53 research outputs found

    Anhedonia and ambivalence in schizophrenic patients with fronto-cerebellar metabolic abnormalities: a fluoro-d-glucose positron emission tomography study.

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    OBJECTIVE: Prefrontal and cerebellar abnormalities have been associated with higher cognitive deficits in schizophrenia. The current study aimed to show whether or not schizophrenic patients with fronto-cerebellar functional abnormalities show more anhedonia or ambivalence. METHODS: Regional cerebral metabolic activity was measured using fluoro-D-glucose positron emission tomography and was compared between 24 patients with chronic schizophrenia and 22 healthy normal volunteers. The existence of regional prefrontal hypofunction and regional cerebellar hyperfunction was investigated in each patient. Demographic and clinical variables including the emotional self-report scales were compared between the subgroups of the patients categorized according to the existence and the absence of the regional dysfunctions. RESULTS: Comparisons between each patient and the total normal controls revealed that 14 of the total twenty-four patients had regional hypofrontal functions, whereas 11 patients had regional hypercerebellar functions. Patients with prefrontal hypofunction showed more severe anhedonia than those without prefrontal hypofunction, whereas patients with cerebellar hyperfunction compared to those without cerebellar hyperfunction had more severe ambivalence. CONCLUSION: It seems that fronto-cerebellar abnormalities may be associated with cardinal emotional features of schizophrenia, such as anhedonia and ambivalenceope

    Efficacy and safety of haloperidol versus atypical antipsychotic medications in the treatment of delirium

