15 research outputs found

    Korean Named Entity Recognition Using Subordinate Clause and Morpheme Segmentation

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    ๊ฐœ์ฒด๋ช… ์ธ์‹์ด๋ž€ ์ฃผ์–ด์ง„ ๋ฌธ์„œ์—์„œ ๊ฐœ์ฒด๋ช…์˜ ๋ฒ”์œ„๋ฅผ ์ฐพ๊ณ  ๊ฐœ์ฒด๋ช…์˜ ๋ฒ”์ฃผ๋ฅผ ๊ฒฐ์ •ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ฐœ์ฒด๋ช… ์ธ์‹์€ ๋ฌธ์„œ ์š”์•ฝ, ์งˆ์˜์‘๋‹ต, ๊ธฐ๊ณ„๋ฒˆ์—ญ, ์žก๋‹ด์ฒ˜๋ฆฌ๊ณผ ๊ฐ™์€ ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ ์ „๋ฐ˜์— ์‚ฌ์šฉ๋œ๋‹ค. ๊ฐœ์ฒด๋ช… ์ธ์‹์˜ ๊ณ ์งˆ์ ์ธ ๋ฌธ์ œ์  ์ค‘ ํ•˜๋‚˜๋Š” ๋ฏธ๋“ฑ๋ก์–ด(out-of-vocabulary) ๋ฌธ์ œ์ด๋‹ค. ๊ธฐ์กด์˜ ํ•œ๊ตญ์–ด ๊ฐœ์ฒด๋ช… ์ธ์‹์˜ ์ž…๋ ฅ์€ ์ฃผ๋กœ ํ˜•ํƒœ์†Œ ๋ถ„์„ ๊ฒฐ๊ณผ์ด๋‹ค. ์ด ๊ฒฝ์šฐ์—๋Š” ๋ฏธ๋“ฑ๋ก์–ด๋กœ ๋ฐœ์ƒ๋œ ์˜ค๋ฅ˜๊ฐ€ ๊ฐœ์ฒด๋ช… ์ธ์‹์— ๊ทธ๋Œ€๋กœ ์ „ํŒŒ๋˜๋ฏ€๋กœ ์—ฌ์ „ํžˆ ๋ฏธ๋“ฑ๋ก์–ด๋กœ ์ธํ•ด ๋ฐœ์ƒ๋˜๋Š” ๋ฌธ์ œ๊ฐ€ ์™„์ „ํžˆ ํ•ด์†Œ๋˜์ง€ ์•Š๋Š”๋‹ค. ์ด์™€ ๊ฐ™์€ ๋ฌธ์ œ๋ฅผ ๋‹ค์†Œ ์™„ํ™”ํ•˜๊ธฐ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ข…์†์ ˆ ๋ฐ ํ˜•ํƒœ์†Œ ๋ถ„๋ฆฌ๋ฅผ ์ด์šฉํ•œ ํ•œ๊ตญ์–ด ๊ฐœ์ฒด๋ช… ์ธ์‹ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ์„ธ ๋‹จ๊ณ„๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ๋‹จ๊ณ„๋Š” ์ข…์†์ ˆ ๋ถ„๋ฆฌ ๋‹จ๊ณ„์ด๋ฉฐ ์ˆœํ™˜์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•˜์—ฌ ์ž…๋ ฅ๋œ ๋ฌธ์žฅ์„ ์ข…์†์ ˆ ๋‹จ์œ„๋กœ ๋ถ„๋ฆฌํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ๋‹จ๊ณ„๋Š” ํ˜•ํƒœ์†Œ ๋ถ„๋ฆฌ ๋‹จ๊ณ„์ด๋ฉฐ, Transformer ๋ชจ๋ธ์„ ์ด์šฉํ•˜์—ฌ ๊ฐ ์ข…์†์ ˆ์˜ ํ˜•ํƒœ์†Œ๋ฅผ ๋ถ„๋ฆฌํ•œ๋‹ค. ์ด๋•Œ ๋ฏธ๋“ฑ๋ก์–ด ๋ฌธ์ œ๋ฅผ ์™„ํ™”ํ•˜๋ ค๊ณ  ํ˜•ํƒœ์†Œ ๋ถ„๋ฆฌ ๋ชจ๋ธ(Transformer ๋ชจ๋ธ)์˜ ์ž…๋ ฅ์œผ๋กœ ๋ถ€๋ถ„๋‹จ์–ด ์ •๋ณด๋ฅผ ์ด์šฉํ•œ๋‹ค. ์„ธ ๋ฒˆ์งธ ๋‹จ๊ณ„๋Š” ๊ฐœ์ฒด๋ช… ์ธ์‹ ๋‹จ๊ณ„์ด๋ฉฐ, ์ˆœํ™˜์‹ ๊ฒฝ๋ง์„ ์ด์šฉํ•ด์„œ ๋ถ„๋ฆฌ๋œ ํ˜•ํƒœ์†Œ์— ๊ฐœ์ฒด๋ช… ํ‘œ์ง€๋ฅผ ๋ถ€์ฐฉํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์„ ํ†ตํ•˜์—ฌ ๋ฌธ์žฅ ๋ถ„๋ฆฌ์—์„œ๋Š” 95%์˜ ๋ฌธ์žฅ ๋ถ„๋ฆฌ ์ •ํ™•๋ฅ ์„ ๋‚˜ํƒ€๋ƒˆ์œผ๋ฉฐ, ํ˜•ํƒœ์†Œ ๋ถ„๋ฆฌ์—์„œ๋Š” 90%์˜ F1-์ ์ˆ˜๋ฅผ ๋‚˜ํƒ€๋‚ด์—ˆ์œผ๋‚˜ ํ•œ๊ธ€๋งž์ถค๋ฒ•์„ ๊ณ ๋ คํ•  ๊ฒฝ์šฐ 98.3%์˜ ์ •ํ™•๋ฅ ์„ ๋ณด์˜€๋‹ค. ๊ฐœ์ฒด๋ช… ์ธ์‹์˜ ๊ฒฝ์šฐ 72%์˜ F1-์ ์ˆ˜๋ฅผ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ์œ„์˜ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆํ•˜๋Š” ๋ฐฉ๋ฒ•์ด ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋ณด๋‹ค ์„ฑ๋Šฅ์ด ์šฐ์ˆ˜ํ•จ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. |Named entity recognition (NER) is a subtask that seeks to locate and classify named entities in a given document into pre-defined categories such as person names, organizations, locations, and so on. NER can be applied to many applications related to natural language processing such as document summarization, question answering, machine translation, and chatbot etc. There is a notorious problem in NER called out-of-vocabulary (OOV). Many previous works have tackled the problem through extension of training corpus and various word representation in deep learning. In addition, most Korean NER systems have used morphological analysis as preprocessors, but Korean morphological analysis has the same problem of OOV of which errors are propagated to the NER system and cause the performance to deteriorate further. In order to alleviate the problem, we propose a novel method for Korean NER using subordinate clause and subword segmentation. The proposal method consists of three steps. The first step is to segment subordinate clauses from a given sentence using a recurrent neural network (RNN), especially Bi-LSTM/CRF. The second step is to segment morphemes from the segmented clauses using the Transformer model developed by Google. The model takes subwords as input in order to mitigate the OOV problem. The third step is to assign the most proper BIO tag to each morpheme using Bi-LSTM/CRF of RNNs. Through experiments, the proposed steps of subordinate clause and morpheme segmentation have been evaluated, achieving F1-scores of about 95% and 98%, respectively. For the proposed NER, experimental results show that our word outperforms the other Korean NER models, carrying out F1-score of about 72%. In the future, we will do research on more accurate morpheme segmentation using the Transformer model with copy mechanism and also on subordinate clause segmentation or subsentence segmentation in linguistics.๋ชฉ ์ฐจ List of Tables iv List of Figures v Abstract vi ์ดˆ๋ก viii ์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 2 ์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ 4 2.1 ๊ฐœ์ฒด๋ช… ์ธ์‹ 4 2.2 ํ˜•ํƒœ์†Œ ๋ถ„๋ฆฌ์™€ ๋ถ€๋ถ„๋‹จ์–ด 7 2.3 ์ข…์†์ ˆ ๋ถ„๋ฆฌ 8 ์ œ 3 ์žฅ ์ข…์†์ ˆ ๋ฐ ํ˜•ํƒœ์†Œ ๋ถ„๋ฆฌ๋ฅผ ์ด์šฉํ•œ ํ•œ๊ตญ์–ด ๊ฐœ์ฒด๋ช… ์ธ์‹ 10 3.1 ์ข…์†์ ˆ ๋ถ„๋ฆฌ 11 3.1.1 ์ข…์†์ ˆ ๋ถ„๋ฆฌ ํ•™์Šต๋ง๋ญ‰์น˜ ์ œ์ž‘ 13 3.1.2 ์ข…์†์ ˆ ๋ถ„๋ฆฌ ๋ชจ๋ธ 15 3.2 ํ˜•ํƒœ์†Œ ๋ถ„๋ฆฌ 16 3.2.1 ๋ถ€๋ถ„๋‹จ์–ด ์‚ฌ์ „ ๊ตฌ์ถ• 17 3.2.2 ํ˜•ํƒœ์†Œ ๋ถ„๋ฆฌ ๋ชจ๋ธ 18 3.3 ๊ฐœ์ฒด๋ช… ์ธ์‹ 22 ์ œ 4 ์žฅ ์‹คํ—˜ ๋ฐ ํ‰๊ฐ€ 24 4.1 ์ข…์†์ ˆ ๋ถ„๋ฆฌ 24 4.1.1 ์‹คํ—˜ ํ™˜๊ฒฝ ๋ฐ ์‹คํ—˜ ์ฒ™๋„ 24 4.1.2 ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 26 4.2 ํ˜•ํƒœ์†Œ ๋ถ„๋ฆฌ 27 4.2.1 ์‹คํ—˜ ํ™˜๊ฒฝ ๋ฐ ์‹คํ—˜ ์ฒ™๋„ 27 4.2.2 ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 28 4.3 ๊ฐœ์ฒด๋ช… ์ธ์‹ 31 4.3.1 ์‹คํ—˜ ํ™˜๊ฒฝ ๋ฐ ์‹คํ—˜ ์ฒ™๋„ 31 4.3.2 ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 30 ์ œ 5 ์žฅ ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ 34 ์ฐธ๊ณ ๋ฌธํ—Œ 36 ๊ฐ์‚ฌ์˜ ๊ธ€ 42Maste

