18 research outputs found

    ๋””์ง€ํ„ธ ๋ฌด์—ญ์˜ ๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ์™€ ํ†ต์ƒํ˜‘์ • : ์œ ๋Ÿฝ์—ฐํ•ฉ๋ฒ• ๋ฐœ์ „์„ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ตญ์ œ๋Œ€ํ•™์› ๊ตญ์ œํ•™๊ณผ, 2018. 2. ์•ˆ๋•๊ทผ.Along with the development of digital technology, digital forms of trade volumes have been enlarged dramatically. These trades inevitably contain transactions of personal data which arouse concerns over possible misuses. However, trade regulations under the World Trade Organization (WTO) regime and other Free Trade Agreements cannot fully prevent privacy infringement and provide proper remedy measures for individuals. This paper studies the long experiences and efforts of the European Union (EU) which has developed the legal foundations for protecting personal data while maintaining its free flow among countries. In particular, the study concentrates on how the efforts are reflected on the international agreements that the EU concluded and compares those agreements with other international trade regulations. What was interesting was that all the regulations in typical trade agreements naturally linked the data protection concern to the human rights which had long been dismissed when dealing with trade concerns. Most of the trade agreements, however, are limited in providing propriate remedy measures for each individual in that these agreements are based on inter-governmental relationships and advocate collective interests of domestic industries. Unlike other trade agreements, the Privacy Shield Principles, which was agreed between the EU and the U.S. following the nullification of previous Safe Harbor Agreement, is introduced as the most concrete and effective regulations for protecting personal data from companies that may infringe the agreement. As a legal area that does not have unified multilateral frame to regulate the digital trade concern, harmonizing the regulation would enhance market efficiency and predictability of participants. The EU-US Privacy Shield Principles that the two trade giants have already compromised can be model clauses for further harmonizing current fragmented international regulations.I. Introduction 1 II. Privacy Protection of European Union 4 1. Early Legal Development for Data Protection 4 2. General Data Protection Regulation 7 3. The Safe Harbor Agreement 8 4. EU-US Privacy Shield Framework Principle 11 III. Privacy Protection of International Trade Agreements 22 1. Regulation under the World Trade Organization 22 2. Regulation under the Free Trade Agreements 33 2.1. Regional Regulations on the Data Protection 33 2.2. Bilateral Free Trade Agreements 35 2.2.1. US-Korea Free Trade Agreement 35 2.2.2. EU-Korea Free Trade Agreement 36 2.2.3. The Comprehensive Economic and Trade Agreement 37 2.2.4. The Transatlantic Trade and Investment Partnership 40 2.3. Trans Pacific Partnership 44 IV. Privacy Protection of Digital Trade 49 1. Special Natures of the Discussion 49 2. Limits of Existing Regulations 52 3. Harmonization of Data Protection Regulations in Trade Area 56 V. Conclusion 59 Bibliography 61 Abstract in Korean 65Maste

