52 research outputs found

    ์ปค๋„ ์„œํฌํŠธ์™€ ํ‰ํ˜•์ ์„ ํ™œ์šฉํ•œ ์ฐจ๋ถ„ ํ”„๋ผ์ด๋ฒ„์‹œ ๋‹ค์ค‘ ํด๋ž˜์Šค ๋ถ„๋ฅ˜ ๊ธฐ๋ฒ•

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2022.2. ์ด์žฌ์šฑ.In this paper, we propose a multi-class classification method using kernel supports and a dynamic system under differential privacy. We find support vector machine (SVM) algorithms have a fundamental weaknesses of implementing differential privacy because the decision function depends on some subset of the training data called the support vectors. Therefore, we develop a method using interior points called equilibrium points (EPs) without relying on the decision boundary. To construct EPs, we utilize a dynamic system with a new differentially private support vector data description (SVDD) by perturbing the sphere center in the kernel space. Empirical results show that the proposed method achieves better performance even on small-sized datasets where differential privacy performs poorly.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ปค๋„ ์„œํฌํŠธ์™€ ํ‰ํ˜•์ ์„ ํ™œ์šฉํ•œ ์ฐจ๋ถ„ ํ”„๋ผ์ด๋ฒ„์‹œ ๋‹ค์ค‘ ํด๋ž˜์Šค ๋ถ„๋ฅ˜ ๊ธฐ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์„œํฌํŠธ ๋ฒกํ„ฐ ๋ถ„๋ฅ˜ ๊ธฐ๋ฒ•์€ ๋ฐ์ดํ„ฐ ๋ถ„์„๊ณผ ๋จธ์‹  ๋Ÿฌ๋‹์— ํ™œ์šฉ์„ฑ์ด ๋†’์•„ ์‚ฌ์šฉ์ž์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๋ณดํ˜ธํ•˜๋ฉฐ ํ•™์Šตํ•˜๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ๊ทธ ์ค‘ ๊ฐ€์žฅ ๋Œ€์ค‘์ ์ธ ์„œํฌํŠธ ๋ฒกํ„ฐ ๋จธ์‹ (SVM)์€ ์„œํฌํŠธ ๋ฒกํ„ฐ๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ์ผ๋ถ€ ๋ฐ์ดํ„ฐ์—๋งŒ ๋ถ„๋ฅ˜์— ์˜์กดํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํ”„๋ผ์ด๋ฒ„์‹œ ์ฐจ๋ถ„ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋ฐ์ดํ„ฐ ํ•˜๋‚˜๊ฐ€ ๋ณ€๊ฒฝ๋˜์—ˆ์„ ๋•Œ ๊ฒฐ๊ณผ์˜ ๋ณ€ํ™”๊ฐ€ ์ ์–ด์•ผ ํ•˜๋Š” ์ฐจ๋ถ„ ํ”„๋ผ์ด๋ฒ„์‹œ ์ƒํ™ฉ์—์„œ ์„œํฌํŠธ ๋ฒกํ„ฐ ํ•˜๋‚˜๊ฐ€ ์—†์–ด์ง„๋‹ค๋ฉด ๋ถ„๋ฅ˜๊ธฐ์˜ ๊ฒฐ์ • ๊ฒฝ๊ณ„๋Š” ๊ทธ ๋ณ€ํ™”์— ๋งค์šฐ ์ทจ์•ฝํ•˜๋‹ค๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ‰ํ˜•์ ์ด๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ๊ตฐ์ง‘ ๋‚ด๋ถ€์— ์กด์žฌํ•˜๋Š” ์ ์„ ํ™œ์šฉํ•˜๋Š” ์ฐจ๋ถ„ ํ”„๋ผ์ด๋ฒ„์‹œ ๋‹ค์ค‘ ํด๋ž˜์Šค ๋ถ„๋ฅ˜ ๊ธฐ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, ๋จผ์ € ์ปค๋„ ๊ณต๊ฐ„์—์„œ ๊ตฌ์˜ ์ค‘์‹ฌ์— ์„ญ๋™์„ ๋”ํ•ด ์ฐจ๋ถ„ ํ”„๋ผ์ด๋ฒ„์‹œ๋ฅผ ๋งŒ์กฑํ•˜๋Š” ์„œํฌํŠธ ๋ฒกํ„ฐ ๋ฐ์ดํ„ฐ ๋””์Šคํฌ๋ฆฝ์…˜(SVDD)์„ ๊ตฌํ•˜๊ณ  ์ด๋ฅผ ๋ ˆ๋ฒจ์ง‘ํ•ฉ์œผ๋กœ ํ™œ์šฉํ•ด ๋™์—ญํ•™๊ณ„๋กœ ๊ทน์†Œ์ ๋“ค์„ ๊ตฌํ•œ๋‹ค. ํ‰ํ˜•์ ์„ ํ™œ์šฉํ•˜๊ฑฐ๋‚˜ ๊ณ ์ฐจ์› ๋ฐ์ดํ„ฐ์˜ ๊ฒฝ์šฐ ์ดˆ์ž…๋ฐฉ์ฒด๋ฅผ ๋งŒ๋“ค์–ด, ํ•™์Šตํ•œ ๋ชจ๋ธ์„ ์ถ”๋ก ์— ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” (1) ์„œํฌํŠธ ํ•จ์ˆ˜๋ฅผ ๊ณต๊ฐœ ํ•˜๋Š” ๋ฐฉ๋ฒ•๊ณผ (2) ํ‰ํ˜•์ ์„ ๊ณต๊ฐœํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. 8๊ฐœ์˜ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ ์ง‘ํ•ฉ์˜ ์‹คํ—˜์ ์ธ ๊ฒฐ๊ณผ๋Š” ์ œ์‹œํ•œ ๋ฐฉ๋ฒ•๋ก ์ด ๋…ธ์ด์ฆˆ์— ๊ฐ•๊ฑดํ•œ ๋‚ด๋ถ€์˜ ์ ์„ ํ™œ์šฉํ•ด ๊ธฐ์กด์˜ ์ฐจ๋ถ„ ํ”„๋ผ์ด๋ฒ„์‹œ ์„œํฌํŠธ ๋ฒกํ„ฐ ๋จธ์‹ ๋ณด๋‹ค ์„ฑ๋Šฅ์„ ๋†’์ด๊ณ , ์ฐจ๋ถ„ ํ”„๋ผ์ด๋ฒ„์‹œ๊ฐ€ ์ ์šฉ๋˜๊ธฐ ์–ด๋ ค์šด ์ž‘์€ ๋ฐ์ดํ„ฐ์…‹์—๋„ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ธฐ์ˆ ์ž„์„ ๋ณด์—ฌ์ค€๋‹ค.Chapter 1 Introduction 1 1.1 Problem Description: Data Privacy 1 1.2 The Privacy of Support Vector Methods 2 1.3 Research Motivation and Contribution 4 1.4 Organization of the Thesis 5 Chapter 2 Literature Review 6 2.1 Differentially private Empirical risk minimization 6 2.2 Differentially private Support vector machine 7 Chapter 3 Preliminaries 9 3.1 Differential privacy 9 Chapter 4 Differential private support vector data description 12 4.1 Support vector data description 12 4.2 Differentially private support vector data description 13 Chapter 5 Differentially private multi-class classification utilizing SVDD 19 5.1 Phase I. Constructing a private support level function 20 5.2 Phase II: Differentially private clustering on the data space via a dynamical system 21 5.3 Phase III: Classifying the decomposed regions under differential privacy 22 Chapter 6 Inference scenarios and releasing the differentially private model 25 6.1 Publishing support function 26 6.2 Releasing equilibrium points 26 6.3 Comparison to previous methods 27 Chapter 7 Experiments 28 7.1 Models and Scenario setting 28 7.2 Datasets 29 7.3 Experimental settings 29 7.4 Empirical results on various datasets under publishing support function 30 7.5 Evaluating robustness under diverse data size 33 7.6 Inference through equilibrium points 33 Chapter 8 Conclusion 34 8.1 Conclusion 34์„