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    BACKGROUND: Most previous studies on the efficacy of antipsychotic medication for the treatment of delirium have reported that there is no significant difference between typical and atypical antipsychotic medications. It is known, however, that older age might be a predictor of poor response to antipsychotics in the treatment of delirium. The objective of this study was to compare the efficacy and safety of haloperidol versus three atypical antipsychotic medications (risperidone, olanzapine, and quetiapine) for the treatment of delirium with consideration of patient age. METHODS: This study was a 6-day, prospective, comparative clinical observational study of haloperidol versus atypical antipsychotic medications (risperidone, olanzapine, and quetiapine) in patients with delirium at a tertiary level hospital. The subjects were referred to the consultation-liaison psychiatric service for management of delirium and were screened before enrollment in this study. A total of 80 subjects were assigned to receive either haloperidol (N = 23), risperidone (N = 21), olanzapine (N = 18), or quetiapine (N = 18). The efficacy was evaluated using the Korean version of the Delirium Rating Scale-Revised-98 (DRS-K) and the Korean version of the Mini Mental Status Examination (K-MMSE). The safety was evaluated by the Udvalg Kliniske Undersogelser side effect rating scale. RESULTS: There were no significant differences in mean DRS-K severity or K-MMSE scores among the four groups at baseline. In all groups, the DRS-K severity score decreased and the K-MMSE score increased significantly over the study period. However, there were no significant differences in the improvement of DRS-K or K-MMSE scores among the four groups. Similarly, cognitive and non-cognitive subscale DRS-K scores decreased regardless of the