    Transitional Entity Graph based Active Learning for Entity Recognition

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ๋ฌธ๋ด‰๊ธฐ.์ตœ๊ทผ ํŠธ์œ„ํ„ฐ๋‚˜ ํŽ˜์ด์Šค๋ถ๊ณผ ๊ฐ™์€ ์†Œ์…œ๋ฏธ๋””์–ด์™€ ์›น ํŽ˜์ด์ง€, ์ด๋ฉ”์ผ, ๋ธ”๋กœ๊ทธ ๋“ฑ ์˜จ๋ผ์ธ์—์„œ ์ƒ์„ฑ๋˜๋Š” ๊ทธ๋ฆผ, ์˜์ƒ, ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์˜๋ฃŒ๊ธฐ๋ก ๋ฐ ๊ฐ์ข… ํŠธ๋žœ์žญ์…˜ ๋ฐ์ดํ„ฐ ๋“ฑ ๋ฐฉ๋Œ€ํ•œ ์–‘์˜ ๋น„์ •ํ˜•๋ฐ์ดํ„ฐ(Unstructured Data)๊ฐ€ ์ƒ์„ฑ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋น„์ •ํ˜•๋ฐ์ดํ„ฐ์˜ ์ฆ๊ฐ€์™€ ๋”๋ถˆ์–ด ์ธ๊ณต์ง€๋Šฅ ๋ฐ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์˜ ๋ฐœ์ „์œผ๋กœ ํ•ด๋‹น ๊ธฐ์ˆ ๋“ค์„ ์ ์šฉํ•˜์—ฌ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ์—์„œ ์ƒˆ๋กœ์šด ์ธ์‚ฌ์ดํŠธ๋ฅผ ์–ป๊ณ ์ž ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ์ค‘ ๊ฐœ์ฒด๋ช… ์ธ์‹์€ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ ๋‚ด์˜ ์ง€๋ช…์—์„œ๋ถ€ํ„ฐ ์งˆ๋ณ‘ ๋ฐ ๋ถ€์ž‘์šฉ์— ์ด๋ฅด๊ธฐ๊นŒ์ง€ ๊ฐœ์ฒด๋ช… ๋ถ„๋ฅ˜์— ์†ํ•˜๋Š” ๋„๋ฉ”์ธ ๋‚ด์˜ ๊ฐœ๋…์„ ๊ฐœ์ฒด๋ช…์œผ๋กœ ์ถ”์ถœํ•œ๋‹ค. ์ „ํ†ต์ ์œผ๋กœ ๊ฐœ์ฒด๋ช… ์ธ์‹์€ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ ๊ฐœ์ฒด๋ช… ์ค‘๋ณต์„ ๊ณ ๋ คํ•˜์—ฌ ๊ทœ์น™ ๊ธฐ๋ฐ˜์œผ๋กœ ์ •๊ทœ์‹์ด๋‚˜ ์‚ฌ์ „์„ ๊ตฌ์„ฑํ•˜๊ณ  ๋งค์นญ๋˜๋Š” ๊ฐœ์ฒด๋ช…์„ ์ถ”์ถœํ•˜๋Š” ์›๊ฑฐ๋ฆฌ ํ•™์Šต ๋ฐฉ์‹์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ, ํ™•์žฅ ๊ฐ€๋Šฅ์„ฑ ๋ฐ ์–ธ์–ด์˜ ๋ชจํ˜ธ์„ฑ๊ณผ ๋‹ค์–‘์„ฑ์„ ๊ณ ๋ คํ•˜๊ธฐ ์œ„ํ•ด ์ง€๋„ ํ•™์Šต ์ ‘๊ทผ๋ฐฉ์‹์„ ํ™œ์šฉํ•œ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์ด ์ œ์‹œ๋˜์—ˆ๋‹ค. ์ง€๋„ ํ•™์Šต์€ ๋ ˆ์ด๋ธ” ๋ฐ์ดํ„ฐ๊ฐ€ ํ•„์š”ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ํ•ด๋‹น ๋ถ„์•ผ์˜ ์ „๋ฌธ๊ฐ€์˜ ๋ ˆ์ด๋ธ”๋ง(Labeling)์ด ์š”๊ตฌ๋˜๋Š”๋ฐ, ์ด๋ฅผ ๋” ๋น ๋ฅด๊ณ  ํšจ์œจ์ ์œผ๋กœ ํ•  ์ˆ˜ ์žˆ๋„๋ก ์•กํ‹ฐ๋ธŒ ๋Ÿฌ๋‹(Active Learning)์ด ์ œ์‹œ๋˜์—ˆ๋‹ค. ์•กํ‹ฐ๋ธŒ ๋Ÿฌ๋‹์€ ๋ชจ๋ธ ํ•™์Šต์— ๊ฐ€์žฅ ์œ ์šฉํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์–ด๋…ธํ…Œ์ดํ„ฐ์—๊ฒŒ ์šฐ์„  ์ˆœ์œผ๋กœ ์ œ์‹œํ•˜์—ฌ, ๋ชจ๋ธ์˜ ์ •ํ™•๋„๋ฅผ ๋†’์ด๋ฉด์„œ ๋™์‹œ์— ๋ ˆ์ด๋ธ”๋ง ๋น„์šฉ์„ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ์ „๋žต์„ ์ œ์‹œํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์•กํ‹ฐ๋ธŒ ๋Ÿฌ๋‹์˜ ๊ธฐ์กด ์ƒ˜ํ”Œ๋ง ์ „๋žต์„ ๋ถ„์„ํ•˜๊ณ  ๊ฐœ์ฒด๋ช… ์ธ์‹์—์„œ ๋ฐ์ดํ„ฐ์…‹ ๋‚ด ๋ฌธ์„œ ๊ฐ„์˜ ์œ ์šฉ์„ฑ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ˆœ์œ„๋ฅผ ์ •์˜ํ•˜๋Š” ์ƒˆ๋กœ์šด ๊ทธ๋ž˜ํ”„ ํ˜•ํƒœ์ธ ์ „์ด ๊ฐœ์ฒด๋ช… ๊ทธ๋ž˜ํ”„(Transitional Entity Graph)๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์•กํ‹ฐ๋ธŒ ๋Ÿฌ๋‹์„ ์ง„ํ–‰ํ•˜๋ฉฐ ์ถ•์ ๋˜๋Š” ๊ฐœ์ฒด๋ช… ๋ ˆ์ด๋ธ”์„ ํ™œ์šฉํ•ด ํ•ด๋‹น ๊ทธ๋ž˜ํ”„๋ฅผ ์ •์ œํ•œ๋‹ค. ์ด์ข… ๊ทธ๋ž˜ํ”„(Heterogeneous Graph)๋ฅผ ์•กํ‹ฐ๋ธŒ ๋Ÿฌ๋‹ ์ง„ํ–‰์— ๋”ฐ๋ผ ํ•จ๊ป˜ ์ง„ํ™”ํ•˜๋Š” ์ „๋žต์„ ์ œ์‹œํ•˜๋Š” ๊ฒƒ์€ ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ์ฒ˜์Œ์ด๋‹ค. ๊ฐœ์ฒด๋ช… ์ธ์‹ ๋ฒค์น˜๋งˆํฌ ๋ฐ์ดํ„ฐ์…‹๊ณผ ๋Œ€ํ‘œ์ ์ธ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์„ ํ™œ์šฉํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ํ•ด๋‹น ์ƒ˜ํ”Œ๋ง ์ „๋žต์ด ํƒ€ ์ƒ˜ํ”Œ๋ง ์ „๋žต์— ๋น„ํ•ด ์ ์€ ์–‘์˜ ํ•™์Šต ๋ฐ์ดํ„ฐ๋กœ ๋†’์€ F1 ์ ์ˆ˜๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํŠนํžˆ ์ œ์•ˆํ•œ ์ „๋žต์ด ํ•™์Šต ์ดˆ๊ธฐ์— ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค.Recently, massive amounts of unstructured data such as medical records, transaction data, and pictures on social media are generated. In addition to the increase of unstructured data, researches are being conducted to gain new insights from text data by applying advanced artificial intelligence and natural language processing technologies. Among them, entity recognition extracts domain-specific concepts such as diseases and side effects from text data. Traditionally, the distant supervision approach of constructing sets of regular expressions or custom dictionaries was used to retrieve matching string entities. However, due to its lack of scalability and difficulty in handling ambiguity and diversity of language, machine learning models were introduced to the task. And as supervised deep learning models have shown significant success in natural language processing, more researches shifted toward the utilization of deep neural networks. On the contrary, supervised learning requires labeled data, which demands human labor. And active learning has been proposed to make the labeling process faster and more efficient. Active learning increases model accuracy and reduces labeling costs by presenting the most useful data for training. In this paper, we analyze existing active learning strategies and propose a transitional entity graph, a novel graph form that evolves along with the active learning process. The transitional entity graph applied with PageRank ranks data samples based on the influence within a dataset network. The graph is refined using the accumulated entity labels during active learning. To the best of our knowledge, this is the first study to propose a sampling strategy by improving a heterogeneous graph as progressing through active learning. Experiments with the entity recognition benchmark datasets and a deep learning model have shown that the suggested sampling strategy showed a higher F1 score with fewer training samples compared to other sampling strategies. In particular, the proposed strategy shows excellent performance in the early stage of learning.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ํ•„์š”์„ฑ 1 1.2 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 4 ์ œ 2 ์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ 5 2.1 ๊ฐœ์ฒด๋ช… ์ธ์‹ 5 2.1.1 ์›๊ฑฐ๋ฆฌ ํ•™์Šต 7 2.2 ๋”ฅ๋Ÿฌ๋‹ 10 2.3 ์•กํ‹ฐ๋ธŒ ๋Ÿฌ๋‹ 13 2.3.1 ๋ถˆํ™•์‹ค์„ฑ ์ƒ˜ํ”Œ๋ง 18 2.3.2 ๋Œ€ํ‘œ์„ฑ ์ƒ˜ํ”Œ๋ง 22 2.4 ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜ ๋žญํ‚น ์•Œ๊ณ ๋ฆฌ์ฆ˜ 26 2.4.1 ํŽ˜์ด์ง€ ๋žญํฌ 28 2.4.2 Hyperlink-Induced Topic Search 30 ์ œ 3 ์žฅ ์ „์ด ๊ฐœ์ฒด๋ช… ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜ ์•กํ‹ฐ๋ธŒ ๋Ÿฌ๋‹ 32 3.1 ์ „์ด ๊ฐœ์ฒด๋ช… ๊ทธ๋ž˜ํ”„ 33 3.1.1 ๋ชฉํ‘œ TEG 38 3.1.2 ์ดˆ๊ธฐ TEG 39 3.1.3 ์ค‘๊ฐ„ TEG 41 3.2 ๋™์ข… ๊ทธ๋ž˜ํ”„ 45 ์ œ 4 ์žฅ ์‹คํ—˜ ๋ฐ ํ‰๊ฐ€ 47 4.1 ๋ฐ์ดํ„ฐ์…‹ ๋ฐ ์ „์ฒ˜๋ฆฌ 47 4.2 ๊ฐœ์ฒด๋ช… ์ธ์‹ ๋ชจ๋ธ 49 4.3 ์ƒ˜ํ”Œ๋ง ์ „๋žต ๋น„๊ต 51 4.4 ์‹คํ—˜ ๊ฒฐ๊ณผ 53 4.4.1 ํ•™์Šต ์ปค๋ธŒ 53 4.4.2 ์ตœ๋Œ€ F1 ๋Œ€๋น„ ์„ฑ๋Šฅ 58 4.4.3 ์•กํ‹ฐ๋ธŒ ๋Ÿฌ๋‹ ์ดˆ๊ธฐ ์‹œ๋”ฉ 59 4.4.4 ์ „์ด ๊ฐœ์ฒด๋ช… ๊ทธ๋ž˜ํ”„ ์ •์ œ ํšจ๊ณผ 63 4.4.5 ์ถ”์ถœ ๊ฐœ์ฒด๋ช… ์ˆ˜์™€ ๋ ˆ์ด๋ธ”๋ง ๋น„์šฉ 66 4.4.6 ์—ฐ์‚ฐ ์‹œ๊ฐ„ 70 ์ œ 5 ์žฅ ๊ณ ์ฐฐ 71 ์ œ 6 ์žฅ ๊ฒฐ๋ก  72 ์ฐธ๊ณ ๋ฌธํ—Œ 73 ๋ถ€๋ก 85 Abstract 96Maste

    ํ•œ๊ตญ์—ฐ์†Œ๋…ธ์ธ์˜ ์‚ฌํšŒ์ฐธ๊ฐ€์™€ ์ž์•„์กด์ค‘๊ฐ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•„๋™๊ฐ€์กฑํ•™๊ณผ, 2012. 2. ํ•œ๊ฒฝํ˜œ.Reflecting the rapid increase in the aged population, the question of what constitutes aging well and how to age well has been gaining attention. There has not yet been a definite answer; part of the reason being that old age is relatively young, and there is still much to be researched. This is especially true for South Korea, where the life span of man has increased with extraordinary rapidity, due to its sudden industrialization. No consensus on many facets of aging ad aging well has been reached, and this could be extremely detrimental to one's aging well. The concept of successful aging is one of the most influential concept that emerged in the process of establishing a cogent theory, and it is noteworthy that it includes a social component: sustained engagement in social and productive activities, which this study translates to social participation. While there are a number of other terms that are used interchangeably in other studies, social participation as a concept could be understood to include most activities that act to relieve social exclusion, and thus allows to measure involvement in the broadest sense. This is the reason this study has chosen this concept in order to examine the young elderly's involvement with life and how it is associated with their self-esteem. The activities chosen specifically under the name of social participation for this study are public association groups, leisure activities, religious ritual, ascriptive association groups, work, volunteering and care-giving. This study examined the social participation status of Korean young elderly men and women, and how it is associated with self-esteem of Korean young elderly men and women. The data for this study was obtained through face-to-face interviews using a structured questionnaire, and consists of 1713 participants, age ranging from 50 to 69, of which 836 were male and 877 were female. Descriptive statistics, correlation analysis, chi-square tests and t-test, and two-step hierarchical regression analyses were conducted, separate by gender. SAS 9.1 program was used. The result are as follows. Firstly, as for male subjects, the activity with most participation was work, followed by ascriptive association activities, leisure activities, volunteering activities, religious activities, public association activities, and care-giving activities, in this order. Female subjects, too, participated in work