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    The effect of discount coupons on lead time driven cancellations

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ฒฝ์˜๋Œ€ํ•™ ๊ฒฝ์˜ํ•™๊ณผ, 2021.8. ์–‘ํ™์„.์ƒ์‚ฐ ์šด์˜ ๊ด€๋ฆฌ์—์„œ ๋ฐฐ์†ก์€ ์„ฑ๊ณต์˜ ํ•ต์‹ฌ์ ์ธ ์š”์ธ์œผ๋กœ ์ž‘์šฉํ•œ๋‹ค. ํ•˜์ง€๋งŒ, ์™„๋ฒฝํ•œ ์šด์˜ ์ฒด๊ณ„๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์ง€ ์•Š์€ ํšŒ์‚ฌ๋“ค์€ ์ œํ’ˆ์˜ ์ธ๊ธฐ๋กœ ์ธํ•œ ๊ฐ‘์ž‘์Šค๋Ÿฌ์šด ์ฃผ๋ฌธ ํญ์ฃผ๋ฅผ ๊ฐ๋‹นํ•  ์ˆ˜ ์—†์–ด ๊ณ ๊ฐ์—๊ฒŒ ์ œ๋•Œ ๋ฐฐ์†ก์„ ํ•  ์ˆ˜ ์—†๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋ฐœ์ƒํ•œ๋‹ค. ์ด๋กœ ์ธํ•ด ๊ณ ๊ฐ์€ ๊ธฐ๋‹ค๋ฆฌ๋‹ค ์ง€์ณ ์ฃผ๋ฌธ์„ ์ทจ์†Œํ•  ์ˆ˜ ์žˆ์œผ๋ฉด ์ด๋Š” ํšŒ์‚ฌ์˜ ์ด์ต ์†์‹ค๊ณผ ์ง๊ฒฐ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ธ์„ผํ‹ฐ๋ธŒ์˜ ์ผ์ข…์ธ ์ฟ ํฐ (ํŒ์ด‰)์„ ์ œ๊ณตํ•˜์—ฌ ๊ธฐ์—…์˜ ์†์‹ค์„ ์ตœ์†Œํ™”ํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. 1) ์ฟ ํฐ ๋„์ž…์ด ์ฃผ๋ฌธ ์ทจ์†Œ๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ์ƒํ™ฉ์—์„œ ๊ธฐ์—… ์ด์ต์— ์–ผ๋งˆ๋‚˜ ๋„์›€์ด ๋˜๋Š”์ง€ 2) ์ตœ์ ์˜ ์ฟ ํฐ ํ• ์ธ ์ˆ˜์ค€์„ ์–ด๋–ป๊ฒŒ ๊ฒฐ์ •ํ•ด์•ผํ•˜๋Š”์ง€ 3) ๊ธฐ์—…์ด ์ฟ ํฐ์„ ๋„์ž…ํ•ด์•ผ ํ•˜๋Š” ์‹œ๊ธฐ๊ฐ€ ์–ด๋–ป๊ฒŒ ๋˜๋Š”์ง€ ์•Œ์•„๋ณผ ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋น„๋™์งˆ์ ์ธ ํฌ์•„์†ก ๊ณผ์ •(nonhomogeneous Poisson process)์„ ํ†ตํ•˜์—ฌ ์˜ˆ์ธก๋œ ์ฃผ๋ฌธ ์ทจ์†Œ ์–‘๊ณผ ์ฟ ํฐ์„ ๋ฐ›์•„๋“ค์ผ์ง€์— ๋Œ€ํ•œ ํ™•๋ฅ  ํ•จ์ˆ˜๋ฅผ ์ด์šฉํ•˜์—ฌ ์ด์ต ํ•จ์ˆ˜๋ฅผ ๊ตฌ์กฐํ™”ํ•œ๋‹ค. ๋‹ค์Œ ์ตœ์ ์˜ ์ฟ ํฐ ๋ ˆ๋ฒจ ๋ณ€ํ™” ์ถ”์ด๋ฅผ ์‚ดํŽด๋ณผ ๊ฒƒ์ด๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ ๊ณ ๊ฐ์—๊ฒŒ ์ฟ ํฐ์„ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์€ ๊ธฐ์—…์˜ ์ด์ต ์†์‹ค์„ ์ตœ์†Œํ™”ํ•˜๋Š”๋ฐ ๋„์›€์ด ๋œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ œํ’ˆ์˜ ์ธ๊ธฐ ๊ธฐ๊ฐ„์ด ๋Š˜์–ด๋‚˜๊ณ  ์ œํ’ˆ์˜ ์ธ๊ธฐ๊ฐ€ ์ฆ๊ฐ€ ํ•จ์ˆ˜๋ฅผ ๋”ฐ๋ฅด๋ฉด, ์–ด๋Š ํŠน์ •ํ•œ ์‹œ์ ๋ถ€ํ„ฐ๋Š” ์ฟ ํฐ์˜ ํšจ์œจ์„ ๊ฐ์†Œ๋˜๊ณ  ์‹ฌ์ง€์–ด ๋ฌด์šฉ์ง€๋ฌผ์ด ๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธธ์–ด์ง„ ๋ฆฌ๋“œ ํƒ€์ž„์œผ๋กœ ์ธํ•œ ์ฃผ๋ฌธ ์ทจ์†Œ๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ์ƒํ™ฉ์—์„œ ๊ธฐ์—… ๊ด€๋ฆฌ์ž๋“ค์ด ๊ธฐ์—…์˜ ์ด์ต์„ ๊ทน๋Œ€ํ™”ํ•˜๊ธฐ ์œ„ํ•ด ์ฟ ํฐ ๋„์ž…์„ ๊ณ ๋ คํ•˜์—ฌ ์–ด๋–ป๊ฒŒ ๋Œ€์ฒ˜ํ•ด์•ผ ํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•˜๊ณ  ํ•™๋ฌธ์ ์ธ ๋ถ€๋ถ„์—์„œ๋Š” ์ฟ ํฐ๊ณผ ๋ฆฌ๋“œ ํƒ€์ž„์œผ๋กœ ์ธํ•œ ์ฃผ๋ฌธ ์ทจ์†Œ๋ฅผ ๊ฐ™์ด ๊ณ ๋ คํ•˜์—ฌ ์ด์ต์„ ๊ทน๋Œ€ํ™”ํ•˜๋Š” ์—ฐ๊ตฌ ๋ถ„์•ผ๋ฅผ ํ™•์žฅํ•˜๋Š”๋ฐ ์˜๋ฏธ๋ฅผ ์ง€๋‹ˆ๊ณ  ์žˆ๋‹ค.Delivery is one of the key success factors in operations management. However, firms may be difficult to deliver products to customers on time because of meagre production system if there is an increase of orders due to popularity. This may cause customers to cancel their orders, which are a loss in profit. Therefore, this paper by providing discount coupon, tries to minimize firmsโ€™ loss. The aims of this study are: 1) to examine how helpful coupon is to the profit in order cancellation situation; 2) to determine the optimal level of discount coupon; and 3) when should firms introduce coupons. This paper, therefore, first find the expected number of order cancellations by using nonhomogeneous Poisson process. Second, by introducing discount coupon with multivariate probability function, profit function will be structured. Then, analysis on the optimal level of discount coupon and point when to introduce discount coupon will be carried out. In addition, by numerical analysis, a trend of optimal level change by time period will be looked. In conclusion, offering coupon to the customers helps a firm to minimize its loss of profit. However, if popularity does not settle down, discount coupon loses its advantage because lead time keep increasing. This paper gives insight of how managers should respond to the situation where order cancellations occur due to the long lead time by considering discount coupon to maximize their profit. For academic insight, this paper offers a broader research on profit maximization by jointly thinking coupon and order cancellations due to lead time.1.Introduction 1 2.Literature Review 3 2.1 History of discount coupon 3 2.2 Order cancellation and lead time related studies 5 3.Model Development 9 3.1 Case dealing in the model 9 3.1.1 Notation 11 3.1.2 Assumption 12 3.2 Basic profit function 13 3.3 Loss of profit due to order cancellation 13 3.4 Profit change due to introduction of coupon 15 4.Model Analysis 19 4.1 Optimal level of , which maximizes 19 4.2 Decision to adopt coupon 21 4.3 Numerical analysis 23 4.3.1 Settings 24 4.3.2 Results 25 5.Conclusion 27 6.Discussion 29 References 32 Abstract in Korean 36์„

    ๋ฌด๊ฒฉ์ž ์œ ๋™ ํ•ด์„์„ ์œ„ํ•œ ์งˆ์  ์ƒ์„ฑ ํ”„๋กœ๊ทธ๋žจ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2016. 2. ๊น€๊ทœํ™.This study aims to develop meshless point generation technique which can be applied to complicated geometry or moving boundary. Unlike the conventional finite volume method, meshless method requires only point system as a computational domain. Therefore Generation of computational domain is relatively easier than conventional FVM. In this study, meshless point generation technique is developed. For the validation, the results obtained from the meshless method were compared with the results obtained from the conventional FVM. Practical models, such as Space shuttle or Missile, were selected as a validation model. For the steady calculation, governing equation is Euler equation. And for the unsteady calculation, Euler equation in Arbitrary Lagrangian Eulerian form is selected as governing equation Meshless method developed by Huh is used for a flow solver. In both meshless method and FVM, AUSMPW+ was used for numerical flux scheme, minmod limiter was used for limiting process and LU-SGS was used for time integration. From the results, the robustness and the accuracy of meshless point generation technique are verified.Chapter 1. Introduction 1.1 Meshless method 1.2 Motivation Chapter 2. Point generation technique 2.1 Meshless point system 2.2 Near surface point system 2.3 Background point system 2.4 Local points cloud 2.5 Moving principle of points Chapter 3. Numerical Method 3.1 Governing Equation 3.2 Least square method 3.3 Spatial Discretization 3.4 Time Integration 3.5 Dual-time stepping for meshless method Chapter 4. Numerical Analysis 4.1 Steady problems 4.2 Unsteady problems Chapter 5. Conclusions Chapter 6. References ๊ตญ๋ฌธ ์ดˆ๋กMaste

    logic & arithmetic operating neuromorphic device using OTS