    Observation of Magnetic Domain of Sr2FeMoO6 Film using MFM

    No full text
    Maste

    2018 Agarwal Award Jin-Sung Park KAIST

    No full text

    The aspect in remineralization of enamel according to the degree of saturation change of organic acid in pH 5.5

    No full text
    ์น˜์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] ์น˜์•„ ์šฐ์‹์ฆ์€ ๊ตฌ๊ฐ• ๊ฒฝ์กฐ์ง ์งˆํ™˜ ์ค‘ ๊ฐ€์žฅ ๋†’์€ ๋ฐœ๋ณ‘๋ฅ ์„ ๋ณด์ด๋Š” ์งˆํ™˜์œผ๋กœ ์น˜์ฃผ ์งˆํ™˜๊ณผ ๋”๋ถˆ์–ด ์น˜์•„์˜ ๋ฐœ๊ฑฐ๋ฅผ ์š”ํ•˜๋Š” ์ค‘์š”ํ•œ ์›์ธ ์ธ์ž๋กœ ์ž‘์šฉํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ดˆ๊ธฐ ๋ฒ•๋ž‘์งˆ ์šฐ์‹์˜ ๊ฒฝ์šฐ ์ฃผ์œ„ ์กฐ๊ฑด์— ๋”ฐ๋ผ ์žฌ๊ด‘ํ™”๊ฐ€ ์ผ์–ด๋‚  ์ˆ˜ ์žˆ์Œ์ด ์ž„์ƒ์ , ์‹คํ—˜์ ์œผ๋กœ ๋ฐํ˜€์ ธ ์™”๊ณ , ์ด๋Š” ์ฃผ์œ„์˜ pH, ์œ ๊ธฐ์‚ฐ์˜ ์ข…๋ฅ˜ ๋ฐ ๋†๋„, ์น˜์•„์˜ ๋ฌด๊ธฐ์งˆ ๋ฐ ์œ ๊ธฐ์งˆ์˜ ํ™”ํ•™์  ์กฐ์„ฑ, ๋ถˆ์†Œ์˜ ๋†๋„ ๋ฐ ์œ ๋ฌด, ์šฉ์•ก์˜ ํฌํ™”๋„์— ์˜ํ–ฅ์„ ๋ฐ›๋Š”๋‹ค๊ณ  ํ•˜์˜€๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” lactic acid๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฒ•๋ž‘์งˆ์„ ์ธ๊ณต ํƒˆํšŒ์‹œํ‚ค๊ณ  ์œ ๊ธฐ์‚ฐ 0.01M, pH 5.5๋กœ ๊ณ ์ •ํ•œ ์ƒํƒœ์—์„œ ์œ ๊ธฐ์‚ฐ ์™„์ถฉ์šฉ์•ก์˜ ํฌํ™”๋„๋ฅผ ๋‹ฌ๋ฆฌํ•˜์—ฌ(0.507, 0.394, 0.301, 0.251) ์žฌ๊ด‘ํ™”๋ฅผ ์‹œํ‚ค๊ณ  ํŽธ๊ด‘ ํ˜„๋ฏธ๊ฒฝ์—์„œ ์–ป์€ ์ƒ์—์„œ ํƒˆํšŒ ๊นŠ์ด์˜ ๋ณ€ํ™”, ์šฐ์‹ ํ‘œ๋ฉด์ธต ํญ์˜ ๋ณ€ํ™”, ๋ฌด๊ธฐ์งˆ์˜ ์–‘์  ๋ณ€ํ™”๋ฅผ ์ปดํ“จํ„ฐ ํ”„๋กœ๊ทธ๋ž˜์„ ์ด์šฉํ•˜์—ฌ ๋น„๊ต, ๋ถ„์„ํ•˜์—ฌ ๋‹ค์Œ ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ๋‹ค.1.pH 5.5 ์—์„œ ๋ชจ๋“  ๊ตฐ์—์„œ ์šฐ์‹ ํ‘œ๋ฉด์ธต์˜ ํญ์€ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค.2.์žฌ๊ด‘ํ™” ์ „์— ๋น„ํ•˜์—ฌ ์žฌ๊ด‘ํ™” ํ›„ ๋ชจ๋“  ๊ตฐ์—์„œ 10-30% ์ •๋„ ์žฌ๊ด‘ํ™” ์–‘์ด ์ฆ๊ฐ€ ํ•˜์˜€๋‹ค.3.์žฌ๊ด‘ํ™” ํ›„ 4๊ตฐ์—์„œ๋Š” ์šฐ์‹ ํ‘œ๋ฉด์ธต๊ณผ ์‹ฌ๋ถ€ ๋ชจ๋‘์—์„œ ์žฌ๊ด‘ํ™”๊ฐ€ ์ผ์–ด๋‚˜๋Š” ์–‘์ƒ์ด ๋‚˜ํƒ€๋‚œ ๋ฐ˜๋ฉด 1๊ตฐ์—์„œ๋Š” ์‹ฌ๋ถ€๋ณด๋‹ค ์šฐ์‹ ํ‘œ๋ฉด์ธต ๋ถ€์œ„์˜ ์žฌ๊ด‘ํ™”๊ฐ€ ๋” ์ผ์–ด๋‚œ ์–‘์ƒ์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค.4.์žฌ๊ด‘ํ™” ํ›„ ์‹œํŽธ์˜ ํƒˆํšŒ๋œ ๊นŠ์ด์˜ ๋ณ€ํ™”๋Š” ํƒˆํšŒ์‹œํ‚จ ํ›„์™€ ๋น„๊ตํ•  ๋•Œ ๋ชจ๋“  ๊ตฐ์—์„œ ์œ ์˜ํ• ๋งŒํ•œ ๋ณ€ํ™”๋Š” ์—†์—ˆ์œผ๋ฉฐ ๊ตฐ๊ฐ„์˜ ํ†ต๊ณ„์  ์œ ์˜์ฐจ๋„ ์—†์—ˆ๋‹ค. ์ฆ‰, pH 5.5 ์—์„œ ์žฌ๊ด‘ํ™” ์šฉ์•ก์˜ ํฌํ™”๋„ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์ถ”๊ฐ€์ ์ธ ํƒˆํšŒ๋Š” ํฌ๊ฒŒ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ์œ ๊ธฐ์‚ฐ 0.01M, pH 5.5๋กœ ๊ณ ์ •ํ•œ ์ƒํƒœ์—์„œ ํฌํ™”๋„๊ฐ€ ๊ฐ€์žฅ ๋‚ฎ์•˜๋˜ ๊ตฐ(0.251)์—์„œ๋Š” ์šฐ์‹ ํ‘œ๋ฉด์ธต์—์„œ ์‹ฌ๋ถ€๊นŒ์ง€ ์žฌ๊ด‘ํ™”๊ฐ€ ์ผ์–ด๋‚œ ๋ฐ˜๋ฉด, ํฌํ™”๋„๊ฐ€ ๊ฐ€์žฅ ๋†’์•˜๋˜ ๊ตฐ(0.507)์—์„œ๋Š” ์šฐ์‹ ํ‘œ๋ฉด์ธต์—์„œ ์žฌ๊ด‘ํ™”๊ฐ€ ์ฃผ๋กœ ์ผ์–ด๋‚ฌ๋‹ค. [์˜๋ฌธ]ope

