43 research outputs found

    ์ƒˆ๋กœ์šด ๋ฉ”๋ชจ๋ฆฌ ๊ธฐ์ˆ ์„ ์‚ฌ์šฉํ•˜๋Š” ์‹œ์Šคํ…œ์˜ ์„ฑ๋Šฅ ํ–ฅ์ƒ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2018. 2. ์ตœ๊ธฐ์˜.Emerging memory technologies such as 3D-stacked memory or STT-RAM have higher density than traditional SRAM technology. As a result, these new memory technologies have recently been integrated with processors on the same chip or in the same package. These integrated emerging memory technologies provide more capacity to the processors than traditional SRAMs. Therefore, in order to improve the performance of the chip or the package, it is also important to effectively manage the memories as well as improve the performance of the processors themselves. This dissertation researches two approaches to improve the performance of systems in which processors and emerging memories are integrated on a single chip or in a single package. The first part of this dissertation focuses on improving the performance of a system in which 3D-stacked memory is integrated with the processor in a package, assuming that the processor is generic and the memory access pattern is not predefined. A DRAM cache technique is proposed, which combines the previous approaches in a synergistic way by devising a module called dirty-block tracker to maintain dirtiness of each block in a dirty-region. The approach avoids unnecessary tag checking for a write operation if the corresponding block in the cache is not dirty. Simulation results show that the proposed technique achieves significant performance improvement on average over the state-of-the-art DRAM cache technique. The second part of this dissertation focuses on improving the performance of a system in which an accelerator and STT-RAM are integrated on a single chip, assuming that certain algorithms, called deep neural networks, are processed on this system. A high-performance, energy-efficient accelerator is designed considering the characteristics of the neural network. While negative inputs for ReLU are useless, it consumes a lot of computing power to calculate them for deep neural networks. A computation pruning technique is proposed that detects at an early stage that the result of a sum of products will be negative by adopting an inverted two's complement expression for weights and a bit-serial sum of products. Therefore, it can skip a large amount of computations for negative results and simply set the ReLU outputs to zero. Moreover, a DNN accelerator architecture is devised that can efficiently apply the proposed technique. The evaluation shows that the accelerator using the computation pruning through early negative detection technique significantly