13 research outputs found

    A Study on the Capacity Determination and the Efficiency Improvement of the Organic Rankine Cycle for Marine Waste Heat Recovery System

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    Global Warming is a phenomenon that air temperature is gradually increased as the concentration of green house gas such as carbon dioxide, methane, etc. is increased. This is very important phenomenon which maintains the earth at certain temperature. But, gas causing green house effect is recently released too much artificially for a short time, so water temperature and sea level rise and abnormal climatic phenomenon is caused all over the world. The release of green house gas in the whole world is continuously increased after the Industrial Revolution and particularly and rapidly increased after 1945. For 200 years, CO2 of more than about 2.3 trillion has been released to the air and more than the half is the quantity released from 1974 to the present. In this situation, one of the technologies which can reduce CO2 generated in ship is WHRS (Marine Waste Heat Recovery System). WHRS means the system generating electric power with organic rankine cycle whose working fluid is Freon or organic media of hydrocarbon which is evaporated at the range of temperature lower than that of water so as to use existing heat source at middle and low temperature, which is released to the air, effectively. This research which is for the selection of quantity of organic rankine cycle for WHRS generation selected fundamental specifications of closed cycle, regeneration cycle and kalina cycle and analyzed characteristics of cycle, 3 kinds of pure refrigerant and 1 kind of mixture refrigerants by temperature changes of coolant. In addition, as a measure to improve efficiency of WHRS generation cycle, the characteristics of Hysys7.3 program of Aspentech, which is widely used as a process design program of industrial field were compared and analyzed for three measures such as addition of superheater, utilization of solar heat system, using of air cooler's waste heat. And, it analyzed economic feasibility in case when WHRS generation system is applied to full line with the data obtained by this research. And, the conclusions were as follows, 1) As the result of the study on the characteristics of WHRS ORC system by output of main engine by kinds of ship, turbine output of 669.0kW for 13,000TEU container ship, 417.2kW for crude oil ship, 159.3kW for 180k bulk ship could be obtained. 2) As the result of the study on the characteristics of WHRS ORC system by kinds of cycle and coolant, regeneration cycle showed higher cycle efficiency of from 0.75% to 4.41% than closed cycle by working fluid. 3) The quantity of kalina cycle for WHRS generation whose working fluid is compound of ammoniaโ€คwater was selected and cycle efficiency of 13.5% could be obtained. And, if composition ratio of compound of ammoniaโ€คwater is changed, cycle output and efficiency were changed by changes of compound characteristics. 4) As the temperature of coolant of central cooling system which is used as collant of WHRS generation was increased, rate of increase range of inquired flux was increased. And, inquired flux was rapidly increased at the temperature of collant at more than 39โ„ƒ. 5) Output increase of 5.42% could be obtained by overheating working fluid in state of saturated steam from the evaporator in the study on characteristics of system by addition of WHRS generation superheater. And, there was no big change for cycle efficiency by addition of superheater. 6) When using warm water generated by solar heat collecting system in WHRS generation system, improved efficiency of from 2.04% to 4.05% was shown by temperature of warm water. 7) Efficiency improvement of 2.63% was shown when installing preheater using waste heat generated in air cooler of main engine in WHRS generation system and preheating working fluid. 