45 research outputs found

    ์ž์„ฑ ๋””์Šคํฌ ๋ฐฐ์—ด ๋‚ด ๊ฒฐํ•ฉ๋œ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด์˜ ๋™์  ๊ฑฐ๋™ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์žฌ๋ฃŒ๊ณตํ•™๋ถ€,2020. 2. ๊น€์ƒ๊ตญ.์ž๊ธฐ ์†Œ์šฉ๋Œ์ด๋Š” ์ˆ˜ ๋งˆ์ดํฌ๋กœ๋ฏธํ„ฐ ํฌ๊ธฐ ํ˜น์€ ๊ทธ ์ดํ•˜์˜ ๊ฐ•์ž์„ฑ ๊ตฌ์กฐ์ฒด์—์„œ ์•ˆ์ •์ ์œผ๋กœ ํ˜•์„ฑ๋˜๋Š” ํŠน์ดํ•œ ๋ฐฐ์—ด ๊ตฌ์กฐ๋ฅผ ๋งํ•œ๋‹ค. ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด๋Š” ๋ฐ•๋ง‰๋ฉด์— ์ˆ˜์งํ•œ ์ˆ˜์‹ญ ๋‚˜๋…ธ๋ฏธํ„ฐ ํฌ๊ธฐ์˜ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด ํ•ต๊ณผ, ๊ทธ ์ฃผ์œ„์˜ ํ‰๋ฉด ๋‚ด ํšŒ์ „ํ•˜๋Š” ๋ชจ์–‘์œผ๋กœ ๋ฐฐ์—ด๋œ ์Šคํ•€๋“ค๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด์— ์™ธ๋ถ€ ์ž๊ธฐ์žฅ ํ˜น์€ ์ „๋ฅ˜ ๋“ฑ์„ ์ธ๊ฐ€ํ•˜๋ฉด ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด ํ•ต์ด ํšŒ์ „์šด๋™์„ ํ•˜๋Š” ์„ฑ์งˆ์ด ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด๋Š” ํ•ต์˜ ๋‘ ๊ฐ€์ง€ ์žํ™”๋ฐฉํ–ฅ๊ณผ ์ฃผ๋ณ€์— ๋ฐฐ์—ด๋œ ์Šคํ•€๋“ค์˜ ๋‘ ๊ฐ€์ง€ ํšŒ์ „๋ฐฉํ–ฅ์˜ ์กฐํ•ฉ์œผ๋กœ ๋„ค ๊ฐœ์˜ ๋™์ผํ•œ ๊ธฐ์ € ์—๋„ˆ์ง€ ์ค€์œ„๋ฅผ ๊ฐ€์งˆ ์ˆ˜ ์žˆ๊ณ , ์—ด์ ์œผ๋กœ ๋งค์šฐ ์•ˆ์ •ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋น„ํœ˜๋ฐœ์„ฑ ์ •๋ณด์ €์žฅ ์†Œ์ž๋กœ ์‘์šฉ ๊ฐ€๋Šฅํ•˜๋‹ค. ๋˜ํ•œ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ๊ฒฐํ•ฉ๋œ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด ์‚ฌ์ด์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด ํ•ต์˜ ์ง‘๋‹จ์  ํšŒ์ „์šด๋™์€ ์ƒˆ๋กœ์šด ์‹ ํ˜ธ์ „๋‹ฌ์˜ ๋งค๊ฐœ์ฒด๋กœ ์ด์šฉ๋  ์ˆ˜ ์žˆ์–ด ์ •๋ณด์ฒ˜๋ฆฌ ์†Œ์ž๋กœ์˜ ์‘์šฉ์„ฑ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์–ด์™”๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฏธ์†Œ์ž๊ธฐ ์ „์‚ฐ๋ชจ์‚ฌ ๋ฐ ์‹คํ—˜์„ ์ด์šฉํ•˜์—ฌ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด์˜ ๋™์  ๊ฑฐ๋™๊ณผ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด ๊ฐ„์˜ ๋™์  ์ƒํ˜ธ์ž‘์šฉ ์—ฐ๊ตฌ์— ์ดˆ์ ์„ ๋‘๊ณ ์žˆ๋‹ค. ์ž๊ธฐ ๋””์Šคํฌ ๋ฐฐ์—ด์—์„œ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด ๊ฒฐํ•ฉ ๋ชจ๋“œ, ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด ํ•ต ๋ฐ˜์ „ ๋ฐฉ๋ฒ• ๋ฐ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด ํ•ต์˜ ํšŒ์ „์šด๋™ ์‹ ํ˜ธ ์ „๋‹ฌ์˜ ์ œ์–ด์— ๊ด€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ฃผ ๋‚ด์šฉ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด์˜ ๋™์  ๊ฑฐ๋™ ์ œ์–ด ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด ์ƒˆ๋กœ์šด ๊ฐœ๋…์˜ RS ๋ž˜์น˜ ๋…ผ๋ฆฌ ์†Œ์ž, ์‹œ๋ถ„ํ•  ๋ฐ ์ฃผํŒŒ์ˆ˜ ๋ถ„ํ•  ๋””๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ์†Œ์ž๋ฅผ ์ œ์•ˆํ•˜๊ณ  ๊ทธ ๋™์ž‘ ํŠน์„ฑ์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด๋ฅผ ์ด์šฉํ•œ ์†Œ์ž๋“ค์€ ๋น„ํœ˜๋ฐœ์„ฑ์ด๋ฉฐ, ๊ฑฐ์˜ ๋ฌด์ œํ•œ์˜ ์ˆ˜๋ช…์„ ๊ฐ€์ง€๊ณ , ์—๋„ˆ์ง€๊ฐ€ ์ ๊ฒŒ ๋“œ๋Š” ๋“ฑ ๋งŽ์€ ์žฅ์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด๋Š” ๊ทธ ํŠน์„ฑ์˜ ์ œ์–ด๊ฐ€ ๋งค์šฐ ์šฉ์ดํ•ด์„œ ํ–ฅํ›„ ๊ฐœ๋ฐœ๋  ์Šคํ•€ํŠธ๋กœ๋‹‰์Šค ์†Œ์ž๋กœ ์‘์šฉ๋  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ฐจ์„ธ๋Œ€ ์Šคํ•€ํŠธ๋กœ๋‹‰์Šค ๊ธฐ์ˆ ๋กœ์„œ ์ž๊ธฐ ์†Œ์šฉ๋Œ์ด์— ๊ธฐ๋ฐ˜ํ•œ ๋…ผ๋ฆฌ ์†Œ์ž ๋ฐ ์ •๋ณด ์ฒ˜๋ฆฌ ์žฅ์น˜์˜ ๊ตฌํ˜„ ๊ฐ€๋Šฅ์„ฑ์„ ๋ณด์—ฌ์ค€๋‹ค.In the sub-micrometer-size ferromagnetic structure, the magnetic vortex is in a strongly stable ground state characterized by an in-plane curling magnetization around and an out-of-plane magnetization in the central region. The magnetic vortex is characterized by clockwise (CW) or counter-clockwise (CCW) curling in-plane magnetizations around a single vortex core in which