improves the energy efficiency and the performance.1 Introduction 1 1.1 A DRAM Cache using 3D-stacked Memory 1 1.2 A Deep Neural Network Accelerator with STT-RAM 5 2 A DRAM Cache using 3D-stacked Memory 7 2.1 Background 7 2.1.1 Loh-Hill DRAM Cache 8 2.1.2 Alloy Cache 9 2.1.3 Mostly-Clean DRAM Cache 10 2.2 Direct-mapped DRAM Cache with Self-balancing Dispatch 12 2.2.1 A Naฤฑve Approach 13 2.2.2 Dirty-Block Tracker (DiBT) 20 2.2.3 Sampling Hit-Miss Predictor 31 2.3 Evaluation Methodology 32 2.3.1 Experimental Setup 32 2.3.2 Workloads 33 2.4 Results 36 2.4.1 Performance 36 2.4.2 Analysis 38 2.4.3 Prediction Accuracy 42 2.4.4 Sensitivity to Sampling Hit-miss Predictor to VUPPER 43 2.4.5 Sensitivity to Dirty-Block Table Size 45 2.4.6 Scalability 46 2.4.7 Implementation Cost 46 2.5 Related Work 49 2.6 Summary 50 3 A Deep Neural Network Accelerator with STT-RAM 52 3.1 Background 52 3.1.1 Computations in CNNs 52 3.1.2 Sign Distribution of Inputs to ReLU 53 3.1.3 Twos Complement Representation 54 3.2 Early Negative Detection 55 3.2.1 Bit-serial Sum of Products 55 3.2.2 Inverted Twos Complement Representation 58 3.2.3 Early Negative Detection 58 3.3 Accelerator 60 3.3.1 Overall Architecture 61 3.3.2 Data block 62 3.3.3 Processing Unit 62 3.3.4 Buffers 65 3.3.5 Memory Controller 65 3.3.6 Providing Network 66 3.3.7 Pipelined Bit-serial Sum of Products 67 3.3.8 Global Controller 68 3.4 Evaluation 71 3.4.1 Methodology 72 3.4.2 Workloads 74 3.4.3 Normalized Runtime 77 3.4.4 Normalized Energy Consumption 80 3.4.5 Power Consumption 83 3.4.6 Normalized EDP and ED2P 85 3.4.7 Area 87 3.5 Related work 87 3.6 Summary 89 4 Conclusion 91 Abstract (In korean) 100Docto

    ๊ณต๊ณต๊ธฐ๊ด€ ์ง€๋ฐฉ์ด์ „์‹œ์ฑ…์˜ ์ถ”์ง„๊ณผ์ • ์—ฐ๊ตฌ(A study on the regional relocation policy of public agency in capital region)

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    ๋…ธํŠธ : ์ด ์—ฐ๊ตฌ๋ณด๊ณ ์„œ์˜ ๋‚ด์šฉ์€ ๊ตญํ† ์—ฐ๊ตฌ์›์˜ ์ž์ฒด ์—ฐ๊ตฌ๋ฌผ๋กœ์„œ ์ •๋ถ€์˜ ์ •์ฑ…์ด๋‚˜ ๊ฒฌํ•ด์™€๋Š” ์ƒ๊ด€์—†์Šต๋‹ˆ๋‹ค

    (A) survey of dental treatment under outpatient general anesthesia in department of pediatric dentistr

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    ์น˜์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€]์ „์‹  ๋งˆ์ทจ๋Š” ์น˜๊ณผ์น˜๋ฃŒ์‹œ ์ผ๋ฐ˜์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ ํ–‰๋™์กฐ์ ˆ์ด ๋ถˆ๊ฐ€๋Šฅํ•œ ํ™˜์ž ๋ฐ ๋ณ„๋„์˜ ์˜ํ•™์  ์ฒ˜์น˜๊ฐ€ ํ•„์š”ํ•œ ํ™˜์ž๋ฅผ ์œ„ํ•œ ํ–‰๋™์กฐ์ ˆ๋ฒ• ์ค‘ ํ•˜๋‚˜๋กœ ์—ฐ๊ตฌ, ๋ฐœ์ „๋˜์–ด ์™”๋‹ค. ์ด ์ค‘ ํ†ต๊ณ„์  ์—ฐ๊ตฌ๋Š” ์น˜๊ณผ ์น˜๋ฃŒ๋ฅผ ์œ„ํ•œ ์ „์‹ ๋งˆ์ทจ๋ฅผ ๋ฐ›๋Š” ํ™˜์ž๋“ค์—๊ฒŒ ๋ณด๋‹ค ์–‘์งˆ์˜ ์ง„๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜๊ธฐ ์œ„ํ•œ ๊ธฐ์ดˆ ์ž๋ฃŒ๋ฅผ ์ œ๊ณตํ•˜๋Š”๋ฐ ๊ทธ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ด์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ง€์†์ ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ ์™”์œผ๋‚˜, ์•„์ง ๊ตญ๋‚ด์—์„œ๋Š” ๋‹ค์ˆ˜์˜ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์žฅ๊ธฐ๊ฐ„์˜ ์—ฐ๊ตฌ๊ฐ€ ๋ถ€์กฑํ•œ ์‹ค์ •์ด๋‹ค. ์ด์— 1997๋…„ 1์›”๋ถ€ํ„ฐ 2008๋…„ 8์›”๋ง๊นŒ์ง€ ์—ฐ์„ธ๋Œ€ํ•™๊ต ์น˜๊ณผ๋ณ‘์› ์†Œ์•„์น˜๊ณผ ๋ฐ ์žฅ์• ์ธ ํด๋ฆฌ๋‹‰์—์„œ ์‹œํ–‰๋œ ์น˜๊ณผ์น˜๋ฃŒ๋ฅผ ์œ„ํ•œ ์ „์‹ ๋งˆ์ทจ 1196๋ก€(1135๋ช…) ์ค‘ ๊ธฐ๋ก์ด ์–‘ํ˜ธํ•œ 1126๋ก€(1065๋ช…)๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜์—ฌ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ๋‹ค. 1. ์—ฐ๋ น๋ฒ”์œ„๋Š” 1๏ฝž66์„ธ๋กœ 5์„ธ ๋ฏธ๋งŒ์ด 410๋ช…(38.5%)์œผ๋กœ ๊ฐ€์žฅ ๋งŽ์•˜์œผ๋ฉฐ, ์„ฑ๋ณ„์€ ๋‚จ์„ฑ์ด 695๋ช…(65.3%)์œผ๋กœ ์—ฌ์„ฑ๋ณด๋‹ค ๋” ๋งŽ์•˜๋‹ค. 2. ์น˜๋ฃŒ ๋‚ด์šฉ์€ ํ†ต๊ณ„์ƒ์˜ ํ‰๊ท ์น˜๋กœ ๋ณผ ๋•Œ ์ˆ˜๋ณต ์น˜๋ฃŒ๊ฐ€ 1์ธ๋‹น 5.6๊ฐœ, ์œ ์น˜ ๋ฐ ์˜๊ตฌ์น˜์— ๋Œ€ํ•œ ์น˜์ˆ˜ ์น˜๋ฃŒ์™€ ๊ทผ๊ด€ ์น˜๋ฃŒ๊ฐ€ 2.3๊ฐœ, ๊ธฐ์„ฑ ๊ธˆ๊ด€์ด 2.5๊ฐœ, ๋ฐœ์น˜๊ฐ€ 1.6 ๊ฐœ์˜€์œผ๋ฉฐ, ์น˜๋ฃŒ ์‹œ๊ฐ„์€ ํ‰๊ท  100๋ถ„์ด์—ˆ๋‹ค. 3. 576๋ช…(53.3%)์˜ ํ™˜์ž๊ฐ€ 6๊ฐœ์›” ๋ฏธ๋งŒ์œผ๋กœ ์žฌ๋‚ด์›ํ•˜์˜€์œผ๋ฉฐ, ๋‚ด์› ํšŸ์ˆ˜๋Š” ํ‰๊ท  4.3ํšŒ์˜€๋‹ค. 4. ์น˜๊ณผ์น˜๋ฃŒ๋ฅผ ์œ„ํ•œ ์ „์‹ ๋งˆ์ทจ๋ฅผ ๋ฐ›์€ ํšŸ์ˆ˜๋Š” 1022๋ช…(95.9%)์ด ์ „์‹ ๋งˆ์ทจ๋ฅผ 1ํšŒ ๋ฐ›์•˜์œผ๋ฉฐ 43๋ช…(4.1%)์˜ ํ™˜์ž๊ฐ€ 2ํšŒ ์ด์ƒ ์ „์‹ ๋งˆ์ทจ๋ฅผ ๋ฐ›์•˜๋‹ค. ๋”ฐ๋ผ์„œ ์ „์‹ ๋งˆ์ทจํ•˜์˜ ์น˜๊ณผ์น˜๋ฃŒ๋Š” ๋Œ€๋ถ€๋ถ„์˜ ์น˜๊ณผ ์น˜๋ฃŒ๋ฅผ ํ•œ๋ฒˆ์— ์‹œํ–‰ํ•  ์ˆ˜ ์žˆ๊ณ  ํ™˜์ž์™€ ๋ณดํ˜ธ์ž์˜ ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ์ตœ์†Œํ™” ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์–‘์งˆ์˜ ์ง„๋ฃŒ ํ™˜๊ฒฝ์„ ์ œ๊ณตํ•จ์œผ๋กœ์„œ ๋งŒ์กฑ์Šค๋Ÿฌ์šด ์น˜๋ฃŒ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋Œ€ํ•  ์ˆ˜ ์žˆ๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ตฌ๊ฐ• ์งˆํ™˜์˜ ํŠน์„ฑ์ƒ ์ ์ ˆํ•œ ์‚ฌํ›„ ๊ด€๋ฆฌ๊ฐ€ ์ด๋ฃจ์–ด์ง€์ง€ ์•Š๋Š”๋‹ค๋ฉด ์ ์ ˆํ•œ ๊ตฌ๊ฐ• ๊ฑด๊ฐ•์„ ์œ ์ง€ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ์•ž์œผ๋กœ์˜ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ „์‹ ๋งˆ์ทจํ•˜ ์น˜๊ณผ์น˜๋ฃŒ์˜ ํšจ์œจ์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ณ  ๊ทธ ์ดํ›„์˜ ์ฒด๊ณ„์ ์ธ ๊ฒ€์ง„ ๊ณ„ํš์„ ํ™•๋ฆฝํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. [์˜๋ฌธ]General anesthesia has been researched and developed in dentistry as on e type of management technique to sedate patients who may be uncontrollable or require medical consideration. Among such studies, the purpose of statistical analysis is to aid in providing superior quality care to patients receiving genenral anesthesia by supplying foundational data. As such, there has been continuous research into this area. Domestically, however, analysis of large of set of patients over a sustained period of time is lacking. Thus, this study analyzes the records of patients who received general anesthesia at the Yonsei University Dental Hospital Department of Pediatric and Clinic for the Disabled. Out of the 1196 records(1135 patients) from January 1997 to August 2008, the study included 1126 records(1065 patients) which were well preserved, yielding the following results. 