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    ๋ฏธ์ง€์˜ ์™ธ๋ž€๊ณผ ์žก์Œ์„ ๋ฐ›๋Š” ํ™•๋ฅ ์‹œ์Šคํ…œ์˜ ์ƒํƒœ์ถ”์ •๊ณผ ์ œ์–ด

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    Abstract PID controllers have been widely used in lots of industrial fields because of several positive characteristics. Its structure is so simple to implement and to apply, while its control performance is higher than those of other type controllers for linear time invariant systems. Especially, PID control technique is very useful and effective as long as the given controlled systems are deterministic without any types of noises. By the way, the real systems found in industrial fields are under noisy circumstance including RMS and/or random noises, and thus they are used to be mathematically modeled as linear time invariant stochastic systems. Although the effect of RMS sinusoidal noises can be reduced by way of comprising a kind of filter such as an RC filter, the effect of random noises can not be reduced by filters. Under noisy circumstance, the D control action of all types of PID controller, for example conventional PID or fuzzy PID, may not be operated normally in steady state by the influence of noises. As a result, the output of PID control systems often exhibits a chattering phenomenon around the given reference input in steady state. When a set of unknown disturbances is driven for the controlled system, even the PID control system may not track the reference input and may resultantly exhibit a steady state error. In order to improve the wrong D control action by the effect of noises, in this paper, a method to comprise PID control system by adopting the separation principle is suggested. A procedure to comprise the controller according to the suggested method by the separation principle is as follows: At first, the state of the PID control system is estimated by the Kalman filter algorithm using the noisy output under the assumption that the system and measurement noise of the PID control system should be white Gaussian random noises. And then the estimated output from the Kalman filter is fed back to the PID controller to generate an error signal. In order to compensate the effect of the unknown disturbance, in this paper, a method to comprise PID control system based on Kalman filter with an unknown disturbance estimator is suggested. The used unknown disturbance estimator is composed based on the fuzzy estimation algorithm. When the unknown disturbance is estimated, the estimated value is fed back into the Kalman filter algorithm to compensate the filtered state and fed back into the PID controller to generate a new control input for the purpose of reducing the steady state error of the PID control system. The eventual PID controller structure to improve the D control action against noises and to compensate the effect of unknown disturbance is suggested, by combining the fuzzy PID controller with Kalman filter based on the unknown disturbance estimator. In order to verify the control performance of the suggested control system, several simulations were accomplished under circumstances with noises and/or unknown disturbance. As a result, the comprised control system exhibited very good control performances for all simulation conditions and conclusively the effectiveness of the suggested method was qualitatively verified.๋ชฉ ์ฐจ Abstractโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ…ฒ List of Figureโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ…ต ์ œ 1 ์žฅ ์„œ ๋ก  โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ1 1.1 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ1 1.2 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ2 ์ œ 2 ์žฅ ๊ฐ€๋ณ€ ํŒŒ๋ผ๋ฏธํ„ฐ ํผ์ง€ PID ์ œ์–ด ์‹œ์Šคํ…œโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ4 2.1 ๊ฐ€๋ณ€ ํŒŒ๋ผ๋ฏธํ„ฐ ํผ์ง€ PID ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ4 2.2 ๊ฐ€๋ณ€ ํŒŒ๋ผ๋ฏธํ„ฐ ํผ์ง€ PID ์ œ์–ด ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ๊ฒ€์ฆโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ11 2.2.1 ์žก์Œ์ด ์—†์„ ๊ฒฝ์šฐ์˜ ์ œ์–ด์„ฑ๋Šฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ11 2.2.2 ์žก์Œ์ด ์žˆ์„ ๊ฒฝ์šฐ์˜ ์ œ์–ด์„ฑ๋Šฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ12 2.3 ์„ฑ๋Šฅ๊ฒ€์ฆ ๊ฒฐ๊ณผ์˜ ๊ณ ์ฐฐ๊ณผ ๋ฌธ์ œ์  ๋ถ„์„โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ14 ์ œ 3 ์žฅ Separation Principle์„ ์ด์šฉํ•œ ํผ์ง€ PID ์ œ์–ด ์‹œ์Šคํ…œยท16 3.1 ์ด์‚ฐ์‹œ๊ฐ„ ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ Kalman ํ•„ํ„ฐ ์ƒํƒœ ์ถ”์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ16 3.2 Kalman ํ•„ํ„ฐ ์ƒํƒœ ์ถ”์ • ๊ธฐ๋ฐ˜ ํผ์ง€ PID ์ œ์–ด ์‹œ์Šคํ…œ ์ œ์•ˆโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ18 3.3 Kalman ํ•„ํ„ฐ ์ƒํƒœ ์ถ”์ • ๊ธฐ๋ฐ˜ ํผ์ง€ PID ์ œ์–ด ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ๊ฒ€์ฆโ€ฅโ€ฅ21 3.3.1 ์žก์Œ๋งŒ ์ธ๊ฐ€๋  ๊ฒฝ์šฐ์˜ ์ œ์–ด์„ฑ๋Šฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ21 3.3.2 ์žก์Œ๊ณผ ๋ฏธ์ง€์˜ ์™ธ๋ž€์ž…๋ ฅ์ด ์ธ๊ฐ€๋  ๊ฒฝ์šฐ์˜ ์ œ์–ด์„ฑ๋Šฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ24 3.4 ์„ฑ๋Šฅ๊ฒ€์ฆ ๊ฒฐ๊ณผ์˜ ๊ณ ์ฐฐ๊ณผ ๋ฌธ์ œ์  ๋ถ„์„โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ26 ์ œ 4 ์žฅ ๋ฏธ์ง€์˜ ์™ธ๋ž€ ์ถ”์ • Kalman ํ•„ํ„ฐ ๊ธฐ๋ฐ˜ ํผ์ง€ PID ์ œ์–ด ์‹œ์Šคํ…œโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ29 4.1 ๋ฏธ์ง€์˜ ์™ธ๋ž€๊ณผ ์žก์Œ์ด ์ธ๊ฐ€๋˜๋Š” ์‹œ์Šคํ…œ์˜ Kalman ํ•„ํ„ฐ ๊ธฐ๋ฐ˜ ํผ์ง€ PID ์ œ์–ด ์‹œ์Šคํ…œโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ29 4.2 Kalman ํ•„ํ„ฐ ์ด๋…ธ๋ฒ ์ด์…˜ ๊ธฐ๋ฐ˜ ๋ฏธ์ง€์˜ ์™ธ๋ž€ ์ถ”์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ31 4.3 ๋ฏธ์ง€์˜ ์™ธ๋ž€ ์ถ”์ •๊ณผ Kalman ํ•„ํ„ฐ ๊ธฐ๋ฐ˜ ํผ์ง€ PID ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์ œ ์•ˆโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ40 4.4 ๋ฏธ์ง€์˜ ์™ธ๋ž€ ์ถ”์ •๊ณผ Kalman ํ•„ํ„ฐ ๊ธฐ๋ฐ˜ ํผ์ง€ PID ์ œ์–ด ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์„ฑ๋Šฅ ๊ฒ€์ฆโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ44 4.5 ์„ฑ๋Šฅ๊ฒ€์ฆ ๊ฒฐ๊ณผ์˜ ๊ณ ์ฐฐโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ48 4.5.1 ๊ธฐ์ค€์ž…๋ ฅ์ด ๋ณ€ํ•˜๋Š” ๊ฒฝ์šฐ์˜ ๊ฒ€์ฆโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ48 4.5.2 ๋ฏธ์ง€์˜ ์™ธ๋ž€ ํฌ๊ธฐ๊ฐ€ ๋ณ€ํ•˜๋Š” ๊ฒฝ์šฐ์˜ ๊ฒ€์ฆโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ50 ์ œ 5 ์žฅ ์ข…ํ•ฉ์  ์„ฑ๋Šฅ๊ฒ€์ฆโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ53 5.1 ํผ์ง€ PID ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ๊ฒ€์ฆ ๋ฐ ๊ณ ์ฐฐโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ53 5.2 Kalman ํ•„ํ„ฐ ๊ธฐ๋ฐ˜์˜ ํผ์ง€ PID ์ œ์–ด์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ๊ฒ€์ฆ ๋ฐ ๊ณ ์ฐฐโ€ฅโ€ฅ54 5.2.1 ์žก์Œ์ด ์ธ๊ฐ€๋œ Kalman ํ•„ํ„ฐ ๊ธฐ๋ฐ˜ ํผ์ง€ PID ์ œ์–ด์‹œ์Šคํ…œโ€ฅโ€ฅโ€ฅ54 5.2.2 ๋ฏธ์ง€์˜ ์™ธ๋ž€๊ณผ ์žก์Œ์ด ์ธ๊ฐ€๋œ Kalman ํ•„ํ„ฐ ๊ธฐ๋ฐ˜ ํผ์ง€ PID ์ œ์–ด ์‹œ์Šคํ…œโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ56 5.3 ๋ฏธ์ง€์˜ ์™ธ๋ž€ ์ถ”์ • Kalman ํ•„ํ„ฐ ๊ธฐ๋ฐ˜ ํผ์ง€ PID ์ œ์–ด์‹œ์Šคํ…œโ€ฅโ€ฅโ€ฅโ€ฅ58 ์ œ 6 ์žฅ ๊ฒฐ ๋ก โ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅโ€ฅ64 ์ฐธ๊ณ ๋ฌธ