region magnetizations are perpendicularly oriented either upward or downward. In isolated disks, applied external forces induce vortex excitations, among which a translational mode exists in which the vortex core gyrates around its equilibrium position at a characteristic eigenfrequency. Vortex-core switching can be accomplished with low power consumption when vortex gyrations are resonantly excited. Moreover, the gyration modes of individual vortex cores in a periodic array of patterned vortex-state disks are coupled with each other, thus yielding collectively coupled motions of the individual cores. On the basis of such novel dynamic characteristics, non-volatile memory and information processing devices using magnetic vortex have been proposed. This work focused on dynamic interaction between vortex-state ferromagnetic structures and its applications, utilizing micromagnetic simulations, analytical calculations, and experiments. The dynamic behaviors of vortex-gyration-coupled modes, vortex-core switching, and propagation of vortex-core gyration signal in magnetic-disk-network devices are investigated. Based on the combinations of the novel dynamic characteristics of vortices in dipolar-coupled disks, a new concept RS latch logic, time- and frequency-division demultiplexer device operations are explored. Magnetic vortex has many advantages such as non-volatility, almost unlimited endurance, and low power operation. Furthermore, a rich tunability of magnetic vortices makes them adoptable as future spintronics devices. This work can pave the way for possible implementation of logic gates and information processing devices based on coupled magnetic vortices.1. Introduction 1 2. Research Background 5 2.1. Magnetization dynamics and micromagnetics 5 2.1.1. Landau-Lifshitz-Gilbert equation 5 2.1.2. Effective fields in the LLG equation 8 2.2. Vortices in magnetic microstructures and their dynamics 10 2.2.1. Vortex core gyration 15 2.2.2. Vortex core switching 18 2.2.3. Interaction between magnetic vortices 18 2.3. Experimental methods 20 2.3.1. Photo lithography 20 2.3.2. Electron beam lithography 20 2.3.3. Anisotropic magneto resistance in vortex 21 3. Vortex Core Switching by Propagation of a Gyration-Coupled Mode 23 3.1. Micromagnetic simulation conditions 23 3.2. Coupled modes of gyration for the two types of vortex-state configurations 26 3.3. Concept design of reset-set latch device 32 3.4. Magnitude of oscillating magnetic field and radius of disks dependent switching behavior 36 3.5. Reset-set latch logic operation 39 4. Control of Gyration Signal Propagation in Coupled Magnetic Vortices 43 4.1. Dynamics of the single and coupled disk array 43 4.2. Control of gyration signal propagation by in-plane bias field 50 4.3. Control of gyration signal propagation by vortex core switching 53 4.4. Concept design of time-division demultiplexer device and its operation 60 4.5. Concept design of frequency-division demultiplexer device and its operation 65 5. Electrical Measurement of the Gyrotropic Resonance of a Magnetic Vortex in Circular and Chopped Disks. 68 5.1. Sample fabrication 68 5.2. DC AMR measurement 73 5.3. AC AMR measurement by rectification technique 78 6. Summary 88 Bibliography 90 Publication List 100 Patent List 102 Presentations in Conferences 103Docto

    ์กฐ์„ ํ›„๊ธฐ ็ถฟ็ดฌๅป› ๅคงๆˆฟ์˜ ้ฝ‹้Œข๊ณผ ็ฆฎ้Œข ์šด์˜: ่ญทๅ–ชๆ‰€ ์ž๋ฃŒ์˜ ์‹ค์ฆ ๋ถ„์„

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    Taebang was at the center of the management of myoฬ†njujoฬ†n. Taebang received money from its members and paid money to them through hosangso, chobigye, paeksagye, and sujuiso. The money was both chaetton and yejoฬ†n. In this study, in order to examine the management of chaetton and yejoฬ†n, three types of account books written by Taebang in relation to hosangso, such as hosangso sangyongchโ€™aek, hosangso chโ€™ahachโ€™aek, and hosangso choฬ†njangtuฬ†ngnok, were empirically analyzed. The volume of chaetton and yejoฬ†n and their long-term trend was identified from 1863 to 1900, when hosangso of myoฬ†njujoฬ†n was disappeared into history. Hosangso sangyongchโ€™aek was the account book of chaetton management and hosangso chโ€™ahachโ€™aek was the account book of yejoฬ†n management. Calculation in red letters was made every six months, but in order to know the specific calculation process, we must check hosangso choฬ†njangtuฬ†ngnok. The management of hosangso by myoฬ†njujoฬ†n can be commonly applied to chobigye, paeksagye, and sujuiso. The management of chaetton and yejoฬ†n did not change significantly even after the amendment of regulations in 1878 and the Kabo Reform in 1894. Considering that both chaetton and yejoฬ†n were applied internally to the members of myoฬ†njujoฬ†n, it can be evaluated that myoฬ†njujoฬ†n had been continued as a cooperative association regardless of its procurement function or the aspect of a merchant organization

    Differential Biases and Variabilities of Deep Learning-Based Artificial Intelligence and Human Experts in Clinical Diagnosis: Retrospective Cohort and Survey Study

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    Background: Deep learning (DL)-based artificial intelligence may have different diagnostic characteristics than human experts in medical diagnosis. As a data-driven knowledge system, heterogeneous population incidence in the clinical world is considered to cause more bias to DL than clinicians. Conversely, by experiencing limited numbers of cases, human experts may exhibit large interindividual variability. Thus, understanding how the 2 groups classify given data differently is an essential step for the cooperative usage of DL in clinical application. Objective: This study aimed to evaluate and compare the differential effects of clinical experience in otoendoscopic image diagnosis in both computers and physicians exemplified by the class imbalance problem and guide clinicians when utilizing decision support systems. Methods: We used digital otoendoscopic images of patients who visited the outpatient clinic in the Department of Otorhinolaryngology at Severance Hospital, Seoul, South Korea, from January 2013 to June 2019, for a total of 22,707 otoendoscopic images. We excluded similar images, and 7500 otoendoscopic images were selected for labeling. We built a DL-based image classification model to classify the given image into 6 disease categories. Two test sets of 300 images were populated: balanced and imbalanced test sets. We included 14 clinicians (otolaryngologists and nonotolaryngology specialists including general practitioners) and 13 DL-based models. We used accuracy (overall and per-class) and kappa statistics to compare the results of individual physicians and the ML models. Results: Our ML models had consistently high accuracies (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%), equivalent to those of otolaryngologists (balanced: mean 71.17%, SD 3.37%; imbalanced: mean 72.84%, SD 6.41%) and far better than those of nonotolaryngologists (balanced: mean 45.63%, SD 7.89%; imbalanced: mean 44.08%, SD 15.83%). However, ML models suffered from class imbalance problems (balanced test set: mean 77.14%, SD 1.83%; imbalanced test set: mean 82.03%, SD 3.06%). This was mitigated by data augmentation, particularly for low incidence classes, but rare disease classes still had low per-class accuracies. Human physicians, despite being less affected by prevalence, showed high interphysician variability (ML models: kappa=0.83, SD 0.02; otolaryngologists: kappa=0.60, SD 0.07). Conclusions: Even though ML models deliver excellent performance in classifying ear disease, physicians and ML models have their own strengths. ML models have consistent and high accuracy while considering only the given image and show bias toward prevalence, whereas human physicians have varying performance but do not show bias toward prevalence and may also consider extra information that is not images. To deliver the best patient care in the shortage of otolaryngologists, our ML model can serve a cooperative role for clinicians with diverse expertise, as long as it is kept in mind that models consider only images and could be biased toward prevalent diseases even after data augmentation.ope

    Usefulness of serial measurement of the mean platelet volume to predict multiple organ dysfunction syndrome in patients with severe trauma

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    Objective: The early prediction of multiple organ dysfunction syndrome (MODS) in trauma patients and provision of prompt treatment may improve their outcomes. We investigated the efficacy of the mean platelet volume (MPV) for predicting MODS in cases of severe trauma. Methods: This retrospective, observational cohort study was performed with patients prospectively integrated in a critical pathway of TRAUMA. We analyzed the severe trauma patients admitted to the emergency department (ED), based on the Advanced Trauma Life Support guideline, between January 1, 2011 and May 31, 2017. The outcomes were developed from MODS at least 48 hours after ED admission. Results: A total of 348 patients were enrolled. An increase in the MPV at 12 hours (odds ratio [OR], 2.611; P8.6 fL (OR, 4.831; P<0.001). The area under the receiver operating characteristic curve (AUROC) value of the MPV at 12 hours (0.751; 95% confidence interval [CI], 0.687-0.818; P<0.01) was not inferior than that of Acute Physiology and Chronic Health Evaluation II score, injury severity score, lactate, and total CO2 for predicting MODS. Conclusion: MPV was an independent predictor of MODS development in severe trauma patients. Emergency physicians can use the MPV as an ancillary biomarker for predicting MODS.ope

    The usefulness of lactate as an early predictor of the severity of emergency department patients with postpartum hemorrhage