    A Study on GNSS User Integrity Monitoring Algorithm for Simultaneous Multiple Satellite Failures

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2013. 2. ๊ธฐ์ฐฝ๋ˆ.GPS ํ˜„๋Œ€ํ™”์™€ Galileo์˜ ์ถœํ˜„, GLONASS์˜ ์žฌ๊ฐœ๋ฐœ์ด ์ง„ํ–‰๋จ์— ๋”ฐ๋ผ ์‚ฌ์šฉ์ž๋Š” ๋งŽ์€ ์ˆ˜์˜ ๊ฐ€์‹œ์œ„์„ฑ์„ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ƒˆ๋กœ์šด GNSS ์œ„์„ฑ๋“ค์€ ๊ธฐ์กด์˜ ์œ„์„ฑ๋“ค์— ๋น„ํ•ด ํ–ฅ์ƒ๋œ ์‹ ํ˜ธ ํ’ˆ์งˆ์„ ๊ฐ–๋Š”๋‹ค. ๋”ฐ๋ผ์„œ ์œ„์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ ์‚ฌ์šฉ์ž๋Š” ๊ฐ€์‹œ์œ„์„ฑ ์ˆ˜์˜ ์ฆ๊ฐ€, ์‹ ํ˜ธ ํ’ˆ์งˆ์˜ ํ–ฅ์ƒ ๋“ฑ์œผ๋กœ ์ธํ•ด ํ–ฅ์ƒ๋œ ์ •ํ™•๋„ ์„ฑ๋Šฅ์„ ๊ฐ–๊ฒŒ ๋  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ •ํ™•๋„ ์„ฑ๋Šฅ ํ–ฅ์ƒ์€ ์‚ฌ์šฉ์ž์˜ ๋ฌด๊ฒฐ์„ฑ ๋ณด์žฅ์„ ์œ„ํ•ด ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ (Receiver Autonomous Integrity Monitoring, RAIM) ๊ธฐ๋ฒ•์„ ํ˜„์žฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋Š” ์‘์šฉ๋ถ„์•ผ๋ณด๋‹ค ๋” ๋†’์€ ์„ฑ๋Šฅ์„ ์š”๊ตฌํ•˜๋Š” ๋ถ„์•ผ์— ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ์—ด์–ด์ค„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์กด์˜ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๋™์‹œ์— ๋‘ ๊ฐœ ์ด์ƒ์˜ ์œ„์„ฑ์— ๊ณ ์žฅ์ด ์ผ์–ด๋‚  ์ˆ˜ ์—†๋‹ค๋Š” ๊ฐ€์ • ํ•˜์— ์ฃผ์–ด์ง„ ๋ฌด๊ฒฐ์„ฑ ์š”๊ตฌ์กฐ๊ฑด์„ ๋งŒ์กฑํ•˜๋„๋ก ์„ค๊ณ„๋˜์—ˆ๊ธฐ ๋•Œ๋ฌธ์— ๋น„๊ต์  ํฐ ์˜ค์ฐจ ํ—ˆ์šฉ๋ฒ”์œ„๋ฅผ ๊ฐ–๋Š” ์ˆ˜ํ‰ ์œ ๋„(Lateral Guidance)์—๋Š” ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์ง€๋งŒ, ์ˆ˜์ง ์œ ๋„ (Vertical Guidance) ๋“ฑ ์ข€ ๋” ๋†’์€ ์„ฑ๋Šฅ (์ˆ˜์ง์˜ค์ฐจ ํ—ˆ์šฉ๋ฒ”์œ„ 15~35m)์„ ์š”๊ตฌํ•˜๋Š” ํ•ญํ–‰ ๋‹จ๊ณ„์—์„œ๋Š” ์‚ฌ์šฉ๋  ์ˆ˜ ์—†๋‹ค. ๊ธฐ์กด์˜ ์ˆ˜ํ‰ ์œ ๋„ ํ•ญํ–‰ ๋‹จ๊ณ„์—์„œ๋Š” ์˜ค์ฐจํ—ˆ์šฉ๋ฒ”์œ„๊ฐ€ ํฌ๊ธฐ ๋•Œ๋ฌธ์— ๊ณ ์žฅ์œผ๋กœ ์ทจ๊ธ‰๋˜์ง€ ์•Š๋˜ ๊ณ ์žฅ์ด ์ˆ˜์ง ์œ ๋„ ๋‹จ๊ณ„์—์„œ๋Š” ๊ณ ์žฅ์œผ๋กœ ์ทจ๊ธ‰๋  ํ•„์š”๊ฐ€ ์žˆ๊ณ , ์ด๋ ‡๊ฒŒ ๋  ๊ฒฝ์šฐ ๋” ์ด์ƒ ๋™์‹œ์— ๋‘ ๊ฐœ ์ด์ƒ์˜ ๊ณ ์žฅ์ด ๋ฐœ์ƒํ•˜์ง€ ์•Š๋Š”๋‹ค๋Š” ๋ณด์žฅ์„ ํ•  ์ˆ˜ ์—†๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋”ฐ๋ผ์„œ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ๋ฅผ ํ™œ์šฉํ•œ ์ˆ˜์ง ์œ ๋„๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ•ญ๋ฒ•์œ„์„ฑ์˜ ๋‹ค์ค‘ ๊ณ ์žฅ์„ ๊ณ ๋ คํ•˜์—ฌ์•ผ ํ•œ๋‹ค. ๋˜ํ•œ ๋ฏธ๋ž˜์˜ ๋‹ค์ค‘ GNSS ํ™˜๊ฒฝ์—์„œ๋Š” ๊ฐ€์‹œ ์œ„์„ฑ์˜ ์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ฒŒ ๋˜์–ด ๋‹ค์ค‘ ๊ณ ์žฅ์˜ ํ™•๋ฅ ์ด ๋”์šฑ ์ฆ๊ฐ€ํ•˜๊ฒŒ ๋  ๊ฒƒ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด์— ๊ฐœ๋ฐœ๋œ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํŠน์„ฑ ๋ฐ ํ•œ๊ณ„๋ฅผ ๋น„๊ต, ๋ถ„์„ํ•˜๊ณ  ๊ธฐ์กด ๋ฐฉ๋ฒ•๋“ค์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ๋จผ์ € ๋‹จ์ผ ๊ณ ์žฅ ๊ฐ€์ • ํ•˜์— ์ธก์ •์น˜ ๋น„์ •๊ทœ ๋ถ„ํฌ ์˜ค์ฐจ๋ฅผ ๊ณ ๋ คํ•œ ๋ฐ˜์†กํŒŒ ์œ„์ƒ ๊ธฐ๋ฐ˜ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜๊ณ , ๋‘ ๋ฒˆ์งธ๋กœ ๋ฏธ๋ž˜์˜ ๋‹ค์ค‘ GNSS ํ™˜๊ฒฝ์— ์ ํ•ฉํ•œ, ๋‹ค์ค‘ ๊ณ ์žฅ์„ ๊ณ ๋ คํ•œ ์ƒˆ๋กœ์šด ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ฐ˜์†กํŒŒ ์œ„์ƒ ๊ธฐ๋ฐ˜ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ๊ธฐ๋ฒ•์€ GNSS ๋ฐ˜์†กํŒŒ ์œ„์ƒ ์ธก์ •์น˜ ์žก์Œ์„ Gaussian Mixture Model (GMM)๋กœ ๋ชจ๋ธ๋งํ•˜์—ฌ ๊ธฐ์กด์˜ ๋ฐ˜์†กํŒŒ ์œ„์ƒ ๊ธฐ๋ฐ˜ ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ๊ธฐ๋ฒ•๋ณด๋‹ค ํ–ฅ์ƒ๋œ ๊ณ ์žฅ ๊ฒ€์ถœ ์„ฑ๋Šฅ๊ณผ ๊ฐ€์šฉ์„ฑ ์„ฑ๋Šฅ์„ ๊ฐ–๋Š”๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ ์ œ์•ˆ๋œ ๋‹ค์ค‘ ๊ณ ์žฅ์„ ๊ณ ๋ คํ•œ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ˆœ์ฐจ์  ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ์œ„์„ฑ์˜ ๊ณ ์žฅ์˜ ๋ฐœ์ƒ ํ˜•ํƒœ์— ๊ด€๊ณ„์—†์ด ์—๋Ÿฌ ๋ฒกํ„ฐ๋ฅผ ์ง์ ‘์ ์œผ๋กœ ์ถ”์ •ํ•˜์—ฌ ํ•ญ๋ฒ• ์œ„์„ฑ์˜ ๊ณ ์žฅ ์—ฌ๋ถ€๋ฅผ ํŒ๋‹จํ•˜๊ฒŒ ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด์˜ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ๊ธฐ๋ฒ•๊ณผ ๋‹ฌ๋ฆฌ ๊ณ ์žฅ ๊ฒ€์ถœ ์„ฑ๋Šฅ์ด ๊ณ ์žฅ ์œ„์„ฑ์˜ ์กฐํ•ฉ, ์œ„์„ฑ์˜ ๊ธฐํ•˜ํ•™์  ๋ฐฐ์น˜ ๋“ฑ์— ์˜ํ–ฅ์„ ๋ฐ›์ง€ ์•Š๊ณ , ๋‹ค์ค‘ ๊ณ ์žฅ ๋ฐœ์ƒ ์‹œ์—๋„ ๋‹จ์ผ ๊ณ ์žฅ ๋ฐœ์ƒ ์‹œ์™€ ๋™์ผํ•œ ์ˆ˜์ค€์˜ ๊ฒ€์ถœ ์„ฑ๋Šฅ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๊ณ ์žฅ ์œ„์„ฑ์˜ ์‹๋ณ„์ด ์šฉ์ดํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ธฐ์กด์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋น„ํ•ด ๊ณ„์‚ฐ ํšจ์œจ์„ฑ์ด ํฌ๊ฒŒ ์ฆ๊ฐ€ํ•˜๊ฒŒ ๋œ๋‹ค. ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์„ฑ๋Šฅ ๊ฒ€์ฆ์„ ์œ„ํ•ด ๊ธฐ์กด์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ ๊ฒ€์ถœ ์„ฑ๊ณต๋ฅ ์€ ์ตœ๋Œ€ 50% ํ–ฅ์ƒ๋˜์—ˆ๊ณ , Cat-I ๊ฐ€์šฉ์„ฑ์€ ์•ฝ 50% ์ •๋„ ํ–ฅ์ƒ๋˜์—ˆ์œผ๋ฉฐ, ๊ณ ์žฅ ๊ฒ€์ถœ ๋ฐ ์‹๋ณ„์— ์†Œ์š”๋œ ๊ณ„์‚ฐ์‹œ๊ฐ„์€ 1/50 ์ˆ˜์ค€์œผ๋กœ ๊ฐ์†Œํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ๊ฒฐ๊ณผ๋Š” GNSS ํ•ญ๋ฒ• ์‚ฌ์šฉ์ž๊ฐ€ ๊ณ ์žฅ ๊ฒ€์ถœ ๊ธฐ๋ฒ•์„ ๊ฐœ์„ ํ•จ์œผ๋กœ์จ ๋ณด๋‹ค ๋†’์€ ํ•ญ๋ฒ• ์„ฑ๋Šฅ์„ ๊ฐ€์งˆ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ฃผ๊ณ , ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ ๋ฏธ๋ž˜์˜ ๋‹ค์ค‘ GNSS ํ™˜๊ฒฝ์—์„œ ์‚ฌ์šฉ์ž์˜ ๋ฌด๊ฒฐ์„ฑ์„ ๋ณด์žฅํ•˜๊ธฐ ์œ„ํ•œ ๊ธฐ๋ฒ•์œผ๋กœ ๋„๋ฆฌ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.๋ชฉ ์ฐจ 1์žฅ. ์„œ ๋ก  ๏ผ‘๏ผ 1. ์—ฐ๊ตฌ ๋™๊ธฐ ๋ฐ ๋ชฉ์  ๏ผ‘๏ผ 2. ์—ฐ๊ตฌ ๋™ํ–ฅ ๏ผ‘๏ผ’ 3. ์—ฐ๊ตฌ ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• ๏ผ‘๏ผ” 1) ์—ฐ๊ตฌ ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• ๏ผ‘๏ผ” 2) ๋…ผ๋ฌธ ๊ตฌ์„ฑ ๏ผ‘๏ผ– 4. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ์˜ ๊ธฐ์—ฌ๋„ ๏ผ‘๏ผ— 1) ๋น„์ •๊ทœ ์ธก์ •์น˜ ์˜ค์ฐจ๋ฅผ ๊ณ ๋ คํ•œ ๋ฐ˜์†กํŒŒ ์œ„์ƒ ๊ธฐ๋ฐ˜ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ๊ธฐ๋ฒ• ์ œ์•ˆ ๏ผ‘๏ผ˜ 2) ๋žจํ”„ ์˜ค์ฐจ๋ฅผ ๊ณ ๋ คํ•œ ๋‹ค์ค‘๊ฐ€์„ค ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ๊ธฐ๋ฒ• ์ œ์•ˆ ๏ผ‘๏ผ™ 2์žฅ. ์œ„์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ๏ผ’๏ผ 1. ์œ„์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ ์˜ค์ฐจ ์š”์ธ ๏ผ’๏ผ 1) ์œ„์„ฑ ๊ถค๋„ ์˜ค์ฐจ ๏ผ’๏ผ 2) ์œ„์„ฑ ์‹œ๊ณ„ ์˜ค์ฐจ ๏ผ’๏ผ‘ 3) ์ „๋ฆฌ์ธต ์ง€์—ฐ (Ionospheric delay) ๏ผ’๏ผ‘ 4) ๋Œ€๋ฅ˜์ธต ์ง€์—ฐ (Tropospheric delay) ๏ผ’๏ผ’ 5) ๋‹ค์ค‘ ๊ฒฝ๋กœ ์˜ค์ฐจ (Multipath) ๏ผ’๏ผ’ 6) ์ˆ˜์‹ ๊ธฐ ๊ด€๋ จ ์˜ค์ฐจ ๏ผ’๏ผ“ 7) ๊ณ ์˜ ์žก์Œ (Selective Availability) ๏ผ’๏ผ“ 8) ์œ„์„ฑ ๋ฐฐ์น˜์— ์˜ํ•œ ์˜ํ–ฅ ๏ผ’๏ผ“ 2. GNSS ์œ„์น˜ํ•ด ๊ฒฐ์ • ์›๋ฆฌ ๏ผ’๏ผ” 3. ์œ„์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ (GNSS Integrity Monitoring) ๏ผ’๏ผ— 1) ํ•ญ๋ฒ• ์š”๊ตฌ ์„ฑ๋Šฅ (RNP, Required Navigation Performance) ๏ผ’๏ผ— 2) ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ (Integrity Monitoring) ๏ผ“๏ผ 3) ๊ธฐ์ค€๊ตญ ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ๏ผ“๏ผ‘ 4) ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ (RAIM, Receiver Autonomous Integrity Monitoring) ๏ผ“๏ผ“ 3์žฅ. ๋‹จ์ผ ๊ณ ์žฅ๋งŒ์„ ๊ณ ๋ คํ•œ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๏ผ“๏ผ– 1. ์˜์‚ฌ๊ฑฐ๋ฆฌ ๊ธฐ๋ฐ˜ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๏ผ“๏ผ– 1) ๊ธฐ์กด์˜ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ๊ธฐ๋ฒ• ๏ผ“๏ผ– 2) ๊ธฐ์กด์˜ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ๊ธฐ๋ฒ•์˜ ํ•œ๊ณ„ ๏ผ”๏ผ— 3) Gaussian Sum Filter๋ฅผ ํ™œ์šฉํ•œ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๏ผ”๏ผ™ 2. ๋ฐ˜์†กํŒŒ ์œ„์ƒ ๊ธฐ๋ฐ˜ ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๏ผ–๏ผ— 1) Absolute RAIM (ARAIM) ๏ผ–๏ผ˜ 2) Relative RAIM (RRAIM) ๏ผ–๏ผ™ 3. ์ตœ์ ํ™”๋œ Gaussian Sum Filter๋ฅผ ์ด์šฉํ•œ ARAIM Algorithm ๏ผ—๏ผ 1) ๋ฐ˜์†กํŒŒ์œ„์ƒ ์žก์Œ ํŠน์„ฑ ๏ผ—๏ผ 2) ํŒŒ๋ผ๋ฏธํ„ฐ ์ตœ์ ํ™” ๊ธฐ๋ฒ•์„ ํ†ตํ•œ Gaussian Mixture ๋ชจ๋ธ๋ง ๏ผ—๏ผ“ 3) ๊ฐœ์„ ๋œ GSF RAIM ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์„ฑ๋Šฅ ๋ถ„์„ ๏ผ—๏ผ— 4์žฅ. ํ•ญ๋ฒ• ์œ„์„ฑ์˜ ๋‹ค์ค‘ ๊ณ ์žฅ ๊ณ ๋ ค์˜ ํ•„์š”์„ฑ ๋ฐ ๊ธฐ์กด์˜ ๋‹ค์ค‘ ๊ฐ€์„ค RAIM ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๏ผ™๏ผ– 1. ์œ„์„ฑํ•ญ๋ฒ•์‹œ์Šคํ…œ ๊ณ ์žฅ์˜ ์›์ธ๊ณผ ์˜ํ–ฅ ๏ผ™๏ผ– 1) ์šฐ์ฃผ/์ง€์ƒ ๋ถ€๋ถ„ (Space/Ground Segment)์˜ ๊ณ ์žฅ ๋ฐœ์ƒ ์›์ธ ๏ผ™๏ผ— 2) ์‚ฌ์šฉ์ž ์žฅ๋น„์˜ ๊ณ ์žฅ ๋˜๋Š” ์‹ ํ˜ธ ์ „๋‹ฌ ์˜ค๋ฅ˜ ๋ฐœ์ƒ ์›์ธ ๏ผ™๏ผ˜ 3) ๊ณ ์žฅ ๋ฐœ์ƒ ํ™•๋ฅ ๊ณผ Time To Alert (TTA) ๏ผ™๏ผ˜ 2. ์œ„์„ฑํ•ญ๋ฒ• ์‹œ์Šคํ…œ ๋‹ค์ค‘ ๊ณ ์žฅ ๏ผ™๏ผ™ 1) ๋‹ค์ค‘ ๊ณ ์žฅ ๋ฐœ์ƒ ์‚ฌ๋ก€ ๏ผ‘๏ผ๏ผ 2) ๋‹ค์ค‘๊ณ ์žฅ ๋ฐœ์ƒ ์›์ธ ๏ผ‘๏ผ๏ผ‘ 3) ๋ฌด๊ฒฐ์„ฑ ์œ„ํ˜‘ ํ™•๋ฅ  ์š”๊ตฌ์กฐ๊ฑด ์„ค์ • ๏ผ‘๏ผ๏ผ’ 3. ๋‹ค์ค‘ ๊ณ ์žฅ ๋ฐœ์ƒ ์‹œ ๋‹จ์ผ ๊ฐ€์„ค RAIM ๊ธฐ๋ฒ•์˜ ๋ฌธ์ œ์  ๏ผ‘๏ผ๏ผ” 1) ๊ธฐ์กด RAIM ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํŠน์„ฑ ๋ฐ ํ•œ๊ณ„ ๏ผ‘๏ผ๏ผ” 2) ๋ฒกํ„ฐ ๊ณต๊ฐ„์—์„œ ์—๋Ÿฌ ๋ฒกํ„ฐ v์™€ ์ž”์ฐจ ๋ฒกํ„ฐ r๊ณผ์˜ ๊ด€๊ณ„ ๏ผ‘๏ผ๏ผ– 4. ๊ธฐ์กด์˜ ๋‹ค์ค‘ ๊ฐ€์„ค RAIM ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋ฐ ํ•œ๊ณ„ ๏ผ‘๏ผ๏ผ™ 1) Multiple Hypothesis Solution Seperation (MHSS) ๏ผ‘๏ผ๏ผ™ 2) Range Consensus (RANCO) ๏ผ‘๏ผ‘๏ผ’ 3) Sequential Multiple Hypothesis RAIM (SMHR) ๏ผ‘๏ผ‘๏ผ— 5์žฅ. ์‹ค์ œ ํ•ญ๋ฒ•์‹œ์Šคํ…œ์— ์ ์šฉ ๊ฐ€๋Šฅํ•˜๋„๋ก ๊ฐœ์„ ๋œ ์ˆœ์ฐจ์  ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๏ผ‘๏ผ’๏ผ— 1. ๋ฐ˜์†กํŒŒ ์œ„์ƒ ์ธก์ •์น˜๋ฅผ ํ™œ์šฉํ•œ ์ƒˆ๋กœ์šด SMHR (Sequential Multiple Hypothesis Relative RAIM, SMHRR) ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๏ผ‘๏ผ’๏ผ— 1) RRAIM ํ•ญ๋ฒ• ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๏ผ‘๏ผ’๏ผ˜ 2. ๋‹ค์ค‘ ๋žจํ”„ ์—๋Ÿฌ๋ฅผ ๊ณ ๋ คํ•œ SMHRR ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๏ผ‘๏ผ“๏ผ“ 1) 2 ์ฐจ์› SMHRR ์ˆ˜์‹ ์ „๊ฐœ ๏ผ‘๏ผ“๏ผ“ 2) N ์ฐจ์› SMHRR ์ˆ˜์‹ ์ „๊ฐœ ๏ผ‘๏ผ“๏ผ— 3) RRAIM ๊ธฐ๋ฒ• ์ ์šฉ์œผ๋กœ ์ธํ•œ ๋ฌธ์ œ์  ๋ฐ ํ•ด๊ฒฐ ๋ฐฉ๋ฒ• ๏ผ‘๏ผ”๏ผ‘ 3. ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์‹œ์Šคํ…œ ํ–‰๋ ฌ ์ตœ์ ํ™” ๋ฐ ๊ทธ ํšจ๊ณผ ๏ผ‘๏ผ”๏ผ” 1) ์‹œ์Šคํ…œ ํ–‰๋ ฌ ๊ตฌ์„ฑ์— ๋”ฐ๋ฅธ ์ถ”์ •๊ฐ’ ํ‘œ์ค€ํŽธ์ฐจ ๏ผ‘๏ผ”๏ผ” 2) ์ธก์ •์น˜ ์ƒ˜ํ”Œ๋ง ๊ฐ„๊ฒฉ ๋ฐ ๊ทธ์— ๋”ฐ๋ฅธ ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์„ฑ๋Šฅ ๏ผ‘๏ผ”๏ผ– 4. ์‚ฌ์šฉ์ž ๋ณดํ˜ธ์ˆ˜์ค€ ๊ณ„์‚ฐ ๋ฐฉ๋ฒ• ๏ผ‘๏ผ”๏ผ˜ 1) ํ•ญ๋ฒ• ์š”๊ตฌ ์กฐ๊ฑด ๋ฐ ๊ทธ์— ๋”ฐ๋ฅธ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์„ค์ • ๏ผ‘๏ผ”๏ผ™ 2) ์‚ฌ์šฉ์ž ๋ณดํ˜ธ์ˆ˜์ค€ ๊ณ„์‚ฐ ๏ผ‘๏ผ•๏ผ— 5. ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๏ผ‘๏ผ–๏ผ‘ 1) ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ตฌ์„ฑ ๏ผ‘๏ผ–๏ผ‘ 2) ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ ๏ผ‘๏ผ–๏ผ“ 6์žฅ. ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ๊ณผ์ œ ๏ผ‘๏ผ˜๏ผ’ ์ฐธ๊ณ  ๋ฌธํ—Œ ๏ผ‘๏ผ˜๏ผ” Abstract ๏ผ‘๏ผ™๏ผ’Docto