the most, and then the other activities followed in the order of leisure activities, ascriptive association activities, religious activities, volunteering activities, public association activities, and care-giving activities. There were significant gender differences in participation in public association activities, religious activities, ascriptive association activities, work, and care-giving activities. More male than female participated in public association activities, work, ascriptive association activities, and the rest, the opposite. Secondly, while male and female participants shared self-rated living status and perceived health variables from sociodemographic variables as significant factors associated with self-esteem, social participation variables that proved to be significant were different for each gender. For male participants, the strongest predictor was leisure activities, followed by volunteering activities, ascriptive association activities, and religious activities. For female participants, the strongest predictor was leisure activities, followed by volunteering activities and care-giving activities. Resultantly, the way Korean young elderly is engaged with life and how it is associated with their self-esteem was made clear. This study has its limitation in which that social participation variables were chosen without clear criteria and appropriate categorization, regardless of the fact that the diversity of the social participation variables are, at the same time, the strength of this study. Further researches are required to investigate the properties of the activities examined that caused some activities to be more significant predictors of self-esteem than the others.๊ณ ๋ น ์ธ๊ตฌ์˜ ๊ธ‰๊ฒฉํ•œ ์ฆ๊ฐ€์— ์˜ํ•ด ๋ฐ”๋žŒ์งํ•œ ๋…ธํ™”์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ๋†’์•„์ง€๊ณ  ์žˆ์œผ๋‚˜, ๋” ์—ฐ๊ตฌ๋  ์—ฌ์ง€๊ฐ€ ๋‚จ์•„ ์žˆ์–ด ์•„์ง ๋ช…ํ™•ํ•œ ๋‹ต์ด ๋‚˜์˜ค์ง€ ์•Š์€ ์‹ค์ •์ด๋‹ค. ์ด์ฒ˜๋Ÿผ ๋…ธํ™”์™€ ๋ฐ”๋žŒ์งํ•œ ๋…ธํ™”์— ๋Œ€ํ•œ ํ•ฉ์˜๊ฐ€ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š์€ ํ˜„ํ™ฉ์€, ์•ž์œผ๋กœ์˜ ์ธ์ƒ์— ๋Œ€ํ•œ ์ง€์นจ์„ ์ฐพ๋Š” ์—ฐ์†Œ ๋…ธ์ธ๋“ค์—๊ฒŒ ๊ฒฐ์ฝ” ๋ฐ”๋žŒ์งํ•œ ์—ฌ๊ฑด์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์—†๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„ฑ๊ณต์  ๋…ธํ™”์˜ ๊ตฌ์„ฑ ์š”์†Œ ์ค‘์— ์‚ฌํšŒ์ , ์ƒ์‚ฐ์  ํ™œ๋™์—์˜ ์ฐธ์—ฌ๋ผ๋Š” ์‚ฌํšŒ์  ์š”์†Œ๊ฐ€ ์žˆ๋Š” ๊ฒƒ์— ์ฃผ๋ชฉํ•˜์—ฌ, ์—ฐ์†Œ ๋…ธ์ธ์˜ ์‚ฌํšŒ ์ฐธ๊ฐ€์— ๋Œ€ํ•˜์—ฌ ์‚ดํŽด๋ณด๊ธฐ๋กœ ํ•œ๋‹ค. ๋ณด๋‹ค ๊ตฌ์ฒด์ ์œผ๋กœ๋Š”, ํ•œ๊ตญ ์—ฐ์†Œ ๋…ธ์ธ์˜ ์‚ฌํšŒ ์ฐธ๊ฐ€ ์–‘์ƒ๊ณผ ๊ทธ๊ฒƒ์ด ์–ด๋–ป๊ฒŒ ๊ทธ๋“ค์˜ ์ž์•„์กด์ค‘๊ฐ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€๋ฅผ ์‚ดํŽด๋ณด๊ธฐ๋กœ ํ•˜์˜€๋‹ค. ์‚ฌํšŒ ์ฐธ๊ฐ€ ๊ฐœ๋…์€ ์‚ฌํšŒ์  ๋ฐฐ์ œ๋ฅผ ์™„ํ™”์‹œํ‚ค๊ธฐ ์œ„ํ•ด ์ž‘๋™ํ•˜๋Š” ๋Œ€๋ถ€๋ถ„์˜ ํ™œ๋™์„ ํฌํ•จํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ ์‚ฌํšŒ ์ฐธ๊ฐ€๋ผ๋Š” ๊ฐœ๋…์œผ๋กœ ์„ ํƒํ•œ ํ™œ๋™๋“ค์€ ๊ณต๊ณต์ง‘๋‹จ, ์—ฌ๊ฐ€ํ™œ๋™, ์ข…๊ตํ™œ๋™, ์—ฐ๊ณ ์ง‘๋‹จ, ์ผ, ์ž์›๋ด‰์‚ฌ, ๊ทธ๋ฆฌ๊ณ  ๋Œ๋ด„์ด๋‹ค. ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ์ž๋ฃŒ๋Š” 50~69์„ธ์˜ ์—ฐ์†Œ๋…ธ์ธ 1713๋ช…์„ (๋‚จ์„ฑ 836๋ช…, ์—ฌ์„ฑ 877๋ช…) ๋Œ€์ƒ์œผ๋กœ, ๊ตฌ์กฐ์  ์งˆ๋ฌธ์ง€๋ฅผ ์‚ฌ์šฉํ•œ ์ผ๋Œ€์ผ ๋ฉด์ ‘์„ ํ†ตํ•ด ํ™•๋ณด๋˜์—ˆ๋‹ค. ๋นˆ๋„๋ถ„์„, ์ƒ๊ด€๊ด€๊ณ„๋ถ„์„, chi-square ๊ฒ€์ •, t-test, ๊ทธ๋ฆฌ๊ณ  ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„์ด ์„ฑ๋ณ„์— ๋”ฐ๋ผ ์‹ค์‹œ๋˜์—ˆ๋‹ค. ๋ถ„์„์—๋Š” SAS 9.1 ํ”„๋กœ๊ทธ๋žจ์„ ์ด์šฉํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๋‚จ์„ฑ ๋…ธ์ธ์˜ ๊ฒฝ์šฐ, ๊ฐ€์žฅ ๋งŽ์ด ์ฐธ๊ฐ€ํ•œ ํ™œ๋™์€ ์ผ์ด์—ˆ๊ณ , ๊ทธ ๋‹ค์Œ์œผ๋กœ ์—ฐ๊ณ ์ง‘๋‹จ, ์—ฌ๊ฐ€ํ™œ๋™, ๋ด‰์‚ฌํ™œ๋™, ์ข…๊ตํ™œ๋™, ๊ณต๊ณต์ง‘๋‹จ, ๊ทธ๋ฆฌ๊ณ  ๋Œ๋ด„์˜ ์ˆœ์„œ์˜€๋‹ค. ์—ฌ์„ฑ ๋…ธ์ธ ์—ญ์‹œ ์ผ์— ๊ฐ€์žฅ ๋งŽ์ด ์ฐธ๊ฐ€ํ–ˆ๊ณ , ๊ทธ ๋‹ค์Œ์œผ๋กœ ์—ฌ๊ฐ€ํ™œ๋™, ์—ฐ๊ณ ์ง‘๋‹จ, ์ข…๊ตํ™œ๋™, ๋ด‰์‚ฌํ™œ๋™, ๊ณต๊ณต์ง‘๋‹จ, ๊ทธ๋ฆฌ๊ณ  ๋Œ๋ด„์ด ์ด์–ด์กŒ๋‹ค. ์—ฌ๊ฐ€ํ™œ๋™๊ณผ ๋ด‰์‚ฌํ™œ๋™์—์„œ๋Š” ์„ฑ๋ณ„ ์ฐจ์ด๊ฐ€ ์—†์—ˆ์œผ๋‚˜, ๊ณต๊ณต์ง‘๋‹จ, ์ข…๊ตํ™œ๋™, ์—ฐ๊ณ ์ง‘๋‹จ, ์ผ, ๊ทธ๋ฆฌ๊ณ  ๋Œ๋ด„์—์„œ๋Š” ์œ ์˜๋ฏธํ•œ ์„ฑ๋ณ„ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ณต๊ณต์ง‘๋‹จ, ์ผ, ์—ฐ๊ณ ์ง‘๋‹จ์˜ ๊ฒฝ์šฐ๋Š” ๋‚จ์„ฑ ๋…ธ์ธ์ด ๋” ๋งŽ์ด ์ฐธ์—ฌํ•˜์˜€๊ณ , ๋‚˜๋จธ์ง€ ์ข…๊ตํ™œ๋™๊ณผ ๋Œ๋ด„์€ ์—ฌ์„ฑ ๋…ธ์ธ์ด ๋” ๋งŽ์ด ์ฐธ์—ฌํ•˜์˜€๋‹ค. ๋‘˜์งธ, ๋‚จ์„ฑ ๋…ธ์ธ๊ณผ ์—ฌ์„ฑ ๋…ธ์ธ ๋ชจ๋‘ ์‚ฌํšŒ์ธ๊ตฌํ•™์  ๋ณ€์ˆ˜ ์ค‘ ์ƒํ™œ์ˆ˜์ค€๊ณผ ๊ฑด๊ฐ• ๋ณ€์ˆ˜๊ฐ€ ์ž์•„์กด์ค‘๊ฐ์— ์œ ์˜๋ฏธํ•œ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค๋Š” ์ ์— ์žˆ์–ด์„œ ๋™์ผํ–ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‚ฌํšŒ์ฐธ๊ฐ€์— ์žˆ์–ด์„œ๋Š” ์„ฑ๋ณ„ ์ฐจ์ด๊ฐ€ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‚จ์„ฑ ๋…ธ์ธ์˜ ๊ฒฝ์šฐ, ์˜ํ–ฅ๋ ฅ์ด ๊ฐ€์žฅ ํฐ ๋ณ€์ˆ˜๋Š” ์—ฌ๊ฐ€ํ™œ๋™์ด์—ˆ๊ณ , ๊ทธ ๋‹ค์Œ์ด ๋ด‰์‚ฌํ™œ๋™, ์—ฐ๊ณ ์ง‘๋‹จ, ๊ทธ๋ฆฌ๊ณ  ์ข…๊ตํ™œ๋™ ์ˆœ์ด์—ˆ๋‹ค. ๋ฐ˜๋ฉด ์—ฌ์„ฑ ๋…ธ์ธ์˜ ๊ฒฝ์šฐ, ๊ฐ€์žฅ ์˜ํ–ฅ๋ ฅ์ด ํฐ ๋ณ€์ˆ˜๋Š” ์—ฌ๊ฐ€ํ™œ๋™์ด๋ผ๋Š” ์ ์—์„œ๋Š” ๋‚จ์„ฑ ๋…ธ์ธ๊ณผ ๋™์ผํ•˜์˜€์œผ๋‚˜, ๊ทธ ๋‹ค์Œ ์ˆœ์„œ๊ฐ€ ๋ด‰์‚ฌํ™œ๋™๊ณผ ๋Œ๋ด„์œผ๋กœ ์ด์–ด์ง„๋‹ค๋Š” ์ ์ด ๋‚จ์„ฑ ๋…ธ์ธ๊ณผ์˜ ์ฐจ์ด์ ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋ณด๋‹ค ์‚ฌํšŒ์ฐธ๊ฐ€ํ™œ๋™์„ ์„ธ๋ถ„ํ•˜์—ฌ ๊ฐ๊ฐ์˜ ํ™œ๋™์ด ์ž์•„์กด์ค‘๊ฐ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๋ ฅ์„ ์‚ดํŽด๋ณด์•˜๋‹ค๋Š” ์ ์—์„œ ์˜์˜๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‚ฌํšŒ์ฐธ๊ฐ€ํ™œ๋™ ๋ณ€์ˆ˜๋ฅผ ๋ช…ํ™•ํ•œ ๊ธฐ์ค€๊ณผ ์ ํ•ฉํ•œ ๋ฒ”์ฃผํ™” ์—†์ด ์„ ํƒํ•˜์˜€๋‹ค๋Š” ์ œํ•œ์ ์„ ๊ฐ€์ง„๋‹ค. ์ด์™€ ๊ฐ™์€ ์ ์„ ๋ณด์™„ํ•˜์—ฌ ํ›„์† ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์ง„๋‹ค๋ฉด, ํ•œ๊ตญ ์—ฐ์†Œ ๋…ธ์ธ์˜ ์‚ฌํšŒ์ฐธ๊ฐ€์™€ ์ž์•„์กด์ค‘๊ฐ์˜ ๊ด€๊ณ„๋ฅผ ๋ณด๋‹ค ์ž˜ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋œ๋‹ค. ๋ง๋ถ™์—ฌ, ์—ฌ๋Ÿฌ ์‚ฌํšŒ์ฐธ๊ฐ€ํ™œ๋™ ์ค‘ ์–ด๋–ค ํ™œ๋™์ด ๋‹ค๋ฅธ ํ™œ๋™๋ณด๋‹ค ์ž์•„์กด์ค‘๊ฐ์— ๋” ํฐ ์˜ํ–ฅ๋ ฅ์„ ๊ฐ€์ง€๋Š” ์›์ธ์„ ๋ฐํžˆ๋Š” ์—ฐ๊ตฌ ๋˜ํ•œ ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค.Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜์–ด์˜๋ฌธํ•™๊ณผ ์˜๋ฌธํ•™์ „๊ณต,1998.Maste

    A study on the correlation between asymmetry of upper dental arch and asymmetry of jaws and cervicothoraic spine scoliosis

