋ฐ˜์‘ํ•˜๋Š” ์˜์—ญ์„ ๋„“ํ˜€ water splitting์„ ๋” ์ž˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ์„ค๊ณ„๋ฅผ ํ•˜์˜€๋‹ค. ํŠนํžˆTantalum์„ doped ์‹œ์ผœ TiO2์˜ conductivity๋ฅผ ํ–ฅ์ƒ ์‹œ์ผœ electron์ด ๋ณด๋‹ค ๋” ํ๋ฅผ ์ˆ˜ ์žˆ๊ฒŒ ํ•˜์—ฌ photo-catalytic property์˜ ํ–ฅ์ƒ์„ ํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๋˜ Tantalum ์ด doped ๋œ TiO2 ์˜ ํ–ฅ์ƒ์„ ์œ„ํ•ด Fe2O3 ์™€ ์ด์ข…์ ‘ํ•ฉ์„ ํ•˜์—ฌ ํŠน์„ฑ์˜ ํ–ฅ์ƒ์„ ์ด๋Œ์–ด ๋‚ด์—ˆ๋‹ค. ๋ณธ ์„์‚ฌ ์กธ์—…๋…ผ๋ฌธ์—์„œ๋Š” nanorod๋กœ ์„ฑ์žฅ์‹œํ‚จTiO2์™€ Ta์ด doped๋œ TiO2๋ฅผ ๋น„๊ตํ•˜์—ฌ ๋ฏธ์„ธ๊ตฌ์กฐ์˜ ๋ณ€ํ™” ๋ฐ ํŠน์„ฑ ํ–ฅ์ƒ์— ๋Œ€ํ•ด ๋…ผํ•˜๊ณ  ์ด๋ฅผ ๋” ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์•ˆ์œผ๋กœ์„œ Fe2O3 ์™€ ์ด์ข…์ ‘ํ•ฉ์„ ํ•˜์—ฌ ๋” ํ–ฅ์ƒ๋œ ๊ด‘์ด‰๋งค๋ฅผ ํ•ฉ์„ฑํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.์ดˆ๋ก.................................................................................................................โ…ฐ List of figures................................................................................................โ…ณ 1. ์„œ๋ก ..............................................................................................................1 2. ๋ฌธํ—Œ์—ฐ๊ตฌ.......................................................................................................4 2.1. ๋ฌผ ๋ถ„ํ•ด ์—ฐ๊ตฌ...........................................................................................4 2.1.1. ๋ฌผ ๋ถ„ํ•ด์˜ ๊ธฐ๋ณธ ์›๋ฆฌ..........................................................................5 2.1.2. ๊ด‘ ์ด‰๋งค์˜ ์ข…๋ฅ˜...................................................................................6 2.1.3. ๊ด‘ ์ด‰๋งค์˜ ํŠน์„ฑ ์ธก์ •..........................................................................9 2.1.4. ๊ด‘ ์ด‰๋งค์˜ ์„ฑ๋Šฅ ํ‰๊ฐ€..........................................................................10 2.2. TiO2 ๊ด‘ ์ด‰๋งค...........................................................................................11 2.2.1. ์ผ ์ฐจ์›์  ๋‚˜๋…ธ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง€๋Š” TiO2.................................................12 2.2.2. TiO2 ์˜ ํ•ฉ์„ฑ๋ฐฉ๋ฒ•.............................................................................13 2.2.3. TiO2 ์˜ ํŠน์„ฑ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋„ํ•‘ ํšจ๊ณผ..............................................15 2.2.4. TiO2 ์˜ ํŠน์„ฑ ํ–ฅ์ƒ์„ ์œ„ํ•œ ์ด์ข…์ ‘ํ•ฉ..............................................16 3. ์‹คํ—˜๋ฐฉ๋ฒ•.....................................................................................................33 3.1. ์‹œํŽธ์˜ ์ค€๋น„...........................................................................................33 3.2. ํŠน์„ฑ ํ‰๊ฐ€..............................................................................................35 4. ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ..............................................................................................38 4.1. HCl ๋†๋„์— ์˜ํ•œ ์˜ํ–ฅ...........................................................................38 4.2. ๊ณต์ • ์‹œ๊ฐ„์— ์˜ํ•œ ์˜ํ–ฅ.........................................................................42 4.3. ๋„ํ•‘ ๋†๋„์— ์˜ํ•œ ์˜ํ–ฅ.........................................................................47 4.4. ์ด์ข… ์ ‘ํ•ฉ์— ์˜ํ•œ ์˜ํ–ฅ.........................................................................69 5. ๊ฒฐ๋ก ...........................................................................................................74 6. ์ฐธ๊ณ ๋ฌธํ—Œ.....................................................................................................76 Abstract.........................................................................................................85Maste

    The Analysis of Mathematical Tasks in KSAT-EBS linked Coursebooks: Focusing on Functions

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌ๋ฒ”๋Œ€ํ•™ ์ˆ˜ํ•™๊ต์œก๊ณผ, 2018. 2. ์ด๊ฒฝํ™”.์ˆ˜ํ•™ ๊ณผ์ œ(Mathematical Tasks)๋Š” ํ•™์Šต ๋‚ด์šฉ๋ฟ ์•„๋‹ˆ๋ผ ์‚ฌ๊ณ ๋ฐฉ์‹๊นŒ์ง€ ๊ฒฐ์ •ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์˜๋ฏธ ์žˆ๋Š” ์ˆ˜ํ•™ ๊ณผ์ œ๋ฅผ ํ•™์Šต์ž์—๊ฒŒ ์ œ์‹œํ•˜๋Š” ๊ฒƒ์€ ์ค‘์š”ํ•œ ๋ฌธ์ œ์ด๋‹ค. ์ด์— ๊ต๊ณผ์„œ์— ํฌํ•จ๋œ ์ˆ˜ํ•™ ๊ณผ์ œ์— ๋Œ€ํ•œ ๋ถ„์„ ์—ฐ๊ตฌ๋Š” ๊พธ์ค€ํžˆ ์ด๋ฃจ์–ด์ ธ ์™”์œผ๋‚˜ EBSโ€“์ˆ˜๋Šฅ ์—ฐ๊ณ„ ์ •์ฑ…์˜ ์˜ํ–ฅ์œผ๋กœ EBS ์ˆ˜๋Šฅ ์—ฐ๊ณ„๊ต์žฌ๊ฐ€ ๊ต๊ณผ์„œ๋ฅผ ๋Œ€์‹ ํ•˜์—ฌ ํ•™๊ต ํ˜„์žฅ์˜ ๊ต์ˆ˜ ํ•™์Šต ๋ฐ ํ‰๊ฐ€์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์žˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์—ฐ๊ณ„๊ต์žฌ์— ๋Œ€ํ•œ ์ˆ˜ํ•™ ๊ต์œก์  ๋ถ„์„์€ ๋ฏธ๋น„ํ•˜์—ฌ ๊ณผ์ œ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์ด ์ œ๊ธฐ๋œ๋‹ค. ํ•จ์ˆ˜๋Š” ๊ต์œก๊ณผ์ • ์ „์ฒด์— ๊ฑธ์นœ ๊ณตํ†ต๋œ ์ฃผ์ œ์ด๊ณ  ์ˆ˜ํ•™์˜ ์—ฌ๋Ÿฌ ์˜์—ญ์„ ํ†ตํ•ฉํ•˜์—ฌ ์ˆ˜ํ•™ ํ•™์Šต์—์„œ ํ•˜๋‚˜์˜ ์ฃผ์ œ ์ด์ƒ์˜ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง€๋ฏ€๋กœ ์—ฐ๊ณ„๊ต์žฌ์˜ ํ•จ์ˆ˜ ๊ณผ์ œ ๋ถ„์„์€ ๊ณผ์ œ์—์„œ ํ•™์ƒ๋“ค์ด ๊ฒฝํ—˜ํ•˜๋Š” ์ˆ˜ํ•™์  ์‚ฌ๊ณ ์— ๋Œ€ํ•˜์—ฌ ๋ณด๋‹ค ํ’์„ฑํ•œ ๋…ผ์˜๋ฅผ ์ด๋Œ์–ด๋‚ผ ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” EBS ์ˆ˜๋Šฅ ์—ฐ๊ณ„๊ต์žฌ์˜ ํ•จ์ˆ˜์˜์—ญ ์ˆ˜ํ•™ ๊ณผ์ œ๊ฐ€ ํ•™์ƒ๋“ค์—๊ฒŒ ์ œ์‹œํ•˜๋Š” ํ•จ์ˆ˜ ํ•™์Šต์˜ ๊ธฐํšŒ๋ฅผ ์‚ดํŽด๋ณธ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ณผ์ œ์˜ ์ธ์ง€์  ์ˆ˜์ค€๊ณผ ํ•จ์ˆ˜์˜ ํ‘œํ˜„ ๋ฐ ํ‘œํ˜„ ๊ฐ„์˜ ๋ฒˆ์—ญํ™œ๋™์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋ฅผ ๋ถ„์„ํ•˜๊ณ  2015 ๊ฐœ์ • ์ˆ˜ํ•™๊ณผ ๊ต์œก๊ณผ์ •์— ๋”ฐ๋ฅธ ํ•จ์ˆ˜์˜์—ญ์˜ ๋‚ด์šฉ์š”์†Œ์™€ ๊ธฐ๋Šฅ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ฌธํ—Œ ๋ถ„์„์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ณผ์ œ์˜ ์ธ์ง€์  ์ˆ˜์ค€, ๊ณผ์ œ์— ๋‚˜ํƒ€๋‚œ ํ•จ์ˆ˜์˜ ํ‘œํ˜„๊ณผ ํ‘œํ˜„ ๊ฐ„ ๋ฒˆ์—ญ๊ณผ์ œ, ๊ณผ์ œ์—์„œ ๋‹ค๋ฃจ๊ณ  ์žˆ๋Š” ํ•จ์ˆ˜์˜ ๋‚ด์šฉ์š”์†Œ์™€ ๊ธฐ๋Šฅ์— ๋”ฐ๋ฅธ ๋ถ„์„ํ‹€์„ ๋งŒ๋“ค๊ณ  ์ด์— ๋”ฐ๋ผ EBS ์ˆ˜๋Šฅ ์—ฐ๊ณ„๊ต์žฌ 4๊ถŒ์œผ๋กœ๋ถ€ํ„ฐ 139๊ฐœ์˜ ํ•จ์ˆ˜ ๊ณผ์ œ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ํŠนํžˆ ๊ณผ์ œ์˜ ์ธ์ง€์  ์ˆ˜์ค€๊ณผ ํ•จ์ˆ˜์˜ ํ‘œํ˜„ ๋ฐ ๋ฒˆ์—ญ์œ ํ˜•์— ๋Œ€ํ•œ ๋ถ„์„์—์„œ ์‚ดํŽด๋ณด์ง€ ๋ชปํ•œ ๋ถ€๋ถ„์„ 2015 ๊ฐœ์ • ์ˆ˜ํ•™๊ณผ ๊ต์œก๊ณผ์ •์—์„œ ์ œ์‹œํ•˜๋Š” ๋‚ด์šฉ์š”์†Œ์™€ ๊ธฐ๋Šฅ์œผ๋กœ๋ถ€ํ„ฐ ์„ธ๋ถ€์ ์œผ๋กœ ๋ถ„์„ํ•จ์œผ๋กœ์จ ๊ณผ์ œ์—์„œ ๋‹ค๋ฃจ๊ณ  ์žˆ๋Š” ํ•จ์ˆ˜์— ๊ด€ํ•œ ์ˆ˜ํ•™์  ์ง€์‹๊ณผ ์ด๋ฅผ ๋‹ค๋ฃจ๋Š” ๋ฐฉ์‹์„ ํ™•์ธํ•˜๋Š” ๊ฒƒ์— ์ค‘์ ์„ ๋‘์—ˆ๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ EBS ์ˆ˜๋Šฅ ์—ฐ๊ณ„๊ต์žฌ์˜ ํ•จ์ˆ˜ ๊ณผ์ œ๋Š” ๊ต์œก๊ณผ์ •์˜ ๋‚ด์šฉ ์š”์†Œ์™€ ์ ˆ์ฐจ์ ์ธ ์ง€์‹์„ ๊ณ ๋ฅด๊ฒŒ ํ•™์Šตํ•˜๊ฒŒ ํ•˜๋ฉฐ ๊ธฐ๋ณธ ์œ ํ˜•์˜ ๊ทธ๋ž˜ํ”„์™€ ํ™œ์šฉ์— ๋Œ€ํ•œ ์—ฐ์Šต ๊ธฐํšŒ๋ฅผ ์ถฉ๋ถ„ํžˆ ์ œ๊ณตํ•˜๊ณ  ์žˆ์—ˆ๋‹ค. ๋‹ค๋งŒ ์ˆ˜ํ•™์  ๋ฌธ์ œํ•ด๊ฒฐ๊ณผ ์ถ”๋ก ๊ณผ ๊ฐ™์€ ๊ณ ์ฐจ์›์  ์‚ฌ๊ณ ์™€ ๋‹ค์–‘ํ•œ ํ•จ์ˆ˜ ๋งฅ๋ฝ๊ณผ ํ‘œํ˜„์„ ๊ฒฝํ—˜ํ•  ๊ธฐํšŒ๋Š” ์ œํ•œ์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ ํ•™์ƒ๋“ค์—๊ฒŒ ๋ณด๋‹ค ์˜๋ฏธ ์žˆ๋Š” ํ•จ์ˆ˜ ํ•™์Šต ๊ธฐํšŒ๋ฅผ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋†’์€ ์ธ์ง€์  ์ˆ˜์ค€์˜ ๊ณผ์ œ์™€ ๊ทธ๋ž˜ํ”„์— ๋Œ€ํ•œ ๋‹ค์–‘ํ•œ ํ™œ๋™์„ ํ•„์š”๋กœ ํ•˜๋Š” ๋ฌธํ•ญ์ด ๋” ์ถ”๊ฐ€๋  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋˜ํ•œ ํ•จ์ˆ˜ ๋งฅ๋ฝ์„ ํฌํ•จํ•˜๋Š” ๊ณผ์ œ๋ฅผ ์ œ์‹œํ•จ์œผ๋กœ์จ ํ•™์ƒ๋“ค์—๊ฒŒ ์‹ค์ƒํ™œ ์†์˜ ์—ฌ๋Ÿฌ ํ˜„์ƒ์„ ์กฐ์งํ•˜๊ณ  ์„ค๋ช…ํ•˜๋Š” ๋„๊ตฌ์ด์ž ์ˆ˜ํ•™์˜ ์—ฌ๋Ÿฌ ์˜์—ญ์„ ํ†ตํ•ฉํ•˜๋Š” ์•„์ด๋””์–ด๋กœ์„œ์˜ ํ•จ์ˆ˜๋ฅผ ๊ฒฝํ—˜ํ•˜๊ฒŒ ํ•  ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค.Exposure of students to meaningful and worthwhile mathematical tasks is an important issue since mathematical tasks determine not only the content to learn but also the way of thinking. Due to importance of mathematical tasks, many analytic studies of tasks in the textbook have been done. However, even though KSAT(Korean Scholastic Aptitude Test)-EBS linked coursebooks have had a great effect on the teaching-learning and the evaluation of high schools under the