    Cross-plane thermoelectric Seebeck coefficients in nanoscale Al2O3/ZnO superlattice films

    No full text
    Superlattice thin films, which are used in thermoelectric (TE) devices for small-scale solid-state cooling and for generating electrical power, have recently been attracting attention due to their low dimensionality, low thermal conductivity, and enhanced power factor. Considering the measurement techniques for characterizing TE properties, very limited information, including cross-plane Seebeck coefficients of superlattice films, has been reported. This information is required for the assessment of the interface between the films and to understand phonon scattering in superlattice films. In this report, thermally stable cross-plane thermoelectric Seebeck coefficients of Al2O3/ZnO (AO/ZnO) superlattice films are presented, at temperature differences (T) ranging from 2 to 12 K. Longitudinal (in-plane) thermal diffusion in the Cu/AO/ZnO/Cu samples, which occurred during the measurements due to the size differences among the samples located between a micro-Peltier and aluminum nitride cooling plate, was investigated. The cross-plane Seebeck coefficients of 3- and 6-cycled AO/ZnO superlattice films were determined to be approximate to 9.4 +/- 0.4 and approximate to 30.6 +/- 0.7 V K-1, respectively, showing stable values in the evaluated T range. Two distinct phenomena, in-plane thermal diffusion and the effect of the environment, were identified in cross-plane Seebeck measurements as dominant factors controlling the temperature coefficient of AO/ZnO superlattice films. In addition, a new TE parameter, the Seebeck temperature coefficient, was proposed for superlattice films.This study was supported by the Priority Research Centers Program and the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (2016R1A2B2012909 and 2017R1D1A1B03031010). This study was supported through Technology Transfer Center for National R & D Program by the Ministry of Science & ICT (2018K000282)

    ์–ด๋–ค 3-์ฐจ์› ๋‹ค์–‘์ฒด์˜ ํ‘œํ˜„ ๊ณต๊ฐ„๊ณผ half density ๋ถˆ๋ณ€๋Ÿ‰

    No full text
    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ˆ˜ํ•™๊ณผ,1997.Docto