    ๊ณ ํšจ์œจ ๊ณ ์ „์•• ํฌ๋ฝ์„  ์ถ”์  ์ „๋ ฅ ์ฆํญ๊ธฐ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2017. 8. ์„œ๊ด‘์„.In this dissertation, two advanced techniques to solve system issues in envelope tracking power amplifier (ET PA) is presented. First of all, a two-stage broadband CMOS stacked FET RF power amplifier (PA) with a reconfigurable interstage matching network is developed for wideband envelope tracking (ET). The proposed RF PA is designed based on Class-J mode of operation, where the output matching is realizedwith a two-section low-pass matching network. To overcome the bandwidth (BW) limitation from the high- interstage impedance, a reconfigurable matching network is proposed, allowing a triple frequency mode of operation using two RF switches. The proposed RF PA is fabricated in a 0.32-ฮผm silicon-on-insulator CMOS process and shows continuous wave (CW) power-added efficiencies (PAEs) higher than 60% from 0.65 to 1.03 GHz with a peak PAE of 69.2% at 0.85 GHz. The complete ET PA system performance is demonstrated using the envelope amplifier fabricated on the same process. When measured using a 20-MHz BW long-term evolution signal, the overall system PAE of the ET PA is higher than 40% from 0.65 to 0.97 GHz while evolved universal terrestrial radio access (E-UTRA) adjacent channel leakage ratios (ACLRs) are better than โ€“33 dBc across the entire BW after memoryless digital pre-distortion. To our knowledge, this study represents the highest overall system performance in terms of PAE and BW among the published broadband ET PAs, including GaAs HBT and SiGe BiCMOS. Second, a high-efficiency gallium-nitride (GaN) envelope amplifier (EA) is developed using class-E2 architecture for wideband LTE applications. The proposed EA consists of a class-E2 resonant converter which output voltage is controlled by a frequency modulator. With a pulse frequency modulation (PFM) signal, the output of the converter can achieve a linear response to the input wideband envelope signal. The frequency modulator with a cross-coupled oscillator and a driver using stacked-FETs structure is fabricated using 0.28-ฮผm SOI CMOS process. The class-E2 converter and PA have been implemented using a commercial GaN device. The envelope amplifier (EA) achieves 74.7% efficiency into a 50 ฮฉ load for a 20-MHz BW LTE signal with a 7.5 dB peak-to-average power ratio (PAPR) and there is no efficiency degradation as the LTE signal bandwidth increases to 160-MHz. The ET transmitter system demonstrated using the CMOS and GaN shows an overall system efficiency of 47.4% at 35.4 dBm with 20-MHz BW LTE signal centered at 3.5 GHz. The measured E-UTRA ACLR of ET PA is โ€“33.8 dBc at 34.4 dBm output power before linearization and โ€“42.9 dBc at the same output power after memory digital pre-destination (DPD). When tested using 80-MHz BW LTE signal, the overall system PAE reaches 46.5% at 35.3 dBm output power and E-UTRA ACLR was measured by โ€“31.5 dBc at 34.4 dBm output power. A wideband performance is characterized using various bandwidth LTE signals which shows only 2.3 dB ACLR degradation without PAE degradation as the signal bandwidth is increased from 20- to 80-MHz. The proposed method is a first demonstration of GaN EA cover 160-MHz BW LTE signals and overcomes the efficiency degradation of the conventional EA as the signal bandwidth increase.Abstract Contents List of Tables List of Figures 1. Introduction 1.1 Motivation 1.2 Dissertation organization 2. Broadband CMOS Stacked RF Power Amplifier Using Reconfigurable Interstage Network for Wideband Envelope Tracking 2.1 Introduction 2.2 Two-stage broadband class-J PA 2.2.1 Review of the class-J PA 2.2.2 BW limitation in multi-stage PAs and proposed solution 2.2.3 Output matching netwok 2.2.4 Reconfigurable interstage matching network 2.3 Design and implementation of ET PA 2.3.1 Power amplifier design 2.3.2 Envelope amplifier design 2.4 Measurement results 2.5 Conclusions 2.6 References 3. A GaN Envelope Amplifier using Class-E2 Architecture for Wideband Envelope Tracking Applications 3.1 Introduction 3.2 Operation principle of the proposed envelope amplifier 3.2.1 Operation principle of class-E inverter and rectifier 3.2.2 Operation comparison of class-E2 between PWM and PFM 3.3 Detailed ET PA design and simulation 3.3.1 Envelope amplifier design using current-starved VCO (CSVCO) 3.3.2 Envelope amplifier design using cross-coupled VCO (CCVCO) 3.4 Measurement results 3.5 Conclusions 3.6 References 4. Conclusions and Future Works Abstract in KoreanDocto

    Polymerization shrinkage and stress of silorane-based dental restorative composite

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ์น˜์˜ํ•™๋Œ€ํ•™์› : ์น˜์˜ํ•™๊ณผ, 2013. 2. ์ด์ธ๋ณต.๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ silorane-๊ธฐ์งˆ ๋ณตํ•ฉ๋ ˆ์ง„์˜ ์ฒด์ ์ค‘ํ•ฉ์ˆ˜์ถ•์˜ ๋™๋ ฅํ•™๊ณผ ์ค‘ํ•ฉ์ˆ˜์ถ•์‘๋ ฅ์„ ๊ธฐ์กด์˜ methacrylate ๊ณ„์—ด์˜ ๋ณตํ•ฉ๋ ˆ์ง„๊ณผ ๋น„๊ต ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. 2๊ฐ€์ง€ methacrylate ๊ณ„์—ด์˜ ๋ณตํ•ฉ๋ ˆ์ง„ (Z250, Z350)๊ณผ 1๊ฐ€์ง€ silorane-๊ธฐ์งˆ์˜ ๋ณตํ•ฉ๋ ˆ์ง„ (P90)์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ์•„๋ฅดํ‚ค๋ฉ”๋ฐ์Šค ์›๋ฆฌ๋ฅผ ์‘์šฉํ•œ ์ฒด์ ์ค‘ํ•ฉ์ˆ˜์ถ• ์ธก์ •์žฅ์น˜๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ด‘์ค‘ํ•ฉ ๊ณผ์ •์—์„œ์˜ ์ฒด์  ๋ณ€ํ™”๋ฅผ ์ธก์ •ํ•˜์˜€๊ณ , ์ค‘ํ•ฉ์ˆ˜์ถ•์‘๋ ฅ ์ธก์ •์„ ์œ„ํ•ด strain gage๊ฐ€ ํ™œ์šฉ๋˜์—ˆ๋‹ค. ๋ฐ์ดํ„ฐ๋Š” ํ†ต๊ณ„์ ์œผ๋กœ one-way ANOVA๋ฅผ ํ†ตํ•ด ๋ถ„์„๋˜์—ˆ๋‹ค. ์ค‘ํ•ฉ์ˆ˜์ถ•๊ณผ ์ตœ๋Œ€์ค‘ํ•ฉ์ˆ˜์ถ•๋ฅ ์€ P90์ด ๊ฐ€์žฅ ๋‚ฎ์€ ๊ฐ’์„, Z350์ด ๊ฐ€์žฅ ๋†’์€ ๊ฐ’์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์ตœ๋Œ€์ค‘ํ•ฉ์ˆ˜์ถ•์‹œ๊ฐ„์€ P90์—์„œ ๊ฐ€์žฅ ๊ธธ๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ค‘ํ•ฉ์ˆ˜์ถ•์‘๋ ฅ์€ ๋‹ค๋ฅธ methacrylate ๊ณ„์—ด์˜ ๋ณตํ•ฉ๋ ˆ์ง„๋ณด๋‹ค silorane-๊ธฐ์งˆ ๋ณตํ•ฉ๋ ˆ์ง„์ธ P90์—์„œ ๋‚ฎ์€ ๊ฐ’์„ ๋‚˜ํƒ€๋ƒˆ๋‹ค.I. ์„œ๋ก  II. ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• III. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ IV. ๊ณ ์ฐฐ V. ๊ฒฐ๋ก  ์ฐธ๊ณ ๋ฌธํ—Œ ํ‘œ ๋ฐ ๊ทธ๋ฆผMaste