influence of the KSAT-EBS linked policy, there have been a lack of research on KSAT-EBS linked coursebooks from the mathematics educational point of view. Function is the common theme throughout the mathematical curriculum and it has the meaning of more than just one theme in mathematical learning in that it integrates a number of areas of mathematics. Therefore, analysis of the tasks on functions in KSAT-EBS linked coursebooks would provide plentiful discussion about the mathematical thinking that students encounter from the tasks. The purpose of this study is to explore how KSAT-EBS linked coursebooks tasks on functions provide students an opportunity-to-learn. For this purpose, this study investigates level of cognitive demand of tasks and studies about the use and translation of functional expressions. Also it investigates content elements and skills of the 2015 revised National Mathematics Curriculum. Based on the investigation, this study analyzes 139 tasks on function content dealt with in four KSAT-EBS linked coursebooks to identify the opportunity-to-learn functions. By analyzing tasks from the viewpoint of content elements and skills, this study focuses on identifying in detail the mathematical knowledge and the way it is dealt with in the tasks on functions. The conclusion is follows: KSAT-EBS linked coursebooks deal with all content element and procedural knowledge without omission and provide sufficient opportunity to practice graphing the basic types of functions. However, the results also show that there are limited opportunities to experience higher-order thinking like mathematical problem solving or reasoning and to deal with diverse context of function and functional expressions. From the results, the study suggests the necessity of additional high-level tasks and diverse graphing activities to provide students more meaningful opportunity to learn functions. Futhermore, it also suggests to provide tasks that include realistic context on functions in order that students can experience the function as a practical tool for organizing and explaining phenomena from daily life and as an idea for integrating multiple areas of mathematics.I. ์„œ๋ก  1 ๊ฐ€. ์—ฐ๊ตฌ์˜ ๋ชฉ์  ๋ฐ ํ•„์š”์„ฑ 1 ๋‚˜. ์—ฐ๊ตฌ ๋ฌธ์ œ 4 II. ๋ฌธํ—Œ ๋ถ„์„ 5 ๊ฐ€. ์ธ์ง€์  ๋…ธ๋ ฅ์ˆ˜์ค€ 5 1. ์ธ์ง€์  ๋…ธ๋ ฅ์ˆ˜์ค€์˜ ์˜๋ฏธ 5 2. ๊ต๊ณผ์„œ ๊ณผ์ œ์˜ ์ธ์ง€์  ๋…ธ๋ ฅ์ˆ˜์ค€ 6 ๋‚˜. ํ•จ์ˆ˜ ์ง€๋„ 9 1. ํ•™๊ต ์ˆ˜ํ•™์˜ ํ•จ์ˆ˜์  ์‚ฌ๊ณ  9 2. ํ•จ์ˆ˜์˜ ํ‘œํ˜„๊ณผ ํ‘œํ˜„ ๊ฐ„ ๋ฒˆ์—ญํ™œ๋™ 11 3. ํ•จ์ˆ˜์˜ ํ‘œํ˜„๊ณผ ํ‘œํ˜„ ๊ฐ„ ๋ฒˆ์—ญ๋Šฅ๋ ฅ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 14 ๋‹ค. 2015 ๊ฐœ์ • ์ˆ˜ํ•™๊ณผ ๊ต์œก๊ณผ์ • 17 1. 