    Changes of ฮฑ- and ฮฒ-calcitonin gene-related peptide expression in rat spinal cord af

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    ์˜ํ•™๊ณผ/๋ฐ•์‚ฌ[ํ•œ๊ธ€] ์ผ์ฐจ๊ฐ๊ฐ์‹ ๊ฒฝ์›์—์„œ ํ†ต๊ฐ์ „๋‹ฌ์— ๊ด€์—ฌํ•˜๋Š” ํŽฉํƒ€์ด๋“œ๋กœ ์ž˜ ์•Œ๋ ค์ ธ ์žˆ๋Š” calcitonin gene-related peptide (CGRP)๋Š”, calcitonin ์œ ์ „์ž๋กœ๋ถ€ํ„ฐ ํ•ฉ์„ฑ๋œ ฮฑ-CGRP์™€, ๊ทธ ๊ตฌ์กฐ๊ฐ€ ๋งค์šฐ ์œ ์‚ฌํ•˜์ง€๋งŒ ๋‹ค๋ฅธ ์œ ์ „์ž์—์„œ ํ•ฉ์„ฑ๋˜๋Š” ฮฒ-CGRP๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋‹ค. ์ด ๋‘ ๊ฐ€์ง€ ํŽฉํƒ€์ด๋“œ๋Š” ์ค‘์ถ”์‹ ๊ฒฝ๊ณ„ํ†ต์—์„œ ๋น„์Šทํ•œ ๋ถ„ํฌ๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ์œผ๋ฉฐ, ๊ธฐ๋Šฅ์ ์ธ ์ฐจ์ด๋„ ์•„์ง ๋ฐํ˜€์ง€์ง€ ์•Š์•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์ž๋Š” ์ฒ™์ˆ˜์ ˆ๋‹จ, ์ฒ™์ˆ˜์‹ ๊ฒฝ ์•ž๋ฟŒ๋ฆฌ ๋ฐ ๋’ค๋ฟŒ๋ฆฌ ์ ˆ๋‹จ์„ ์‹œํ–‰ํ•œ ํ›„ ์ถ•์‚ญ์ด ์†์ƒ๋˜๊ฑฐ๋‚˜ ๊ตฌ์‹ฌ์„ฑ ์—ฐ๊ฒฐ์ด ์†Œ์‹ค๋œ ์ฒ™์ˆ˜ ์•ž๋ฟ”์„ธํฌ์˜ CGRP ๋ฐ ฮฑ- ๋˜๋Š” ฮฒ-CGRP mRNA ๋ฐœํ˜„์˜ ๋ณ€ํ™”๋ฅผ ๊ด€์ฐฐํ•˜๊ณ , ์ด๋“ค ํŽฉํƒ€์ด๋“œ์˜ ๊ธฐ๋Šฅ์  ์ฐจ์ด๋ฅผ ๊ตฌ๋ช…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ฒด์ค‘ 250 gm์ •๋„์˜ ํฐ์ฅ๋ฅผ ์ „์‹ ๋งˆ์ทจํ•œ ํ›„ ๋„ท์งธ-์—ฌ์„ฏ์งธ ์š”์ˆ˜์‹ ๊ฒฝ ์•ž๋ฟŒ๋ฆฌ, ๋„ท์งธ-์—ฌ์„ฏ์งธ ์š”์ˆ˜์‹ ๊ฒฝ ๋’ค๋ฟŒ๋ฆฌ, ๋˜๋Š” ํ‰์ˆ˜๋ถ„์ ˆ ํ•˜๋ถ€์˜ ์ฒ™์ˆ˜๋ฅผ ์ ˆ๋‹จํ•˜๊ณ , 1์ฃผ์ผ๊ฐ„ ์‚ฌ์œกํ•œ ํ›„ ๋‹ค์„ฏ์งธ ์š”์ˆ˜๋ถ„์ ˆ์„ ์ ์ถœํ•˜์—ฌ ๋ฉด์—ญ์กฐ์งํ™”ํ•™์—ผ์ƒ‰๊ณผ in situ hybridization ์กฐ์งํ™”ํ•™์—ผ์ƒ‰์„ ์‹œํ–‰ํ•˜์˜€ ๋‹ค. ๋Œ€์กฐ๊ตฐ์—์„œ๋Š” ์กฐ์ง์ ˆํŽธ๋‹น ํ‰๊ท  4.45ยฑ1.51๊ฐœ์˜ ์šด๋™์‹ ๊ฒฝ์›์—์„œ CGRP ๋ฉด์—ญ๋ฐ˜์‘์ด ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ, ์ขŒ์šฐ์˜ ์ฐจ์ด๋Š” ์—†์—ˆ๋‹ค. ํ•œ์ชฝ ์ฒ™์ˆ˜์‹ ๊ฒฝ ์•ž๋ฟŒ๋ฆฌ๋ฅผ ์ ˆ๋‹จํ•œ ๊ฒฝ์šฐ ๋™์ธก์˜ ๋ฉด์—ญ๋ฐ˜์‘ ์„ธํฌ์ˆ˜๊ฐ€ 9.12ยฑ2.52๊ฐœ๋กœ ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ๋’ค๋ฟŒ๋ฆฌ๋ฅผ ์ ˆ๋‹จํ•œ ๊ฒฝ์šฐ์—๋Š” ๋™์ธก 7.29ยฑ3.69๊ฐœ, ๋ฐ˜๋Œ€์ธก 6.26ยฑ 1.53๊ฐœ๋กœ ์–‘์ชฝ ๋ชจ๋‘์—์„œ ๋ฉด์—ญ๋ฐ˜์‘ ์„ธํฌ์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ํ•œํŽธ ํ‰์ˆ˜๋ถ„์ ˆ ํ•˜๋ถ€์˜ ์ฒ™์ˆ˜๋ฅผ ์ ˆ๋‹จํ•œ ๊ฒฝ์šฐ์—๋Š”, ์–‘์ชฝ ๋ชจ๋‘์—์„œ ๋ฉด์—ญ๋ฐ˜์‘ ์„ธํฌ๊ฐ€ ๊ฑฐ์˜ ์™„์ „ํžˆ ์†Œ์‹ค๋˜์—ˆ๋‹ค. In situ hybridization์„ ์ด์šฉํ•˜์—ฌ ฮฑ- ๋ฐ ฮฒ-CGRP mRNA ๋ฐœํ˜„์„ ๊ด€์ฐฐํ•œ ๊ฒฐ๊ณผ, ๋Œ€์กฐ๊ตฐ์—์„œ ฮฑ-CGRP myNA ๋ฐœํ˜„ ์„ธํฌ์ˆ˜๋Š” 4.16ยฑ 1.32๊ฐœ์˜€๊ณ , ฮฒ-CGRP mRNA ๋ฐœํ˜„ ์„ธํฌ์ˆ˜๋Š” 5.50ยฑ1.38๊ฐœ์˜€์œผ๋ฉฐ, ์ขŒ์šฐ ์ฐจ์ด๋Š” ์—†์—ˆ๋‹ค. ์ฒ™์ˆ˜์‹ ๊ฒฝ ์•ž๋ฟŒ๋ฆฌ๋ฅผ ์ ˆ๋‹จํ•œ ๊ฒฝ์šฐ์—๋Š” ๋™์ธก์—์„œ ฮฑ-CGRP mRNA ๋ฐœํ˜„ ์„ธํฌ์ˆ˜๊ฐ€ 10.07ยฑ2.86๊ฐœ๋กœ ์ฆ๊ฐ€ํ•˜์˜€์œผ๋‚˜, ฮฒ-CGRP mRNA ๋ฐœํ˜„ ์„ธํฌ์ˆ˜๋Š” ๋ณ€ํ™”๊ฐ€ ์—†์—ˆ์œผ๋ฉฐ, ๋’ค๋ฟŒ๋ฆฌ๋ฅผ ์ ˆ๋‹จํ•œ ๊ฒฝ์šฐ์—๋Š” ฮฑ-CGRP mRNA ๋ฐœํ˜„ ์„ธํฌ์ˆ˜๋Š” ๋Œ€์กฐ๊ตฐ๊ณผ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜์œผ๋‚˜, ฮฒ-CGRP mRNA๋ฐœํ˜„ ์„ธํฌ์ˆ˜๋Š” ๋™์ธก 7.45ยฑ2.04๊ฐœ, ๋ฐ˜๋Œ€์ธก 7.02ยฑ 1.38๊ฐœ๋กœ ์–‘์ชฝ์—์„œ ๋ชจ๋‘ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ์ฒ™์ˆ˜๋ฅผ ์ ˆ๋‹จํ•œ ๊ฒฝ์šฐ์—๋Š” ์ขŒ์šฐ ๋ชจ๋‘์—์„œ ฮฑ- ๋ฐ ฮฒ-CGRP mRNA ๋ฐœํ˜„ ์šด๋™์‹ ๊ฒฝ์›์ด ๊ฑฐ์˜ ์†Œ์‹ค๋˜์—ˆ๋‹ค. ์ด์ƒ์˜ ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉํ•˜์—ฌ ๋ณผ ๋•Œ, ์ฒ™์ˆ˜์˜ ์•ž๋ฟ”์„ธํฌ์— ์žˆ์–ด์„œ ์ถ•์‚ญ ์†์ƒ์‹œ์—๋Š” ฮฑ-CGRP๊ฐ€, 1์ฐจ ๊ฐ๊ฐ์‹ ๊ฒฝ์›์„ ํ†ตํ•œ ๊ตฌ์‹ฌ์„ฑ ์—ฐ๊ฒฐ์ด ์†Œ์‹ค๋˜์—ˆ์„ ๊ฒฝ์šฐ์—๋Š” ฮฒ-CGRP๊ฐ€ ์ฆ๊ฐ€ํ•˜๋ฉฐ, ์ฒ™์ˆ˜ ์ ˆ๋‹จ์‹œ์—๋Š” ๋‘ ํŽฉํƒ€์ด๋“œ๊ฐ€ ๋ชจ๋‘ ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๊ณ , ์ด๋“ค ๋‘ ํŽฉํƒ€์ด๋“œ๊ฐ€ ์„œ๋กœ ๋‹ค๋ฅธ ์ž‘์šฉ์„ ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ๊ฐ„์ ‘์ ์œผ๋กœ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. [์˜๋ฌธ] Calcitonin gene-related peptide (CGRf), a well-known neuropeptide in primary sensory neurons carrying nociceptive information, includes two different peptides of similar structure, the ฮฑ - and ฮฒ -CGRP. The distribution of these two peptides in the central nervous system is known to be similar and no functional differences have been reported. The aim of this study is to investigated the changes of ฮฑ - and ฮฒ -CGRP expression following efferent or afferent disconnection of anterior horn cells in the rat spinal cord. One week after ventral rhizotomy (left L4โˆผ6), dorsal rhizotomy (left L4โˆผ6) or spinal cord transection(at lower thoracic level), the animals were sacrified and the L5 segments of the spinal card were excised to perform immunohistochemistry and in situ hybridization histochemistry. In the control group, 4.45ยฑ 1.51 anterior horn cells showed CGRP immunoreactivity per tissue section in one side. After ventral rhizotomy, the number of CGRP immunoreactive neurons increased to 9.12 ยฑ 2.52 at the ipsilateral ventral hors. After dorsal rhizotomy, CGRP immunoreactive neurons increased to 7.29 ยฑ 3.69 at the ipsilateral ventral loom and 6.26 ยฑ 1.53 at the contralateral ventral horn. In cases of spinal cord transection, almost all the anterior horn cells lost CGRP immunoreactivity in both sides. Neurons expressing ฮฑ - or ฮฒ -CGRP mRNA could be distinguished by in situ hybridization histochemistry. In the control group, anterior horn cells expressing ฮฑ-CGRP mRNA numbered 4.16ยฑ 1.32 per section and ฮฒ -CGRP cells numbered 5.50ยฑ 1.38. After ventral rhizotomy, the number of cells expressing ฮฑ -CGRP mRNA increased to 10.07ยฑ2.86 in the ipsilateral side without any changes in ฮฒ-CGRP mRNA expression. After dorsal rhizotomy, no significant changes in ฮฑ-CGRP mRNA expression were detected, but the number of cells expressing ฮฒ-CGRP mRNA increased to 7.45 ยฑ2.04 in the ipsilateral side and to 7.02ยฑ 1.38 in the contrallateral side. In cases of spinal cord transection, the anterior horn cells lost ฮฑ - and ฮฒ -CGRP mRNA signals almost completely in both sides. These results showed that ฮฑ-CGRP expression increased in axotomized anterior horn cells of the spinal cord, and that ฮฒ-CGRP expression increased in anterior horn cells which had lost their afferent input through the primary sensory neurons. These findings provide evidence showing the functional difference of the two peptides in anterior horn cells of the spinal cord.restrictio