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์น˜์˜ํ•™๊ณผB, 2017. 2. ๊น€ํƒœ์šฐ.๋‘๋ถ€์™€ ๊ฒฝ์ถ”, ๋ชธํ†ต์˜ ์—ฐ๊ฒฐ๋œ ๊ทผ๊ณจ๊ฒฉ๊ณ„๋ฅผ ์ด๋ฃจ๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์–ด๋Š ํ•œ ๋ถ€๋ถ„์— ๋น„๋Œ€์นญ์ด ์žˆ๋Š” ๊ฒฝ์šฐ์— ์ด๋ฅผ ๋ณด์ƒํ•˜๋ ค๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ๋‹ค๋ฅธ ๋ถ€๋ถ„์—๋„ ๋น„๋Œ€์นญ์ด ๋ฐœ์ƒํ•œ๋‹ค๋Š” ์ฃผ์žฅ์ด ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์น˜์—ด๊ถ์˜ ๋น„๋Œ€์นญ๊ณผ ์ •์ค‘๋ฉด ์ƒ์˜ ์•…๊ณจ์˜ ๋น„๋Œ€์นญ, ์ฒ™์ถ”์˜ ์ธก๋งŒ ์‚ฌ์ด์˜ ์—ฐ๊ด€์„ฑ์„ ๋ถ„์„ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ํ™˜์ž์˜ ๊ต์ •์น˜๋ฃŒ ์ „ ์ƒ์•… ์ง„๋‹จ๋ชจํ˜•์„ ์ดฌ์˜ํ•œ ์‚ฌ์ง„ ์ƒ์—์„œ ์ •์ค‘๊ตฌ๊ฐœ๋ด‰ํ•ฉ์„ ๋Œ€์นญ์ถ•์œผ๋กœ ๋™๋ช…์น˜๊ฐ„ ์œ„์น˜์˜ ์ฐจ์ด๋ฅผ ์ค‘์ ˆ์น˜๋ถ€ํ„ฐ ์ œ1๋Œ€๊ตฌ์น˜๊นŒ์ง€ ๋”ํ•œ ๊ฐ’์œผ๋กœ ์ƒ์•…๊ถ์˜ ๋น„๋Œ€์นญ์ •๋„(โˆ†)๋ฅผ ์ •์˜ํ•˜์—ฌ ๊ตฌํ•˜์˜€๋‹ค. ๊ต์ •์น˜๋ฃŒ ์ „ Posteroanterior cephalogram ์ƒ์—์„œ Cg์™€ ANS๋ฅผ ์—ฐ๊ฒฐํ•˜๋Š” Facial midline์— ๋Œ€ํ•œ ์ƒยทํ•˜์•…๊ณจ์˜ ๋น„๋Œ€์นญ ์ •๋„ ๋ฐ ๊ฒฝ์ถ”์˜ ๋ณ€์œ„๋Ÿ‰์„ ๊ฐ๊ฐ 2๊ฐ€์ง€ ์ˆ˜์น˜๋กœ ์ธก์ •ํ•˜์˜€๋‹ค. ์ƒ์•…๊ณจ์˜ ๋น„๋Œ€์นญ ์ •๋„๋Š” ์–‘์ธก Jugular point๋ฅผ ์—ฐ๊ฒฐํ•œ ์ง์„ ์ด Facial midline๊ณผ ์ด๋ฃจ๋Š” ๊ฐ๋„ ๋ฐ ์–‘์ธก ์ƒ์•… ์ œ1๋Œ€๊ตฌ์น˜์˜ ์น˜๊ฒฝ๋ถ€๊ฐ€ ํ˜‘์ธก ์น˜์กฐ๊ณจ๊ณผ ๋งŒ๋‚˜๋Š” ์ ์„ ์—ฐ๊ฒฐํ•œ ์ง์„ ์ด Facial midline๊ณผ ์ด๋ฃจ๋Š” ๊ฐ๋„๋กœ ์ธก์ •ํ•˜์˜€๋‹ค. ํ•˜์•…๊ณจ์˜ ๋น„๋Œ€์นญ ์ •๋„๋Š” ์–‘์ธก Antegonial notch๋ฅผ ์—ฐ๊ฒฐํ•œ ์ง์„ ์ด Facial midline๊ณผ ์ด๋ฃจ๋Š” ๊ฐ๋„ ๋ฐ ANS-Menton์„ ์—ฐ๊ฒฐํ•œ ์ง์„ ์ด Facial midline๊ณผ ์ด๋ฃจ๋Š” ๊ฐ๋„๋กœ ์ธก์ •ํ•˜์˜€๋‹ค. ๊ฒฝ์ถ”์˜ ๋ณ€์œ„๋Ÿ‰์€ ์ œ1๊ฒฝ์ถ” ์–‘์ธก ๋‚ด์ธก๋ฉด์˜ ์ค‘์ ๊ณผ ์ œ4๊ฒฝ์ถ” ์™ธ์ธก๋ฉด์˜ ํ•จ์š”๋ถ€์˜ ์ค‘์ ์„ ์—ฐ๊ฒฐํ•œ ์ง์„ ๊ณผ Facial midline์ด ์ด๋ฃจ๋Š” ๊ฐ๋„ ๋ฐ Odontoid process์˜ ์ฒจ์ ๊ณผ ์ œ4๊ฒฝ์ถ” ํ•˜์—ฐ์˜ ์ค‘์ ์„ ์—ฐ๊ฒฐํ•œ ์ง์„ ์ด Facial midline๊ณผ ์ด๋ฃจ๋Š” ๊ฐ๋„๋กœ ๊ฐ๊ฐ ์ธก์ •ํ•˜์˜€๋‹ค. ์•…๊ต์ • ์ˆ˜์ˆ  ์ „ Chest PA x-ray ์‚ฌ์ง„์—์„œ Cobbs angle์„ ์ธก์ •ํ•˜์—ฌ ๊ฒฝ์ถ” ๋ฐ ํ‰์ถ”๋ถ€์œ„์˜ ์ธก๋งŒ์ •๋„๋ฅผ ์ธก์ •ํ•˜์˜€๋‹ค. ๋ชจ๋“  ๊ณ„์ธก์€ Microsoft Visual Studio 2015์˜ C# ์–ธ์–ด๋ฅผ ์ด์šฉํ•˜์—ฌ ์ œ์ž‘ํ•œ ํ”„๋กœ๊ทธ๋žจ์„ ํ†ตํ•ด ์ด๋ฃจ์–ด์กŒ์œผ๋ฉฐ 5๋ฒˆ์”ฉ ๊ณ„์ธกํ•˜์—ฌ ํ‰๊ท ๊ฐ’์„ ๋ถ„์„์— ์‚ฌ์šฉํ–ˆ๋‹ค. ์น˜์—ด๊ถ์˜ ๋น„๋Œ€์นญ๊ณผ ์•…๊ณจ์˜ ๋น„๋Œ€์นญ, ์ฒ™์ถ”์˜ ์ธก๋งŒ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ SPSS statics 23์„ ์ด์šฉํ•˜์—ฌ ํ†ต๊ณ„๋ถ„์„์„ ์‹œํ–‰ํ–ˆ๋‹ค. ๊ฐ๊ฐ์˜ ์ธก์ •๊ฐ’์— ๋Œ€ํ•˜์—ฌ Pearson ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๊ณ„์‚ฐํ–ˆ๋‹ค. ์ƒ์•…๊ถ ๋น„๋Œ€์นญ ์ •๋„(โˆ†)์˜ ํฌ๊ธฐ๋ฅผ ์ค‘์•™๊ฐ’ 10์„ ๊ธฐ์ค€์œผ๋กœ ํ‘œ๋ณธ์„ ์ƒ์•…๊ถ ๋Œ€์นญ๊ตฐ๊ณผ ๋น„๋Œ€์นญ๊ตฐ์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์—ฌ ๊ฐ ์ธก์ •๊ฐ’์„ ๋…๋ฆฝ t-test๋กœ ๋น„๊ตํ•˜์˜€๋‹ค. ๋ชจ๋“  ๋ถ„์„์—์„œ ์œ ์˜์ˆ˜์ค€์€ 5%๋กœ ์„ค์ •ํ–ˆ๋‹ค. ์ƒ์•… ์น˜์—ด๊ถ์˜ ๋น„๋Œ€์นญ ์ •๋„์™€ ๊ฒฝ์ถ” ๋ฐ ํ‰์ถ” ๋ถ€์œ„์˜ ๋งŒ๊ณก๋„ ๊ฐ„์— ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ƒ๊ด€๊ด€๊ณ„๊ฐ€ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜์œผ๋ฉฐ, ์ƒ์•… ์น˜์—ด๊ถ ๋น„๋Œ€์นญ๊ตฐ๊ณผ ๋Œ€์นญ๊ตฐ ์‚ฌ์ด์— ๊ฒฝ์ถ” ๋ฐ ํ‰์ถ” ๋ถ€์œ„์˜ ๋งŒ๊ณก๋„์— ์œ ์˜ํ•œ ์ฐจ์ด๋Š” ์—†์—ˆ๋‹ค. ์น˜์—ด๊ถ๊ณผ ์•…๊ณจ, ์•…๊ณจ๊ณผ ๊ฒฝ์ถ”์˜ ๋ณ€์œ„๋Ÿ‰์€ ๊ฐ๊ฐ ๋‚ฎ์€ ์ •๋„์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค. Menton์˜ ๋ณ€์œ„๋Ÿ‰์œผ๋กœ ์ธก์ •ํ•œ ์•ˆ๋ฉด ๋น„๋Œ€์นญ ์ •๋„๋Š” ์ฒ™์ถ”์˜ ๋งŒ๊ณก(๊ฒฝ์ถ”์˜ ๋ณ€์œ„)๊ณผ ๋‚ฎ์€ ์ •๋„์˜ ์ƒ๊ด€๊ด€๊ณ„(r=0.354, p<0.05)๋ฅผ ๋ณด์˜€๋‹ค.โ… . ์„œ๋ก  1 โ…ก. ์—ฐ๊ตฌ๋Œ€์ƒ ๋ฐ ๋ฐฉ๋ฒ• 3 1.์—ฐ๊ตฌ ๋Œ€์ƒ 3 2.์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 3 1. ์‚ฌ์ง„ ๊ณ„์ธก ํ”„๋กœ๊ทธ๋žจ ์ œ์ž‘ 3 2. ์ƒ์•… ์น˜์—ด๊ถ ๋น„๋Œ€์นญ ๋ถ„์„ 4 3. ์ƒยทํ•˜์•…๊ณจ์˜ ๋น„๋Œ€์นญ ์ •๋„ ๋ฐ ๊ฒฝ์ถ”์˜ ๋ณ€์œ„๋Ÿ‰ ๋ถ„์„ 6 4. ์ฒ™์ถ” ์ธก๋งŒ๋„์˜ ์ธก์ • 7 5. ํ†ต๊ณ„๋ถ„์„ 7 โ…ข. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 9 1. ์ƒ๊ด€๋ถ„์„ 9 2. ๋…๋ฆฝ t-test๋ฅผ ์ด์šฉํ•œ ํ‰๊ท  ๋ถ„์„ 11 โ…ฃ. ๊ณ ์ฐฐ 13 โ…ค. ๊ฒฐ๋ก  18 ์ฐธ๊ณ ๋ฌธํ—Œ 19 ํ‘œ 22 ๊ทธ๋ฆผ 27 ๋ถ€๋ก 35 Abstract 41Maste

    Analysis of Expression Appeared in a Movie Poster Tenebrism

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    Role of Demand Response in Small Power Consumer Market and a Pilot Study

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    ์ตœ๊ทผ ์ง€์†์ ์ธ ์ „๋ ฅ ์ˆ˜์š” ์ฆ๊ฐ€์™€ ์ด์— ๋”ฐ๋ฅธ ์‚ฌํšŒ์  ๋น„์šฉ ๋ฐ ๊ฐˆ๋“ฑ ํ•ด๊ฒฐ์„ ์œ„ํ•œ ๋ฐฉ์•ˆ์œผ๋กœ ์ˆ˜์š”์ž์› ๊ฑฐ๋ž˜์‹œ์žฅ(Demand Response Market)์˜ ํ™œ์„ฑํ™”๊ฐ€ ํฌ๊ฒŒ ๋Œ€๋‘๋˜๊ณ  ์žˆ๋‹ค. ๊ตญ๋‚ด์˜ ๊ฒฝ์šฐ 2014๋…„๋ถ€ํ„ฐ ์ˆ˜์š”์ž์› ๊ฑฐ๋ž˜์‹œ์žฅ์„ ์šด์˜์ค‘์ด๋‚˜ ๋Œ€๊ทœ๋ชจ ์ˆ˜์š”์ž์› ๊ฑฐ๋ž˜์— ์ง‘์ค‘ํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ „๋ ฅ ์†Œ๋น„ ๋น„์ค‘์˜ ์ฆ๊ฐ€, ํฐ ๊ฐ์ถ• ์œ ๋™์„ฑ, ํ•ญ์ƒ ํ™œ์šฉ ๊ฐ€๋Šฅ, ์‚ฌํšŒ ์ธ์‹ ๊ฐœ์„  ๋“ฑ์˜ ๋ฉด์—์„œ ์†Œ๊ทœ๋ชจ ์ „๋ ฅ ์†Œ๋น„์ž์˜ ์‹œ์žฅ ์ฐธ์—ฌ๋Š” ์„ฑ๊ณต์ ์ธ ์ˆ˜์š”์ž์› ๊ฑฐ๋ž˜์‹œ์žฅ ์šด์˜์˜ ํ•ต์‹ฌ์  ์š”์†Œ๋กœ ๋ณผ ์ˆ˜ ์žˆ๋‹ค. ์ด ๋…ผ๋ฌธ์€ 2016๋…„ ์—ฌ๋ฆ„ ์Šค๋งˆํŠธํฐ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ํ™œ์šฉํ•˜์—ฌ 5,000๋ช… ์ด์ƒ์˜ ์†Œ๊ทœ๋ชจ ์ „๋ ฅ ์†Œ๋น„์ž๋ฅผ๋Œ€์ƒ์œผ๋กœ ํ•œ ์ธ์„ผํ‹ฐ๋ธŒ ๊ธฐ๋ฐ˜(incentive-based) ์ˆ˜์š”๋ฐ˜์‘(Demand Response) ํ”„๋กœ๊ทธ๋žจ์˜ ์‹ค์ œ ์‹œ๋ฒ”์šด์˜ ๊ฒฐ๊ณผ๋ฅผ ๋ถ„์„ํ•˜๊ณ  ๊ทธ ํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๋ฉฐ, ์ด๋ฅผ ํ†ตํ•˜์—ฌ ์†Œ๊ทœ๋ชจ ์ „๋ ฅ ์†Œ๋น„์ž ๋Œ€์ƒ ์ˆ˜์š”์ž์› ๊ฑฐ๋ž˜์‹œ์žฅ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•œ๋‹ค. Demand Response Market (DR Market) has risen as one of the key solutions to address the growth and fluctuation of electricity consumptions. In Korea, DR market has been in operation since 2014, where the focus has been mainly on large-scale loads. Small-scale DR market, however, is becoming increasingly important because small power consumers contribution to the national power consumption has been increasing and because small loads tend to show large fluctuations. Furthermore, small-scale DR can improve social awareness on energy issues which can bring additional impacts. In this paper, we provide the findings from a small-scale consumer DR pilot. The pilot was conducted in the summer of 2016 on over 5,000 small-scale users in Korea, and smartphone applications were used in the pilot. The effectiveness of small-scale DR Market is analyzed and addressed, and the results indicate a promising future of small-scale DR Market.OAIID:RECH_ACHV_DSTSH_NO:T201708078RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A079687CITE_RATE:0DEPT_NM:์œตํ•ฉ๊ณผํ•™๋ถ€EMAIL:[email protected]_YN:NN
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