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    Objective: Only a few studies have examined the role of lactate reflecting on tissue hypoperfusion determining the severity of postpartum hemorrhage (PPH) patients in the emergency department (ED). This study evaluated the utility of the arterial lactate level as a prognostic marker of severity in PPH patients admitted to an ED. Method: This retrospective, observational cohort study was conducted on patients integrated prospectively in a critical pathway of SPEED (Severance Protocol to save postpartum bleeding through Expeditious care Delivery). Adult primary PPH patients admitted to the ED between July 1, 2010 and March 31, 2017 were analyzed. The outcomes were the development of severe PPH including death, hysterectomy, surgical treatment, and massive transfusion. Results: A total of 112 patients were enrolled in this study. An increase in the arterial lactate value was a strong independent predictor of severe PPH. The increasing predictability of severe PPH was closely associated with an arterial lactate โ‰ฅ3.15 mL/L at admission (odds ratio, 13.870; P<0.001). Conclusion: Lactate is an independent predictor of severe PPH and is suitable for a rapid and simple estimation of the severity of PPH. Emergency physicians can use lactate to determine the initial treatment strategies more precisely.ope

    ่พฒๆž—ๅธ‚ๅ ด์—์„œ์˜ ๅ•†ไบบๅœ˜้ซ”์— ๊ด€ํ•œ ็ก็ฉถ : ๅฟ ๅ— ๆดชๅŸŽ ไธ€ๅธถ์˜ ่ค“่ฒ ๅ•†ๅœ˜์„ ไธญๅฟƒ์œผ๋กœ

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

    Coastal Trade Brokers and their Rights in Traditional Korea Revisited Using a Document Database from the Kyujanggak Collection