    ๊ธฐ์ˆ ์ง€์‹์˜ ์ฐฝ์ถœ๊ณผ ๊ณต์œ ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    1999-12Drucker(1993)๊ฐ€ ไผๆฅญ็ซถ็ˆญๅŠ›์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ์œ ์ผํ•œ ์›์ฒœ์ด ็Ÿฅ่ญ˜(knowledge)์ด๋ผ๊ณ  ์ฃผ์žฅํ•œ ์ด๋ž˜, ํ˜„๋Œ€ ๊ธฐ์—…๋“ค์€ ์ง€์‹์„ ๋งค์šฐ ์ค‘์š”ํ•œ ์ž์›์œผ๋กœ ์—ฌ๊ธฐ๊ณ  ์žˆ๋‹ค. ๋‹ค์‹œ ๋งํ•˜๋ฉด ๆƒ…ๅ ฑ์™€ ็Ÿฅ่ญ˜์ด ์—ฌ๋Ÿฌ ์‚ฐ์—…์—์„œ ้‡่ฆํ•œ ็”Ÿ็”ฃ่ฆ็ด ๋กœ ๋ถ€๊ฐ๋˜๊ณ  ์žˆ๋Š” ๊ฒƒ์ด๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ๋ณด๋ฉด, ๊ธฐ์—…์˜ ์ƒ์‚ฐ ์š”์†Œ๋Š” ๏คฏๅ‹•๊ณผ ่ณ‡ๆœฌ์ด ๊ทธ ์›์ฒœ์œผ๋กœ ๋˜์–ด ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ 90๋…„๋Œ€ ํ›„๋ฐ˜๋ถ€ํ„ฐ ์ง€์‹์ด ๅฏŒ์˜ ๅ€‰้€ ์™€ ์ง€์†์ ์ธ ็ซถ็ˆญๅ„ชไฝ์˜ ๆบๆณ‰์ด๋ผ๋Š” ์‚ฌ์‹ค์ด ํ™•์ธ๋˜๊ณ  ์žˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด ํšŒ๊ณ„์žฅ๋ถ€์ƒ ์ž์‚ฐ์ด GM ็คพ์˜ 1/15 ๋ฐ–์— ๋˜์ง€ ์•Š๋Š” Microsoft ็คพ๊ฐ€ ์ฃผ์‹๊ฐ€๊ฒฉ์œผ๋กœ ํ™˜์‚ฐํ•œ ์‹œ์žฅ๊ฐ€์น˜๋Š” GM์˜ 3๋ฐฐ๋ฅผ ๊ธฐ๋กํ•˜๊ณ  ์žˆ๋Š” ๊ฒฝ์šฐ๋‚˜ ์ฃผ๋ผ๊ธฐ๊ณต์› ์˜ํ™”1ํŽธ์ด ์šฐ๋ฆฌ ๋‚˜๋ผ ์ž๋™์ฐจ 150๋งŒ๋Œ€์˜ ์ˆ˜์ถœ ๋ณด๋‹ค ๋ถ€๊ฐ€๊ฐ€์น˜๊ฐ€ ๋†’์€ ๊ฒฝ์šฐ ๋“ฑ์„ ๋“ค ์ˆ˜ ์žˆ๋‹ค

    In-depth Analysis of Affective Engineering Process : a case study on vehicle instrument panel

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ธ๋ฌธ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ์ธ์ง€๊ณผํ•™์ „๊ณต, 2019. 2. ์œค๋ช…ํ™˜.This dissertation aims to propose quantitative methods for elaborating the existing affective engineering methodology and to demonstrate the proposed methods by conducting a case analysis. Since the mid-1980s, the affective engineering research has been recognized as a methodology of developing user-oriented products. There are a few remarkable differences between the affective engineering research and other engineering researches. First, the percentage of qualitative studies is relatively higher in affective engineering. A study plan tends to be determined based on a researchers subjective decision about scope, methods, detailed plans and evaluation criteria through the whole process. Second, the subject of the affective engineering research is humans and thus evaluation results are more difficult to generalize and elaborate. These two differences are both characteristics and limitations of the affective engineering research. Focusing on this fact, five quantitative analysis methods are proposed which could complement the existing affective engineering research. Study 1 performed a sematic network analysis of Internet review data and proposed a method of deriving affective structures and main affects, which were necessary for affective evaluation. A case of car instrumental panels was also examined. Online reviews about car interior designs were collected and preprocessed. A semantic network analysis of the preprocessed data was conducted to identify centrality values of each word and connectivity. Based on the analysis result, luxuriousness and naturalness were determined as the target affects related to car instrument panels, and 20 sub-affective words were selected which seemed to constitute the target affects. Study 2 proposed a quantitative method of examining the validity of each affective word selected for affective evaluation, and verified the proposed method by applying it to cases. An affective evaluation was performed for car instrument panels. In the affective evaluation, degrees of feeling corresponding to each affective word were measured by the semantic differential method, when subjects saw and felt six samples. Besides, a survey was also performed to investigate subjects understanding about each affective word and their subjective perception of the distinctness of the samples. The evaluation results were analyzed by 5 statistical methods in order to see the validity of each affective word. The analysis results showed that the affective words formed a regular distribution when the numbers of statistical methods satisfied by them were arranged in descending order from 5 to 0. Accordingly, the proposed method revealed that each affective word had a different degree of validity. Study 3 was a case study aiming to see which of the three conventional semantic differential methods was effective in affective evaluation. An affective evaluation was performed for car instrument panels. The affective evaluation was repeated by applying the three semantic differential methods, that is, Absolute evaluation 1, Absolute evaluation 2 and Relative evaluation. Three quantitative analysis methods were used to the performance of each evaluation method. It turned out that the relative evaluation produced better results than the remaining absolute evaluation types. However, the relative evaluation requires a long time for evaluation. Accordingly, an appropriate semantic differential method needed to be determined by considering various factors influencing experiments such as the number of evaluation samples, the number of participants in an experiment and the duration of an experiment. Study 4 proposed a method of processing data, which were obtained from affective evaluation, and presenting a significant statistical model. The proposed method was verified by a case study. The difference in the explanatory power of the model was identified by comparing the product reviews of the ordinary participants in the experiment and those of experts. A method of enhancing the explanatory power of the model, which was derived from the ordinary participants, was proposed. The key of the method was dividing participants into different groups by the evaluation criterion for a specific affective word. Two evaluation cases were distinguished. In the one case, the target affect was valued highly when the embossing was large. In the other case, the target affect was valued highly when the embossing was small. Models for each case were constructed, and the explanatory power of each model was examined. The explanatory power obtained by applying the proposed method was better than that obtained from the conventional analysis. Then, the problems of the proposed method and counterarguments were discussed. In addition, the model based on the evaluation results of the ordinary users was compared with another model based on the evaluation of experts in order to consider the difference between the models and the causes for such a difference. Study 5 derived a positioning map by using the affective evaluation results of Study 4. Based on the result of Study 4, it turned out that 13 affective words influenced affective models. A principal component analysis of those 13 words was performed to determine 4 components. The multidimensional scaling method was applied to the component scores of the four components in each sample, and thus a positioning map was derived for relative positions in each sample. Two dimensions constituting the positing map were compared with the scores of luxuriousness and naturalness, which were obtained in Study 4, in order to examine the validity. Relative positions of 12 samples on the positioning map were compared. In addition, a method of applying the comparison of samples to marketing and product development was also considered. This dissertation proposed five quantitative analysis methods to elaborate the existing affective engineering methodology. The proposed methods were demonstrated by conducting a case study for car instrument panels. As the development of user-oriented products has become the philosophy of product design, reliable and highly valid affective evaluation results need to be obtained and applied for product development in order to attract consumers in the market. The findings of this dissertation will contribute to developing a new methodology of yielding more valid and significant results in affective engineering.