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€(ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์žฌ๋ฃŒ), 2022. 8. ๊น€์ƒ๋ฒ”.์—๋„ˆ์ง€ ๋น„ํšจ์œจ์ ์ธ ํฐ ๋…ธ์ด๋งŒ ์ปดํ“จํŒ… ๋ฐฉ์‹์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด, ๋‰ด๋กœ๋ชจํ”ฝ ์ปดํ“จํŒ…์˜ ํ•„์š”์„ฑ์ด ์ œ๊ธฐ๋˜๋ฉด์„œ ์‹ ๊ฒฝ์„ ๋ชจ์‚ฌํ•˜๋ ค๋Š” ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. 1943๋…„, ์ธ๊ฐ„์˜ ๋‡Œ๋Š” '๋…ผ๋ฆฌ ์—ฐ์‚ฐ์ด ๊ฐ€๋Šฅํ•œ' ๋‰ด๋Ÿฐ ๋„คํŠธ์›Œํฌ๋กœ ๊ตฌ์„ฑ๋˜์–ด์žˆ๋‹ค๋Š” ๊ฐ€์„ค์ด ์ œ์•ˆ๋˜์—ˆ๊ณ , ๊ทธ ์ค‘์—์„œ๋„ XOR ์—ฐ์‚ฐ์€ ์ธ๊ฐ„์˜ ํ•™์Šต๊ณผ ๊ธฐ์–ต์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค๊ณ  ์•Œ๋ ค์ ธ ์žˆ์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ , ์ตœ๊ทผ ์ธ๊ฐ„ ๋‡Œ์˜ ์ผ๋ถ€ ๊ฐ€์ง€ ๋Œ๊ธฐ(dendrite)๊ฐ€ XOR ์—ฐ์‚ฐ์„ ํ•˜๊ณ  ์žˆ์Œ์ด ์‹คํ—˜์ ์œผ๋กœ ๋ฐํ˜€์ง€๋ฉด์„œ, ๋‰ด๋Ÿฐ์ด ์—ฐ์‚ฐ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๋Š” ํ•˜๋‚˜์˜ ๋‹จ์œ„๋ผ๋Š” ๊ฐ€์„ค์„ ๋’ท๋ฐ›์นจํ•˜์˜€๋‹ค. ์ด์ฒ˜๋Ÿผ ๋…ผ๋ฆฌ ์—ฐ์‚ฐ์ด ๊ฐ€๋Šฅํ•œ ๋‰ด๋Ÿฐ์˜ ํ•„์š”์„ฑ์— ๋น„ํ•ด, ์ด๋ฅผ ๋ชจ์‚ฌํ•˜๋Š” ์ธ๊ณต ๋‰ด๋Ÿฐ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ถ€์กฑํ•œ ์ƒํ™ฉ์ด๋‹ค. ์šฐ๋ฆฌ๋Š” OTS(Ovonic threshold switch) ์†Œ์ž๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋น„์„ ํ˜•์ ์ธ XOR ์—ฐ์‚ฐ์ด ๊ฐ€๋Šฅํ•œ ๋‹จ์ˆœ ์ธ๊ณต ๋‰ด๋Ÿฐ ํšŒ๋กœ์™€ ๋”๋ถˆ์–ด, ๋น„์„ ํ˜•์  ๋…ผ๋ฆฌ ์—ฐ์‚ฐ ๋ฐ ์‚ฐ์ˆ  ์—ฐ์‚ฐ์ž๋ฅผ ์ตœ์ดˆ๋กœ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ, XOR ์—ฐ์‚ฐ ๋‰ด๋Ÿฐ์€ ๊ธฐ์กด OTS ์ธ๊ณต ๋‰ด๋Ÿฐ์˜ ํšŒ๋กœ๋ฅผ ํ™œ์šฉํ•˜์—ฌ, ๋‘ ๊ฐœ์˜ ํŽ„์Šค ์ž…๋ ฅ์„ ๋ฐ›๊ณ  ์—ฐ์‚ฐ ๊ฒฐ๊ณผ๋ฅผ ๋‰ด๋Ÿฐ ์ŠคํŒŒ์ดํฌ ํ˜•ํƒœ๋กœ ์ถœ๋ ฅํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ, 5V์˜ ์ž…๋ ฅ ํŽ„์Šค ์ „์••์— ๋Œ€ํ•œ ์—ฐ์‚ฐ ๊ฒฐ๊ณผ๋Š” 4MHz ์ฃผํŒŒ์ˆ˜์˜ ๋‰ด๋Ÿฐ ์ŠคํŒŒ์ดํฌ๋กœ ์ถœ๋ ฅ์ด ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ–ˆ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, XOR ์—ฐ์‚ฐ ๋‰ด๋Ÿฐ 2๊ฐœ๋ฅผ ํ™œ์šฉํ•ด ๋Œ€๋‡Œ ํ”ผ์งˆ์˜ 2-3์ธต์— ์กด์žฌํ•˜๋Š” ํ”ผ๋ผ๋ฏธ๋“œ ๋‰ด๋Ÿฐ(L2/3 pyramidal neuron)์—์„œ ๋ณด์ด๋Š” ๋™์ผํ•œ ํŠน์„ฑ์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ƒ์œผ๋กœ ๊ตฌํ˜„ํ–ˆ๋‹ค. ๋น„์Šทํ•œ ์›๋ฆฌ๋ฅผ ์‘์šฉํ•˜์—ฌ, ์„ ํ˜•์ ์ธ ์—ฐ์‚ฐ(NOT, AND, OR, NAND, NOR)์ด ๊ฐ€๋Šฅํ•œ ์ธ๊ณต ๋‰ด๋Ÿฐ๊ณผ ํ•จ๊ป˜, ์‹ค์ œ ๋‰ด๋Ÿฐ์—์„œ ์ž์ฃผ ๋ฐœ๊ฒฌ๋˜๋Š” ํŠน์„ฑ์ธ flip-flop ๋„ ์‹คํ—˜์ ์œผ๋กœ ๊ตฌํ˜„ํ–ˆ๋‹ค. ์ด์ฒ˜๋Ÿผ ๋‹ค์–‘ํ•œ ๋…ผ๋ฆฌ ์—ฐ์‚ฐ ๋‰ด๋Ÿฐ๋“ค์€ ์ŠคํŒŒ์ดํฌ ๊ธฐ๋ฐ˜์˜ ๋ถ€์šธ ๋…ผ๋ฆฌ ์—ฐ์‚ฐ์ด ๊ฐ€๋Šฅํ•จ๊ณผ ๋™์‹œ์—, ์ธ๊ณต ๋‰ด๋Ÿฐ์„ ๊ตฌ์„ฑํ•˜๋Š” ๋ฐ์— ์žˆ์–ด์„œ ํ•˜๋‚˜์˜ ๋นŒ๋”ฉ ๋ธ”๋ก์œผ๋กœ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ์ž…๋ ฅ ์ „์••์˜ ํ•ฉ์„ ๋‰ด๋Ÿฐ ์ŠคํŒŒ์ดํฌ ์ฃผํŒŒ์ˆ˜์˜ ํฌ๊ธฐ๋กœ ์ถœ๋ ฅ์ด ๊ฐ€๋Šฅํ•œ Rate coding ๊ธฐ๋ฐ˜์˜ ์ธ๊ณต ์‚ฐ์ˆ  ์—ฐ์‚ฐ ๋‰ด๋Ÿฐ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ์ธ๊ณต ๋‰ด๋Ÿฐ์€ OTS ์†Œ์ž์™€ ๊ฐ€๋ณ€ ์ €ํ•ญ์ฒ˜๋Ÿผ ๋™์ž‘ํ•˜๋Š” FET๋ฅผ ์กฐํ•ฉํ•˜์˜€๊ณ , FET์˜ ๊ฐ ๊ฒŒ์ดํŠธ๋ฅผ ์ž…๋ ฅ ๋‹จ์ž๋กœ ์‚ฌ์šฉํ•œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ, ๊ฐ ์ž…๋ ฅ ์ „์••์˜ ํ•ฉ(-2V~4V)์€ ์ŠคํŒŒ์ดํฌ ์ฃผํŒŒ์ˆ˜์˜ ํฌ๊ธฐ(0.03~0.62MHz)์— ๋Œ€ํ•ด ์„ ํ˜•์ ์œผ๋กœ ๋น„๋ก€ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์œ„ ์‚ฐ์ˆ  ๋‰ด๋Ÿฐ์€ ๋Œ€๋‡Œ ํ”ผ์งˆ์˜ 5์ธต์— ์กด์žฌํ•˜๋Š” ํ”ผ๋ผ๋ฏธ๋“œ ๋‰ด๋Ÿฐ(L5 pyramidal neuron)์˜ ํŠน์„ฑ์„ ๋™์ผํ•˜๊ฒŒ ๋ชจ์‚ฌํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์•ž์„œ ์–ธ๊ธ‰ํ•œ ์ธ๊ณต ๋‰ด๋Ÿฐ๋“ค์€ ๋ชจ๋‘ OTS ์†Œ์ž๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š”๋ฐ, ์ด ์†Œ์ž๋Š” ํ•„์—ฐ์ ์œผ๋กœ '์ „๊ธฐ์  ํ˜•์„ฑ(electro forming)' ๊ณผ์ •์„ ๊ฐ€์ง„๋‹ค. ์ด ๊ณผ์ •์€ OTS ์†Œ์ž์˜ ๋‚ด๊ตฌ์„ฑ ๋ฌธ์ œ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์ถ”๊ฐ€์ ์ธ ๊ตฌ๋™ ํšŒ๋กœ๋ฅผ ์š”๊ตฌํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ธ๊ณต ๋‰ด๋Ÿฐ ๋„คํŠธ์›Œํฌ ์„ค๊ณ„์—์„œ ํฐ ์žฅ์• ๋ฌผ๋กœ ์ž‘์šฉํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ „๊ธฐ์  ํ˜•์„ฑ ๊ณผ์ •์„ ์™„ํ™”ํ•˜๊ธฐ ์œ„ํ•ด, ๊ธฐ์กด GeSe ๊ธฐ๋ฐ˜์˜ ์นผ์ฝ”์ง€๋‚˜์ด๋“œ ์žฌ๋ฃŒ์—์„œ์˜ ์ „๊ธฐ์  ํ˜•์„ฑ ์ „, ํ›„๋กœ ์ˆ˜์†ก ํŠน์„ฑ์„ ๋น„๊ตํ•˜์˜€๋‹ค. ์ „๊ธฐ์  ํ˜•์„ฑ ์ดํ›„ EC-EF ๊ฐ’์ด 1.34eV์—์„œ 0.26eV๋กœ, ํŠธ๋žฉ ์‚ฌ์ด์˜ ๊ฑฐ๋ฆฌ(ฮ”z)๊ฐ€ 26.5nm์—์„œ 13.5nm๋กœ ํฌ๊ฒŒ ๊ฐ์†Œํ•œ ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๊ฐ€ ์žฌ๋ฃŒ ๋‚ด ๊ฒฐํ•ฉ ์ƒํƒœ ๋ณ€ํ™”์— ์˜ํ•ด ๋ฐœ์ƒํ•œ ๊ฒƒ์ด๋ผ๊ณ  ํŒ๋‹จํ•˜์˜€์œผ๋ฉฐ, ์ „๊ธฐ์  ํ˜•์„ฑ ๊ณผ์ •์„ ์™„ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ์žฌ๋ฃŒ๋ฅผ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ๋น„์ •์งˆ ์นผ์ฝ”์ง€๋‚˜์ด๋“œ ์žฌ๋ฃŒ ๋‚ด ํŠธ๋žฉ์˜ ๊ธฐ์›์œผ๋กœ ์•Œ๋ ค์ง„ VAP (Valence alternation pair) ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ, ๊ตญ์†Œ ๊ฒฐํ•ฉ ๊ตฌ์กฐ์˜ ๋ณ€ํ™”๋ฅผ ํ†ตํ•ด ํŠธ๋žฉ ๋ฐ€๋„๋ฅผ ์ฆ๊ฐ€์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” Sn์„ ์น˜ํ™˜ ๋„ํ•‘ํ–ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” GSS (Ge1-xSnxSe1) ๋ฐ•๋ง‰์„ ๋†๋„ ๋ณ„๋กœ ์ œ์ž‘ํ•˜์˜€๊ณ (x=0-0.47), XPS ๋ฐ Raman ๋ถ„์„์„ ํ†ตํ•ด ์˜ˆ์ƒํ•œ ๊ฒฐํ•ฉ ๊ตฌ์กฐ์˜ ๋ณ€ํ™”๋ฅผ ๊ฒ€์ฆํ•˜์˜€๋‹ค. GSS ๋ฐ•๋ง‰ OTS์˜ ์ „๋ฅ˜-์ „์•• ํŠน์„ฑ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, Sn ๋†๋„๊ฐ€ ์ฆ๊ฐ€ํ• ์ˆ˜๋ก ์ „๊ธฐ์  ํ˜•์„ฑ ์ „์••(Vform)์ด 9V-6.5V ์œผ๋กœ ์ ์ฐจ ๊ฐ์†Œํ•˜์˜€๊ณ , ฮ”z๋Š” 26.5nm์—์„œ 17.5nm๋กœ ์ ์ง„์ ์œผ๋กœ ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์œ„ ๊ฒฐ๊ณผ๋Š” OTS์˜ ์ „๊ธฐ์  ํ˜•์„ฑ ๊ณผ์ •์ด ฮ”z์™€ ๊ด€๋ จ์ด ์žˆ์Œ์„ ๋ณด์—ฌ์ฃผ๋ฉฐ, ๊ถ๊ทน์ ์œผ๋กœ ์ „๊ธฐ์  ํ˜•์„ฑ ๊ณผ์ •์ด ์—†๋Š” OTS๋ฅผ ๊ฐœ๋ฐœํ•˜๋Š” ๋ฐ์— ์‹ค๋งˆ๋ฆฌ๊ฐ€ ๋  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š”, ๋‹ค์–‘ํ•œ ๋…ผ๋ฆฌ ๋ฐ ์‚ฐ์ˆ  ์—ฐ์‚ฐ ๋‰ด๋Ÿฐ๋“ค์„ ํ†ตํ•ด ๋‡Œ์—์„œ ์˜๊ฐ์„ ๋ฐ›์€ ์ปดํ“จํŒ…(๋‰ด๋กœ๋ชจํ”ฝ ์ปดํ“จํŒ…) ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์„ ์œ„ํ•ด ํ•„์ˆ˜์ ์ธ ๊ตฌ์„ฑ ์š”์†Œ๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๋˜, OTS ์†Œ์ž์˜ '์ „๊ธฐ์  ํ˜•์„ฑ' ๋ฌธ์ œ๋ฅผ ์™„ํ™”์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š” ์žฌ๋ฃŒ๋ฅผ ์„ค๊ณ„ํ•˜๋ฉด์„œ, ์ธ๊ณต ์‹ ๊ฒฝ๋ง ์„ค๊ณ„๋ฅผ ๋” ๋‹จ์ˆœํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฐ˜์ด ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.To overcome the limitations of energy-efficient von Neumann computing methods, research has been actively conducted to simulate nerves as the need for neuromorphic computing has been raised. In 1943, a hypothesis was proposed that the human brain consists of a network of neurons capable of 'logical operations', and it was recently experimentally verified that some dendrites are performing XOR operations in the human brain. Compared to the importance of neurons capable of logical operations, research on artificial neurons that simulate them is insufficient. We are the first to propose a variety of logical and arithmetic operators, in addition to a simple artificial neuron circuit capable of XOR computation using OTS (Ovonic threshold switch) elements. In addition, with a material design that can alleviate