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    In the 1980s, a series of lists and research of Changt"o Munjลk, a unique collection of royal properties at Kyujanggak Institute, were published. Despite the rapid improvement in the understanding of Yลgaek Chuin, or coastal trade brokers in traditional Korea, no additional progress has been made since the 1990s. This paper attempts to conduct a critical review of existing research and present a novel method of analysis for Korean economic history research through the introduction of a database of Changt"o Munjลk related to Yลgaek Chuin in the provincial areas of Kyลnggi and Ch"ungch"ลng. Three hypotheses are developed in this study. First, the coastal trade brokers did not become major merchants or accumulate huge commercial capital. Second, the rights of coastal trade brokers were converged, standardized, and even concentrated into the royal force in the nineteenth century, while such rights were disputed in the eighteenth century. Lastly, the purchasing price, which is a key indicator of the secondary market of those rights, did not seem to rise sharply, unlike that depicted in existing research.ๆœ้ฎฎ์˜ ๋Œ€ํ‘œ์  ์ƒ์—…๊ธฐ๊ด€์ค‘ ๅฎข็Ž‹๋Š” ๅฎขๅ•†์˜ ไธปไบบ์ด๊ณ ๏ผŒๆ—…้–ฃ์€ ๆ—…ๅ•†์˜ ๅฎฟๅฑ‹์ด๋ผ๋Š” ์„ค๋ช…์ด 20์„ธ๊ธฐ ์ดˆ์— ์ œ์‹œ๋œ ๋ฐ” ์žˆ๋‹ค. ์ด์™€ ๊ฐ™์ด ๊ฐ์ฃผ์™€ ์—ฌ๊ฐ์„์ธ์œ„์ ์œผ๋กœ ๊ตฌ๋ถ„ํ•œ ๊ฒƒ์€ ๊ฐ์ฃผ๊ฐ€ ์ˆ˜ํ–‰ํ•˜๋Š” ์—ฌ๋Ÿฌ ์—…๋ฌด๊ฐ€ ์กฐ์„  ํ›„๊ธฐ ็Ž‹ไบบๆฌŠ์—์„œ ํŒŒ์ƒ๋˜์–ด ๋‹ค์–‘ ํ•˜๊ฒŒ ๋ถ„ํ™”๏ผŒ๋ฐœ์ „๋œ ์‚ฌ์ •์„ ์ดํ•ดํ•˜์ง€ ๋ชปํ•œ ๊ฒฐ๊ณผ ๋ฐœ์ƒํ•œ ์˜ค๋ฅ˜์˜€์Œ์ด 21์„ธ๊ธฐ ์ดˆ์— ๋“ค์–ด ์ง€์ ๋˜์—ˆ๋‹ค. ์•ฝ 100๋…„์ด ๊ฒฝ๊ณผํ•œ ์‹œ์ ์—์„œ๋‚˜๋งˆ ์ด์™€ ๊ฐ™์€ ์ธ์‹์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋œ ๊ฒƒ์€ 1980๋…„๋Œ€์— ์ง‘์ค‘์ ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๋˜ ์ผ๋ จ์˜ ๆ—…ๅฎขไธปไบบ ์—ฐ๊ตฌ์— ํž˜์ž…์€ ๋ฐ” ํฌ๋‹ค. ์—ฌ๊ฐ์ฃผ์ธ์„ ๋‚ด๋ฅ™์˜ ๋„์‹œ๋‚˜ ์ƒ์—…์ค‘์‹ฌ์ง€๋“ค์—์„œ ํ™œ๋™ํ•˜๋˜ ์ƒ์—…์ž๋ณธ์œผ๋กœ๏ผŒ์„ ์ƒ์ฃผ์ธ์„ ๊ฐ•๊ฐ€๋‚˜ ๋ฐ”๋‹ค๊ฐ€์˜ ํฌ๊ตฌ๋“ค์— ํ˜•์„ฑ๋œ ๋„์‹œ๋‚˜ ์ƒ์—…์ค‘์‹ฌ์ง€๋“ค์—์„œ ํ™œ๋™ํ•˜๋˜ ์ƒ์—…์ž๋ณธ์œผ๋กœ ๊ตฌ๋ถ„ํ•˜๋Š” ๊ฒฌํ•ด๋„ ์žˆ์œผ๋‚˜๏ผŒ์ด๋Š” ์–ด๋””๊นŒ์ง€๋‚˜ ๊ฐ์ข… ๅนดไปฃ่จ˜์— ๋‚˜ํƒ€๋‚˜๋Š” ๋ถ€์‹คํ•˜๊ณ ๋„ ๋‹จํŽธ์ ์ธ ๊ธฐ์‚ฌ์— ์˜์กดํ•œ ๊ฒƒ์ด๋‹ค. ์—ฌ๊ฐ์ฃผ์ธ์— ๊ด€ํ•œ ์ „๋ฉด์  ์—ฐ๊ตฌ๊ฐ€ ๊ฐœ์‹œ๋˜๊ณ  ๋‚œ ํ›„์—๋Š” ์—ฌ๊ฐ์ฃผ์ธ์˜ ๋ฒ”์ฃผ๋ฅผ ๆตฆๅฃๅ•†ๆฅญ๊ณผ ๊ด€๊ณ„๋œ ๊ฒƒ์œผ๋กœ ํ•œ์ •ํ•˜์—ฌ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์ด ์ผ๋ฐ˜์ ์ด๋ฉฐ๏ผŒ์ด๋Š” ๊ฐ์ข… ๅคๆ–‡ๆ›ธ์— ๊ธฐ์žฌ๋œ ํ‘œํ˜„์— ๊ทผ๊ฑฐํ•œ๋‹ค.์ด ๋…ผ๋ฌธ์€ 2008๋…„๋„ ์ •๋ถ€(๊ต์œก๊ณผํ•™๊ธฐ์ˆ ๋ถ€)์˜ ์žฌ์›์œผ๋กœ ํ•œ๊ตญ์—ฐ๊ตฌ์žฌ๋‹จ์˜ ์ง€์›์„ ๋ฐ›์•„ ์—ฐ๊ตฌ๋˜์—ˆ์Œ(NRF-2008-361-A00007)

    ์ฒดํ—˜์˜ ๊ด€์ ์—์„œ ๋ณธ J.W. von Goethe์˜ ๊ต์–‘์†Œ์„คใ€ŽWilhelm Meisters Lehrjahreใ€์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๋…์–ด๋…๋ฌธํ•™๊ณผ,1995.Maste

    ํ™•๋ฅ ๋ถ„ํฌ๋ฅผ ์ด์šฉํ•œ ๋ณตํ•ฉ์žฌ๋ฃŒ์˜ ๊ฐ•๋„์˜ˆ์ธก

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์„ฌ์œ ๊ณ ๋ถ„์ž๊ณตํ•™๊ณผ,1999.Maste
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