treatment group. The treatment response rate was lower in patients over 75 years old than in patients under 75 years old. Particularly, the response rate to olanzapine was poorer in the older age group. Fifteen subjects experienced a few adverse events, but there were no significant differences in adverse event profiles among the four groups. CONCLUSIONS: Haloperidol, risperidone, olanzapine, and quetiapine were equally efficacious and safe in the treatment of delirium. However, age is a factor that needs to be considered when making a choice of antipsychotic medication for the treatment of delirium.ope

    ๋„์‹œ์ง€์—ญ ์•ฝ์ˆ˜ ์‚ฌ์šฉ์˜ ์‚ฌํšŒ๋ฌธํ™”์  ์˜๋ฏธ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ธ๋ฅ˜ํ•™๊ณผ ์ธ๋ฅ˜ํ•™์ „๊ณต,2000.Maste

    The Space Design for the Support of Local Festivals in Rural Neighborhood Parks

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ƒํƒœ์กฐ๊ฒฝยท์ง€์—ญ์‹œ์Šคํ…œ๊ณตํ•™๋ถ€(์ƒํƒœ์กฐ๊ฒฝํ•™์ „๊ณต), 2013. 2. ์ •์šฑ์ฃผ.2011๋…„ ํ˜„์žฌ, ์„œ์šธ๊ณผ 6๊ฐœ ๊ด‘์—ญ์‹œ๋ฅผ ์ œ์™ธํ•œ ์ง€๋ฐฉ ์ค‘์†Œ๋„์‹œ์— ๊ณ„ํš๋œ ๊ทผ๋ฆฐ๊ณต์›์€ ์ด 3,432๊ฐœ์†Œ, 406,604,415ใŽก์˜ ๋ฉด์ ์„ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ๋Š”๋ฐ, ์ด๋Š” ๋Œ€๋„์‹œ(1์ธ๋‹น 8.44ใŽก)์— ๋น„ํ•ด ์ธ๊ตฌ๋Œ€๋น„ ํ›จ์”ฌ ๋งŽ์€ ๋ฉด์ (1์ธ๋‹น 14.78ใŽก)์„ ์ง€๋‹ˆ๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ง€๋ฐฉ์˜ ๋†์–ด์ดŒ๋„์‹œ์— ๋„์‹œ๊ณ„ํš์‹œ์„ค์˜ ์ผ๋ถ€์ธ ๊ณต์›์ด ์˜คํžˆ๋ ค ๋Œ€๋„์‹œ์ง€์—ญ๋ณด๋‹ค ๋งŽ๋‹ค๋Š” ๊ฒƒ์€ ์˜๋ฌธ์ด๋ฉฐ, ๊ณต์›์˜ ๊ธฐ๋Šฅ์„ ์ถฉ์‹คํžˆ ํ•˜์ง€ ๋ชปํ•˜๊ณ  ์žˆ๋‹ค๊ณ  ํŒ๋‹จ๋œ๋‹ค. ์ค‘์†Œ๋„์‹œ์˜ ๊ทผ๋ฆฐ๊ณต์›์€ ๋†์–ด์ดŒ๊ณต์›(country park)์œผ๋กœ ๋ถˆ๋ฆฌ๋ฉฐ ๋Œ€๋„์‹œ์˜ ๊ณต์›๊ณผ๋Š” ์ „ํ˜€ ๋‹ค๋ฅธ ํŠน์ง•์„ ์ง€๋‹Œ๋‹ค. ์ฒซ์งธ, ์ง€๋ฆฌ์ƒ์œผ๋กœ ๊ณต์›์ด ์ œ๊ณตํ•˜๋Š” ์ž์—ฐ๋…น์ง€์˜ ๊ธฐ๋Šฅ์ด ํ•„์š”ํ•˜์ง€ ์•Š๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ๋‘˜์งธ, ์ด์šฉ์ž์  ์ธก๋ฉด์—์„œ ์ง€๋ฐฉ์— ํฐ ๊ทœ๋ชจ์˜ ๊ทผ๋ฆฐ๊ณต์›์€ ์ ํ•ฉํ•˜์ง€ ์•Š๋‹ค. ์…‹์งธ, ์‹ค์งˆ์ ์ธ ๊ณต์› ์ด์šฉ์ž๊ฐ€ ์ ๊ธฐ ๋•Œ๋ฌธ์— ๊ณต์›์กฐ์„ฑ๊ณผ ๊ด€๋ฆฌ์— ๊ด€์‹ฌ์ด ์ ๊ณ  ์˜ˆ์‚ฐ์ฑ…์ •์— ์žˆ์–ด ๋‚ญ๋น„๊ฐ€ ๋  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋ฏธ ์ง€๋ฐฉ์— ๋งŽ์€ ๊ณต์›์ด ์กฐ์„ฑ๋œ ์ƒํƒœ์ด๋ฉฐ ๋งค๋…„ ๋งŽ์€ ์œ ์ง€๋น„์šฉ์„ ๋“ค์—ฌ ๊ด€๋ฆฌํ•˜๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ณต์›์˜ ์ˆ˜๋ฅผ ์ค„์ด๋Š” ๊ฒƒ๋ณด๋‹ค, ๋Œ€๋„์‹œ์™€ ๋‹ค๋ฅธ ๊ธฐ๋Šฅ์„ ์ง€๋‹Œ ์ง€๋ฐฉ ๋†์–ด์ดŒ๊ณต์›์˜ ์—ญํ• ์„ ์ œ์‹œํ•  ํ•„์š”์„ฑ์ด ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ด€์ ์—์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์ง€๋ฐฉ ๊ทผ๋ฆฐ๊ณต์›์˜ ์ด์Šˆ์™€ ์ง€์ž์ฒด์˜ ๊ณต๊ฐ„ ํ™œ์šฉ ์ด์Šˆ๋ฅผ ์‚ดํŽด๋ณด์•˜์œผ๋ฉฐ, ๊ทผ๋ฆฐ๊ณต์›์˜ ํ™œ์šฉ ๋ฐฉ์•ˆ ์ค‘ ํ•˜๋‚˜๋กœ ์ง€์—ญ์ถ•์ œ์™€์˜ ์—ฐ๊ณ„๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค. ํ•œ๊ตญ๋ฌธํ™”๊ด€๊ด‘์ •์ฑ…์—ฐ๊ตฌ์›์—์„œ ์ˆ˜ํ–‰ํ•œ ใ€Žํ•œ๊ตญ ์ง€์—ญ์ถ•์ œ ์กฐ์‚ฌํ‰๊ฐ€ ๋ฐ ๊ฐœ์„ ๋ฐฉ์•ˆ ์—ฐ๊ตฌใ€์— ์˜ํ•˜๋ฉด 2006๋…„ ํ˜„์žฌ ํ•œ๊ตญ ์ง€์—ญ์ถ•์ œ์˜ ์ˆ˜๋Š” 1,176๊ฐœ, ๊ตญํ† ํ•ด์–‘๋ถ€์˜ ๋„์‹œ๊ณ„ํš์‹œ์„ค ์ž๋ฃŒ์— ๋“œ๋Ÿฌ๋‚œ ๊ตญ๋‚ด ๊ทผ๋ฆฐ๊ณต์›์˜ ์ˆ˜๋Š” ์•ฝ 4700์—ฌ๊ฐœ์ด๋‹ค. ๊ทผ๋ฆฐ๊ณต์›์€ ์ˆ˜์ ์œผ๋กœ, ๊ณต๊ฐ„์ ์œผ๋กœ ์ถ•์ œ๋ฅผ ์ˆ˜์šฉํ•˜๊ธฐ์— ์šฉ์ดํ•˜๋ฉฐ, ๋•Œ๋ฌธ์— ์ด๋Š” ํŒฝ์ฐฝํ•˜๊ณ  ์žˆ๋Š” ์ถ•์ œ๋“ค์„ ๊ณต์› ๊ณต๊ฐ„์—์„œ ์ˆ˜์šฉ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ์—ฌ๊ธฐ์„œ ์ถ•์ œ๋Š” ๊ณต์›์˜ ์ž˜ ๊ตฌ์ถ•๋œ ํ•˜๋“œ์›จ์–ด ์ธํ”„๋ผ ์†์—์„œ ์ €๋น„์šฉ์œผ๋กœ ๊ฐœ์ตœ ๊ฐ€๋Šฅํ•˜๋ฉฐ, ์ผ์‹œ์  ๊ณต๊ฐ„ํ”„๋กœ๊ทธ๋žจ์œผ๋กœ์„œ ์ˆ˜์šฉ๋˜์–ด ๊ณต๋™์ฒด์  ์ปค๋ฎค๋‹ˆํ‹ฐ๋ฅผ ์ œ๊ณตํ•˜๊ณ  ๊ณต์›์˜ ์ด์šฉ๋ฅ ์„ ๋†’์ด๋Š” ์—ญํ• ์„ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‹ค์‹œ ๋งํ•ด, ๊ทผ๋ฆฐ๊ณต์›๊ณผ ์ถ•์ œ์˜ ์—ฐ๊ฒฐ์„ ํ†ตํ•ด ์–‘์ž๊ฐ„์— ๊ณต๊ฐ„์ , ๋น„์šฉ์  ํšจ์œจ์„ฑ์„ ๊ทน๋Œ€ํ™” ์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋งฅ๋ฝ์—์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์šฐ์„  ๊ทผ๋ฆฐ๊ณต์›๊ณผ ์ถ•์ œ์žฅ์˜ ๊ณต๊ฐ„์†์„ฑ์„ ๋ถ„์„ํ•˜๊ณ  ์ด๋ฅผ ๋น„๊ตํ•˜์—ฌ ์–‘์ž ๊ฐ„์— ๊ด€๊ณ„์„ฑ์„ ๋„์ถœํ•˜์˜€์œผ๋ฉฐ ๋ฌธํ™”๊ด€๊ด‘์ถ•์ œ์ข…ํ•ฉํ‰๊ฐ€ ๋ณด๊ณ ์„œ์— ๊ธฐ์žฌ๋œ ์ถ•์ œ์ „๋ฌธ๊ฐ€๋“ค์˜ ํ‰๊ฐ€๋‚ด์šฉ์„ ์ข…ํ•ฉโ€ค๋ถ„๋ฅ˜ํ•˜์—ฌ ์ถ•์ œ๊ณต๊ฐ„ ์กฐ์„ฑ ์‹œ ๊ณ ๋ คํ•ด์•ผ ํ•  ์‚ฌํ•ญ๋“ค์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ด์ฒœ์˜ ์„ค๋ด‰๊ณต์›์„ ๋Œ€์ƒ์ง€๋กœ ํ•˜์—ฌ ์‹ค์ œ ์ถ•์ œ์ง€์›๊ณต๊ฐ„ ์„ค๊ณ„๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ด ๊ณผ์ •์—์„œ ๋“œ๋Ÿฌ๋‚œ ์—ฐ๊ตฌ์˜ ์˜์˜๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ง€๋ฐฉ ๊ทผ๋ฆฐ๊ณต์›์˜ ์ •์ฒด์„ฑ์„ ํƒ๊ตฌํ•ด ๋ณด๊ณ  ๋Œ€์•ˆ(์ถ•์ œ๊ณต์›)์„ ์ œ์‹œํ•˜์˜€์œผ๋ฉฐ, ๋‘˜์งธ, ์ง€์—ญ์ถ•์ œ์˜ ์ด์Šˆ๋ฅผ ๊ณต๊ฐ„์  ์ธก๋ฉด์—์„œ ๋ฐ”๋ผ๋ณด๊ณ  ์กฐ๊ฒฝ์„ค๊ณ„์  ๊ด€์ ์—์„œ ํ•ด๊ฒฐ๋ฐฉ์•ˆ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์…‹์งธ, ํ˜„์žฌ ์ง€๋ฐฉ์˜ ๊ณต๊ฐ„์ด์Šˆ์™€ ๋ฐ€์ ‘ํ•˜๊ฒŒ ๊ด€๋ จํ•˜์—ฌ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ, ๋„ท์งธ, ์‹ค์งˆ์ ์œผ๋กœ ๋ฐ˜์˜๋  ์ˆ˜ ์žˆ๋Š” ์„ค๊ณ„์•ˆ์„ ์ˆ˜๋ฆฝํ•˜์˜€๋‹ค๋Š” ๊ฒƒ์— ๋ณธ ๋…ผ๋ฌธ์˜ ์˜์˜๊ฐ€ ์žˆ๋‹ค.โš ๋ชฉ ์ฐจ โš ๊ตญ๋ฌธ์ดˆ๋ก I โš ๋ชฉ์ฐจ โ…ฃ โš ํ‘œ ๋ชฉ์ฐจ โ…จ โš ๊ทธ๋ฆผ ๋ชฉ์ฐจ XI โš ๋„๋ฉด ๋ชฉ์ฐจ XIV ์ œ 1์žฅ. ์„œ๋ก  1 1์ ˆ. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 2. ์—ฐ๊ตฌ์˜ ๋ชฉ์  6 2์ ˆ. ์„ ํ–‰์—ฐ๊ตฌ ๊ณ ์ฐฐ 7 1. ์ถ•์ œ ์ „๋ฐ˜์˜ ์—ฐ๊ตฌ๋™ํ–ฅ 7 2. ์ถ•์ œ๊ณต๊ฐ„์„ ๋‹ค๋ฃฌ ์—ฐ๊ตฌ 7 3. ๋ณธ ์—ฐ๊ตฌ์˜ ์ฐจ๋ณ„์„ฑ 8 3์ ˆ. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• ๋ฐ ๋ฒ”์œ„ 9 1. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 9 2. ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ 10 ์ œ 2์žฅ. ์ด๋ก ์  ๊ณ ์ฐฐ 11 1์ ˆ. ์ถ•์ œ์˜ ์ผ๋ฐ˜์  ํŠน์ง• 11 1. ์ถ•์ œ์˜ ๊ธฐ์›๊ณผ ์–ด์›์  ์˜๋ฏธ 11 2. ์ถ•์ œ์˜ ๊ฐœ๋… 12 3. ํ˜„๋Œ€ ์ง€์—ญ์ถ•์ œ์˜ ๋ณธ์งˆ 14 2์ ˆ. ์ถ•์ œ์˜ ๊ณต๊ฐ„์  ํŠน์ง• 19 1. ์ถ•์ œ์žฅ์˜ ๊ธฐ๋Šฅ๊ณผ ์—ญํ•  19 2. ์ถ•์ œ์žฅ์˜ ๊ณต๊ฐ„์†์„ฑ 21 3. ์ถ•์ œ์žฅ์˜ ๊ณต๊ฐ„์œ ํ˜• 24 3์ ˆ. ๊ทผ๋ฆฐ๊ณต์›์˜ ๊ณต๊ฐ„์  ํŠน์ง• 27 1. ๊ทผ๋ฆฐ๊ณต์›์˜ ๊ฐœ๋…๊ณผ ๋ถ„๋ฅ˜ 27 2. ๊ทผ๋ฆฐ๊ณต์›์˜ ๊ณต๊ฐ„์†์„ฑ 