๋ณธ ๋…ผ๋ฌธ์˜ ๋ชฉ์ ์€ ๊ธฐ์กด ๊ฐ์„ฑ ๊ณตํ•™ ๋ฐฉ๋ฒ•๋ก ์„ ์ •๊ตํ™” ํ•  ์ˆ˜ ์žˆ๋Š” ์ •๋Ÿ‰์ ์ธ ๋ฐฉ๋ฒ•๋“ค์„ ์ œ์•ˆํ•˜๊ณ , ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•๋“ค์ด ์–ด๋–ค ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜๋Š”์ง€ ์‹ค์ฆํ•˜๋Š” ๊ฒƒ์ด๋‹ค. 1980๋…„ ์ค‘๋ฐ˜ ์ดํ›„ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ ์ œํ’ˆ ๊ฐœ๋ฐœ์˜ ํ•œ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•๋ก ์œผ๋กœ ์ธ์ •๋ฐ›๊ฒŒ ๋œ ๊ฐ์„ฑ ๊ณตํ•™ ์—ฐ๊ตฌ๋Š” ๋‹ค๋ฅธ ๊ณตํ•™ ์—ฐ๊ตฌ์™€ ๊ตฌ๋ณ„๋˜๋Š” ๋ช‡ ๊ฐ€์ง€ ์ฐจ์ด์ ๋“ค์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ํ•œ ๊ฐ€์ง€ ์ฐจ์ด์ ์€ ๊ฐ์„ฑ ๊ณตํ•™์—์„œ๋Š” ์ •์„ฑ์ ์ธ ์—ฐ๊ตฌ์˜ ๋น„์œจ์ด ์ƒ๋Œ€์ ์œผ๋กœ ํฌ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์—ฐ๊ตฌ ๋ฒ”์œ„, ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•, ์—ฐ๊ตฌ์˜ ์„ธ๋ถ€ ๊ณ„ํš, ํ‰๊ฐ€ ๊ธฐ์ค€ ๋“ฑ, ์—ฐ๊ตฌ ์ „๋ฐ˜์— ๊ฑธ์ณ ์—ฐ๊ตฌ์ž์˜ ์ฃผ๊ด€์  ์˜์‚ฌ๊ฒฐ์ •์„ ๋ฐ”ํƒ•์œผ๋กœ ์—ฐ๊ตฌ ๊ณ„ํš์„ ์ˆ˜๋ฆฝํ•˜๊ฒŒ ๋œ๋‹ค. ๋˜ ๋‹ค๋ฅธ ์ฐจ์ด์ ์€ ์‚ฌ๋žŒ์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋Š” ์—ฐ๊ตฌ์ด๊ธฐ ๋•Œ๋ฌธ์— ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋ฅผ ์ผ๋ฐ˜ํ™”ํ•˜๊ณ , ์ •๊ตํ™”ํ•˜๊ธฐ๊ฐ€ ์ƒ๋Œ€์ ์œผ๋กœ ์–ด๋ ต๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด 2๊ฐ€์ง€ ์ฐจ์ด์ ์€ ๊ฐ์„ฑ ๊ณตํ•™ ์—ฐ๊ตฌ์˜ ํŠน์ง•์ด๋ฉด์„œ, ํ•œ๊ณ„์ ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ด ์‚ฌ์‹ค์— ์ดˆ์ ์„ ๋งž์ถ”์–ด, ๊ธฐ์กด์˜ ๊ฐ์„ฑ ๊ณตํ•™ ์—ฐ๊ตฌ๋ฅผ ๋ณด์กฐํ•  ์ˆ˜ ์žˆ๋Š” ์ด 5๊ฐ€์ง€์˜ ์ •๋Ÿ‰์ ์ธ ๋ถ„์„ ๋ฐฉ๋ฒ•๋“ค์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ 1์—์„œ๋Š” ์ธํ„ฐ๋„ท ๋ฆฌ๋ทฐ ๋ฐ์ดํ„ฐ๋ฅผ ๋Œ€์ƒ์œผ๋กœ network analysis๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ ๊ฐ์„ฑ ํ‰๊ฐ€์— ํ•„์š”ํ•œ ๊ฐ์„ฑ ๊ตฌ์กฐ ๋ฐ ์ฃผ์š” ๊ฐ์„ฑ์„ ๋„์ถœํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ , ์ฐจ๋Ÿ‰์šฉ instrument panel์„ ๋Œ€์ƒ์œผ๋กœ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์šฐ์„  ์ธํ„ฐ๋„ท์—์„œ ์ฐจ๋Ÿ‰ ์‹ค๋‚ด ๋””์ž์ธ๊ณผ ๊ด€๋ จ๋œ ๋ฆฌ๋ทฐ๋“ค์„ ์ˆ˜์ง‘ํ•˜์—ฌ, ์ „์ฒ˜๋ฆฌ ๊ณผ์ •์„ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ์ „์ฒ˜๋ฆฌ๋œ ์ž๋ฃŒ๋ฅผ ๋Œ€์ƒ์œผ๋กœ network analysis๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ, ๊ฐ ์–ดํœ˜ ๋ณ„ centrality ๊ฐ’์„ ๊ตฌํ•˜๊ณ  ์—ฐ๊ฒฐ์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ† ๋Œ€๋กœ, ๊ณ ๊ธ‰๊ฐ๊ณผ ์ฒœ์—ฐ๊ฐ์„ ์ฐจ๋Ÿ‰์šฉ instrument panel๊ณผ ๊ด€๋ จ๋œ ๋ชฉํ‘œ ๊ฐ์„ฑ์œผ๋กœ ์„ ์ •ํ•˜์˜€๊ณ , ๋‘ ๋ชฉํ‘œ ๊ฐ์„ฑ์„ ๊ตฌ์„ฑํ•œ๋‹ค๊ณ  ์ƒ๊ฐ๋˜๋Š” ํ•˜์œ„ ๊ฐ์„ฑ ์–ดํœ˜๋ฅผ ์„ ์ •ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ 2์—์„œ๋Š” ๊ฐ์„ฑ ํ‰๊ฐ€๋ฅผ ์œ„ํ•ด ์„ ์ •๋œ ๊ฐœ๋ณ„ ๊ฐ์„ฑ ์–ดํœ˜์˜ ํƒ€๋‹น์„ฑ์„ ์ •๋Ÿ‰์ ์ธ ๋ถ„์„์„ ์ด์šฉํ•˜์—ฌ ํ™•์ธํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ , ์‚ฌ๋ก€ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•œ ๊ฒฐ๊ณผ๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ์ฐจ๋Ÿ‰์šฉ instrument panel์„ ๋Œ€์ƒ์œผ๋กœ ๊ฐ์„ฑ ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ๊ฐ์„ฑ ํ‰๊ฐ€๋Š” 6๊ฐœ์˜ ํ‰๊ฐ€ ์ƒ˜ํ”Œ์„ ๋ณด๊ณ  ๋งŒ์งˆ ๋•Œ ๊ฐ์„ฑ ์–ดํœ˜์˜ ๋Š๋‚Œ์„ ๋ฐ›๋Š” ์ •๋„๋ฅผ ์˜๋ฏธ๋ฏธ๋ถ„๋ฒ• ๋ฐฉ์‹์œผ๋กœ ์ธก์ •ํ•˜์˜€์œผ๋ฉฐ, ์ถ”๊ฐ€์ ์œผ๋กœ ๊ฐ์„ฑ ์–ดํœ˜ ๋ณ„ ์ดํ•ด๋„์™€ ์ƒ˜ํ”Œ ๊ฐ„ ๊ตฌ๋ณ„์„ฑ์— ๋Œ€ํ•œ ์ฃผ๊ด€์  ์ธ์‹์— ๋Œ€ํ•ด ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ด 5๊ฐ€์ง€์˜ ํ†ต๊ณ„ ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์—ฌ, ๊ฐ์„ฑ ์–ดํœ˜ ๋ณ„ ํƒ€๋‹น์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, 5๊ฐœ ํ†ต๊ณ„ ๋ถ„์„ ๋ชจ๋‘๋ฅผ ๋งŒ์กฑ์‹œํ‚ค๋Š” ๊ฐ์„ฑ ์–ดํœ˜๋ถ€ํ„ฐ ํ•˜๋‚˜์˜ ํ†ต๊ณ„ ๋ถ„์„๋„ ๋งŒ์กฑ์‹œํ‚ค์ง€ ๋ชปํ•˜๋Š” ๊ฐ์„ฑ ์–ดํœ˜๊นŒ์ง€ ๊ฐ์„ฑ ์–ดํœ˜๊ฐ€ ์ผ์ •ํ•œ ๋ถ„ํฌ๋ฅผ ํ˜•์„ฑํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด, ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ๊ธฐ์ค€์œผ๋กœ ํ–ˆ์„ ๋•Œ, ์–ดํœ˜ ํƒ€๋‹น์„ฑ์˜ ์ •๋„๊ฐ€ ๊ฐ์„ฑ ์–ดํœ˜ ๋ณ„๋กœ ์ฐจ์ด๊ฐ€ ๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์—ฐ๊ตฌ 3์—์„œ๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๋Š” 3์ข…๋ฅ˜์˜ ์˜๋ฏธ๋ฏธ๋ถ„๋ฒ• ์ค‘ ์–ด๋–ค ๋ฐฉ์‹์˜ ์˜๋ฏธ๋ฏธ๋ถ„๋ฒ•์ด ๊ฐ์„ฑ ํ‰๊ฐ€์— ํšจ๊ณผ์ ์ธ์ง€๋ฅผ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ์ฐจ๋Ÿ‰์šฉ instrument panel์„ ๋Œ€์ƒ์œผ๋กœ ๊ฐ์„ฑ ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ–ˆ๋‹ค. ๊ฐ์„ฑ ํ‰๊ฐ€๋Š” ์ ˆ๋Œ€ํ‰๊ฐ€ 1, ์ ˆ๋Œ€ํ‰๊ฐ€ 2, ์ƒ๋Œ€ํ‰๊ฐ€์˜ 3๊ฐ€์ง€ ๋ฐฉ์‹์˜ ์˜๋ฏธ๋ฏธ๋ถ„๋ฒ•์„ ๊ฐ€์ง€๊ณ  ๋ฐ˜๋ณต์ ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๊ณ , ์ด 3๊ฐ€์ง€ ์ •๋Ÿ‰์ ์ธ ๋ถ„์„ ๋ฐฉ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ๊ฐ ์˜๋ฏธ๋ฏธ๋ถ„๋ฒ• ๋ฐฉ์‹์˜ ํ‰๊ฐ€ ์šฐ์ˆ˜์„ฑ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ์ƒ๋Œ€ํ‰๊ฐ€ ๋ฐฉ์‹์ด ๋‚˜๋จธ์ง€ 2๊ฐœ์˜ ์ ˆ๋Œ€ํ‰๊ฐ€ ํƒ€์ž…์— ๋น„ํ•ด์„œ ์šฐ์ˆ˜ํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ƒ๋Œ€ํ‰๊ฐ€ ๋ฐฉ์‹์€ ํ‰๊ฐ€ ์‹œ๊ฐ„์ด ์˜ค๋ž˜ ๊ฑธ๋ฆฐ๋‹ค๋Š” ๋‹จ์ ์ด ์žˆ์—ˆ๋‹ค. ํ‰๊ฐ€ ์ƒ˜ํ”Œ์˜ ๊ฐœ์ˆ˜, ์‹คํ—˜์ฐธ์—ฌ์ž์˜ ์ˆ˜, ์‹คํ—˜ ์ง„ํ–‰ ์‹œ๊ฐ„ ๋“ฑ ์‹คํ—˜์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ์š”์†Œ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์‹คํ—˜์—์„œ ์‚ฌ์šฉํ•  ์˜๋ฏธ๋ฏธ๋ถ„๋ฒ• ํƒ€์ž…์„ ๊ฒฐ์ •ํ•ด์•ผ ํ•œ๋‹ค๋Š” ๊ฒฐ๋ก ์„ ๋„์ถœํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ 4์—์„œ๋Š” ๊ฐ์„ฑ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ๋„์ถœ๋œ ๋ฐ์ดํ„ฐ๋ฅผ ๊ฐ€๊ณตํ•˜์—ฌ ์œ ์˜๋ฏธํ•œ ํ†ต๊ณ„์  ๋ชจ๋ธ์„ ์ œ์‹œํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ , ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์˜ ๊ฒฐ๊ณผ๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ œํ’ˆ ํ‰๊ฐ€์— ๋Œ€ํ•œ ์ผ๋ฐ˜์ธ ์‹คํ—˜์ฐธ์—ฌ์ž๋“ค์˜ ๊ฒฐ๊ณผ๋ฅผ ์ „๋ฌธ๊ฐ€์™€ ๋น„๊ตํ•˜์—ฌ ์„ค๋ช…๋ ฅ์˜ ์ฐจ์ด๋ฅผ ํ™•์ธํ•˜๊ณ , ์ผ๋ฐ˜ ์‹คํ—˜์ฐธ์—ฌ์ž๋“ค๋กœ๋ถ€ํ„ฐ ๋„์ถœ๋˜๋Š” ๋ชจ๋ธ์˜ ์„ค๋ช…๋ ฅ์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์„ค๋ช…๋ ฅ์„ ๋†’์ด๊ธฐ ์œ„ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ, ํŠน์ • ๊ฐ์„ฑ ์–ดํœ˜๋ฅผ ํ‰๊ฐ€ํ•œ ๊ธฐ์ค€์œผ๋กœ ๊ทธ๋ฃน์„ ๋‚˜๋ˆ„๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์— ๋ณด ํฌ๊ธฐ๊ฐ€ ํด ๋•Œ, ๋ชฉํ‘œ ๊ฐ์„ฑ์„ ๋†’๊ฒŒ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒฝ์šฐ์™€ ์— ๋ณด ํฌ๊ธฐ๊ฐ€ ์ž‘์„ ๋•Œ, ๋ชฉํ‘œ ๊ฐ์„ฑ์„ ๋†’๊ฒŒ ํ‰๊ฐ€ํ•˜๋Š” ๊ฒฝ์šฐ๋กœ ํ‰๊ฐ€ ์ผ€์ด์Šค๋ฅผ ๋‚˜๋ˆ„์–ด ๊ฐ๊ฐ์— ๋Œ€ํ•œ ๋ชจ๋ธ์„ ๋งŒ๋“ค๊ณ , ๋งŒ๋“ค์–ด์ง„ ๋ชจ๋ธ์˜ ์„ค๋ช…๋ ฅ์„ ํ™•์ธํ–ˆ๋‹ค. ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ–ˆ์„ ๋•Œ์˜ ์„ค๋ช…๋ ฅ์ด ์ผ๋ฐ˜์ ์ธ ๋ถ„์„์œผ๋กœ ๋‚˜์˜จ ์„ค๋ช…๋ ฅ๋ณด๋‹ค ์ข‹๋‹ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ œ์•ˆ ๋ฐฉ๋ฒ•์˜ ๋ฌธ์ œ์ ๊ณผ ๊ทธ์— ๋Œ€ํ•œ ๋ฐ˜๋ก  ๋“ฑ์„ ๋…ผ์˜ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ „๋ฌธ๊ฐ€ ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋กœ๋ถ€ํ„ฐ ๋‚˜์˜จ ๋ชจ๋ธ๊ณผ ์ผ๋ฐ˜ ์‚ฌ์šฉ์ž ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋กœ๋ถ€ํ„ฐ ๋‚˜์˜จ 2๊ฐ€์ง€ ๋ชจ๋ธ์„ ๋น„๊ตํ•˜์—ฌ, ๋ชจ๋ธ ๊ฐ„ ์ฐจ์ด์ ์„ ํ™•์ธํ•˜๊ณ  ๊ทธ ์ฐจ์ด์˜ ์›์ธ์„ ๊ณ ์ฐฐํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ 5์—์„œ๋Š” ์—ฐ๊ตฌ 4์—์„œ ๋„์ถœ๋œ ๊ฐ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํฌ์ง€์…”๋‹ ๋งต์„ ๋„์ถœํ–ˆ๋‹ค. ์—ฐ๊ตฌ4์˜ ๊ฒฐ๊ณผ๋กœ 13๊ฐœ์˜ ๊ฐ์„ฑ ์–ดํœ˜๊ฐ€ ๊ฐ์„ฑ ๋ชจ๋ธ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋๊ณ , ์ด 13๊ฐœ ์–ดํœ˜๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ฃผ์„ฑ๋ถ„๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ด 4๊ฐœ์˜ ์„ฑ๋ถ„์„ ๋„์ถœํ•˜์˜€๋‹ค. ๋„์ถœ๋œ 4๊ฐœ ์„ฑ๋ถ„์˜ ์ƒ˜ํ”Œ ๋ณ„ ์„ฑ๋ถ„ ์ ์ˆ˜๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๋‹ค์ฐจ์› ์ฒ™๋„๋ฒ•์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ƒ˜ํ”Œ ๋ณ„ ์ƒ๋Œ€์  ์œ„์น˜์— ๋Œ€ํ•œ ํฌ์ง€์…”๋‹ ๋งต์„ ๋„์ถœํ•˜์˜€๋‹ค. ํฌ์ง€์…”๋‹ ๋งต์„ ๊ตฌ์„ฑํ•˜๋Š” 2๊ฐœ์˜ ์ฐจ์›์„ ์—ฐ๊ตฌ 4์—์„œ ๋„์ถœ๋œ ๊ณ ๊ธ‰๊ฐ ๋ฐ ์ฒœ์—ฐ๊ฐ ์ ์ˆ˜์™€ ๋น„๊ตํ•˜์—ฌ ๊ทธ ํƒ€๋‹น์„ฑ์— ๋Œ€ํ•ด ๋…ผ์˜ํ•˜์˜€๋‹ค. ํฌ์ง€์…”๋‹ ๋งต ์ƒ์—์„œ์˜ 12๊ฐœ ์ƒ˜ํ”Œ์˜ ์ƒ๋Œ€์ ์ธ ์œ„์น˜๋ฅผ ๋น„๊ตํ•˜์˜€๊ณ , ์ƒ˜ํ”Œ ๊ฐ„ ์ƒ๋Œ€์ ์ธ ๋น„๊ต๋ฅผ ๋งˆ์ผ€ํŒ… ๋ฐ ์ œํ’ˆ ๊ฐœ๋ฐœ ์ธก๋ฉด์— ์ ์šฉํ•˜๋Š” ๋ฐฉ์•ˆ์— ๋Œ€ํ•ด ๋…ผ์˜ํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ 5๊ฐ€์ง€์˜ ์ •๋Ÿ‰์ ์ธ ๋ถ„์„ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜์—ฌ ๊ธฐ์กด์˜ ๊ฐ์„ฑ ๊ณตํ•™ ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•๋ก ์„ ์ •๊ตํ™”ํ•˜๋Š” ๋ฐฉ์•ˆ์— ๋Œ€ํ•ด ๋…ผ์˜ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ฐจ๋Ÿ‰์šฉ instrument panel์„ ๋Œ€์ƒ์œผ๋กœ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์—ฌ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•๋“ค์„ ์‹ค์ฆํ•˜์˜€๋‹ค. ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ์ œํ’ˆ ๊ฐœ๋ฐœ์ด๋ผ๋Š” ๊ฐœ๋ฐœ ๋ฐฉํ–ฅ์„ฑ์ด ์ œํ’ˆ ์„ค๊ณ„์˜ ์ฃผ์š” ์ฒ ํ•™์œผ๋กœ ์ž๋ฆฌ์žก์€ ์ƒํ™ฉ์—์„œ ์‹ ๋ขฐ๋„ ๋ฐ ํƒ€๋‹น์„ฑ ๋†’์€ ๊ฐ์„ฑ ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜์—ฌ ์ด๋ฅผ ์ œํ’ˆ ๊ฐœ๋ฐœ์— ์ ์šฉํ•˜๋Š” ๊ฒƒ์€ ์‹œ์žฅ์—์„œ ์†Œ๋น„์ž์˜ ์„ ํƒ ๋ฐ›๊ธฐ ์œ„ํ•ด ํ•„์ˆ˜์ ์ธ ์š”์†Œ๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด, ๊ธฐ์กด์— ๋น„ํ•ด ํƒ€๋‹น์„ฑ์ด ๋†’๊ณ  ์œ ์˜๋ฏธํ•œ ํ‰๊ฐ€ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ์„ฑ ๊ณตํ•™ ๋ฐฉ๋ฒ•๋ก ์„ ๊ฐœ๋ฐœํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Abstract i Contents v List of Tables ix List of Figures xi Chapter 1. Introduction 1 1.1. Research Background 1 1.2. Research Objective 8 1.3. Organization of the Dissertation 10 Chapter 2. Literature Review 13 2.1. Overview 13 2.2. Affect 14 2.2.1. Definition of Affect 14 2.2.2. Concepts related to Affect 15 2.2.3. Neural Bases of Affect 21 2.3. Affective Engineering 28 2.3.1. Definition of Affective Engineering 28 2.3.2. Definition of Affect in Affective Engineering 29 2.3.3. Affective Engineering Methodologies proposed in the Early Days 29 2.3.4. Integrated Affective Engineering Process by Combining Various Types 35 2.3.5. Investigation Methodologies 37 2.3.6. Evaluation Methodologies 39 2.4. Conclusion 43 Chapter 3. Affective Structure Extraction using Network Analysis for Web Data 45 3.1. Introduction 45 3.2. Data Structuring 47 3.3. Results 52 3.3.1. Network Formation and Frequency Analysis 52 3.3.2. Centrality Value Analysis 53 3.4. Discussion 57 Chapter 4. Evaluating the Validity of Affective Words 63 4.1. Introduction 63 4.2. Method 65 4.2.1. Selection of Samples and Affective Words 65 4.2.2. Evaluation Environment 70 4.2.3. Questionnaire 71 4.2.4. Analysis Method 72 4.3. Results 73 4.3.1. Analysis Results of the Understanding of Affective Words 73 4.3.2. Analysis Result of the Distinctness of Samples 80 4.4. Discussion 89 Chapter 5. Comparing Semantic Differential Methods 93 5.1. Introduction 93 5.2. Method 97 5.2.1. Questionnaires 99 5.2.2. Analysis Methods 99 5.2.3. Design Parameter of Samples 100 5.3. Results 103 5.3.1. Analysis of the Statistically Significant Difference in Affective Word Scores among Samples in each Semantic Differential Method 103 5.3.2. Post Analysis for Details of the Semantic Differential Methods 107 5.3.3. Analysis of the Correlation between Affective Words and Design Variables 110 5.4. Discussion 114 Chapter 6. Improving the Explanatory Power of Models using Clustering Analysis 121 6.1. Introduction 121 6.2. Method 123 6.2.1. Evaluation Environment 123 6.2.2. Samples 124 6.2.3. Participants 128 6.2.4. Interaction Methods between Samples and Participants in Evaluation 129 6.2.5. Evaluation Method and Questionnaire 129 6.2.6. Selection of the Variables (Affective Words) for Cluster Analysis 132 6.3. Results 134 6.3.1. Regression Analysis for Customer before Grouping 134 6.3.2. Grouping Customers 135 6.3.3. Regression Analysis 140 6.3.4. Developing Affect Prediction Model 143 6.4. Discussion 146 Chapter 7. Developing a Positioning Map 153 7.1. Introduction 153 7.2. Principal Component Analysis 156 7.3. Multi-dimensional Scaling Analysis 158 7.4. Discussion 161 Chapter 8. Conclusion 165 8.1. Summary of Study and Findings 165 8.2. Contribution and Limitation 170 8.3. Future Work 174 References 177 Appendix A 191 Appendix B 195 Appendix C 205 Appendix D 215 Appendix E 223 Abstract (in Korean) 225Docto

    ๊ตฌ๊ฐ„ ์ค‘๋„์ ˆ๋‹จ ์ž๋ฃŒ์—์„œ Cox ๋น„๋ก€์œ„ํ—˜๋ชจํ˜•์˜ ์ถ”์ •๋ฐฉ๋ฒ• ๋น„๊ต

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    ์˜๊ณผ๋Œ€ํ•™/์„์‚ฌ์ƒ์กด๋ถ„์„์€ ์ž„์ƒ์—ฐ๊ตฌ์—์„œ ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” ๋ถ„์„ ๋ฐฉ๋ฒ• ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ƒ์กด๋ถ„์„ ์—ฐ๊ตฌ์—์„œ ๊ด€์‹ฌ ์žˆ๋Š” ์‚ฌ๊ฑด์˜ ๋ฐœ์ƒ์€ ์ •ํ™•ํ•œ ์‹œ์ ์œผ๋กœ ๊ด€์ธก๋˜๊ธฐ ๋ณด๋‹จ ์ •ํ™•ํ•œ ๊ด€์ธก์‹œ์ ์„ ํฌํ•จํ•œ ๊ตฌ๊ฐ„์œผ๋กœ ๊ด€์ธก๋˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋Œ€๋ถ€๋ถ„์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ž๋ฃŒ๋ฅผ ๊ตฌ๊ฐ„์ค‘๋„์ ˆ๋‹จ ์ž๋ฃŒ(interval censored data)๋ผ ํ•œ๋‹ค. ์ตœ๊ทผ ์ž„์ƒ์—์„œ๋Š” ์ด์ฒ˜๋Ÿผ ๊ด€์‹ฌ์‚ฌ๊ฑด ๋ฐœ์ƒ์ด ๊ตฌ๊ฐ„์œผ๋กœ ๊ด€์ธก๋˜๋Š” ๊ฒฝ์šฐ, ๋‘ ๊ตฌ๊ฐ„์˜ ํ‰๊ท ๊ฐ’์„ ๋Œ€์น˜ํ•˜์—ฌ ๋ถ„์„ํ•˜๋Š” ํ‰๊ท ๋Œ€์น˜๋ฒ•(mean imputation)์ด๋‚˜ ๋งˆ์ง€๋ง‰ ๊ด€์ธก๋œ ๊ตฌ๊ฐ„์˜ ์šฐ์ธก ๊ฐ’์œผ๋กœ ๋Œ€์น˜ํ•˜์—ฌ ๋ถ„์„ํ•˜๋Š” ์šฐ๋Œ€์น˜๋ฒ•(right imputation)์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋Œ€๋ถ€๋ถ„์ด๋‹ค. ํ•˜์ง€๋งŒ ์ด์™€ ๊ฐ™์€ ๋ถ„์„์˜ ๊ฒฝ์šฐ ๋ฐ”์ด์–ด์Šค ๋œ ๊ฒฐ๊ณผ๋ฅผ ์ดˆ๋ž˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ตฌ๊ฐ„์ค‘๋„์ ˆ๋‹จ ์ž๋ฃŒ๊ฐ€ ๊ด€์ธก๋˜์—ˆ์„ ๊ฒฝ์šฐ cox ๋น„๋ก€์œ„ํ—˜๋ชจํ˜•์„ ์ด์šฉํ•˜์—ฌ ๊ณต๋ณ€๋Ÿ‰์„ ์ถ”์ •ํ•˜๋Š”๋ฐ ์žˆ์–ด ํ‰๊ท ๋Œ€์น˜๋ฒ•๊ณผ ์šฐ๋Œ€์น˜๋ฒ• ๊ทธ๋ฆฌ๊ณ  Pan์ด ์ œ์•ˆํ•œ iterative convex minorant (ICM) ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•œ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ๋…ผ์˜ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ชจ์˜์‹คํ—˜์„ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ์„ค์ • ํ•˜์—์„œ ์„ธ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ๋น„๊ต ๋ฐ ํ‰๊ฐ€๋ฅผ ํ•˜์˜€๋‹ค. ๋ชจ์˜์‹คํ—˜ ๊ฒฐ๊ณผ ํ‘œ๋ณธํฌ๊ธฐ n์ด 100๋ณด๋‹ค ์ž‘์€ ๊ฒฝ์šฐ Intcox๋ฐฉ๋ฒ•์€ ํ‰๊ท ๋Œ€์น˜๋ฒ•๊ณผ ์šฐ๋Œ€์น˜๋ฒ•์— ๋น„ํ•ด ๋ฐ”์ด์–ด์Šค๋Š” ๋†’๊ฒŒ ๋‚˜์™”์œผ๋‚˜ 95% corverage rate์€ ๋” ์•ˆ์ •์ ์ธ ๊ฒƒ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ํ‘œ๋ณธํฌ๊ธฐ n์ด ์ปค์งˆ์ˆ˜๋ก ํ‰๊ท ๋Œ€์น˜๋ฒ•์ด๋‚˜ ์šฐ๋Œ€์น˜๋ฒ•์€ ์‹ค์ œ๊ฐ’ ์ถ”์ •์— ์žˆ์–ด ๊ณผ์†Œ์ถ”์ •๋จ์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋งŽ์€ ์ž„์ƒ์—ฐ๊ตฌ์—์„œ ํ‰๊ท ๋Œ€์น˜๋ฒ•๊ณผ ์šฐ๋Œ€์น˜๋ฒ•์€ ๊ณ„์‚ฐํ•˜๊ธฐ ์‰ฝ๊ณ  ๋น„๊ต์  ์•ˆ์ •์ ์ธ ์ถ”์ •์ด ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์ด์œ ๋กœ ๋งŽ์ด ์‚ฌ์šฉ๋˜์—ˆ์ง€๋งŒ ๊ฒฐ๊ณผ๊ฐ€ ๊ณผ์†Œ์ถ”์ • ๋  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ๊ตฌ๊ฐ„์ค‘๋„์ ˆ๋‹จ ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜๋Š” ๊ฒฝ์šฐ ์ฃผ์˜๊ฐ€ ํ•„์š”ํ•˜๋‹ค.ope