8) If assuming that WHRS generation system is installed and operated in 13,000TEU container ship, oil costs of from 384,915.43to384,915.43 to 331,336.00 was expected to be saved per a year.Abstract Nomenclature ์ œ 1 ์žฅ ์„œ ๋ก  1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1.2 ์—ฐ๊ตฌ ๋ชฉ์  ์ œ 2 ์žฅ ์„ ๋ฐ•ํ์—ดํšŒ์ˆ˜๋ฐœ์ „ ์‹œ์Šคํ…œ 2.1 ์„ ๋ฐ•ํ์—ดํšŒ์ˆ˜๋ฐœ์ „ 2.2 ์œ ๊ธฐ๋žญํ‚จ์‚ฌ์ดํด 2.2.1 ์œ ๊ธฐ๋žญํ‚จ์‚ฌ์ดํด์˜ ๊ฐœ๋… 2.2.2 ์œ ๊ธฐ๋žญํ‚จ์‚ฌ์ดํด์˜ ์ข…๋ฅ˜ 2.2.3 ์œ ๊ธฐ๋žญํ‚จ์‚ฌ์ดํด์˜ ์ž‘๋™์œ ์ฒด 2.3 ์ €์˜จ๋ถ€์‹ ์ œ 3 ์žฅ ์„ ๋ฐ•ํ์—ดํšŒ์ˆ˜๋ฐœ์ „ ์‹œ์Šคํ…œ์˜ ์šฉ๋Ÿ‰์„ ์ •์„ ์œ„ํ•œ ์—ฐ๊ตฌ 3.1 ์„ ๋ฐ•์˜ ์ข…๋ฅ˜๋ณ„ ์ฃผ๊ธฐ๊ด€ ์ถœ๋ ฅ์— ๋”ฐ๋ฅธ ์‹œ์Šคํ…œ ํŠน์„ฑ 3.1.1 ์„ ๋ฐ•ํ์—ดํšŒ์ˆ˜๋ฐœ์ „ ๊ธฐ๋ณธ ์‚ฌ์ดํด์˜ ์šฉ๋Ÿ‰์„ ์ • 3.1.2 ์„ ๋ฐ•์˜ ์ข…๋ฅ˜๋ณ„ ์ฃผ๊ธฐ๊ด€ ์ถœ๋ ฅ์— ๋”ฐ๋ฅธ ์‹œ์Šคํ…œ ํŠน์„ฑ ๋ถ„์„ 3.2 ๋ƒ‰๋งค ์ข…๋ฅ˜์— ๋”ฐ๋ฅธ ๋ฐ€ํ์‚ฌ์ดํด๊ณผ ์žฌ์ƒ์‚ฌ์ดํด์˜ ํŠน์„ฑ 3.2.1 R-245fa 3.2.2 R-134a 3.2.3 R-22 3.2.4 ๋ƒ‰๋งค์ข…๋ฅ˜์— ๋”ฐ๋ฅธ ์‹œ์Šคํ…œ ํŠน์„ฑ ๊ฒฐ๊ณผ ๋ถ„์„ 3.3 ์นผ๋ฆฌ๋‚˜ ์‚ฌ์ดํด์˜ ํŠน์„ฑ 3.3.1 ์นผ๋ฆฌ๋‚˜ ์‚ฌ์ดํด์˜ ๊ฐœ๋… 3.3.2 ์•”๋ชจ๋‹ˆ์•„โ€ค๋ฌผ ํ˜ผํ•ฉ๋ฌผ 3.3.3 ์„ ๋ฐ•ํ์—ดํšŒ์ˆ˜๋ฐœ์ „ ์šฉ ์นผ๋ฆฌ๋‚˜ ์‚ฌ์ดํด์˜ ์šฉ๋Ÿ‰์„ ์ • 3.3.4 ์•”๋ชจ๋‹ˆ์•„โ€ค๋ฌผ ํ˜ผํ•ฉ๋ฌผ์˜ ์กฐ์„ฑ๋น„์— ๋”ฐ๋ฅธ ์‚ฌ์ดํด ํŠน์„ฑ 3.4 ๋ƒ‰๊ฐ์ˆ˜ ์˜จ๋„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ์‹œ์Šคํ…œ ํŠน์„ฑ ์ œ 4 ์žฅ ์„ ๋ฐ•ํ์—ดํšŒ์ˆ˜๋ฐœ์ „ ์‹œ์Šคํ…œ์˜ ํšจ์œจํ–ฅ์ƒ์„ ์œ„ํ•œ ์—ฐ๊ตฌ 4.1 ๊ณผ์—ด๊ธฐ ์ถ”๊ฐ€์— ๋”ฐ๋ฅธ ์‚ฌ์ดํด ํŠน์„ฑ 4.1.1 ๊ธฐ๋ณธ ์‚ฌ์ดํด 4.1.2 ๊ณผ์—ด๊ธฐ ์ถ”๊ฐ€ ์‚ฌ์ดํด 4.1.3 ๊ณผ์—ด๊ธฐ ์ถ”๊ฐ€ ์ „ํ›„ ๋น„๊ต 4.1.4 ์—ด์› ์œ ๋Ÿ‰๋น„์œจ์กฐ์ ˆ์— ๋”ฐ๋ฅธ ์‚ฌ์ดํด ํŠน์„ฑ 4.1.5 ๊ณผ์—ด๊ธฐ ์ถ”๊ฐ€ ์‚ฌ์ดํด์˜ ๋ฐฐ๊ด€์†์‹ค ๊ณ„์‚ฐ 4.1.6 ๊ณผ์—ด๊ธฐ ์ถ”๊ฐ€ ์‚ฌ์ดํด์˜ ๋™ํŠน์„ฑ ๊ฒฐ๊ณผ ๋ถ„์„ 4.2 SOTEC์„ ์‘์šฉํ•œ ์‚ฌ์ดํด ํŠน์„ฑ 4.3 ์ฃผ๊ธฐ๊ด€์˜ Air Cooler ํ์—ด์„ ์ด์šฉํ•œ ์‚ฌ์ดํด ํŠน์„ฑ ์ œ 5 ์žฅ ์„ ๋ฐ•ํ์—ดํšŒ์ˆ˜๋ฐœ์ „ ์‹œ์Šคํ…œ์˜ ๊ฒฝ์ œ์„ฑ ์ œ 6 ์žฅ ๊ฒฐ ๋ก  ์ฐธ๊ณ ๋ฌธํ—Œ ๊ฐ์‚ฌ์˜

    ์ฝœ-๋ฆฌํ„ด ์ง์ด ๋งž๋Š” ๊ฒฝ๋กœ์— ๋Œ€ํ•œ ์‚ฌ์šฉ์ž ์ƒํ˜ธ์ž‘์šฉ์„ ํ†ตํ•ด ์ •๋ณดํ๋ฆ„ ๋ถ„์„ ๊ฒฝ๋ณด๋ฅผ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฐฉ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2017. 2. ์ด๊ด‘๊ทผ.