28 4์ ˆ. ์†Œ๊ฒฐ 30 ์ œ 3์žฅ. ์—ฐ๊ตฌ ์ด์Šˆ ๊ณ ์ฐฐ 31 1์ ˆ. ์ง€๋ฐฉ ๊ทผ๋ฆฐ๊ณต์›์˜ ์ถ•์ œ ์ˆ˜์šฉ ๊ฐ€๋Šฅ์„ฑ ํƒ๊ตฌ 31 1. ์ง€๋ฐฉ ๊ทผ๋ฆฐ๊ณต์›์˜ ๊ธฐ๋Šฅ๊ณผ ์—ญํ•  ๋ณ€ํ™” 31 2. ๊ทผ๋ฆฐ๊ณต์›ํ˜• ์ถ•์ œ์žฅ์˜ ํ˜„ํ™ฉ 33 3. ์ถ•์ œ์žฅ๊ณผ ๊ทผ๋ฆฐ๊ณต์›์˜ ๊ณต๊ฐ„์†์„ฑ ๋น„๊ต 36 4. ์†Œ๊ฒฐ 39 2์ ˆ. ์ถ•์ œ์žฅ์˜ ์‹ค์งˆ์  ๊ณต๊ฐ„์ด์Šˆ ๊ณ ์ฐฐ 41 1. ๊ณ ์ฐฐ๋ฐฉ๋ฒ• 41 2. ๊ณ ์ฐฐ๊ฒฐ๊ณผ 42 2.1. ์ถ•์ œ์žฅ ๊ณต๊ฐ„์ด์Šˆ์˜ ์œ ํ˜•๋ถ„๋ฅ˜ 42 2.2. ์ถ•์ œ์žฅ ๊ณต๊ฐ„์ด์Šˆ์˜ ์œ ํ˜•๋ณ„ ๋นˆ๋„ ๋ถ„์„ 44 2.3. ์ถ•์ œ์žฅ์˜ ๋ฌธ์ œ์  ๋„์ถœ 46 2.4. ๊ณต๊ฐ„์ด์Šˆ๋ณ„ ๊ณ„ํš๋ฐฉํ–ฅ ๊ณ ์ฐฐ 48 3์ ˆ. ์†Œ๊ฒฐ 55 ์ œ 4์žฅ. ๋Œ€์ƒ์ง€ ๋ถ„์„ 56 1์ ˆ. ์„ค๋ด‰๊ณต์› ๋ถ„์„ 56 1. ๋Œ€์ƒ์ง€ ์œ„์น˜์™€ ์ ‘๊ทผ์„ฑ 56 2. ๋Œ€์ƒ์ง€ ์ฃผ๋ณ€ ์ปจํ…์ŠคํŠธ์™€ ์„ค๊ณ„๋ฒ”์œ„ 56 3. ์ง€ํ˜•๋ถ„์„ 58 4. ๊ณต๊ฐ„๊ตฌ์„ฑ ๋ถ„์„ 59 5. ์„ธ๋ถ€๊ณต๊ฐ„ ๋ถ„์„ 62 2์ ˆ. ๊ฐœ์ตœ ์ถ•์ œ ๋ถ„์„ 67 1. ์ด์ฒœ๋„์ž๊ธฐ์ถ•์ œ 67 2. ์ด์ฒœ๊ตญ์ œ์กฐ๊ฐ์‹ฌํฌ์ง€์—„ 69 3. ์„ค๋ด‰์‚ฐ ๋ณ„๋น›์ถ•์ œ 71 4. ์ด์ฒœ์Œ€๋ฌธํ™”์ถ•์ œ 71 5. ์„ค๋ด‰๋ฌธํ™”์ œ 76 ์ œ 5์žฅ. ๋Œ€์ƒ์ง€ ์„ค๊ณ„ 78 1์ ˆ. ๊ธฐ๋ณธ๋ฐฉํ–ฅ ์„ค์ • 78 1. ์ง€์—ญ์ฃผ๋ฏผ์˜ ์ผ์ƒ์  ์ด์šฉ ๊ณต๊ฐ„ ์กฐ์„ฑ 78 2. ๊ทผ๋ฆฐ๊ณต์›์˜ ์ถ•์ œ์ธํ”„๋ผ ์กฐ์„ฑ 80 3. ์ง€๋ฐฉ๋„์‹œ์˜ ๋ฌธํ™”์  ๊ตฌ์‹ฌ์  ํ˜•์„ฑ 81 2์ ˆ. ๊ณต๊ฐ„์ด์Šˆ ๋„์ถœ 82 3์ ˆ. ์„ค๊ณ„ ํ”„๋กœ์„ธ์Šค ๋ฐ ์ „๋žต 84 1. ์žฅ์†Œ์ „๋žต 84 2. ๊ณต๊ฐ„์กฐ์„ฑ์ „๋žต 88 3. ์‹์žฌ์ „๋žต 93 4์ ˆ. ์ข…ํ•ฉ๊ณ„ํš๋„ 97 5์ ˆ. ์ƒ์„ธ๊ณ„ํš 99 1. ๊ณต๊ฐ„ โ€ค ํ”„๋กœ๊ทธ๋žจ ๊ณ„ํš 99 2. ๋™์„ ๊ณ„ํš 102 3. ์‹์žฌ๊ณ„ํš 104 4. ํฌ์žฅ๊ณ„ํš 107 5. ์‹œ์„ค๋ฌผ๊ณ„ํš 108 6. ์ฃผ์ฐจ๊ณต๊ฐ„๊ณ„ํš 110 6์ ˆ. ๊ณต๊ฐ„๋ณ„ ์ƒ์„ธ๊ณ„ํš 112 1. ์ง„์ž…๊ณต๊ฐ„ 112 2. ์•ผ์™ธ์นดํŽ˜&์ „์‹œ๊ณต๊ฐ„ 114 3. ๊ด‘์žฅ(์นดํŽ˜)&์ถ•์ œ๋งˆ๋‹น๊ณต๊ฐ„ 115 4. ํ•„๋“œ 117 5. ์กฐ๊ฐ๊ณต์› 120 6. ๋ถ„์ˆ˜์‰ผํ„ฐ 121 7. ์ˆ˜๋ชฉ๋ณด์Šคํฌ์‰ผํ„ฐ 122 7์ ˆ. ๋„๋ฉด์ง‘ 124 ์ œ 6์žฅ. ๊ฒฐ๋ก  129 โ–ฎ์ฐธ๊ณ ๋ฌธํ—Œ 131 โ–ฎ๋ถ€ ๋ก 134 โ–ฎAbstract 156Maste

    Beam Pattern Synthesis based on Shape Model of Turning Towed Line Array SONAR