    ์ˆ˜์ข…์˜ ์ƒ์•„์งˆ ์ ‘์ฐฉ์ œ์™€ ๋ณตํ•ฉ๋ ˆ์ง„์˜ ์ ํ•ฉ์„ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์น˜์˜ํ•™๊ณผ ๋ณด์กดํ•™์ „๊ณต,1995.Maste

    ์„ฑ๋ณ„ ๊ทผ๋กœ์‹œ๊ฐ„ ๊ฒฉ์ฐจ ์—ฐ๊ตฌ

    No full text
    ์š” ์•ฝ i ์ œ1์žฅ ์„œ ๋ก  (๊ณฝ์€ํ˜œ) 1 ์ œ1์ ˆ ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 ์ œ2์ ˆ ์—ฐ๊ตฌ ๋‚ด์šฉ 3 ์ œ2์žฅ ์„ฑ๋ณ„์— ๋”ฐ๋ฅธ ๋…ธ๋™๊ณต๊ธ‰์˜ ์žฅ๊ธฐ ์ถ”์„ธ (๋ฐ•์ง„์„ฑ) 6 ์ œ1์ ˆ ์„œ ๋ก  6 ์ œ2์ ˆ ๋ฐ์ดํ„ฐ:๊ฒฝ์ œํ™œ๋™์ธ๊ตฌ์กฐ์‚ฌ(1981๏ฝž2022๋…„) 8 ์ œ3์ ˆ ๊ฒฝ์ œํ™œ๋™์ฐธ๊ฐ€์œจ๊ณผ ๊ทผ๋กœ์‹œ๊ฐ„์˜ ์žฅ๊ธฐ ์ถ”์„ธ 10 ์ œ4์ ˆ ์†Œ ๊ฒฐ 35 ์ œ3์žฅ ์ฝ”๋กœ๋‚˜19์™€ ๋ถ€๋ถ€์˜ ๊ฐ€์‚ฌ๋…ธ๋™ ๋ถ„๋‹ด (๊น€์˜์•„) 42 ์ œ1์ ˆ ์„œ ๋ก  42 ์ œ2์ ˆ ์„ ํ–‰์—ฐ๊ตฌ 45 ์ œ3์ ˆ ๊ฐ€์‚ฌ๋…ธ๋™ ๋ถ„๋‹ด๋ฅ  ๋ณ€ํ™” ์ถ”์ด 46 ์ œ4์ ˆ ์ฝ”๋กœ๋‚˜19 ์ดํ›„ ๊ฐ€์‚ฌ๋…ธ๋™ ๋ถ„๋‹ด ์œ ํ˜• ๋ถ„์„:์‚ฌํšŒ์ธ๊ตฌํ•™์  ํŠน์„ฑ ๋ฐ ๋…ธ๋™์‹œ์žฅ ํŠน์„ฑ 64 ์ œ5์ ˆ ์†Œ ๊ฒฐ 72 ์ œ4์žฅ ์„ฑ๋ณ„ ๊ทผ๋กœ์‹œ๊ฐ„ ๊ฒฉ์ฐจ์™€ ์ž„๊ธˆ ๊ฒฉ์ฐจ (๊ณฝ์€ํ˜œ) 77 ์ œ1์ ˆ ์„œ ๋ก  77 ์ œ2์ ˆ ์„ฑ๋ณ„ ๊ทผ๋กœ์‹œ๊ฐ„ ๊ฒฉ์ฐจ์™€ ์ž„๊ธˆ ๊ฒฉ์ฐจ์˜ ๊ด€๊ณ„ 79 ์ œ3์ ˆ ์ดˆ๊ณผ๊ทผ๋กœ์‹œ๊ฐ„ ์ œํ•œ์ด ์„ฑ๋ณ„ ๊ทผ๋กœ์‹œ๊ฐ„ ๊ฒฉ์ฐจ ๋ฐ ์ž„๊ธˆ ๊ฒฉ์ฐจ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ 91 ์ œ4์ ˆ ์†Œ ๊ฒฐ 105 ์ œ5์žฅ ์š”์•ฝ ๋ฐ ์ •์ฑ… ์ œ์–ธ (๊ณฝ์€ํ˜œ) 108 ์ œ1์ ˆ ์š” ์•ฝ 108 ์ œ2์ ˆ ์ •์ฑ…์ œ์–ธ 111 ์ฐธ๊ณ ๋ฌธํ—Œ 11
    • โ€ฆ
    corecore