๋ณธ ๋…ผ๋ฌธ์€ ์ •๋ณด ํ๋ฆ„ ๋ถ„์„๊ธฐ์˜ ๊ฒฝ๋ณด ๋ถ„๋ฅ˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ์ž์ƒํ˜ธ์ž‘์šฉ์„ ํ†ตํ•œ ํšจ์œจ์ ์ธ ๊ฒฝ๋ณด ๋ถ„๋ฅ˜ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ์‚ฌ์šฉ์ž์ œ์•ฝ์‹์„ ๋งŒ์กฑํ•˜๋Š” ์ฝœ-๋ฆฌํ„ด ์ง์ด ๋งž๋Š” ์ตœ๋‹จ ํ•จ์ˆ˜ ํ˜ธ์ถœ ๊ฒฝ๋กœ๋ฅผ ์•ˆ์ „ํ•˜๊ณ ํšจ์œจ์ ์œผ๋กœ ์ฐพ์Œ์œผ๋กœ์จ ์‚ฌ์šฉ์ž์˜ ๊ฒฝ๋ณด ๋ถ„๋ฅ˜๋ฅผ ๋•๋Š”๋‹ค. ์šฐ๋ฆฌ๋Š” ์ด ๋ฐฉ๋ฒ•์˜์‹ค์šฉ์„ฑ์„ ๋ณด์ด๊ธฐ ์œ„ํ•˜์—ฌ ๊ฒฝ๋ณด ๋ถ„๋ฅ˜ ์‹œ์Šคํ…œ SHOVEL์„ ๋””์ž์ธ ๋ฐ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ๊ตฌํ˜„๋œ ๊ฒฝ๋ณด ๋ถ„๋ฅ˜๊ธฐ SHOVEL๊ณผ ์ •์  ๋ถ„์„๊ธฐ SPARROW๋ฅผ ํ†ตํ•ด์ด 44๊ฐœ์˜ ์˜คํ”ˆ์†Œ์Šค C ํ”„๋กœ๊ทธ๋žจ์œผ๋กœ๋ถ€ํ„ฐ 360๊ฐœ์˜ ๊ฒฝ๋ณด๋ฅผ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ์ด๊ณผ์ •์—์„œ ๊ฒฝ๋ณด์˜ ์ง„์œ„์—ฌ๋ถ€๋ฅผ ํ‰๊ท  2.93ํšŒ์˜ ์ ์€ ์ˆ˜์˜ ์‚ฌ์šฉ์ž์ƒํ˜ธ์ž‘์šฉ์œผ๋กœ ํŒ๋‹จํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ๊ฒฝ๋ณด ๋ถ„๋ฅ˜๋ฅผ ํ†ตํ•ด 48๊ฐœ์˜ ๋ฒ„๊ทธ๋ฅผ๋ฐœ๊ฒฌํ•˜์˜€๊ณ  ๊ทธ์ค‘ 3๊ฐœ์˜ ๋ฒ„๊ทธ์— ๋Œ€ํ•ด CVE๋ฒˆํ˜ธ๋ฅผ ๋ถ€์—ฌ๋ฐ›์•˜๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 1.1 ๋™๊ธฐ 1 1.2 ํ•ด๊ฒฐ์ฑ… 1 1.3 ์‚ฌ์šฉ์ž ์ƒํ˜ธ์ž‘์šฉ 2 1.3.1 ์‚ฌ์šฉ์ž ์ƒํ˜ธ์ž‘์šฉ ์˜ˆ์ œ 3 1.4 ๊ฒฐ๊ณผ 6 1.5 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 7 ์ œ 2 ์žฅ ์ฝœ-๋ฆฌํ„ด ์ง์ด ๋งž๋Š” ํ•จ์ˆ˜ ํ˜ธ์ถœ ๊ฒฝ๋กœ ํ‘œํ˜„ 8 2.1 ํ•จ์ˆ˜ ํ˜ธ์ถœ ๊ฒฝ๋กœ 8 2.2 ์ฝœ-๋ฆฌํ„ด ์ง์ด ๋งž๋Š” ๊ฒฝ๋กœ ์ •์˜ 8 2.3 ์ฝœ-๋ฆฌํ„ด ์ง์ด ๋งž๋Š” ๊ฒฝ๋กœ์˜ ํšจ์œจ์ ์ธ ํ‘œํ˜„ 10 ์ œ 3 ์žฅ ์ฝœ-๋ฆฌํ„ด ์ง์ด ๋งž๋Š” ํ•จ์ˆ˜ ํ˜ธ์ถœ ๊ฒฝ๋กœ ํƒ์ƒ‰ 12 3.1 ์•Œ๊ณ ๋ฆฌ์ฆ˜ 12 3.2 ์ฝœ-๋ฆฌํ„ด ์ง์ด ๋งž๋Š” ๊ฒฝ๋กœ์˜ ๋ถ€์šธ์‹ ์ธ์ฝ”๋”ฉ 13 3.2.1 ๋ถ€์šธ์‹ ์ธ์ฝ”๋”ฉ ํ•จ์ˆ˜ ฮฆ 14 3.2.2 ฮฆ ์ ์šฉ 16 ์ œ 4 ์žฅ ์‹คํ—˜ ๊ฒฐ๊ณผ 22 4.1 ์‹คํ—˜ ํ™˜๊ฒฝ 22 4.2 ํ‰๊ฐ€ 23 4.3 ๋ฐœ๊ฒฌ๋œ ์‹ค์ œ ์ทจ์•ฝ์  26 ์ œ 5 ์žฅ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ๋ณด์™„ ์‚ฌํ•ญ 29 5.1 ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ 29 5.2 ๋ณด์™„ ์‚ฌํ•ญ 29 ์ œ 6 ์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ 31 ์ œ 7 ์žฅ ๊ฒฐ๋ก  33 ์ œ A ์žฅ ๋ถ€๋ก 34 ์ฐธ๊ณ ๋ฌธํ—Œ 39 Abstract 44Maste

    Analysis of the Participation in Social Network Group Using Balance Triangle Structure