    A study on the Reconstruction of Reality through Sense

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋ฏธ์ˆ ๋Œ€ํ•™ ๋ฏธ์ˆ ํ•™๊ณผ, 2020. 8. ์œค๋™์ฒœ.๋ณธ ์—ฐ๊ตฌ๋Š” ํ˜„๋Œ€์‚ฌํšŒ๋ฅผ ๋Š์ž„์—†์ด ์ •๋ณด๋ฅผ ์Ÿ์•„๋‚ด๋Š” ๋ณต์žกํ•˜๊ณ  ๊ฑฐ๋Œ€ํ•œ ๋Œ€์ƒ์œผ๋กœ ๋ณด๋Š” ๊ฒƒ์—์„œ ์ถœ๋ฐœํ•˜์˜€๋‹ค. ์‚ฌํšŒ๋ฅผ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•œ ๋งฅ๋ฝ์€ ์ •๋ณด๋“ค๋กœ ๊ตฌ์„ฑ๋˜๋Š”๋ฐ ๋„ˆ๋ฌด๋‚˜ ๋งŽ์€ ์ •๋ณด๋“ค๋กœ ์ธํ•ด ๊ทธ ๋งฅ๋ฝ๋งˆ์ € ํ—๊ฑฐ์›Œ์ง€๊ณ  ์ •๋ณด๋“ค๋„ ๋งฅ๋ฝ์—์„œ ๋–จ์–ด์ ธ ๋‚˜๊ฐ€๊ฒŒ ๋œ๋‹ค. ์ด ๋ถ„๋ฆฌ๋œ ์ •๋ณด๋Š” ๊ธฐ์กด์˜ ์ด์„ฑ์  ํ‹€๋งŒ์œผ๋กœ๋Š” ํ•ด์„ํ•˜๊ธฐ ํž˜๋“ค๊ฒŒ ๋˜์—ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ž์—ฐ์„ ๋ณผ ๋•Œ ๊ฐ์ž์˜ ๊ฒฝํ—˜๊ณผ ์ง€๊ฐ์„ ํ†ตํ•ด ์ง๊ด€์ ์œผ๋กœ ํŒŒ์•…ํ•˜๊ณ  ์ดํ•ดํ•œ๋‹ค. ๊ฐ€๋ณ€์ ์ธ ํ˜„๋Œ€์˜ ์ธ๊ฐ„์‚ฌํšŒ๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋„ ์ž์—ฐ์„ ๋ณผ ๋•Œ์ฒ˜๋Ÿผ ์œ ๋™์ ์ธ ์ž์„ธ๊ฐ€ ํ•„์š”ํ•˜๋ฉฐ, ๊ทธ๊ฒƒ์€ ๋†€์ด์  ๋ชฐ์ž…๊ณผ ๊ด€์กฐ๋ฅผ ํ†ตํ•œ ๋Œ€์ƒ์˜ ์ง€์†์  ๊ด€์ฐฐ์„ ํ†ตํ•ด ๊ฐ€๋Šฅํ•˜๋‹ค๊ณ  ์ƒ๊ฐํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐฉ์‹์œผ๋กœ ์‹ค์ œ ๋Œ€์ƒ์— ์ฃผ๋ชฉํ–ˆ์„ ๋•Œ, ๋Œ€์ƒ ๋ณธ์—ฐ์˜ ์†์„ฑ๋“ค์„ ์—ด๋ฆฐ ์‹œ๊ฐ์—์„œ ๊ฒฝํ—˜ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋Œ€์ƒ์€ ์ธ๊ฐ„์˜ ๊ฐœ์ž… ์—†์ด๋„ ์กด์žฌ ๊ฐ€๋Šฅํ•œ ๋…๋ฆฝ์ ์ธ ์ž์œจ์  ๊ฐ์ฒด(object)์ผ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๊ด€์ฐฐ์ž์˜ ํƒ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ทธ ๋ณธ๋ž˜์˜ ์†์„ฑ๋“ค์„ ์ง€๊ฐํ•  ์ˆ˜ ์žˆ๋‹ค๊ณ  ๋ณธ๋‹ค. ๊ฐ๊ฐ์„ ํ†ตํ•œ ๋Œ€์ƒ์˜ ์žฌ๊ตฌ์„ฑ์„ ๋‹จ์ˆœํ•˜๊ฒŒ ์ •๋ฆฌํ•˜์ž๋ฉด, ๊ด€์ฐฐ์ž์˜ ์›€์ง์ž„์„ ํ†ตํ•ด ๋Œ€์ƒ์„ ๊ฒฝํ—˜ํ•˜๊ณ  ๊ทธ๋กœ๋ถ€ํ„ฐ ํŒŒ์•…ํ•œ ๋Œ€์ƒ์˜ ์„ฑ์งˆ๋“ค์„ ์ž‘์—…์„ ํ†ตํ•ด ๋“œ๋Ÿฌ๋‚ด๋Š” ๊ฒƒ์ด๋‹ค. ์ด ๋Œ€์ƒ์„ ๋ณด๋Š” ์‹œ๊ฐ์—๋Š” ๋‹น์—ฐํžˆ ๊ด€์ฐฐ์ž์˜ ์ทจํ–ฅ์ด ๋ฐ˜์˜๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ์˜ค๋žซ๋™์•ˆ ์‚ฌํšŒ ํ™˜๊ฒฝ์  ์š”์ธ์— ์˜ํ•ด ์Šต๋“๋œ ์ทจํ–ฅ์€ ๋ฏธ์  ๊ฒฝํ—˜์œผ๋กœ ์Šนํ™”๋˜์–ด ๋Œ€์ƒ์„ ๊ตฌ๋ณ„ํ•˜๊ณ  ์„ ํƒํ•˜๋Š” ๊ธฐ์ค€์˜ ์—ญํ• ์„ ํ•˜๊ฒŒ ๋œ๋‹ค. ์ง€๊ฐ์„ ํ†ตํ•ด ๋Œ€์ƒ์œผ๋กœ๋ถ€ํ„ฐ ์–ป์€ ๊ฐ๊ฐ์  ๊ธฐ์–ต๋“ค์€ ๊ฐ๊ฐ์ƒํƒœ(sensations) ์ฆ‰, ๋Š๋‚Œ์ด๋‚˜ ์ธ์ƒ๊ณผ ๊ฐ™์€ ๊ฒƒ์œผ๋กœ์„œ ๋Œ€์ƒ์— ๋Œ€ํ•œ ๊ฒฝํ—˜์ด ๋ฐ˜๋ณต๋ ์ˆ˜๋ก ์Œ“์—ฌ์ ธ ๊ทธ ๋‚˜๋ฆ„์˜ ์—ฐ๊ฒฐ๊ด€๊ณ„-๋งฅ๋ฝ์„ ํ˜•์„ฑํ•˜๊ฒŒ ๋œ๋‹ค. ์ด๋ ‡๋“ฏ ๋Œ€์ƒ์˜ ์„ฑ์งˆ์— ์˜ํ•ด ๋งŒ๋“ค์–ด์ง€๋Š” ๋งฅ๋ฝ์€ ๊ธฐ์กด์˜ ๋Œ€์ƒ์ด ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์šฉ๋„์  ๋งฅ๋ฝ๊ณผ๋Š” ์ฐจ๋ณ„ํ™”๋˜๋Š” ๊ฒƒ์ด๋‹ค. ์ƒˆ๋กœ์šด ๋งฅ๋ฝ์€ ๋Œ€์ƒ์˜ ์šฐ์—ฐํ•œ ๋ฐœ๊ฒฌ๊ณผ ์ฒดํ—˜์„ ํ†ตํ•ด ์–ป์€ ๊ฒƒ๊ณผ ์กฐํ˜•์  ์‹œ๊ฐ์œผ๋กœ ํŒŒ์•…ํ•œ ์ธก๋ฉด์„ ํ•จ๊ป˜ ๊ตฌ์„ฑํ•จ์œผ๋กœ์จ ํ˜•์„ฑ๋˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๊ฒƒ์€ ์˜ˆ์ˆ ์ž‘ํ’ˆ์„ ๋ถ„๋ณ„ํ•˜๋Š” ์ •ํ˜•ํ™”๋œ ๊ธฐ์ค€์„ ๋„˜์–ด์„œ ์˜๋„์™€ ๋ฌด๊ด€ํ•˜๊ฒŒ ์ž‘๋™ํ•˜๋Š” ์ฃผ์˜(attention)์™€ ๊ด€์กฐ, ๊ณต๊ฐ์— ์˜ํ•ด ํŒŒ์•…๋˜๋Š” ๋Œ€์ƒ์˜ ์†”์งํ•œ ๋ณธ์งˆ๊ณผ ๊ฐ€๊น๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋Œ€์ƒ์˜ ์ƒˆ๋กœ์šด ๋งฅ๋ฝ๊ณผ ๊ธฐ์กด์˜ ๋งฅ๋ฝ์„ ๊ฒฐํ•ฉํ•˜์—ฌ ์žฌ๊ตฌ์„ฑํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ทธ๊ฒƒ์€ ๋Œ€์ƒ์„ ์„ธ์ƒ์˜ ํ•˜์œ„ ๊ตฌ์„ฑ์š”์†Œ๋กœ ๋ณด๋Š” ์‹œ๊ฐ์—์„œ ๋ฒ—์–ด๋‚˜ ์ž์œ ๋กœ์šด ๊ฐœ๋ณ„์  ๊ฐ์ฒด๋กœ ์ธ์‹ํ•จ์œผ๋กœ์จ ๋ณธ๋ž˜์˜ ๋Œ€์ƒ์ด ๊ฐ€์ง„ ๋‹จ๋ฉด์  ์‹ค์žฌ์— ๊ฐ€๊นŒ์ด ๊ฐ€๊ณ ์ž ํ•œ ์‹œ๋„์˜€๋‹ค. ์—ฐ๊ตฌ์ž๋Š” ๋“œ๋กœ์ž‰์„ ํ†ตํ•ด ๋ณธ๊ฒฉ์ ์œผ๋กœ ์ž‘์—…์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋Œ€์ƒ์˜ ๊ด€์ฐฐ์€ ์ง€๋‚˜๊ฐ€๋ฉฐ ์Šค์ณ๋ณด๊ฑฐ๋‚˜ ์—ฟ๋ณด๋Š” ๋ฐฉ์‹๊ณผ ๊ฐ™์€ ์œ ๋™์ ์ธ ์›€์ง์ž„์„ ํ†ตํ•ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ๊ทธ๊ฒƒ์€ ์–ด๋–ค ์˜๋„๋ฅผ ๋ฐฐ์ œํ•˜๊ณ  ๋Œ€์ƒ์„ ๋ณด๋Š” ๊ฒƒ์œผ๋กœ์จ ๋Œ€์ƒ์˜ ์žˆ๋Š” ๊ทธ๋Œ€๋กœ์˜ ์†”์งํ•œ ์ƒํƒœ๋ฅผ ํŒŒ์•…ํ•˜๊ณ ์ž ํ•œ ๊ฒƒ์ด์—ˆ๋‹ค. ์ง€๊ฐ์„ ํ†ตํ•ด ๊ธฐ์–ต๋œ ๋Œ€์ƒ์˜ ๊ฐ๊ฐ์  ์†์„ฑ๋“ค์€ ๋“œ๋กœ์ž‰์„ ํ†ตํ•ด ํ‘œํ˜„์˜ ๋ณ€ํ™”๊ณผ์ •์„ ๊ฑฐ์น˜๊ฒŒ ๋˜์—ˆ๋‹ค. ๊ทธ๋ฆผ์ผ๊ธฐ๋กœ๋ถ€ํ„ฐ ์‹œ์ž‘ํ•œ ๊ธฐ๋ก ๋ฐฉ์‹์€ ํŒŒํŽธํ™”, ๊ฐ๊ฐ ์†Œํ†ต, ์กฐํ•ฉ๊ณผ ์žฌ์กฐํ•ฉ์˜ ๊ณผ์ •์„ ๊ฑฐ์น˜๋ฉฐ ๋‚˜๋ฆ„์˜ ํ‘œํ˜„๋ฐฉ์‹์„ ์ฐพ์•„ ๋‚˜๊ฐ”๋‹ค. ์ž‘์—…์˜ ์ฃผ์š” ๋Œ€์ƒ์ด ๋œ ๊ฒƒ์€ ์‹ ๋ฐœ, ๊ฐ„ํŒ, ์ง€๋„์˜€๋‹ค. ์ด ์†Œ์žฌ๋“ค์€ ๊ฐ๊ฐ ๊ฐœ๋ณ„์ ์ธ ๊ฐ์ฒด์ธ ๋™์‹œ์— ์„œ๋กœ ๊ด€๊ณ„๋ง์„ ํ˜•์„ฑํ•จ์œผ๋กœ์จ ์—ฐ๊ตฌ์ž๋กœ ํ•˜์—ฌ๊ธˆ ์„ธ์ƒ์„ ํ•˜๋‚˜์˜ ์••์ถ•๋œ ํ˜•ํƒœ์˜ ๋ชจ๋ธ๋กœ์จ ์ธ์‹ํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ์‹ ๋ฐœ์€ ๋ถ„ํ•ด์™€ ์žฌ์กฐํ•ฉ์˜ ๊ณผ์ •์„ ๊ฑฐ์น˜๋ฉฐ ๊ฐœ์ธ์˜ ์ž์˜์‹์„ ๋ฐ˜์˜ํ•˜๋Š” ์†Œ์žฌ๋กœ์จ ์„ ํƒ๋˜์—ˆ๋‹ค. ๊ฐ„ํŒ์€ ๊ฐ๊ฐ์˜ ๋†€์ด๋ฅผ ํ†ตํ•œ ๋Œ€์ƒ๊ณผ์˜ ์†Œํ†ต ๋ฐฉ์‹์„ ๋“œ๋Ÿฌ๋‚ด๋Š” ๊ฒƒ์ด๊ณ , ์ง€๋„๋Š” ์‹ ๋ฐœ๊ณผ ๊ฐ„ํŒ ์ž‘์—…์—์„œ ๋ฐœ์ „๋œ ๊ฒฝ๋กœ์™€ ์ด๋™์˜ ์˜๋ฏธ๋ฅผ ๋‹ด๊ณ  ์žˆ๋‹ค. ์ž‘์—…์ด ์‹ฌํ™”ํ•˜๋ฉด์„œ ๋Œ€์ƒ์˜ ๋ณตํ•ฉ์  ์†์„ฑ- ์ฆ‰ ์˜๋ฏธ์˜ ๊ฐ€๋ณ€์„ฑ๊ณผ ๋‹ค์ธต์„ฑ์„ ํ‘œํ˜„ํ•˜๊ธฐ ์œ„ํ•ด ํ•ด์ฒด์  ์กฐํ•ฉ๋ฐฉ์‹์„ ์ ๊ทน์ ์œผ๋กœ ์‹œ๋„ํ•˜์˜€๋‹ค. ์ด๋Š” ๋Œ€์ƒ์˜ ๋ณธ๋ž˜ ํŠน์„ฑ์„ ๋“œ๋Ÿฌ๋‚ด๋ ค๋Š” ๋…ธ๋ ฅ์˜ ์ผํ™˜์ด๋‹ค. ๋˜ํ•œ, ํ›„๋ฐ˜๋ถ€ ์„ ํ†ตํ•ด์„œ๋Š”, ๋Œ€์ƒ์„ ํ•ด์„ํ•˜๋Š” ๊ธฐ์กด์˜ ์‚ฌํšŒยท์ •์น˜์  ๋งฅ๋ฝ๊ณผ ์—ฐ๊ตฌ์ž ์Šค์Šค๋กœ๊ฐ€ ํŒŒ์•…ํ•œ ๋Œ€์ƒ์˜ ๊ฒฝํ—˜์  ์ธ์ƒ๊ณผ ์กฐํ˜•์  ์ธก๋ฉด ๋“ฑ ๋‹ค์–‘ํ•œ ๋งฅ๋ฝ๋“ค์„ ํ•˜๋‚˜๋กœ ๊ฒฐํ•ฉํ•ด ์žฌ๊ตฌ์„ฑํ•˜์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ๊ธฐ์กด ๋งฅ๋ฝ์˜ ์„ธ๋ถ€ ๋ฌธ๋งฅ์„ ๊ต๋ž€ํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์ƒˆ๋กœ์šด ๋งฅ๋ฝ์„ ์žฌ๊ตฌ์„ฑํ•˜์—ฌ ๋ณด๋‹ค ๋” ์‹ค์žฌ์— ๋‹ค๊ฐ€์„œ๊ณ ์ž ์˜๋„ํ•˜์˜€๋‹ค. ์„ค์น˜์ž‘์—…์„ ํ†ตํ•ด ์‹ค์ œ ์žฅ์†Œ๋‚˜ ์„ธ๊ณ„์ง€๋„๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๊ฐ€์ƒ์  ์ƒํ™ฉ์„ ์—ฐ์ถœํ•œ ์ผ๋ จ์˜ ์ž‘์—…๋“ค, ์˜์ƒ๋งค์ฒด๋กœ ๋””์ง€ํ„ธ ์ด๋ฏธ์ง€์˜ ๋ชฝํƒ€์ฅฌ์  ํŠน์„ฑ์„ ํ™•์žฅํ•œ ์• ๋‹ˆ๋ฉ”์ด์…˜ ํ˜•์‹์˜ ์ด๋ฏธ์ง€ํŽธ์ง‘ ์ž‘์—…, ์‹ ์ฒด์˜ ์›€์ง์ž„๊ณผ ๋ชฉ์†Œ๋ฆฌ๋ฅผ ์˜์ƒ์— ๊ฐœ์ž…์‹œ์ผœ ๊ฐ€์ƒ์  ๊ณต๊ฐ„์œผ๋กœ์„œ์˜ ์˜์ƒ์„ ์‹ค์ œ ๊ณต๊ฐ„์˜ ์˜์—ญ์œผ๋กœ ๋Œ์–ด๋‚ธ ์ž‘์—…๋„ ๋ชจ๋‘ ๊ฐ™์€ ์„ ์ƒ์˜ ์ž‘์—…์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋Œ€์ƒ์—์„œ ํŒŒ์•…ํ•œ ๋‚ด์šฉ์„ ํ‘œํ˜„ํ•œ ์ผ๋ จ์˜ ์ž‘์—…์— ๋‹ด๊ธด ์ฃผ์ œ์™€ ์˜๋ฏธ๋ฅผ ์ •๋ฆฝํ•˜๋ ค๋Š” ๊ฒƒ์ด๋‹ค. ์—ฐ๊ตฌ์ž๋Š” ์ž‘์—…์„ ํ†ตํ•ด ๊ธฐ์กด์˜ ์ด์„ฑ์  ํŒ๋‹จ์ด๋‚˜ ๋…ผ๋ฆฌ์  ์‚ฌ๊ณ ๋กœ ์‚ฌํšŒ๋ฅผ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์„ ๋„˜์–ด, ์ธ๊ฐ„์ด ๊ฐ€์ง„ ์ž์œ ๋กœ์šด ๊ฐ๊ฐ์  ๋ณธ์„ฑ์„ ํ™•์žฅํ•จ์œผ๋กœ์จ ์„ธ์ƒ์„ ๊ตฌ์„ฑํ•˜๋Š” ์—ฐ๊ฒฐ๊ณ ๋ฆฌ๋กœ์„œ์˜ ๋Œ€์ƒ์˜ ๋˜ ๋‹ค๋ฅธ ์‹ค์žฌ๋ฅผ ๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋‹ค์–‘ํ•œ ๋งฅ๋ฝ์œผ๋กœ ์—ฐ๊ฒฐ๋œ ์„ธ์ƒ์˜ ๋ชจ์Šต์„ ๋ณผ ์ˆ˜ ์žˆ๋Š” ์‹œ๊ฐ์„ ์–ป๊ณ ์ž