the problem of 'electroforming' of OTS devices, the utilization of OTS computational neurons has been enhanced. The proposed circuits provide essential components for the development of brain-inspired computing (neuromorphic computing) systems, and expect to be the basis for simplifying artificial neural network design.I. ์„œ ๋ก  1 1 Neuromorphic computing์˜ ๋ฐฐ๊ฒฝ 1 2 ํ˜„ Neuromorphic computing์˜ ๋ฌธ์ œ์  3 3 ์ด๋ก ์  ๋ฐฐ๊ฒฝ 5 3.1 ๋น„์ •์งˆ ์นผ์ฝ”์ง€๋‚˜์ด๋“œ ์žฌ๋ฃŒ ๊ธฐ๋ฐ˜ OTS 5 3.2 Simple artifical neuron based on OTS 10 II. ๋ณธ ๋ก  13 1 Neural operator 13 1.1 Boolean logic operator 19 1.1.1 Boolean logic operator 19 1.1.2 XOR 21 1.1.3 AND 25 1.1.4 OR 27 1.1.5 NOR 29 1.1.6 NAND 31 1.1.7 Flip-flop 33 1.2 Arithmetic operator 35 1.2.1 Arithmetic operator 35 1.2.2 SUM 36 1.2.3 Parallel 39 1.3 ์ธ๊ฐ„ ๋‡Œ์˜ L2/3 ์ธต Pyramidal ๋‰ด๋Ÿฐ์˜ dCaAP ๋ชจ์‚ฌ 41 1.4 ์š”์•ฝ ๋ฐ ๋…ผ์˜ 43 2 OTS ์†Œ์ž์˜ ์ „๊ธฐ์  ํ˜•์„ฑ(Electro-forming) ์™„ํ™” ์—ฐ๊ตฌ 44 2.1 ํ˜„ OTS ์†Œ์ž์˜ ๋ฌธ์ œ์ ๊ณผ ํ•ด๊ฒฐ ๋ฐฉ์•ˆ 44 2.2 Sn ๋„ํ•‘๋œ GeSe OTS ์†Œ์ž ์ œ์ž‘ ๋ฐ ๊ฒ€์ฆ ๋ฐฉ๋ฒ• 48 2.3 GSS film OTS ์†Œ์ž์™€ ๊ฒ€์ฆ 51 2.3.1 OTS์˜ ์ „๊ธฐ์  ํ˜•์„ฑ ๊ณผ์ • ์ „, ํ›„ ์ˆ˜์†ก ํŠน์„ฑ ๋ณ€ํ™” 51 2.3.2 Sn ๋„ํ•‘ GeSe ์„ค๊ณ„์™€ ๊ฒฐํ•ฉ ๊ตฌ์กฐ ๋ถ„์„ 55 2.3.3 OTS์˜ ์ „๊ธฐ์  ํ˜•์„ฑ ์ „์•• ์™„ํ™”์™€ ๋ชจ๋ธ 64 2.4 ์š”์•ฝ ๋ฐ ๋…ผ์˜ 70 III. ๊ฒฐ ๋ก  72 ์ฐธ ๊ณ  ๋ฌธ ํ—Œ 76์„

    Microstructure control and solidification process analysis of hypereutectic Al-Si alloys

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ธˆ์†๊ณตํ•™๊ณผ,1998.Docto

    ํ•œ๊ตญ๋ฒ•ํ•™ 50๋…„์˜ ์„ฑ๊ณผ์™€ 21์„ธ๊ธฐ์  ๊ณผ์ œ : ์ œ5์ฃผ์ œ ๋ฐœํ‘œ๋…ผ๋ฌธ ; ํ•œ๊ตญ ํ˜•์‚ฌ๋ฒ•ํ•™ 50๋…„์˜ ์„ฑ๊ณผ์™€ 21์„ธ๊ธฐ์  ๊ณผ์ œ

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    1945๋…„ 8์›” 15์ผ ์šฐ๋ฆฌ๋‚˜๋ผ๊ฐ€ ์ผ์ œ๋กœ๋ถ€ํ„ฐ ํ•ด๋ฐฉ๋œ ์ง€ 50๋…„์ด ๋˜์—ˆ๋‹ค. ๋น„๋ก ํ•ด๋ฐฉ๋œ ํ›„ ์ƒ๋‹นํ•œ ์‹œ๊ฐ„์ด ์ง€๋‚œ ํ›„์ธ 1953๋…„์— ํ˜•๋ฒ•์ด, ๊ทธ๋ฆฌ๊ณ  1954๋…„์— ํ˜•์‚ฌ์†Œ์†ก๋ฒ•์ด ๋น„๋กœ์†Œ ์‹œํ–‰๋˜์–ด ์šฐ๋ฆฌ์˜ ํ˜•์‚ฌ๋ฒ•์„ ๊ฐ–๊ฒŒ ๋œ ๊ฒƒ์€ ์•„์ง 40์—ฌ ๋…„์— ๋ถˆ๊ณผํ•˜์ง€๋งŒ ์ผ๋ณธ์ธ์— ์˜ํ•˜์—ฌ ๋…์ ๋˜์–ด ํ•œ๊ตญ์ธ์˜ ์ฐธ์—ฌ๊ฐ€ ๋ง‰ํ˜€ ์žˆ์—ˆ๋˜ ํ˜•์‚ฌ๋ฒ•ํ•™์ด ์šฐ๋ฆฌ์˜ ์†์œผ๋กœ ํ˜•์„ฑ๋˜๊ธฐ ์‹œ์ž‘ํ•œ ๊ฒƒ์€ ํ•ด๋ฐฉ๊ณผ ๋•Œ๋ฅผ ๊ฐ™์ดํ–ˆ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด์— ์˜ํ•˜์—ฌ ์šฐ๋ฆฌ ๋ฒ•ํ•™์˜ ๋ฌ˜ํฌ๊ฐ€ ๋งˆ๋ จ๋˜์—ˆ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œ๊ตญ๋ฒ•ํ•™ 50๋…„์„ ๊ธฐ๋…ํ•˜๋Š” ๋ชจ์ž„์„ ๊ฐ–๊ฒŒ ๋œ ์ด์œ ๋„ ์—ฌ๊ธฐ์— ์žˆ๋‹ค๊ณ  ์ƒ๊ฐ๋œ๋‹ค. ๋‹ค๋งŒ, ํ•ด๋ฐฉํ›„ 50๋…„์˜ ๊ธฐ๊ฐ„ ์ค‘ 1945๋…„๋ถ€ํ„ฐ 1950๋…„๊นŒ์ง€์— ์žˆ์–ด์„œ๋Š” ๋จผ์ € 1948๋…„ ์ •๋ถ€๊ฐ€ ์ˆ˜๋ฆฝ๋  ๋•Œ๊นŒ์ง€์˜ ๋ฏธ๊ตฐ์ •์‹œ๋Œ€์—๋Š” ๊ตฐ์ •๋ฒ•๋ น ์ œ21ํ˜ธ(1945๋…„ 11์›” 2์ผ)์— ์˜ํ•˜์—ฌ ํ•œ๊ตญ์ธ์—๊ฒŒ ์ฐจ๋ณ„๋Œ€์šฐ๋ฅผ ํ–ˆ๋˜ ๋ฒ•๋ น ์ด์™ธ์˜ ๋ฒ•๋ น์€ ํšจ๋ ฅ์„ ๊ฐ–๊ฒŒ ๋˜์—ˆ์œผ๋ฏ€๋กœ ์ผ๋ณธํ˜•๋ฒ•์ธ ๊ตฌํ˜•๋ฒ•์˜ ํšจ๋ ฅ๋„ ์ง€์†๋˜์—ˆ๊ณ  ์ด์— ๋”ฐ๋ผ ์ผ๋ณธ์˜ ๊ต๊ณผ์„œ๊ฐ€ ์šฐ๋ฆฌ ํ•™๊ณ„์— ๊ทธ๋Œ€๋กœ ์‚ฌ์šฉ๋˜์ง€ ์•Š์„ ์ˆ˜ ์—†์—ˆ๋‹ค. 1947๋…„์— ๋‚จ์กฐ์„ ๊ณผ๋„์ •๋ถ€ ํ–‰์ •๋ช…๋ น ์ œ3ํ˜ธ์— ์˜ํ•˜์—ฌ ๋ฒ•์ „๊ธฐ์ดˆ์œ„์›ํšŒ๊ฐ€ ์„ค์น˜๋˜์—ˆ๊ณ , ๊ตฐ์ •์žฅ๊ด€์œผ๋กœ๋ถ€ํ„ฐ ์ž„๋ช…๋ฐ›์€ ๋ฒ•์ „ํŽธ์ฐฌ์œ„์›๋“ค์— ์˜ํ•˜์—ฌ ์šฐ๋ฆฌ์˜ ํ˜•๋ฒ•์„ ๋งŒ๋“ค๊ธฐ ์œ„ํ•œ ํ˜•๋ฒ•๊ธฐ์ดˆ์š”๊ฐ•์ด ๋ฐœํ‘œ๋œ๋ฐ” ์žˆ์œผ๋‚˜, ์ •๋ถ€์ˆ˜๋ฆฝ์— ์˜ํ•˜์—ฌ ์ด ์œ„์›ํšŒ๋Š” ํ•ด์ฒด๋˜๊ณ  ๋ง์•˜๋‹ค. ํ˜•์‚ฌ์†Œ์†ก๋ฒ•์˜ ๋ถ„์•ผ์—์„œ๋Š” ์ •๋„์˜€๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค

    M-CPBA mediated oxidation of (benzenesulfonyl)ketene s,s-acetals and 7,12-dihydro[5,6][1,3]thiazepino[3,2-ฮฑ] benzimidazole 6,6-dioxides : regio- and stereochemistry

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    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ™”ํ•™๋ถ€,2003.Docto

    Prediction of Steady-state Strip Profile in Flat Rolling

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    DoctorA prerequisite for automatic process set-up and control in a hot strip mill, in a cold rolling mill, and in a plate mill is to acquire a precision mathematical model for the prediction of the strip profile and the residual stresses. In this paper, an issue is raised regarding the importance of taking into account the effect of post-deformation. Also, demonstrated is how to rigorously implement it into the calculation of the strip profile and residual stress profile. The validity of our post-deformation model is verified through comparison with predictions from Finite Element simulation and also with actual measurements
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