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    ์†Œ์…œ ๋„คํŠธ์›Œํฌ ๊ทธ๋ฃน์€ ์ •๋ณด ์ „๋‹ฌ ๋ฐ ์˜๊ฒฌ ๊ต๋ฅ˜์˜ ์ธก๋ฉด์—์„œ ๊ทธ ํŠน์œ ์˜ ๊ฐœ๋ฐฉ์„ฑ์œผ๋กœ ํ™œ๋ฐœํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ ๋‹ค. ์ด์— ๋”ฐ๋ผ, ์†Œ์…œ ๋„คํŠธ์›Œํฌ ๊ทธ๋ฃน ์•ˆ์—์„œ ๊ทธ๋ฃน ์ฐธ์—ฌ์ž๊ฐ€ ๋ฐ›๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์ด ๋Œ€๋‘๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” Heider์˜ Balance Theory์— ์ฐฉ์•ˆํ•˜์—ฌ ๊ทธ๋ฃน ๋„คํŠธ์›Œํฌ ๋‚ด์˜ ๋‹ค๋ฅธ ๊ตฌ์„ฑ์›์œผ๋กœ๋ถ€ํ„ฐ ๋ฐ›๋Š” ์˜ํ–ฅ ์ด ๊ฐœ์ธ์˜ ์ฐธ์—ฌ ๊ฒฝํ–ฅ์„ ์–ด๋–ป๊ฒŒ ๋ณ€ํ™”์‹œํ‚ค๋Š”์ง€์— ๋Œ€ํ•ด ์•Œ์•„๋ณด์•˜๋‹ค. ํ•œ๊ตญ์–ด ์œ„ํ‚คํ”ผ๋””์•„ ๊ทธ๋ฃน์˜ ํŽธ์ง‘ ๋‚ด์—ญ ๋ฐ ์ดํ„ฐ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ, ๊ตฌ์„ฑ์›๋“ค๊ณผ์˜ ์ดˆ๊ธฐ ๊ด€๊ณ„๋„์™€ ์ด์— ๋”ฐ๋ฅธ ๊ทธ๋ฃน ๊ตฌ์„ฑ์›์˜ ํ–‰๋™ ์–‘์ƒ์„ ์ „์ฒด ๋„คํŠธ์›Œํฌ ๊ตฌ์กฐ์—์„œ์˜ Balance Triangle ๊ตฌ์กฐ ๋น„์œจ๊ณผ ๋น„๊ตํ•˜์˜€๊ณ , ์ด์™€ ๊ฐ™์€ ์ธ์ง€๊ณผํ•™์  ํ†ต์ฐฐ์ด ํ˜„์‹ค์˜ ์ธ๊ฐ„๊ด€๊ณ„๊ฐ€ ์•„๋‹Œ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ๊ทธ๋ฃน ์ƒ์—์„œ์˜ ์ธ๊ฐ„๊ด€๊ณ„์—์„œ๋„ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง์„ ๋ณด์˜€๋‹ค.์ด ๋…ผ๋ฌธ์€ 2015๋…„๋„ ์ •๋ถ€(๋ฏธ๋ž˜์ฐฝ์กฐ๊ณผํ•™๋ถ€)์˜ ์žฌ์›์œผ๋กœ ํ•œ๊ตญ์—ฐ๊ตฌ์žฌ๋‹จ์˜ ์ง€์›์„ ๋ฐ›์•„ ์ˆ˜ํ–‰๋œ ์—ฐ๊ตฌ์ž„. (No. NRF-2015R1A2A1A01007400)OAIID:RECH_ACHV_DSTSH_NO:A201620370RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A001118CITE_RATE:DEPT_NM:์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€EMAIL:[email protected]_YN:CONFIRM:

    Reliable and Efficient Backpressure Routing using DODAG Structure

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    ๋ฐฐ์•• ๋ผ์šฐํŒ… ๊ธฐ๋ฒ•์€ ๋ฉ€ํ‹ฐํ™‰ ํ†ต์‹ ์—์„œ ๋„คํŠธ์›Œํฌ ์ „์†ก๋Ÿ‰์„ ์ตœ๋Œ€ํ™”ํ•œ๋‹ค๋Š” ๊ฐ•๋ ฅํ•œ ํŠน์ง• ๋•Œ๋ฌธ์— ํ•ด๋งˆ๋‹ค ๊พธ์ค€ ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์ ธ์˜ค๊ณ  ์žˆ์œผ๋‚˜ ์‹ฌ๊ฐํ•œ ์ง€์—ฐ ๋ฐ ๋ฃจํ”„ ๋ฌธ์ œ๋ฅผ ๊ฐ€์ง„๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ €์ „๋ ฅ ์†์‹ค ๋„คํŠธ์›Œํฌ ๋ผ ์šฐํŒ… ๊ธฐ๋ฒ•์— ์‚ฌ์šฉ๋˜๋Š” DODAG ๊ตฌ์กฐ๋ฅผ ๋ฐฐ์•• ๋ผ์šฐํŒ… ๊ธฐ๋ฒ•์— ์ ์šฉํ•จ์œผ๋กœ์จ, ์ผ๋ฐ˜์  ๋ฐฐ์•• ๋ผ์šฐํŒ… ๊ธฐ๋ฒ•์˜ ๋ฉ” ์‹œ์ง€ ์ „์†ก์— ๋ฐฉํ–ฅ์„ฑ์„ ๋ถ€์—ฌํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ, ๊ธฐ์กด์˜ ๋ฐฐ์•• ๋ผ์šฐํŒ… ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ๊ฐ€์ง€๋Š” ์ตœ๋Œ€ ๊ฐ•์ ์ธ ๋„คํŠธ์›Œํฌ ๋‚ด ์ „์†ก๋Ÿ‰ ์ตœ๋Œ€ํ™” ํŠน์„ฑ์„ ์œ ์ง€ํ•˜๋ฉด์„œ, ๊ธฐ์กด ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ๊ฐ€์ง€๋˜ ๋ฃจํ”„ ๋ฌธ์ œ ๋˜๋Š” ์‹ฌ๊ฐํ•œ ์ „์†ก ์ง€์—ฐ ๋ฌธ์ œ๋ฅผ ์™„ ํ™”ํ•˜์˜€๋‹ค. ์„ฑ๋Šฅ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด ์ „์†ก ์ง€์—ฐ ์‹œ๊ฐ„์„ ๊ธฐ์กด ๋Œ€๋น„ ์•ฝ 65% ๊ฐ์†Œ, ๋ฃจํ”„ ๋ฐฉ์ง€ ๋ฐ ์ „์†ก ํšจ์œจ์„ ๋†’์ž„์œผ ๋กœ์จ ์ „์†ก๋ฅ ์„ 97%์—์„œ 99%๋กœ ํ–ฅ์ƒ์‹œํ‚ค๊ณ  ์žˆ์Œ์„ ๋ณด์˜€๋‹ค.์ด ๋…ผ๋ฌธ์€ 2016๋…„๋„ ์ •๋ถ€(๋ฏธ๋ž˜์ฐฝ์กฐ๊ณผํ•™๋ถ€)์˜ ์žฌ์›์œผ๋กœ ์ •๋ณดํ†ต์‹ ๊ธฐ์ˆ ์ง„ํฅ์„ผํ„ฐ์˜ ์ง€์›(No.B0190-16-2017,IoT ๊ธฐ๊ธฐ ์˜ ๋ฌผ๋ฆฌ์  ์†์„ฑ, ๊ด€๊ณ„, ์—ญํ•  ๊ธฐ๋ฐ˜ Resilient/Fault-Tolerant ์ž์œจ ๋„คํŠธ์›Œํ‚น ๊ธฐ์ˆ  ์—ฐ๊ตฌ) ๋ฐ ๋ฏธ๋ž˜์ฐฝ์กฐ๊ณผํ•™๋ถ€ ๋ฐ ์ •๋ณดํ†ต ์‹ ๊ธฐ์ˆ ์ง„ํฅ์„ผํ„ฐ์˜ ๋Œ€ํ•™ICT์—ฐ๊ตฌ์„ผํ„ฐ์œก์„ฑ ์ง€์›์‚ฌ์—…์˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ์Œ" (IITP-2015-R0992-15-1023)OAIID:RECH_ACHV_DSTSH_NO:A201620365RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A001118CITE_RATE:DEPT_NM:์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€EMAIL:[email protected]_YN:CONFIRM:

    Performance Evaluation of Review Spam Detection for Domestic Shopping Site Application

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    ์ƒํ’ˆ ๋˜๋Š” ์ƒ์ ์— ๋Œ€ํ•ด ๊ฑฐ์ง“๋œ ํ›„๊ธฐ๋ฅผ ๋‚จ๊ธฐ๋Š” ์•…์˜์ ์ธ ์‚ฌ์šฉ์ž๊ฐ€ ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์‚ฌ์šฉ์ž์—๊ฒŒ ์‹ ๋ขฐ์„ฑ ์žˆ๋Š” ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋Š” ๋ฐ ์–ด๋ ค์›€์„ ๊ฒช๊ณ  ์žˆ๋‹ค. ๊ตญ๋‚ด ์‡ผํ•‘ ์‚ฌ์ดํŠธ์—์„œ๋„ ๋ฆฌ๋ทฐ ์ŠคํŒธ์€ ํ”ํžˆ ์ ‘ํ•  ์ˆ˜ ์žˆ์œผ๋‚˜, ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ๋ชจ๋‘ ์™ธ๊ตญ ์‚ฌ์ดํŠธ์—์„œ๋งŒ ํ‰๊ฐ€๋˜์—ˆ๋‹ค. ๋”ฐ ๋ผ์„œ, ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋„ค์ด๋ฒ„ ์‡ผํ•‘์˜ ๋ฆฌ๋ทฐ ํŠน์„ฑ์„ ํŒŒ์•…ํ•˜๊ณ , ๋ฆฌ๋ทฐ ์ŠคํŒธ์„ ํƒ์ง€ํ•˜๋Š” ์—ฌ๋Ÿฌ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์„ ๋„ค์ด๋ฒ„ ์‡ผํ•‘์— ์ ์šฉํ•˜์—ฌ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค.์ด ๋…ผ๋ฌธ์€ 2015๋…„๋„ ์ •๋ถ€(๋ฏธ๋ž˜์ฐฝ์กฐ๊ณผํ•™๋ถ€)์˜ ์žฌ์›์œผ๋กœ ํ•œ๊ตญ์—ฐ๊ตฌ์žฌ๋‹จ์˜ ์ง€์›์„ ๋ฐ›์•„ ์ˆ˜ํ–‰๋œ ์—ฐ๊ตฌ์ž„(No. NRF2015R1A2A1A01007400)OAIID:RECH_ACHV_DSTSH_NO:A201620371RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A001118CITE_RATE:DEPT_NM:์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€EMAIL:[email protected]_YN:CONFIRM:

    Using Rank Correlation Coefficient to identify Abnormal Energy Consumption in Buildings

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    ์—๋„ˆ์ง€ ์ ˆ์•ฝ ๋ฌธ์ œ๋Š” ํ˜„์žฌ๊นŒ์ง€ ํ•ด๊ฒฐ๋˜์ง€ ์•Š์€ ๋ฌธ์ œ์ด๋ฉฐ, ์—๋„ˆ์ง€๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์‚ฌ์šฉํ•˜๊ธฐ ์œ„ํ•ด ์—ฌ๋Ÿฌ ๋ฐฉ ๋ฒ•๋“ค์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. IT ๊ธฐ์ˆ ์˜ ๋ฐœ๋‹ฌ๊ณผ ๋”๋ถˆ์–ด ์„ผ์„œ, ์˜จ๋„์กฐ์ ˆ์žฅ์น˜, ์—์–ด์ปจ, ์กฐ๋ช… ๋“ฑ์˜ ๊ธฐ๊ธฐ๋“ค์„ ํ†ตํ•ฉํ•˜์—ฌ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๊ณ  ๊ด€๋ฆฌํ•˜๋Š” ๊ณต์กฐ ์‹œ์Šคํ…œ (HVAC: Heating Ventilation Air Conditioning)์ด ๊ฑด๋ฌผ์— ๋„์ž…๋˜์–ด ํ™œ ์šฉ๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด ์‹œ์Šคํ…œ์„ ํ†ตํ•˜์—ฌ ์—๋„ˆ์ง€ ์†Œ๋น„์˜ ๋ฌธ์ œ์ ์„ ์ฐพ๊ณ  ์—๋„ˆ์ง€๋ฅผ ํšจ์œจ์ ์œผ๋กœ ๊ด€๋ฆฌํ•  ์ˆ˜ ์žˆ๋‹ค. ์—๋„ˆ์ง€ ํšจ์œจ์  ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•˜์—ฌ ์ด์ƒ ํ˜„์ƒ์„ ํšจ์œจ์ ์œผ๋กœ ๊ฐ์ง€ํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•œ ๋ฐฉ๋ฒ•๋“ค๋„ ๋งŽ์ด ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ์—์„œ๋Š” ์—๋„ˆ์ง€ ์„ผ์„œ๊ฐ„์˜ ์ „๋ ฅ ์†Œ๋ชจ ํŒจํ„ด์„ 3๊ฐœ์˜ ๋ฐด๋“œ ์˜์—ญ์œผ๋กœ ๋‚˜๋ˆ„์–ด ์ด์ƒ ํ˜„์ƒ์„ ํƒ์ง€ ํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ๊ฐ ๋ฐด๋“œ ์˜์—ญ ๊ฐ„์˜ ๊ด€๊ณ„์— ์น˜์šฐ์ณ ๊ธฐ๊ธฐ๋“ค๊ฐ„์˜ ๋งŽ์€ ๊ด€๊ณ„๋ฅผ ํƒ์ƒ‰ํ•˜์ง€ ๋ชปํ•œ๋‹ค๋Š” ๋‹จ์ ์ด ์žˆ ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์€ ์ „๋ ฅ ์†Œ๋ชจ ํŒจํ„ด์— ๋”ฐ๋ผ ๋ฐด๋“œ ์˜์—ญ์„ ๊ตฌ๋ถ„ํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ์ „ ๋ฐด๋“œ ์˜์—ญ์—์„œ ๊ฐ ๊ธฐ๊ธฐ๊ฐ„์˜ ์ˆœ์œ„ ๊ด€๊ณ„ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•จ์œผ๋กœ์จ ์ด์ƒ ํ˜„์ƒ ํƒ์ง€์˜ ํšจ์œจ์„ฑ์„ ๋†’์ด๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•˜๊ณ  ์žˆ๋‹ค.