    Distributio of visceral substance P- and calcitonin gene-related peptide immunoreactive neurons in the dorsal root ganglia of the rats

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    ์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] ์ฒ™์ˆ˜์‹ ๊ฒฝ์ ˆ์˜ ์ผ์ฐจ๊ฐ๊ฐ์‹ ๊ฒฝ์›์—์„œ, ํ†ต๊ฐ์„ ์ „๋‹ฌํ•˜๋Š” ์‹ ๊ฒฝ์ „๋‹ฌ๋ฌผ์งˆ๋กœ substance P์™€ CGRP๊ฐ€ ์ž˜ ์•Œ๋ ค์ ธ ์žˆ์ง€๋งŒ, ์ด ๋‘ ๊ฐ€์ง€ ์‹ ๊ฒฝ์ „๋‹ฌ๋ฌผ์งˆ์„ ํ•จ์œ ํ•œ ์‹ ๊ฒฝ์›์— ์žˆ์–ด์„œ ์ฒด๊ตฌ์‹ฌ์„ฑ์‹ ๊ฒฝ์› ๋ฐ ๋‚ด์žฅ๊ตฌ์‹ฌ์„ฑ์‹ ๊ฒฝ์› ๋น„์œจ์— ๋Œ€ํ•ด์„œ๋Š” ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ•œ์ชฝ T9-T11 ์ฒ™์ˆ˜์‹ ๊ฒฝ์˜ ์•ž๊ฐ€์ง€์™€ ๋’ค๊ฐ€์ง€๋ฅผ ์ ˆ๋‹จํ•˜์—ฌ ์ฒด๊ตฌ์‹ฌ์„ฑ์‹ ๊ฒฝ์›์œผ๋กœ ๋“ค์–ด์˜ค๋Š” ์„ฑ๋ถ„์„ ์ œ๊ฑฐํ•œ ํ›„ ์–‘์ชฝ T9-T11 ์ฒ™์ˆ˜์‹ ๊ฒฝ์ ˆ์„ substance P ๋ฐ CGRP๋ฅผ ํ•ญ์›์œผ๋กœ ํ•˜๋Š” ๋ฉด์—ญ์กฐ์งํ™”ํ•™์—ผ์ƒ‰์„ ์‹œํ–‰ํ•˜์—ฌ, ์ฒ™์ˆ˜์‹ ๊ฒฝ์ ˆ์˜ ์ผ์ฐจ๊ฐ๊ฐ์‹ ๊ฒฝ์› ์ค‘ ๋‚ด์žฅ๊ตฌ์‹ฌ์„ฑ substance P ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ์™€ ๋‚ด์žฅ๊ตฌ์‹ฌ์„ฑ CGRP ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ๊ฐ€ ์ฐจ์ง€ํ•˜๋Š” ๋น„์œจ๊ณผ ํฌ๊ธฐ์˜ ๋ถ„ํฌ๋ฅผ ๋ฐํ˜”๋‹ค. ๋Œ€์กฐ์ธก T9-T11 ์ฒ™์ˆ˜์‹ ๊ฒฝ์ ˆ์—์„œ ์ „์ฒด ์‹ ๊ฒฝ์› ์ค‘ 12.02%๊ฐ€ substance P ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ์˜€๊ณ , 11.67%๊ฐ€ CGRP ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ์˜€๋‹ค. Substance P ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ์˜ ํ‰๊ท  ๋‹จ๋ฉด์ ์€ 533.17 ฮผm**2 ์˜€๊ณ  200-300 ฮผm **2 ์˜ ์„ธํฌ๊ฐ€ ๊ฐ€์žฅ ๋งŽ์•˜์œผ๋ฉฐ, CGRP ๋จผ์—ญ๋ฐ˜์‘์„ธํฌ๋Š” ํ‰๊ท  ๋‹จ๋ฉด์ ์ด 394.14 ฮผm**2 ์˜€๊ณ  300-400 ฮผm**2 ์˜ ์„ธํฌ๊ฐ€ ๊ฐ€์žฅ ๋งŽ์•˜๋‹ค. ๋‚ด์žฅ๊ตฌ์‹ฌ์„ฑ substance P ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ๋Š” ์ „์ฒด substance P ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ์˜ 43.00% ์ดํ•˜๋ฅผ ์ฐจ์ง€ํ–ˆ์œผ๋ฉฐ, ๋‚ด์žฅ๊ตฌ์‹ฌ์„ฑ CGRP ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ๋Š” ์ „์ฒด CGRP ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ์˜ 15.59% ์ดํ•˜๋ฅผ ์ฐจ์ง€ํ–ˆ๋‹ค. ๋‚ด์žฅ๊ตฌ์‹ฌ์„ฑ substance P ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ๋Š” ์ „์ฒด substance P ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ์— ๋น„ํ•ด ๋‹จ๋ฉด์  300-700 ฮผm**2 ๋ฒ”์œ„์˜ ์„ธํฌ๊ฐ€ ์ฐจ์ง€ํ•˜๋Š” ๋น„์œจ์ด ๋†’์•˜๊ณ , ๋‚ด์žฅ๊ตฌ์‹ฌ์„ฑ CGRP ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ๋Š” ์ „์ฒด CGRP ๋ฉด์—ญ๋ฐ˜์‘์„ธํฌ์— ๋น„ํ•ด ๋‹จ๋ฉด์  500 ฮผm**2 ์ด์ƒ์˜ ์„ธํฌ๊ฐ€ ์ฐจ์ง€ํ•˜๋Š” ๋น„์œจ์ด ๋†’์•„, ๋‘ ๊ฐ€์ง€ ์„ธํฌ ๋ชจ๋‘ ์ฒด๊ตฌ์‹ฌ์„ฑ์‹ ๊ฒฝ์›์— ๋น„ํ•ด ํฐ ์‹ ๊ฒฝ์›์ด ๋งŽ์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. Distribution of visceral afferent substance P- and calcitonin gene-related peptide immunoreactive neurons in the dorsal root ganglia of the rats. Ho Yoon Department of Medical Science The Graduate School, Yonsei University (Directed by Assistant Professor Won Taek Lee) Although substance P (SP) and calcitonin gene-related peptide (CGRP) are well known as neurotransmitters concerned in pain transmission there are no information about the number or size distribution of visceral primary afferent neurons containing SP and CGRP of the rats. T9-T11 ventral and dorsal rami were transected at the right side arid 10 days later, T9-T11 dorsal root ganglia (DRG) of both side were obtained, The DRG were cut in 10 ฮผm thickness and each section was stained immunohistochemically against SP or CGRP with peroxidase anti-peroxidase method. The number and the size distribution of the cells with immunoreactivity were obtained, and the data of the right side was compared with that of the contralateral side. From entire DRG cells 12.02 % had SP immunoreactivity and 11.67% had CGRP-immunoreactivity, at the control (left) side. The percentage of visceral afferent neurons in SP- or CGRP-immunoreactive cells was loss than 43.00% and 15.59%, respectively. The peak incidence of total SP immunoreactive colls was at the range of 200-300 ฮผm**2 and that of visceral afferent SP immunoreactive cells was 300-400 ฮผm**2 . In the case of CGRP immunoreactive cells, peaks of the two groups were at 300-400 ฮผm**2 , but visceral afferent cells were slightly larger than somatic afferent cells. [์˜๋ฌธ] Although substance P (SP) and calcitonin gene-related peptide (CGRP) are well known as neurotransmitters concerned in pain transmission there are no information about the number or size distribution of visceral primary afferent neurons containing SP and CGRP of the rats. T9-T11 ventral and dorsal rami were transected at the right side arid 10 days later, T9-T11 dorsal root ganglia (DRG) of both side were obtained, The DRG were cut in 10 ฮผm thickness and each section was stained immunohistochemically against SP or CGRP with peroxidase anti-peroxidase method. The number and the size distribution of the cells with immunoreactivity were obtained, and the data of the right side was compared with that of the contralateral side. From entire DRG cells 12.02 % had SP immunoreactivity and 11.67% had CGRP-immunoreactivity, at the control (left) side. The percentage of visceral afferent neurons in SP- or CGRP-immunoreactive cells was loss than 43.00% and 15.59%, respectively. The peak incidence of total SP immunoreactive colls was at the range of 200-300 ฮผm**2 and that of visceral afferent SP immunoreactive cells was 300-400 ฮผm**2 . In the case of CGRP immunoreactive cells, peaks of the two groups were at 300-400 ฮผm**2 , but visceral afferent cells were slightly larger than somatic afferent cells.restrictio

    ์Œ๊ณก๋ฅ  ๊ณต๊ฐ„์—์„œ์˜ ์ค‘์•™๊ฐ’-ํ‰๊ท  ์ถ”์ •๋Ÿ‰์˜ ์ง€์ˆ˜์  ์ง‘์ค‘

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ†ต๊ณ„ํ•™๊ณผ, 2022.2. ๋ฐ•๋ณ‘์šฑ.In Euclidean spaces, the empirical mean vector as a mean estimator has polynomial concentration unless a strong tail assumption is imposed. The idea of median-of-means tournament has been considered as a way of robustification for the empirical mean vector. In this paper, to address the sub-optimal performance of the empirical mean in a more general setting, we consider general Polish spaces with a general metric, which are allowed to be non-compact and of infinite-dimension. We discuss the estimation of the associated population Frechet mean, and for this we extend the existing notion of median-of-means to this general setting. We devise several new notions and inequalities associated with the geometry of the underlying metric, and using them we show that the new estimators achieve exponential concentration under the only second moment condition on the underlying distribution, while the empirical Frechet mean has polynomial concentration. We focus our study on spaces with non-positive Alexandrov curvature since they afford slower rates of convergence than spaces with positive curvature.์œ ํด๋ฆฌ๋“œ๊ณต๊ฐ„์—์„œ ํ‘œ๋ณธํ‰๊ท  ๋ฒกํ„ฐ๋Š” ํ‰๊ท  ์ถ”์ •๋Ÿ‰์— ๋Œ€ํ•ด ์˜ค์ง ๋‹คํ•ญ์  ์ง‘์ค‘๋งŒ์„ ๊ฐ€์ง„๋‹ค. ์ด์—, ์ค‘์•™๊ฐ’-ํ‰๊ท (median-of-means) ์ถ”์ •๋Ÿ‰์ด ํ‘œ๋ณธํ‰๊ท  ๋ฒกํ„ฐ์˜ ๊ฐ•๊ฑดํ™”์˜ ์ผํ™˜์œผ๋กœ ์ œ์‹œ๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„์˜น๊ณจ(non-compact) ํ˜น์€ ๋ฌดํ•œ ์ฐจ์› ํด๋ž€๋“œ ๊ณต๊ฐ„(Polish space)์—์„œ ์ผ๋ฐ˜์ ์ธ ๊ฑฐ๋ฆฌ๊ฐ€ ์ฃผ์–ด์กŒ์„ ๋•Œ์˜ ํ”„๋ ˆ์…ฐ(Frechet) ๋ชจํ‰๊ท  ์ถ”์ •์— ๊ด€ํ•œ ๋ฌธ์ œ๋ฅผ ๋‹ค๋ฃฌ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, ๊ธฐ์กด์˜ ์ค‘์•™๊ฐ’-ํ‰๊ท ์˜ ์ •์˜๋ฅผ ํ™•์žฅํ•˜์˜€๊ณ , ์ฃผ์–ด์ง„ ๊ณต๊ฐ„์˜ ๊ธฐํ•˜ํ•™์  ์„ฑ์งˆ์„ ๋ฐ˜์˜ํ•˜๊ธฐ ์œ„ํ•ด ๊ณ ์•ˆ๋œ ๊ฐœ๋…๋“ค๊ณผ ๋ถ€๋“ฑ์‹์„ ์ด์šฉํ•˜์—ฌ ํ‘œ๋ณธ ํ”„๋ ˆ์…ฐ ํ‰๊ท ์€ ๋‹คํ•ญ์  ์ง‘์ค‘๋งŒ์„ ๊ฐ€์ง€๋Š” ๋ฐ˜๋ฉด, ๋ณธ๋ฌธ์—์„œ ์ œ์‹œํ•œ ์ƒˆ๋กœ์šด ์ถ”์ •๋Ÿ‰์€ ์ง€์ˆ˜์  ์ง‘์ค‘์„ ๊ฐ€์ง์„ ๋ณด์ธ๋‹ค. ํŠนํžˆ, ๋ณธ ์—ฐ๊ตฌ๋Š” ์–‘๊ณก๋ฅ  ๊ณต๊ฐ„๋ณด๋‹ค ์ˆ˜๋ ด์†๋„๊ฐ€ ๋” ๋Š๋ฆฐ ์Œ๊ณก๋ฅ  ๊ณต๊ฐ„์— ์ดˆ์ ์„ ๋‘๊ณ  ์žˆ๋‹ค.1. Introduction (p1) 2. Assumptions (p6) 3. Empirical Frechet Means (p12) 4. Consideration of Assumptions (p15) 5. Geometric-Median-of-Means (p25) 6. Discussion (p37) 7. Proofs (p38) 8. Reference (p52) 9. Abstract in Korean (p58)์„

    Multiple-Hypothesis RAIM Algorithm with an RRAIM Concept

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    ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฏธ๋ž˜์˜ ๋‹ค์ค‘ Global Navigation Sattelite System (GNSS) ๋ฐ ๋‹ค์ค‘ ์ฃผํŒŒ์ˆ˜ ํ•ญ๋ฒ•์‹ ํ˜ธ ์‚ฌ์šฉ์ž๋ฅผ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋‹ค์ค‘ ๊ฐ€์„ค ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๊ธฐ์กด์˜ Weighted Least Squares (WLS) ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ (Receiver Autonomous Integrity Monitoring, RAIM) ๊ธฐ๋ฒ•์€ ๋™์‹œ์— ๋‘ ๊ฐœ ์ด์ƒ์˜ ์œ„์„ฑ์ด ๊ณ ์žฅ ๋‚˜์ง€ ์•Š๋Š”๋‹ค๋Š” ๊ฐ€์ • ํ•˜์— ์‚ฌ์šฉ์ž ์ธก์ •์น˜์˜ ๋ฌด๊ฒฐ์„ฑ์„ ๊ฐ์‹œํ•˜๋ฏ€๋กœ ๋™์‹œ์— ๋‘ ๊ฐœ ์ด์ƒ์˜ ์ธก์ •์น˜ ์ด์ƒ์ด ๋ฐœ์ƒํ•˜์˜€์„ ๋•Œ ์ ์ ˆํ•œ ๋Œ€์‘์„ ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์—ฌ๋Ÿฌ epoch์˜ ์ธก์ •์น˜ ์ž”์ฐจ์™€ ์œ„์„ฑ ๊ด€์ธกํ–‰๋ ฌ์˜ ๋ณ€ํ™”๋Ÿ‰์„ ํ™œ์šฉํ•˜์—ฌ ๋‹จ์ผ ๊ณ ์žฅ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‹ค์ค‘ ๊ณ ์žฅ์„ ํšจ์œจ์ ์œผ๋กœ ๊ฒ€์ถœํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ ๋ฐ˜์†กํŒŒ ์œ„์ƒ ์ธก์ •์น˜์˜ ๋ณ€ํ™”์œจ์„ ํ™œ์šฉํ•˜๋Š” Relative RAIM (RRAIM) ๊ธฐ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ์ตœ์†Œ ๊ฒ€์ถœ๊ฐ€๋Šฅ ๋ฐ”์ด์–ด์Šค(Minimum Detectable Bias, MDB)์˜ ํฌ๊ธฐ๋ฅผ ์ตœ์†Œํ™”ํ•˜์˜€๊ณ , ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ์ˆ˜ ์‹ญ m ์ •๋„ ํฌ๊ธฐ์˜ ๋‹ค์ค‘ ๊ณ ์žฅ์„ ๊ฒ€์ถœํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.N

    Modeling of GPS measurement noise for estimating smoothed pseudorange and ionospheric delay

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    GPS ์‹ ํ˜ธ์˜ ์ฃผ์š” ์˜ค์ฐจ ์š”์ธ ์ค‘ ์ „๋ฆฌ์ธต ์ง€์—ฐ ์˜ค์ฐจ๋Š” ์‹ ํ˜ธ ์ฃผํŒŒ์ˆ˜์— ๋”ฐ๋ผ ์ง€์—ฐ๋Ÿ‰์ด ๋‹ฌ๋ผ์ง€๋Š” ํŠน์„ฑ์„ ๊ฐ€์ง„๋‹ค. ์ด์ค‘ ์ฃผํŒŒ์ˆ˜ ์‚ฌ์šฉ์ž๋Š” L1, L2 ์ฃผํŒŒ์ˆ˜์˜ ์˜์‚ฌ๊ฑฐ๋ฆฌ ์ธก์ •์น˜์˜ ์ฐจ์ด ๊ฐ’์„ ์ด์šฉํ•˜์—ฌ ๋ณด์ •ํ•˜๊ฒŒ ๋˜๋Š”๋ฐ, ์ด๋ ‡๊ฒŒ ์ถ”์ •๋œ ์ „๋ฆฌ์ธต ์ง€์—ฐ ์ถ”์ •์น˜์—๋Š” ์˜์‚ฌ๊ฑฐ๋ฆฌ ์žก์Œ์— ์˜ํ•œ ์˜ค์ฐจ๊ฐ€ ํฌํ•จ๋˜๊ฒŒ ๋˜๋ฏ€๋กœ ์ผ๋ฐ˜์ ์œผ๋กœ ํ•„ํ„ฐ๋ฅผ ํ†ตํ•ด ์˜์‚ฌ๊ฑฐ๋ฆฌ ์ธก์ •์น˜๋ฅผ ํ‰ํ™œํ™” ์‹œํ‚จ ํ›„ ์ „๋ฆฌ์ธต ์ง€์—ฐ์„ ๊ณ„์‚ฐํ•˜๊ฒŒ ๋œ๋‹ค. Weighted hatch filter๋Š” ์ธก์ •์น˜์˜ ์žก์Œ ์ˆ˜์ค€์„ ๊ณ ๋ คํ•˜์—ฌ ์ตœ์ ์˜ ํ‰ํ™œํ™” ๋œ ์˜์‚ฌ๊ฑฐ๋ฆฌ ์ธก์ •์น˜๋ฅผ ๊ณ„์‚ฐํ•ด ๋‚ผ ์ˆ˜ ์žˆ์œผ๋‚˜, ์ด๋ฅผ ์ด์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ธก์ •์น˜ ์žก์Œ์— ๋Œ€ํ•œ ๋ชจ๋ธ๋ง์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” NDGPS ๊ธฐ์ค€๊ตญ๋“ค์— ๋Œ€ํ•˜์—ฌ ์ธก์ •์น˜ ์žก์Œ ๋ชจ๋ธ๋ง์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ชจ๋ธ๋ง ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ weighted hatch filter๋ฅผ ๊ตฌ์„ฑํ•˜์—ฌ ํ‰ํ™œํ™” ๋œ ์˜์‚ฌ๊ฑฐ๋ฆฌ ์ธก์ •์น˜ ๋ฐ ์ „๋ฆฌ์ธต ์ง€์—ฐ์„ ์ถ”์ •ํ•œ ๊ฒฐ๊ณผ ํ•„ํ„ฐ๋ฅผ ์ ์šฉํ•˜์ง€ ์•Š์€ ๊ฒƒ์— ๋น„ํ•˜์—ฌ ์ „๋ฆฌ์ธต ์ง€์—ฐ ์˜ค์ฐจ์˜ ํ‘œ์ค€ํŽธ์ฐจ๊ฐ€ 1/25 ๊ฐ€๋Ÿ‰์œผ๋กœ ์ค„์–ด๋“œ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค.N