ํ•œ๋‹ค.This study started from seeing the modern society as a complex and huge object that constantly releases information. The context for understanding society is composed of information, but too much information looses the context and the information is also removed from the context. This separated information has become difficult to interpret with the existing rational framework. When we look at nature, we intuitively grasp and understand it through our own experiences and perceptions. In order to grasp the modern human society that is variable, it is necessary to have a fluid attitude as when looking at nature, and I think it is possible through continuous observation of objects through 'playful immersion' and 'contemplation'. In this way, when we focused on the real object, we could experience the nature of the object from an open perspective. It is considered that the object can be an independent autonomous object that can exist without human intervention, and that the original attributes can be perceived through the observation of the observer. To summarize the 'reconstruction of the object through the senses', the experience of the object through the movement of the observer is revealed through the work. Obviously, the viewer's taste was reflected in the view of this object. In addition, tastes acquired by social and environmental factors for a long time are sublimated into aesthetic experiences and serve as criteria for distinguishing and selecting objects. The sensory memories obtained from objects through perception are such as sensations, that is, feelings or impressions, and are accumulated as the experiences of objects are repeated to form their own connection-context. In this way, the context created by the nature of the object is different from the purposeful context of the existing object. The new context is formed by constructing the aspects obtained through the accidental discovery and experience of the object and the aspects grasped through the formative perspective. It is close to the canonical nature of the object grasped by attention, contemplation, and empathy that operates regardless of intention beyond the standardized standard of discriminating art works. Based on this, the new context of the object and the existing context were combined to reconstruct it. It was an attempt to get closer to the cross-section reality of the original object by recognizing it as an individual object free from the view of the object as a sub-element of the world. The researcher worked in earnest through drawing. Observation of objects was accomplished through fluid movements such as passing and peeping. It was intended to grasp the honest state of the object as it is by excluding the intention and looking at the object. The sensory attributes of objects remembered through perception went through a process of change of expression through drawing. The recording method, which started from the diary, went through fragmentation, sensory communication, combination, and recombination to find out its own expression method. The main objects of the work were shoes, signs, and maps. Each of these materials is an individual object, and at the same time, by forming a network of relationships with each other, the researchers recognized the world as a model of a compressed form. Shoes were selected as a material that reflects the 'individual self-consciousness' through the process of disassembly and recombination. The signboard reveals the 'communication method with the object' through the play of the senses, and the map contains the meanings of 'path' and 'movement' developed in shoes and signboard work. As the work deepened, the deconstructive combination method was actively attempted to express the complex attributes of the object-variability and multi-layer of meaning. This is part of an effort to reveal the original characteristics of the object. In addition, through the latter part of , various contexts, such as the existing social and political context of interpreting objects and the empirical impression and formative aspects of the objects that the researcher himself grasped, were combined into one and reconstructed. Furthermore, the intention was to reorganize the new context in a way that disturbs the detailed context of the existing context to reach more reality. Through a series of installations, a series of works that create a virtual situation on a real place or a world map, an animation-type image editing work that expands the montage characteristics of a digital image with a video medium, and the movement and voice of the body which are intervened in the moving image, the work of pulling the moving image in a virtual space into the real space is also a work on the same line. The purpose of this study is to establish the subject and meaning contained in a series of works expressing the contents grasped by the subject. The researcher tried to see 'another reality of the object' as a link that constitutes the world by expanding the free sensory nature of human beings, beyond understanding the society through existing rational judgment or logical thinking through work. Through this, I want to get a perspective to see the world connected in various contexts.I. ์„œ๋ก  1 II. ์‹ค์žฌ์˜ ์ž๊ฐ๊ณผ ๋Œ€์ƒ์˜ ์ธ์‹ 7 1. ์‹ค์žฌ์˜ ์ž๊ฐ 7 1) ๋‹ค์›์  ์ž์•„ ์ •์ฒด์„ฑ 8 2) ์‚ฌํšŒ์˜ ๋‹ค์–‘์„ฑ๊ณผ ํš์ผ์„ฑ์˜ ๊ณต์กด 12 2. ๋Œ€์ƒ์˜ ์ธ์‹ 1) ๋Œ€์ƒ๊ณผ์˜ ์œ ๊ธฐ์  ๊ฑฐ๋ฆฌ๋‘๊ธฐ 16 2) ๊ฐ๊ฐ์˜ ํšŒ๋ณต์„ ํ†ตํ•œ ์‹ค์žฌ์˜ ์ธ์‹ 21 III. ๋Œ€์ƒ์˜ ํƒ์ƒ‰๊ณผ ๋ฐœ๊ฒฌ 32 1. ์ด๋™์„ ํ†ตํ•œ ํƒ์ƒ‰ 32 1) ์—ฟ๋ณด๊ธฐ๋ฅผ ํ†ตํ•œ ๊ด€์ฐฐ 32 2) ๊ทธ๋ฆฌ๊ธฐ๋ฅผ ํ†ตํ•œ ๊ธฐ๋ก 36 2. ๋Œ€์ƒ์˜ ๋ฐœ๊ฒฌ 1) ์‹ ๋ฐœ; ์ž์˜์‹์˜ ๋ฐœ๊ฒฌ 42 2) ๊ฐ„ํŒ; ์„ธ์ƒ๊ณผ์˜ ์†Œํ†ต ๊ฒฝ๋กœ 45 3) ์ง€๋„; ๊ธฐํ˜ธ๋“ค์˜ ์ง‘ํ•ฉ์ฒด 49 IV. ๋Œ€์ƒ์˜ ์กฐํ•ฉ๊ณผ ๋งฅ๋ฝ์˜ ์žฌ๊ตฌ์„ฑ 57 1. ๋Œ€์ƒ์˜ ์†์„ฑ๊ณผ ์กฐํ•ฉ 57 1) ๋Œ€์ƒ์˜ ๋ณตํ•ฉ์  ์†์„ฑ 57 2) ํ•ด์ฒด์  ์กฐํ•ฉ 65 2. ๋งฅ๋ฝ์˜ ์žฌ๊ตฌ์„ฑ 1) ์‚ฌํšŒ์  ๋งฅ๋ฝ๊ณผ ์กฐํ˜•์  ๋งฅ๋ฝ์˜ ๊ฒฐํ•ฉ 71 2) ๋ฌธ๋งฅ์˜ ๊ต๋ž€ 77 V. ๋Œ€์ƒ์˜ ์—ฐ๊ฒฐ๊ณผ ํ™•์žฅ 81 1. ๋Œ€์ƒ์˜ ์—ฐ๊ฒฐ์ฒด์  ์†์„ฑ 81 2. ๋Œ€์ƒ ์ธ์‹์˜ ํ™•์žฅ 85 1) ์„ค์น˜; ์ง€๋„ ๊ณต๊ฐ„์˜ ํ™•์žฅ 85 2) ์• ๋‹ˆ๋ฉ”์ด์…˜; ์ด๋ฏธ์ง€์˜ ๋ชฝํƒ€์ฅฌ์  ์—ฐ๊ฒฐ 93 3) ์˜์ƒ; ๊ฐ€์ƒ ๊ณต๊ฐ„์˜ ์‹ค์ œํ™” 95 VI. ๊ฒฐ๋ก  101Docto

    Clinical study of ketamine anesthesia for cesarean section