์ด ๋…ผ๋ฌธ์€ 2016๋…„๋„ ์ •๋ถ€(๋ฏธ๋ž˜์ฐฝ์กฐ๊ณผํ•™๋ถ€)์˜ ์žฌ์›์œผ ๋กœ ์ •๋ณดํ†ต์‹ ๊ธฐ์ˆ ์ง„ํฅ์„ผํ„ฐ์˜ ์ง€์›์„ ๋ฐ›์•„ ์ˆ˜ํ–‰๋œ ์—ฐ๊ตฌ (No.B0190-16-2017,IoT ๊ธฐ๊ธฐ์˜ ๋ฌผ๋ฆฌ์  ์†์„ฑ, ๊ด€๊ณ„, ์—ญ ํ•  ๊ธฐ๋ฐ˜ Resilient/Fault-Tolerant ์ž์œจ ๋„คํŠธ์›Œํ‚น ๊ธฐ์ˆ  ์—ฐ๊ตฌ) ๋ฐ ๋ฏธ๋ž˜์ฐฝ์กฐ๊ณผํ•™๋ถ€ ๋ฐ ์ •๋ณดํ†ต์‹ ๊ธฐ์ˆ ์ง„ํฅ์„ผํ„ฐ์˜ ๋Œ€ํ•™ICT์—ฐ๊ตฌ์„ผํ„ฐ์œก์„ฑ ์ง€์›์‚ฌ์—…์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ ์Œ" (IITP-2015-R0992-15-1023)OAIID:RECH_ACHV_DSTSH_NO:A201620368RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A001118CITE_RATE:DEPT_NM:์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€EMAIL:[email protected]_YN:CONFIRM:

    Bipartite Preference aware Robust Recommendation System

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    ์˜จ๋ผ์ธ ์‹œ์Šคํ…œ์ด ํ™œ์„ฑํ™” ๋˜๊ณ  ์ ‘๊ทผ ๊ฐ€๋Šฅํ•œ ์ •๋ณด์˜ ์–‘์ด ๋Š˜์–ด๋‚˜๋ฉด์„œ ์ถ”์ฒœ ์‹œ์Šคํ…œ์˜ ์˜ํ–ฅ๋ ฅ ๋˜ํ•œ ์ปค์ง€๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ์ผ๋ถ€ ์•…์˜์ ์ธ ์œ ์ €๋“ค์˜ ๊ณต๊ฒฉ์œผ๋กœ ์ธํ•ด ์‹œ์Šคํ…œ์— ๋Œ€ํ•œ ์‹ ๋ขฐ๋„๋ฅผ ์ €ํ•˜์‹œํ‚ค๊ณ  ์กฐ์ž‘ํ•˜๋ ค๋Š” ์‹œ๋„๊ฐ€ ๋Š˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌํŒ€์€ํ•ด๋‹น ๋ฆฌ๋ทฐ์— ๋Œ€ํ•œ ๊ณต๊ฐ, ๋น„๊ณต๊ฐ ๋น„์œจ์„ ๋ถ„์„ํ•˜๊ณ  ์ด๋ฅผ ์ถ”์ฒœ ์‹œ์Šคํ…œ์— ์ ์šฉํ•จ์œผ๋กœ์จ ์ถ”์ฒœ ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ณ ๊ฐ•๊ฑดํ•œ ์‹œ์Šคํ…œ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์‹ค์ œ ์˜ํ™” ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ ์ ์šฉํ•ด ๋ณธ ๊ฒฐ๊ณผ ๊ธฐ์กด์˜ ์ถ”์ฒœ ์‹œ์Šคํ…œ๋ณด๋‹ค ํ–ฅ์ƒ๋œ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. Due to the prevalent use of online systems and the increasing amount of accessible information, the influence of recommender systems is growing bigger than ever. However, there are several attempts by malicious users who try to compromise or manipulate the reliability of recommender systems with cyber-attacks. By analyzing the ratio of 'sympathy' against 'apathy' responses about a concerned review and reflecting the results in a recommendation system, we could present a way to improve the performance of a recommender system and maintain a robust system. After collecting and applying actual movie review data, we found that our proposed recommender system showed an improved performance compared to the existing recommendation systems.N
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