    Multiple-Hypothesis RAIM Algorithm with an RRAIM Concept

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    author's final manuscript๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ฏธ๋ž˜์˜ ๋‹ค์ค‘ Global Navigation Sattelite System (GNSS) ๋ฐ ๋‹ค์ค‘ ์ฃผํŒŒ์ˆ˜ ํ•ญ๋ฒ•์‹ ํ˜ธ ์‚ฌ์šฉ์ž๋ฅผ ์œ„ํ•œ ์ƒˆ๋กœ์šด ๋‹ค์ค‘ ๊ฐ€์„ค ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๊ธฐ์กด์˜ Weighted Least Squares (WLS) ์‚ฌ์šฉ์ž ๋ฌด๊ฒฐ์„ฑ ๊ฐ์‹œ (Receiver Autonomous Integrity Monitoring, RAIM) ๊ธฐ๋ฒ•์€ ๋™์‹œ์— ๋‘ ๊ฐœ ์ด์ƒ์˜ ์œ„์„ฑ์ด ๊ณ ์žฅ ๋‚˜์ง€ ์•Š๋Š”๋‹ค๋Š” ๊ฐ€์ • ํ•˜์— ์‚ฌ์šฉ์ž ์ธก์ •์น˜์˜ ๋ฌด๊ฒฐ์„ฑ์„ ๊ฐ์‹œํ•˜๋ฏ€๋กœ ๋™์‹œ์— ๋‘ ๊ฐœ ์ด์ƒ์˜ ์ธก์ •์น˜ ์ด์ƒ์ด ๋ฐœ์ƒํ•˜์˜€์„ ๋•Œ ์ ์ ˆํ•œ ๋Œ€์‘์„ ํ•  ์ˆ˜ ์—†๋‹ค๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์—ฌ๋Ÿฌ epoch์˜ ์ธก์ •์น˜ ์ž”์ฐจ์™€ ์œ„์„ฑ ๊ด€์ธกํ–‰๋ ฌ์˜ ๋ณ€ํ™”๋Ÿ‰์„ ํ™œ์šฉํ•˜์—ฌ ๋‹จ์ผ ๊ณ ์žฅ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋‹ค์ค‘ ๊ณ ์žฅ์„ ํšจ์œจ์ ์œผ๋กœ ๊ฒ€์ถœํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ๊ธฐ๋ฒ•์€ ๋ฐ˜์†กํŒŒ ์œ„์ƒ ์ธก์ •์น˜์˜ ๋ณ€ํ™”์œจ์„ ํ™œ์šฉํ•˜๋Š” Relative RAIM (RRAIM) ๊ธฐ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ ์ตœ์†Œ ๊ฒ€์ถœ๊ฐ€๋Šฅ ๋ฐ”์ด์–ด์Šค(Minimum Detectable Bias, MDB)์˜ ํฌ๊ธฐ๋ฅผ ์ตœ์†Œํ™”ํ•˜์˜€๊ณ , ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฒฐ๊ณผ ์ˆ˜ ์‹ญ m ์ •๋„ ํฌ๊ธฐ์˜ ๋‹ค์ค‘ ๊ณ ์žฅ์„ ๊ฒ€์ถœํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.OAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000003405/3SEQ:3PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000003405ADJUST_YN:YEMP_ID:A000360DEPT_CD:446CITE_RATE:0FILENAME:์ตœ์ข…๋…ผ๋ฌธ_์œคํ˜ธ.hwpDEPT_NM:๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€EMAIL:[email protected]_YN:NCONFIRM:

    Modeling of GPS measurement noise for estimating smoothed pseudorange and ionospheric delay

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    author's final versionGPS ์‹ ํ˜ธ์˜ ์ฃผ์š” ์˜ค์ฐจ ์š”์ธ ์ค‘ ์ „๋ฆฌ์ธต ์ง€์—ฐ ์˜ค์ฐจ๋Š” ์‹ ํ˜ธ ์ฃผํŒŒ์ˆ˜์— ๋”ฐ๋ผ ์ง€์—ฐ๋Ÿ‰์ด ๋‹ฌ๋ผ์ง€๋Š” ํŠน์„ฑ์„ ๊ฐ€์ง„๋‹ค. ์ด์ค‘ ์ฃผํŒŒ์ˆ˜ ์‚ฌ์šฉ์ž๋Š” L1, L2 ์ฃผํŒŒ์ˆ˜์˜ ์˜์‚ฌ๊ฑฐ๋ฆฌ ์ธก์ •์น˜์˜ ์ฐจ์ด ๊ฐ’์„ ์ด์šฉํ•˜์—ฌ ๋ณด์ •ํ•˜๊ฒŒ ๋˜๋Š”๋ฐ, ์ด๋ ‡๊ฒŒ ์ถ”์ •๋œ ์ „๋ฆฌ์ธต ์ง€์—ฐ ์ถ”์ •์น˜์—๋Š” ์˜์‚ฌ๊ฑฐ๋ฆฌ ์žก์Œ์— ์˜ํ•œ ์˜ค์ฐจ๊ฐ€ ํฌํ•จ๋˜๊ฒŒ ๋˜๋ฏ€๋กœ ์ผ๋ฐ˜์ ์œผ๋กœ ํ•„ํ„ฐ๋ฅผ ํ†ตํ•ด ์˜์‚ฌ๊ฑฐ๋ฆฌ ์ธก์ •์น˜๋ฅผ ํ‰ํ™œํ™” ์‹œํ‚จ ํ›„ ์ „๋ฆฌ์ธต ์ง€์—ฐ์„ ๊ณ„์‚ฐํ•˜๊ฒŒ ๋œ๋‹ค. Weighted hatch filter๋Š” ์ธก์ •์น˜์˜ ์žก์Œ ์ˆ˜์ค€์„ ๊ณ ๋ คํ•˜์—ฌ ์ตœ์ ์˜ ํ‰ํ™œํ™” ๋œ ์˜์‚ฌ๊ฑฐ๋ฆฌ ์ธก์ •์น˜๋ฅผ ๊ณ„์‚ฐํ•ด ๋‚ผ ์ˆ˜ ์žˆ์œผ๋‚˜, ์ด๋ฅผ ์ด์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ธก์ •์น˜ ์žก์Œ์— ๋Œ€ํ•œ ๋ชจ๋ธ๋ง์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” NDGPS ๊ธฐ์ค€๊ตญ๋“ค์— ๋Œ€ํ•˜์—ฌ ์ธก์ •์น˜ ์žก์Œ ๋ชจ๋ธ๋ง์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ชจ๋ธ๋ง ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ weighted hatch filter๋ฅผ ๊ตฌ์„ฑํ•˜์—ฌ ํ‰ํ™œํ™” ๋œ ์˜์‚ฌ๊ฑฐ๋ฆฌ ์ธก์ •์น˜ ๋ฐ ์ „๋ฆฌ์ธต ์ง€์—ฐ์„ ์ถ”์ •ํ•œ ๊ฒฐ๊ณผ ํ•„ํ„ฐ๋ฅผ ์ ์šฉํ•˜์ง€ ์•Š์€ ๊ฒƒ์— ๋น„ํ•˜์—ฌ ์ „๋ฆฌ์ธต ์ง€์—ฐ ์˜ค์ฐจ์˜ ํ‘œ์ค€ํŽธ์ฐจ๊ฐ€ 1/25 ๊ฐ€๋Ÿ‰์œผ๋กœ ์ค„์–ด๋“œ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค.OAIID:oai:osos.snu.ac.kr:snu2012-01/102/0000003405/4SEQ:4PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:0000003405ADJUST_YN:YEMP_ID:A000360DEPT_CD:446CITE_RATE:0FILENAME:ํ•ญํ–‰ํ•™ํšŒ์ง€๋…ผ๋ฌธ_ํ•œ๋•ํ™”.hwpDEPT_NM:๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€EMAIL:[email protected]_YN:NCONFIRM:
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