2015 ๊ฐœ์ • ์ˆ˜ํ•™๊ณผ ๊ต์œก๊ณผ์ •์˜ ๋‚ด์šฉ์ฒด๊ณ„ ๋ฐ ์„ฑ์ทจ๊ธฐ์ค€ 17 2. 2015 ๊ฐœ์ • ์ˆ˜ํ•™๊ณผ ๊ต์œก๊ณผ์ •์˜ ํ•จ์ˆ˜์˜์—ญ ์ง€๋„๋ฐฉํ–ฅ 20 โ…ข. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 23 ๊ฐ€. ๋ถ„์„ ๋Œ€์ƒ 23 ๋‚˜. ๋ถ„์„ ๋ฐฉ๋ฒ• 25 1. ์ธ์ง€์  ๋…ธ๋ ฅ์ˆ˜์ค€ ๋ถ„์„ํ‹€ 25 2. ํ•จ์ˆ˜์˜ ํ‘œํ˜„๊ณผ ํ‘œํ˜„ ๊ฐ„ ๋ฒˆ์—ญ ๋ถ„์„ํ‹€ 26 3. 2015 ๊ฐœ์ • ์ˆ˜ํ•™๊ณผ ๊ต์œก๊ณผ์ •์˜ ๋‚ด์šฉ์š”์†Œ์™€ ๊ธฐ๋Šฅ์— ๋”ฐ๋ฅธ ์„ธ๋ถ€ ๋ถ„์„ํ‹€ 28 IV. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 32 ๊ฐ€. ๊ณผ์ œ์˜ ์ธ์ง€์  ๋…ธ๋ ฅ์ˆ˜์ค€ 32 ๋‚˜. ํ•จ์ˆ˜์˜ ํ‘œํ˜„๊ณผ ๋ฒˆ์—ญํ™œ๋™ 34 1. ์ œ์‹œ๋œ ํ•จ์ˆ˜ ํ‘œํ˜„ 35 2. ํ•จ์ˆ˜ ํ‘œํ˜„ ๊ฐ„์˜ ๋ฒˆ์—ญํ™œ๋™ 40 ๋‹ค. 2015 ๊ฐœ์ • ์ˆ˜ํ•™๊ณผ ๊ต์œก๊ณผ์ •์˜ ๋‚ด์šฉ์š”์†Œ์™€ ๊ธฐ๋Šฅ์— ๋”ฐ๋ฅธ ์„ธ๋ถ€ ๋ถ„์„ ๊ฒฐ๊ณผ 43 1. M ๊ณผ์ œ ์„ธ๋ถ€ ๋ถ„์„ ๊ฒฐ๊ณผ 43 2. PNC ๊ณผ์ œ ์„ธ๋ถ€ ๋ถ„์„ ๊ฒฐ๊ณผ 45 2.1. ๊ณ„์‚ฐํ•˜๊ธฐ์™€ ์ดํ•ดํ•˜๊ธฐ 47 2.2. ํ•จ์ˆ˜ ๊ตฌํ•˜๊ธฐ 48 2.3. ๊ทธ๋ž˜ํ”„ ๊ทธ๋ฆฌ๊ธฐ์™€ ํ‘œํ˜„ํ•˜๊ธฐ 51 2.4. ํ™œ์šฉํ•˜๊ธฐ์™€ ๋ฌธ์ œํ•ด๊ฒฐํ•˜๊ธฐ 56 V. ๋…ผ์˜ ๋ฐ ๊ฒฐ๋ก  61 ๊ฐ€. ๋…ผ์˜ ๋ฐ ๊ฒฐ๋ก  61 ์ฐธ๊ณ ๋ฌธํ—Œ 69Maste

    PbTiOโ‚ƒ ๋ฏธ์„ธ๋ถ„๋ง ํ•ฉ์„ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์žฌ๋ฃŒ๊ณตํ•™๋ถ€,2002.Maste

    Effect on environmental pollution of population increases in seoul

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    ๋ณด๊ฑดํ•™๊ณผ/์„์‚ฌIn this paper environmental pollution is considered as a by - product of population increase and economic development. The anticipated rise in environmental pollution is expressed as a result of urban expansion in size and functions. Owing to the over-population and great density of the industrial area in Seoul environmental disruption has been brought out as a result of pollution. From this point of view, in order to determine the degree of environmental pollution, a time series analysis, a principal component analysis, a multiple and partial correlation analysis have been undertaken with the data taken from the annual reports of Seoul metorpolitian Government statisitical Year book and the reports of the Seoul Institute of Hygiene. 1969 to 1974. Eleven testing items were selected as variables, including population, concentration of sulfur dioxide, dust fall-out, total exhaust quantity of refuse and excrements, number of motor vehicles, use of Gasoline, Anthracite, Bunker c-oil, production of water and consumption level of electric power. The results were as follows: 1. According to the time series analysis, as popolation increased, 10 variables environmental pollutants increased year by year. 2. Of the given variables from principal component analysis, three were classified as the causative factors to be identified as follows 1) Factor of Energy consumption 2) Factor of self-purificaton 3) Fcator of Environmental pollution 3. The cumulative percentage of eigen values from the three identified variables totaled 77.7%. 