1. Patient's age ranged from 1 to 66, with under 5 being the largest group with 410 members(38.5%). The study included more men than women, with 695 male members(65.3%). 2. Type of dental procedure performed were as follows(per person) : 5.6 Dental restoration; 2.3 Endodontic treatment of deciduous and primary teeth; 2.5 preformed crowning; and 1.6 extractions. Procedures took an average of 100 minutes. 3. 489 patients(46.7%) received care for longer than 6 months, with an average of 4.3 visits. 4. 1022 patients(95.9%) received dental care under general anesthesia once and 43 patients(4.1%) received dental care under general anesthesia two or more times. Dentistry under general anesthesia has the benefit of increasing the likelihood of successful treatment by performing most of dental procedures in a single session, minimizing the patient's and the guardian's stress level, and providing superior treatment environment. Due to the nature of oral disorders, however, without appropriate post-treatment care, it is difficult to maintain good oral health. Therefore, not only is it important to improve the efficiency and safety of general anesthesia through future research, it is also important to establish a systematic check-up plan.ope

    ํ•œ๊ตญ์˜ ๋ง๋ผ๋ฆฌ์•„ ์—ญํ•™์กฐ์‚ฌ ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅธ ์ถ”์ • ๊ฐ์—ผ ์ง€์—ญ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋ณด๊ฑด๋Œ€ํ•™์› : ๋ณด๊ฑดํ•™๊ณผ, 2014. 8. ์กฐ์„ฑ์ผ.์—ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ง๋ผ๋ฆฌ์•„๋Š” ๋ชจ๊ธฐ์— ์˜ํ•ด ์ „ํŒŒ๋˜๋Š” ๊ธ‰์„ฑ์—ด์„ฑ ์งˆํ™˜์œผ๋กœ ํ•œ๊ตญ์—์„œ๋Š” ์‚ผ์ผ์—ด ๋ง๋ผ๋ฆฌ์•„๊ฐ€ ๊ฐ€์žฅ ์ฃผ๋œ ์›์ธ๋ณ‘์›์ฒด๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ ๋ณด๊ฑด๋‹น๊ตญ์€ ์ง‘์ค‘์ ์ธ ๊ฐ์‹œ, ์˜ˆ๋ฐฉ, ๋ฐฉ์—ญํ™œ๋™์„ ํ†ตํ•ด 1979๋…„๋„์— ์šฐ๋ฆฌ๋‚˜๋ผ์—์„œ ๋ง๋ผ๋ฆฌ์•„ ์™„์ „ ํ‡ด์น˜๋ฅผ ์„ ์–ธํ•œ ๋ฐ” ์žˆ์œผ๋‚˜, 1993๋…„๋ถ€ํ„ฐ ํœด์ „์„  ์ธ๊ทผ์—์„œ ํ™˜์ž๊ฐ€ ๋‹ค์‹œ ๋ฐœ์ƒํ•œ ์ด๋ž˜๋กœ ์ผ์‹œ์  ์ฆ๊ฐ€ ํ›„ ๊ฐ์†Œ์ถ”์„ธ๋ฅผ ๋ณด์ด๋Š” ํ™˜์ž ๋ฐœ์ƒ๊ณผ ๊ด€๋ จ๋œ ์ง€์†์ ์ธ ์˜ˆ๋ฐฉยท๊ด€๋ฆฌ ์‚ฌ์—…์„ ์ˆ˜ํ–‰์ค‘์ด๋‹ค. ์งˆ๋ณ‘๊ด€๋ฆฌ๋ณธ๋ถ€๋Š” 2001๋…„ ์ดํ›„ ์›น๊ธฐ๋ฐ˜ ๊ฐ์—ผ๋ณ‘ ๊ฐ์‹œ์ฒด๊ณ„๋ฅผ ๊ตฌ์ถ•ํ•ด ์‹œํ–‰ํ•ด์˜ค๊ณ  ์žˆ์œผ๋‚˜, ์ด ์ž๋ฃŒ๋Š” ํ™˜์ž์˜ ์ฃผ์†Œ์ง€๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋“ฑ๋กํ•˜๊ฒŒ ๋˜์–ด ์žˆ์–ด, ๋‹ค๋ฅธ ๊ฐ์—ผ๋ณ‘๊ณผ๋Š” ๋‹ฌ๋ฆฌ ๋ง๋ผ๋ฆฌ์•„๋Š” ์‹ค์ œ ๊ฐ์—ผ์ง€์—ญ ์–‘์ƒ๊ณผ๋Š” ์ฐจ์ด๋ฅผ ๋ณด์ผ ์ˆ˜ ์žˆ๋‹ค๋Š” ์˜๊ฒฌ๋“ค์ด ์ œ์‹œ๋˜์—ˆ๋‹ค. ์ด๋ฅผ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•ด ์งˆ๋ณ‘๊ด€๋ฆฌ๋ณธ๋ถ€๋Š” ์ž์ฒด์ ์œผ๋กœ ์ž ๋ณต๊ธฐ ๋ฐ ๊ณผ๊ฑฐ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์„ ์ •๋œ 22๊ฐœ ์œ„ํ—˜์ง€์—ญ ๋‚ด ๊ฑฐ์ฃผยท๊ทผ๋ฌด, ๊ตฐ๋ณต๋ฌด, ์—ฌํ–‰์—ฌ๋ถ€์™€ ๋ชจ๊ธฐ ๋…ธ์ถœ ๊ฐ€๋Šฅ์„ฑ, ๋ชจ๊ธฐ ์„œ์‹ ํ™˜๊ฒฝ ๋“ฑ์„ ์‚ฌ๋ก€๋ณ„๋กœ ์ถ”๊ฐ€ ์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜๊ณ , ์ด ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉ์ ์œผ๋กœ ๊ฒ€ํ† ํ•˜์—ฌ ์‚ฌ๋ก€๋ณ„ ๊ฐ์—ผ๊ฒฝ๋กœ ๋ฐ ๊ฐ์—ผ์ง€์—ญ์„ ์ถ”์ •ํ•˜๋Š” ์—ญํ•™์กฐ์‚ฌ ๊ณผ์ •์„ ๋‘๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์—ญํ•™์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์ถ”์ •๋œ ๊ฐ์—ผ๊ฒฝ๋กœ ๋ฐ ๊ฐ์—ผ์ง€์—ญ ํ˜„ํ™ฉ์— ๋Œ€ํ•œ ๋ถ„์„๊ฒฐ๊ณผ๋ฅผ ์ฃผ์†Œ์ง€ ๊ธฐ์ค€ ์ง€์—ญ๋ถ„ํฌ์™€ ๋น„๊ต ๋ถ„์„ํ•˜์—ฌ ๊ทธ ์˜๋ฏธ๋ฅผ ์ œ์‹œํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 2001๋…„์—์„œ 2010๋…„๊นŒ์ง€ ๊ฐ์—ผ๋ณ‘์›น๋ณด๊ณ ์‹œ์Šคํ…œ์„ ํ†ตํ•ด ์งˆ๋ณ‘๊ด€๋ฆฌ๋ณธ๋ถ€์— ์‹ ๊ณ ๋œ ๋ง๋ผ๋ฆฌ์•„ ํ™•์ง„ํ™˜์ž 16,206๋ช… ์ค‘ ํ˜„์—ญ๊ตฐ์ธ(3,551๋ช…), ํ•ด์™ธ์œ ์ž… ์‚ฌ๋ก€(454๋ช…), ์žฌ๋ฐœ ๋ฐ ์žฌ๊ฐ์—ผ ์‚ฌ๋ก€(372๋ช…), ์ˆ˜ํ˜ˆ๊ฐ์—ผ ์‚ฌ๋ก€(2๋ช…) ๋“ฑ์„ ์ œ์™ธํ•œ 11,425๋ช…์˜ ๋ฏผ๊ฐ„์ธ(์ „์—ญ์ž๋Š” ํฌํ•จ)์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์—ญํ•™์กฐ์‚ฌ ์ž๋ฃŒ๋ฅผ ๋ถ„์„๋Œ€์ƒ์œผ๋กœ ํ•˜์˜€๋‹ค. ์—ญํ•™์กฐ์‚ฌ ํ•ญ๋ชฉ์—๋Š” ๊ณผ๊ฑฐ ์œ„ํ—˜์ง€์—ญ ๊ฑฐ์ฃผ์—ฌ๋ถ€, ์—ฌํ–‰๋ ฅ, ์•ผ์™ธํ™œ๋™๋ ฅ ๋“ฑ์ด ํฌํ•จ๋˜์–ด ์žˆ์–ด, ์ฆ์ƒ๋ฐœ์ƒ ์ด์ „ 2๋…„ ์ด๋‚ด์— ๋ง๋ผ๋ฆฌ์•„ ์œ„ํ—˜์ง€์—ญ์—์„œ ๊ฑฐ์ฃผํ•˜๊ฑฐ๋‚˜ ์ง์žฅ์„ ๋‹ค๋‹Œ ๊ฒฝ์šฐ, ์•ผ์™ธํ™œ๋™์„ ๋™๋ฐ˜ํ•œ ์—ฌํ–‰์„ ๋‹ค๋…€์˜จ ๊ฒฝ์šฐ, ๊ตฐ๋ณต๋ฌด๋ฅผ ํ•œ ๊ฒฝ์šฐ์—๋Š” ์œ„ํ—˜์š”์ธ์— ๋…ธ์ถœ๋œ ๊ฒƒ์œผ๋กœ ๊ฐ„์ฃผํ•˜์—ฌ ๊ฐ์—ผ๊ฒฝ๋กœ ๋ฐ ๊ฐ์—ผ์ง€์—ญ์„ ํ•ด๋‹น ์œ„ํ—˜์ง€์—ญ์œผ๋กœ ์ถ”์ •ํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ์ถ”์ •๋œ ๋ง๋ผ๋ฆฌ์•„ ํ™˜์ž ์ถ”์ • ๊ฐ์—ผ ์ง€์—ญ์„ Arc GIS๋ฅผ ์ด์šฉํ•˜์—ฌ ๋„์‹œํ•œ ๋’ค, ๋ณด๊ณ ๋œ ์ฃผ์†Œ์ง€ ๊ธฐ์ค€ ์ง€์—ญ ๋ถ„ํฌ ํŠน์„ฑ๊ณผ ํ•จ๊ป˜ ๋น„๊ตํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ ๋ง๋ผ๋ฆฌ์•„ ์œ„ํ—˜์ง€์—ญ์— ๊ฑฐ์ฃผํ•˜๊ฑฐ๋‚˜ ์ง์žฅ์„ ๋‹ค๋‹ˆ๋ฉด์„œ ๊ฐ์—ผ๋œ ๊ฒฝ์šฐ๊ฐ€ ์ „์ฒด์˜ ์•ฝ 50%์˜€์œผ๋ฉฐ, ๊ตฐ๋ณต๋ฌด๋กœ ์ธํ•œ ๊ฐ์—ผ์ž๋Š” 2005๋…„ ์ด์ „ 30-40% ์ˆ˜์ค€์—์„œ ์ดํ›„ 30% ์ดํ•˜๋กœ ๊ฐ์†Œํ•˜์˜€๊ณ , ์œ„ํ—˜์ง€์—ญ ์—ฌํ–‰์„ ํ†ตํ•œ ๊ฐ์—ผ ์‚ฌ๋ก€๋Š” ์•ฝ 10%๋ฅผ ์ฐจ์ง€ํ•˜์˜€๋‹ค. ๊ฐ์—ผ์ง€์—ญ ๋ฐ ๊ฒฝ๋กœ๋ฅผ ์ฃผ์–ด์ง„ ์ž๋ฃŒ๋กœ ์ถ”์ •ํ•ด ๋‚ด๊ธฐ ์–ด๋ ต๊ฑฐ๋‚˜, ๋น„์œ„ํ—˜์ง€์—ญ์—์„œ ๊ฐ์—ผ ๊ฒƒ์œผ๋กœ ์ถ”์ •๋œ ๊ฒฝ์šฐ๋Š” ๋งค๋…„ 4-11% ์ •๋„๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ์—ญํ•™์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ์ถ”์ •๋œ ๊ฐ์—ผ์ง€์—ญ์ด ์ฃผ์†Œ์ง€์™€ ๋‹ค๋ฅธ ๊ฒฝ์šฐ๋Š” 5,528๋ช…(48.3%) (๋งคํ•‘๊ธฐ์ค€ 5,473๋ช…(48.1%))์ด์—ˆ์œผ๋ฉฐ, ์ฃผ์†Œ์ง€ ๊ธฐ์ค€์œผ๋กœ๋Š” ์œ„ํ—˜์ง€์—ญ ์™ธ์—์„œ ์ฃผ๋กœ ์ง€๋ฐฉ ๊ด‘์—ญ์‹œ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ๋ถ„ํฌํ•˜๋˜ ์‚ฌ๋ก€๋“ค์ด, ๊ฐ์—ผ์ง€์—ญ ์ถ”์ •์‹œ์—๋Š” ๋Œ€๋ถ€๋ถ„ ์œ„ํ—˜์ง€์—ญ์—์„œ์˜ ๊ฑฐ์ฃผยท๊ทผ๋ฌด, ๊ตฐ๋ณต๋ฌด, ์—ฌํ–‰ ๋“ฑ์˜ ๊ฒฝ๋ ฅ์ด ํ™•์ธ๋˜์–ด ๋ง๋ผ๋ฆฌ์•„๊ฐ€ ์ง€๋ฐฉ๋Œ€๋„์‹œ๋กœ ํ™•์‚ฐ๋˜๊ธฐ ๋ณด๋‹ค๋Š” ์œ„ํ—˜์ง€์—ญ์—์„œ์˜ ๋…ธ์ถœ๋กœ ์ธํ•œ ๊ฐ์—ผ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. ๊ฒฐ๋ก  ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด, ์ง€๋‚œ 10๋…„๊ฐ„ ํœด์ „์„ ์„ ์ค‘์‹ฌ์œผ๋กœ ํ•œ ๋ง๋ผ๋ฆฌ์•„ ํ† ์ฐฉํ™” ํ˜„์ƒ์€ ์ง€์†์ ์œผ๋กœ ๊ด€์ฐฐ๋˜์—ˆ์œผ๋‚˜, ์ „์ฒด์ ์ธ ํ™˜์ž์ˆ˜๋Š” ๊ฐ์†Œํ•œ ์ƒํ™ฉ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋ง๋ผ๋ฆฌ์•„ ๋ฐœ์ƒ์˜ ์ง€์—ญ์  ๋ถ„ํฌ ๋ถ„์„์„ ์œ„ํ•ด์„œ๋Š” ์—ญํ•™์กฐ์‚ฌ๋ฅผ ํ†ตํ•œ ๊ฐ์—ผ์ง€์—ญ ์ถ”์ •์ด ํ•„์š”ํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๊ณผ์ •์„ ํ†ตํ•ด ๊ตญ๋‚ด ๋ง๋ผ๋ฆฌ์•„์˜ ํšจ๊ณผ์ ์ธ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•ด์„œ๋Š” ๊ธฐํ›„ ๋ณ€ํ™” ๋“ฑ์— ๋”ฐ๋ฅธ ๋ง๋ผ๋ฆฌ์•„์˜ ๋น„์œ„ํ—˜์ง€์—ญ ํ™•์‚ฐ์„ ์˜ˆ๋ฐฉํ•˜๋Š” ๊ฒƒ ๋ชป์ง€์•Š๊ฒŒ, ๊ธฐ์กด์— ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋Š” ๋ง๋ผ๋ฆฌ์•„ ํ† ์ฐฉ ์œ„ํ—˜์ง€์—ญ์—์„œ์˜ ๋ฐฉ์—ญํ™œ๋™๊ณผ ํ‡ด์น˜ ๊ด€๋ฆฌ ๋Œ€์ฑ…์„ ์†Œํ˜ํžˆ ํ•˜์ง€ ์•Š๋Š” ๊ฒƒ์ด ์—ฌ์ „ํžˆ ๋ง๋ผ๋ฆฌ์•„ ๊ด€๋ฆฌ ์ •์ฑ…์˜ ์ค‘์š”ํ•œ ์›์น™์ด ๋˜์–ด์•ผ ํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.I. ์„œ๋ก  1.1 ๋ง๋ผ๋ฆฌ์•„ ๊ฐœ์š” 1 1.2 ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๋ง๋ผ๋ฆฌ์•„ 4 1.3 ์—ฐ๊ตฌ๋ฐฐ๊ฒฝ 9 1.4 ์—ฐ๊ตฌ๋ชฉ์  11 II. ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 2.1 ์—ฐ๊ตฌ๋Œ€์ƒ 12 2.2 ์ž๋ฃŒํ•ญ๋ชฉ๊ณผ ์œ„ํ—˜์ง€์—ญ ์„ ์ •๊ธฐ์ค€ 13 2.3 ๊ฐ์—ผ์ง€์—ญ ์ถ”์ • 14 2.4 ๋ถ„์„๋ฐฉ๋ฒ• 16 III. ๊ฒฐ๊ณผ 3.1 ์ธ๊ตฌํ•™์  ํŠน์„ฑ 17 3.2 ์ถ”์ •์š”์ธ๋ณ„ ํŠน์„ฑ 17 3.3 ์ง€๋ฆฌ์  ๋ถ„ํฌ ํŠน์„ฑ 20 IV. ๊ณ ์ฐฐ 4.1 ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์˜๋ฏธ 23 4.2 ์ œํ•œ์  27 4.3 ๋ณธ ์—ฐ๊ตฌ์˜ ํšจ๊ณผ 28 4.4 ๋ฐฉ์—ญ๊ด€์  ๋ฐ ํ–ฅํ›„ ์ „๋ง 28 V. ๊ฒฐ๋ก  31 VI. ์ฐธ๊ณ ๋ฌธํ—Œ 32 VII. ๋ถ€๋ก ๋ถ€๋ก1. ์งˆ๋ณ‘๊ด€๋ฆฌ๋ณธ๋ถ€ ๋ง๋ผ๋ฆฌ์•„ ์—ญํ•™์กฐ์‚ฌ์„œ (2012) 35 ๋ถ€๋ก2. ๋งคํ•‘ ์‹ค์‹œ๋ฅผ ์œ„ํ•œ DATA SET ํ˜•์‹ 37 VIII. ์˜๋ฌธ์ดˆ๋ก 38Maste

    Development of digital twin system for Busan coastal waters using sensor fusion

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    Digital twin refers to the reproduction and implementation of a physical model of the real world in a digital environment. Using digital twins, we can predict what happens in reality in advance in the virtual world, and gain advantages such as improving product quality and reducing development costs. In practice, research and development are being actively conducted in the field of smart city and manufacturing. If digital twin system is built and utilized to verify the algorithm of an autonomous ship, benefits can be obtained in terms of time and cost. However, there are currently few examples of implementing a port