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    ์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] Phencylidine ์œ ๋„์ฒด์— ์†ํ•˜๋Š” Ketamine์€ 1965๋…„ Domino ๋“ฑ์— ์˜ํ•ด ๊ฐ•ํ•œ ์ง„ํ†ตํšจ๊ณผ๊ฐ€ ์žˆ๋‹ค๊ณ  ๋ณด๊ณ ๋œ ์ด๋ž˜ ์—ฌ๋Ÿฌ ๋ถ„์•ผ์˜ ์ˆ˜์ˆ ์„ ์œ„ํ•œ ๋งˆ์ทจ์— ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ์‚ฌ์šฉ๋˜์–ด ์™”๋‹ค. ์‚ฐ๊ณผ๋งˆ์ทจ์— ์žˆ์–ด์„œ ๋‘๊ฐ€์ง€ ์  ์ฆ‰ ๋งˆ์ทจ์œ ๋„์‹œ ์ผ์‹œ์ ์ธ ํ˜ˆ์••์ƒ์Šน์ž‘์šฉ๊ณผ ํšŒ๋ณต์‹œ ์ •์‹ ํฅ๋ถ„ ํ˜„์ƒ์„ ์ œ์™ธํ•œ๋‹ค๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ด์œ ๋กœ ์ ์ ˆํ•˜๊ฒŒ ์“ธ ์ˆ˜ ์žˆ๋‹ค๊ณ  ํ•˜์˜€๋‹ค. ์ฆ‰ โ‘  ์‹ ์ƒ์•„๋ฅผ ์–ต์••์‹œํ‚ค๋Š” ์ผ์ด ์ ๊ณ , โ‘ก ๋งˆ์ทจ์œ ๋„์‹œ ํ›„๋‘๋ฐ˜์‚ฌ์™€ ๊ธฐ๋„๊ฐ€ ์œ ์ง€๋˜๋ฉฐ โ‘ข ์•ˆ์ „๋ฒ”์œ„๊ฐ€ ๋„“๊ณ  ํ˜ธํก์ด๋‚˜ ์‹ฌ์žฅ์— ๋Œ€ํ•œ ์–ต์••์ž‘์šฉ์ด ์ ์œผ๋ฉฐ โ‘ฃ ์ž๊ถ์ˆ˜์ถ•์ด ๊ฐ•ํ•˜์—ฌ ์ถœํ˜ˆ์ด ์ ๊ณ  โ‘ค ์ˆ˜์ˆ  ํ›„ ์˜ค์‹ฌ ๊ตฌํ† ๊ฐ€ ์ ์œผ๋ฉฐ ํšŒ๋ณต์ด ๋น ๋ฅด๋‹ค.(Galloon, 1971; Galloon ๋ฐ Dick, 1971; Spoerel, 1971). Corssen(1974) ๊ณผ McDonald๋“ฑ (1972)๋„ ์ œ์™•์ ˆ๊ฐœ์ˆ ์— Ketamine์„ ์‚ฌ์šฉํ•˜๊ณ  ์‚ฐ๋ชจ์™€ ํƒœ์•„์—๊ฒŒ ์ข‹์€ ์ ์ด ์žˆ์Œ์„ ๋ณด๊ณ ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์ด์ ์— ์ฐฉ์•ˆํ•˜์—ฌ ์ €์ž๋Š” ์ œ์™•์ ˆ๊ฐœ์ˆ ์— ๋Œ€ํ•œ ๋งˆ์ทจ์ค‘ thiopental๋กœ ๋งˆ์ทจ์œ ๋„ํ•œ 25์˜ˆ์™€ ketamine์œผ๋กœ ๋งˆ์ทจํ•œ 27์˜ˆ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€์œผ๋ฉฐ ํ›„์ž๋Š” ๋‹ค์‹œ ๊ทธ ๋งˆ์ทจ๋ฐฉ๋ฒ•์— ๋”ฐ๋ผ ์ฆ‰ โ‘  ketamine ๋‹จ๋…๋งˆ์ทจ์‹œ โ‘ก ketamine ๊ณผ succinylcholine์œผ๋กœ ๋งˆ์ทจ์œ ๋„ ๋ฐ ๊ธฐ๊ด€๋‚ด์‚ฝ๊ด€์‹œ ๊ทธ๋ฆฌ๊ณ  โ‘ข ketamine ๊ณผ pancuronium์œผ๋กœ ์œ ๋„ ๋ฐ ์‚ฝ๊ด€ํ•œ 3๊ตฐ์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์—ฌ ๋งˆ์ทจ์ „, ์ค‘ ๋ฐ ํ›„์˜ ์‚ฐ๋ชจ ๋ฐ ํƒœ์•„์˜ ์ƒํƒœ๋ฅผ ๋น„๊ต๊ด€์ฐฐํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๋ก ์„ ์–ป์—ˆ๋‹ค. 1) Ketamine์€ ๊ฐ•๋ ฅํ•˜๊ณ ๋„ ๋น ๋ฅธ ์ง„ํ†ตํšจ๊ณผ๋ฅผ ๋‚˜ํƒ€๋ƒˆ์œผ๋ฉฐ ์‘๊ธ‰ ๋ฐ ์„ ํƒ์ˆ˜์ˆ ์— ๋ชจ๋‘ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. 2) ๋งˆ์ทจ๋ฐฉ๋ฒ•์— ์žˆ์–ด์„œ๋Š” thiopental ๋˜๋Š” ketamine ๋‹จ๋…๋งˆ์ทจ๋ณด๋‹ค ์•„์‚ฐํ™”์งˆ์†Œ(N^^2 O)์™€ ๊ทผ์œก์ด์™„์ œ๋ฅผ ํ•จ๊ป˜ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด ์ข‹์œผ๋ฉฐ succinylcholine์„ ๋ฐ˜๋ณต์ •์ฃผํ•˜๋Š” ๊ฒƒ๋ณด๋‹ค pancuronium์„ 1ํšŒ ์ •์ฃผํ•˜๋Š” ๊ฒƒ์ด ์ข‹์•˜๋‹ค. 3) ๋งˆ์ทจ์œ ๋„์‹œ ketamine์€ 2mg/kg ์ดํ•˜์˜ ์ •์ฃผ๊ฐ€ ์ถ”์ฒœํ•  ๋งŒํ•˜๋ฉฐ ๊ทธ๋ณด๋‹ค ์šฉ๋Ÿ‰์ด ๋งŽ์„ ๋•Œ๋Š” ํƒœ์•„๋ฅผ ์–ต์ œ์‹œํ‚ค๋Š” ๊ฒฝํ–ฅ์ด ์žˆ์—ˆ๋‹ค. 4) Ketamine์€ ์‚ฐ๋ชจ์˜ ํ˜ˆ์••์„ ์ƒ์Šน์‹œํ‚ค๋Š” ํšจ๊ณผ๊ฐ€ ์žˆ์—ˆ์œผ๋ฉฐ ketamine-pancuronium ๋งˆ์ทจ๋•Œ๋Š” ์ˆ˜์ถ•๊ธฐ ๋ฐ ํŠนํžˆ ํ™•์žฅ๊ธฐํ˜ˆ์••์„ ํ˜„์ €ํžˆ ์ฆ๊ฐ€์‹œํ‚ด์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. 5) Ketamine์€ ์‚ฐ๋ชจ์˜ ๋งฅ๋ฐ•์ˆ˜๋ฅผ ์ฆ๊ฐ€์‹œ์ผฐ์œผ๋ฉฐ ketamine-pancuronium ๋ฐฉ๋ฒ•์—์„œ๋Š” ํƒ€๊ตฐ์— ๋น„ํ•ด ๋งˆ์ทจ์œ ๋„์‹œ 28.5%์˜ ์˜์˜์žˆ๋Š” ์ฆ๊ฐ€๋ฅผ ๋ณด์•˜๋‹ค. 6) Ketamine ๊ณผ๋Ÿ‰์œผ๋กœ ์ธํ•œ 1์˜ˆ๋ฅผ ์ œ์™ธํ•˜๊ณ ๋Š” ํƒœ์•„์–ต์••์˜ ์˜ˆ๋Š” ์—†์—ˆ์œผ๋ฉฐ I.D.I.๊ฐ€ ๊ธธ์–ด์งˆ ๋•Œ Apgar score๊ฐ€ ๋‚˜๋น ์ง€๋Š” ๊ฒƒ์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. 7) Ketamine ๋งˆ์ทจ์‹œ ํƒœ์•„๋งŒ์ถœํ›„ ์ด์™„์„ฑ์ž๊ถ์ถœํ˜ˆ์˜ ์˜ˆ๋Š” ์—†์—ˆ๋‹ค. 8) ์ˆ˜์ˆ  ๋ฐ ๋งˆ์ทจ์‹œ๊ฐ„์ด ๊ฐ€์žฅ ์งง๊ฒŒ ๊ฑธ๋ฆฐ ๊ตฐ์€ hiopental ๊ตฐ ์ด์—ˆ์œผ๋ฉฐ ํšŒ๋ณต์ด ๊ฐ€์žฅ ๋นจ๋ž๋˜ ๊ตฐ์€ ketamine-pancuronium-ether ๋งˆ์ทจ๋ฐฉ๋ฒ•์ด์—ˆ๋‹ค. ์ด์ƒ์˜ ์„ฑ์ ์„ ์ข…ํ•ฉํ•˜์—ฌ ๋ณผ ๋•Œ ketamine ๊ทผ์œก์ด์™„์ œ N^^2 O ๊ธฐ๊ด€๋‚ด ๋งˆ์ทจ๋ฒ•์€ ๊ณ ํ˜ˆ์••์„ ๋™๋ฐ˜ํ•œ ์ž„์‹ ์ค‘๋…์ฆ์ด๋‚˜ ์ •์‹ ๊ณผ์  ๋ฌธ์ œ๋ฅผ ๊ฐ€์ง„ ์‚ฐ๋ชจ๋‚˜ ํƒœ์•„ ์ ˆ๋ฐ•์ƒํƒœ๋ฅผ ์ œ์™ธํ•œ๋‹ค๋ฉด ์ œ์™•์ ˆ๊ฐœ์ˆ ์‹œ ์‚ฐ๋ชจ์™€ ํƒœ์•„์— ๋Œ€ํ•œ ์•ˆ์ „ํ•˜๊ณ ๋„ ๋งŒ์กฑ์Šค๋Ÿฌ์šด ์œ ์šฉํ•œ ๋งˆ์ทจ๋ฐฉ๋ฒ•์˜ ํ•˜๋‚˜๋ผ๊ณ  ์ƒ๊ฐํ•œ๋‹ค. [์˜๋ฌธ] The pharmacological actions of ketamine in human volunteers were reported by Domino et al. in 1965, and use in 130 patients by Corssen and Domino (1966). Since then, its use in a wide variety of surgical procedures has been reported throughout the world. Several authors (Galloon, 1977; Galloon and Dick, 1971; Spoerel, 1971) reported that ketamine gas several advantages over conventional anesthetics. The advantages of using ketamine anesthesia are : preservation of pharyngeal reflex and airway maintenance during induction of anesthesia, stimulation, fast recovery, little nausea and vomiting after anesthesia, little depression of the fetus and good uterine contraction with minimal bleeding. On the other hand, ketamine has also disadvantages : elevation of arterial pressure and pulse rate temporarily during induction of anesthesia, poor muscle relaxation and post-operative psychotic reactions are not uncommonly found. The author tried to find out the easibility of ketamine anesthesia for Cesarean section over the conventional method of thiopental-muscle relaxant-N^^2 O with IPPV technique. Materials and Methods 52 Korean parturients were selected for Cesarean section including emergency and elective operation for this study. Thioental group. 25 cases were induced for anesthesia with about 3.5ใŽŽ/ใŽ of thiopental and intubated with the help of 1ใŽŽ/ใŽ of succinylcholine. After delivering the baby, anesthesia was maintained with N^^2 O - O^^2-ether throughout the procedures. Ketamine A group. 9 cases, just before skin incision, were injected intravenously with ketamine 1.9ใŽŽ/ใŽ slowly for over one minute with or without N^^2 O:O^^2 (2:1 L/min) through a mask. After delivering the fetus, a supplement of ketamine and diazepam 10ใŽŽ โ…ฃ was given intermittently. Ketamine B group. Anesthesia was induced by 1.9ใŽŽ/ใŽ ketamine and 1ใŽŽ/ใŽ of succinylcholine with endotracheal intubation. After delivery, N^^2O with O^^2 and additional ketamine were given to 9 patients. Ketamine C group. Anesthesia was performed with 1.7ใŽŽ/ใŽ of ketamine, 0.08ใŽŽ/ใŽ of pancuronium, N^^2 0, with endotracheal intubation for 9 patients, ether supplement was given following delivery. Conclusion With these mentioned methods of anesthesia, the author formed several conclusions about ketamine anesthesia in Cesarean section. 1. Ketamine can be used as the main anesthetic or for induction in elective and emergency Cesarean section because of its rapid onset and intense analgestic effect. 2. As in the method of anesthesia, it is useful to combine N^^2 O-O^^2 mixture and muscle relaxants such as succinylcholine or pancuronium. The technique is more suitable for maintenance of anesthesia because of the poor muscle relaxation of ketamine alone. 3. For induction of anesthesia, under 2ใŽŽ/ใŽ of ketamine is advisable. Exceeding this dose, the infant respiration is more likely to be depressed because of hypertonicity of the skeletal musculature. 4. Ketamine has a maternal cardiovascular stimulation effect particularly diastolic blood pressure and pulse rate in the Ketamine-pancuronium-N^^2 O-intubation group. 5. Less bleeding was found during and after the delivery, recovery due to an increased uterine contraction from ketamine. 6. Disadvantage of ketamine included a prolonged maternal recovery period, and newborn respiratory depression and these seemed to be dose related. From th above, ketamine anesthesia appears to be another safe and satisfactory method of anesthesia for Cesarean section, provided that toxemia of pregnancy patients with hypertension and patients who have had psychotic problems previously are avoided.restrictio

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์„œ์–‘ํ™”๊ณผ ์„œ์–‘ํ™”์ „๊ณต,2000.Maste
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