4. In the partial correlation between sulfur dioxide and population increase, it was shown that population increase would not give a direct influence to the increase of sulfur dioxide in the Environment. 5. There was no correlation between the quantity of dust fall-out and the population, but the increasement of population has some correlations with the refuse from the city (r=0.332), and with the use of anthracite for fuel (r=0.488). 6. As far as the population increase, the motor vehicles increase with the partial correlation of r=0.696. 7. the population in crease shows the increasement of anthracite use with r=0.361.restrictio

    ๋ถํ•œ์˜ ๊ฒฝ์ œํ˜„ํ™ฉ ๋ฐ ๋Œ€์™ธ๊ฒฝ์ œํ˜‘๋ ฅ์˜ ํ•„์š”์„ฑ

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    Performance Evaluation of Safety Envelop Based Path Generation and Tracking Algorithm for Autonomous Vehicle

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    This paper describes the tracking algorithm performance evaluation for autonomous vehicle using a safety envelope based path. As the level of autonomous vehicle technologies evolves along with the development of relevant supporting modules including sensors, more advanced methodologies for path generation and tracking are needed. A safety envelope zone, designated as the obstacle free regions between the roadway edges, would be introduced and refined for further application with more detailed specifications. In this paper, the performance of the path tracking algorithm based on the generated path would be evaluated under safety envelop environment. In this process, static obstacle map for safety envelope was created using Lidar based vehicle information such as current vehicle location, speed and yaw rate that were collected under various driving setups at Seoul National University roadways. A level of safety was evaluated through CarSim simulation based on paths generated with two different references: a safety envelope based path and a GPS data based one. A better performance was observed for tracking with the safety envelop based path than that with the GPS based one.N
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