environment with a digital twin. In the field of autonomous ship, verification of unmanned ships is important, but enormous costs are incurred when manufacturing autonomous ships. Besides, there are many difficulties in demonstration due to problems such as accessibility to the experimental environment and the risk of accidents. Therefore, this study proposes a digital twin system that can verify the autonomous ship algorithm in Busan port using sensor fusion. It implements the Busan port sea area on the simulator and real ships existing in the sea. To implement the digital twin, ROS Gazebo ship simulator is used. In order to acquire the location of target ships, sensor data is collected from the sea area data collection device installed on the ground. The data collection device consists of AIS, radar, and camera. The radar obtains the location information of other ships through image processing from PPI (Plan Position Indicator) image information, and the camera obtains the pixel coordinates of a specific ship through deep learning to calculate the location information. An Extended Kalman filter is used to predict and correct the position of other ships. In addition, a data association technique is applied to associate some of the sensor data with a specific ship. Finally, the digital twin is implemented using the result of convergence of multiple sensor data such as AIS, Radar, and Camera.|๋””์ง€ํ„ธ ํŠธ์œˆ์€ ํ˜„์‹ค ์„ธ๊ณ„์˜ ๋ฌผ๋ฆฌ์  ๋ชจ๋ธ์„ ๋””์ง€ํ„ธ ํ™˜๊ฒฝ์— ๋ณต์ œํ•˜์—ฌ ๊ตฌํ˜„ํ•˜๋Š” ๊ฒƒ์„ ๋œปํ•œ๋‹ค. ๋””์ง€ํ„ธ ํŠธ์œˆ์„ ์ด์šฉํ•˜๋ฉด ํ˜„์‹ค์—์„œ ์ด๋ฃจ์–ด์ง€๋Š” ์ผ๋“ค์„ ๊ฐ€์ƒ์˜ ์„ธ๊ณ„์—์„œ ๋ฏธ๋ฆฌ ์ง„ํ–‰ํ•˜์—ฌ, ๋ฏธ๋ž˜๋ฅผ ์˜ˆ์ธกํ•  ์ˆ˜ ์žˆ๊ณ  ์ œํ’ˆ ํ’ˆ์งˆ ํ–ฅ์ƒ, ๊ฐœ๋ฐœ๋น„์šฉ ์ ˆ๊ฐ ๋“ฑ์˜ ์ด์ ์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ์‹ค์ œ๋กœ ์Šค๋งˆํŠธ์‹œํ‹ฐ๋‚˜ ์ œ์กฐ๋ถ„์•ผ์—์„œ๋Š” ์—ฐ๊ตฌ ๋ฐ ๊ฐœ๋ฐœ์ด ํ™œ๋ฐœํ•˜๊ฒŒ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ์ด์ฒ˜๋Ÿผ ์ž์œจ์šดํ•ญ ์„ ๋ฐ•์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ์‹ค์ œ ํ•ญ๋งŒ์— ์กด์žฌํ•˜๋Š” ์„ ๋ฐ•๋“ค์„ ์žฌํ˜„ํ•˜๋Š” ๋””์ง€ํ„ธ ํŠธ์œˆ์„ ๊ตฌ์ถ•ํ•˜๊ณ  ์ด๋ฅผ ํ™œ์šฉํ•œ๋‹ค๋ฉด ์‹œ๊ฐ„์ ยท๋น„์šฉ์  ์ธก๋ฉด์—์„œ ์ด๋“์„ ์–ป์„ ์ˆ˜ ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ํ˜„์žฌ ๋””์ง€ํ„ธ ํŠธ์œˆ์œผ๋กœ ํ•ญ๋งŒ ํ™˜๊ฒฝ์„ ๊ตฌํ˜„ํ•˜๋Š” ์‚ฌ๋ก€๋Š” ๋ฏธ๋น„ํ•œ ์ƒํ™ฉ์ด๋‹ค. ์ž์œจ์šดํ•ญ ๋ถ„์•ผ์—์„œ๋Š” ๋ฌด์ธ์„ ์˜ ๊ฒ€์ฆ์ด ์ค‘์š”ํ•œ๋ฐ, ์‹ค์ œ ํฌ๊ธฐ์˜ ์ž์œจ์šดํ•ญ ์„ ๋ฐ• ์ œ์ž‘ ์‹œ ๋ง‰๋Œ€ํ•œ ๋น„์šฉ์ด ๋ฐœ์ƒํ•˜๋ฉฐ ์‹คํ—˜ํ™˜๊ฒฝ์œผ๋กœ์˜ ์ ‘๊ทผ์„ฑ๊ณผ ์‚ฌ๊ณ  ๋ฐœ์ƒ ์œ„ํ—˜์„ฑ ๋“ฑ์˜ ๋ฌธ์ œ๋กœ ์‹ค์ฆ์— ๋‹ค์ˆ˜์˜ ์–ด๋ ค์›€์ด ์กด์žฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์„ผ์„œ ์œตํ•ฉ ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•œ ๋ถ€์‚ฐํ•ญ ํ•ด์—ญ ๋””์ง€ํ„ธ ํŠธ์œˆ์œผ๋กœ ์ž์œจ์šดํ•ญ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ๊ฒ€์ฆํ•  ์ˆ˜ ์žˆ๋Š” ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ ์ƒ์— ์‹ค์ œ ๋ถ€์‚ฐํ•ญ๊ณผ ์œ ์‚ฌํ•œ ํ•ด์—ญ ํ™˜๊ฒฝ์„ ๊ตฌ์„ฑํ•˜๊ณ , ํ•ด์ƒ์— ์กด์žฌํ•˜๋Š” ์‹ค์ œ ์„ ๋ฐ•์„ ๋ฐฐ์น˜ํ•œ๋‹ค. ๋””์ง€ํ„ธ ํŠธ์œˆ์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด ROS Gazebo ์„ ๋ฐ• ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๊ฐ€ ํ™œ์šฉ๋œ๋‹ค. ํƒ€์„ ์˜ ์œ„์น˜์ •๋ณด๋ฅผ ํš๋“ํ•˜๊ธฐ ์œ„ํ•ด ์ง€์ƒ์— ์„ค์น˜๋œ ํ•ด์—ญ ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์žฅ์น˜๋กœ๋ถ€ํ„ฐ ์„ผ์„œ ๋ฐ์ดํ„ฐ๋ฅผ ์ˆ˜์ง‘ํ•œ๋‹ค. ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘์žฅ์น˜๋Š” AIS, ๋ ˆ์ด๋”, ์นด๋ฉ”๋ผ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ๋ ˆ์ด๋”๋Š” PPI(Plan Position Indicator) ์˜์ƒ์ •๋ณด์—์„œ ์ด๋ฏธ์ง€ ์ฒ˜๋ฆฌ๋ฅผ ํ†ตํ•ด ํƒ€์„ ์˜ ์œ„์น˜ ์ •๋ณด๋ฅผ ํš๋“ํ•˜๊ณ , ์นด๋ฉ”๋ผ๋Š” ๋”ฅ๋Ÿฌ๋‹์„ ํ†ตํ•ด ํŠน์ • ์„ ๋ฐ•์˜ ํ”ฝ์…€ ์ขŒํ‘œ๋ฅผ ํš๋“ํ•˜์—ฌ ์œ„์น˜ ์ •๋ณด๋ฅผ ๊ณ„์‚ฐํ•œ๋‹ค. ํƒ€์„ ์˜ ์œ„์น˜๋ฅผ ์˜ˆ์ธกํ•˜๊ณ  ๋ณด์ •ํ•˜๊ธฐ ์œ„ํ•ด ํ™•์žฅ ์นผ๋งŒ ํ•„ํ„ฐ๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค. ๋˜ํ•œ ์—ฌ๋Ÿฌ ์„ผ์„œ ๋ฐ์ดํ„ฐ ์ค‘ ์ผ๋ถ€๋ฅผ ํŠน์ • ์„ ๋ฐ•๊ณผ ์—ฐ๊ด€์‹œํ‚ค๊ธฐ ์œ„ํ•œ Data association ๊ธฐ๋ฒ•์„ ์ ์šฉํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, AIS, Radar, Camera ๋“ฑ, ๋‹ค์ˆ˜์˜ ์„ผ์„œ ๋ฐ์ดํ„ฐ๋ฅผ ์œตํ•ฉํ•œ ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋””์ง€ํ„ธ ํŠธ์œˆ์„ ๊ตฌํ˜„ํ•œ๋‹ค.1. ์„œ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋ชฉํ‘œ ๋ฐ ๋ฐฉ๋ฒ• 3 1.3 ๋…ผ๋ฌธ ๊ตฌ์„ฑ 5 1.4 ๋””์ง€ํ„ธ ํŠธ์œˆ ์‹œ์Šคํ…œ ๊ตฌ์„ฑ 6 2. ์„ ํ–‰ ์—ฐ๊ตฌ ์กฐ์‚ฌ 9 2.1 ์„ผ์„œ ์œตํ•ฉ ๊ธฐ์ˆ  9 2.2 ๋””์ง€ํ„ธ ํŠธ์œˆ 10 3. ์„ ๋ฐ• ์ถ”์  ์‹œ์Šคํ…œ 15 3.1 ์„ผ์„œ ๊ตฌ์„ฑ ๋ฐ ์ด๋ก  15 3.1.1 AIS 17 3.1.2 ๋ ˆ์ด๋” 19 3.1.3 ์นด๋ฉ”๋ผ 23 3.1.4 ์„ผ์„œ๋ณ„ ํŠน์ง• 32 3.2 ์„ผ์„œ ์œตํ•ฉ ๊ธฐ์ˆ  33 3.2.1 ์นผ๋งŒ ํ•„ํ„ฐ 33 3.2.2 ํ™•์žฅ ์นผ๋งŒ ํ•„ํ„ฐ 34 3.2.3 ๋ฐ์ดํ„ฐ ์—ฐ๊ด€ 36 3.3 ์ถ”์  ๊ฒฐ๊ณผ 38 3.3.1 ์‹œ์Šคํ…œ ๋ชจ๋ธ 38 3.3.2 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์„ฑ๋Šฅ ๊ฒ€์ฆ 39 3.3.3 ์‹ค๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ์ถ”์  48 4. ๋””์ง€ํ„ธ ํŠธ์œˆ ์‹œ์Šคํ…œ 51 4.1 ROS Gazebo ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ 51 4.2 ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ™˜๊ฒฝ ๊ฐœ๋ฐœ 52 4.2.1 ๋ถ€์‚ฐ ํ•ด์—ญ ์ง€ํ˜• ๊ตฌํ˜„ 52 4.2.2 ์‹คํ•ด์—ญ ํƒ€์„  ๊ตฌํ˜„ 55 4.3 ์ž์œจ์šดํ•ญ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๊ฒ€์ฆ ์„ ๋ฐ• 58 4.3.1 ํ…Œ์ŠคํŠธ ์„ ๋ฐ• 58 4.3.2 ๊ฐ€์ƒ ์„ผ์„œ 58 5. ๊ฒฐ๋ก  60Maste

    ์ „์•• ๋ฏผ๊ฐ๋„๋ฅผ ์ด์šฉํ•œ UPFC์™€ Shunt Capacitor์˜ ์ •์ƒ์ƒํƒœ ํ˜‘์กฐ์šด์ „์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ „๊ธฐ. ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2008.2Maste
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