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2012. 2. ์„ฑ๊ต‰๋ชจ.ํ•ด์–‘์—์„œ ํ‘œ์ ์„ ํƒ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜๋Š” ์ˆ˜์ค‘ ์Œํ–ฅ ํƒ์ง€ ์‹œ์Šคํ…œ์€ ๋ชฉํ‘œ๋ฌผ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ŒํŒŒ๋ฅผ ์ˆ˜์‹ ํ•˜๊ณ , ๋ถ„์„ํ•˜๋Š” ๊ณผ์ •์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ˆ˜์ค‘ ์Œํ–ฅ ํƒ์ง€ ์‹œ์Šคํ…œ์„ ์†Œ๋‚˜(SONAR, Sound Navigation And Ranging)๋ผ๊ณ  ํ•˜๋Š”๋ฐ, ๊ทธ ์ค‘ ํ‘œ์ ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์†Œ์Œ์„ ํƒ์ง€ํ•˜๋Š” ์ˆ˜๋™ ์†Œ๋‚˜๋Š” ์ž ์ˆ˜ํ•จ์˜ ์ €์ฃผํŒŒ๋ฅผ ํƒ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ๋งค์šฐ ๊ธด ์„ ๋ฐฐ์—ด์„ ํ•จ์ •์— ๊ฒฌ์ธํ•˜๋Š” ํ˜•ํƒœ๋กœ ์šด์šฉ๋œ๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ๊ฒฌ์ธ ์„ ๋ฐฐ์—ด ์†Œ๋‚˜๋Š” ์œ ์ฒด์˜ ํ๋ฆ„์— ๋”ฐ๋ผ ํ˜•์ƒ์ด ์™œ๊ณก๋˜๊ณ , ์ด์— ๋”ฐ๋ผ ํƒ์ง€ ์„ฑ๋Šฅ์ด ์ €ํ•˜๋œ๋‹ค. ํŠนํžˆ ์„ ๋ฐฐ์—ด์„ ์šด์šฉํ•˜๋Š” ๊ฒฌ์ธํ•จ์ด ํšŒ์ „ํ•˜๋ฉด์„œ ํ˜•์ƒ์ด ์‹ฌํ•˜๊ฒŒ ์™œ๊ณก๋œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฒฌ์ธํ•จ์˜ ํšŒ์ „๊ฐ๋„๋ฅผ ์ด์šฉํ•ด์„œ ์„ ๋ฐฐ์—ด์˜ ํ˜•์ƒ์„ ์ˆ˜ํ•™์ ์œผ๋กœ ๋ชจ๋ธ๋งํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์˜€๊ณ , ๋ชจ๋ธ๋งํ•œ ๊ฒฐ๊ณผ๋ฅผ ์ด์šฉํ•˜์—ฌ ์„ผ์„œ๊ฐ€ ๋‹จ์œ„ ์‘๋‹ต์„ ๊ฐ–๋Š” ๊ฒฝ์šฐ์˜ ๋น” ํŒจํ„ด์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ๋น” ํŒจํ„ด์€ ์„ ๋ฐฐ์—ด์— ๋ฐฐ์น˜๋œ ์„ผ์„œ์˜ ์ˆ˜์™€ ์„ผ์„œ๊ฐ„ ๊ฐ„๊ฒฉ ๋“ฑ ๋ฌผ๋ฆฌ์ ์ธ ์š”์†Œ์— ์˜ํ•ด ๊ฒฐ์ •๋˜๊ธฐ ๋•Œ๋ฌธ์—, ์šด์šฉ์ž๊ฐ€ ์›ํ•˜๋Š” ๋ชฉ์  ๋น” ํŒจํ„ด์„ ์„ค๊ณ„ํ•˜๊ธฐ ์œ„ํ•ด์„œ ๊ฐ ์„ผ์„œ์˜ ์‘๋‹ต์„ ์กฐ์ ˆํ•œ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฒฌ์ธํ•จ์ด ํšŒ์ „ํ•˜๋ฉด์„œ ํ˜•์ƒ์ด ์™œ๊ณก๋œ ์ƒํƒœ์—์„œ, ์šด์šฉ์ž๊ฐ€ ์›ํ•˜๋Š” ๋ชฉ์  ๋น” ํŒจํ„ด์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด ์„ ํ˜•์ œ์•ฝ์ตœ์†Œ๋ถ„์‚ฐ์— ๊ธฐ๋ฐ˜ํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ์ตœ์†Œ ์ž์Šน๋ฒ•์— ๊ธฐ๋ฐ˜ํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ ์šฉํ•˜์˜€์œผ๋ฉฐ, ๋ชจ๋ธ๋งํ•œ ์„ ๋ฐฐ์—ด์˜ ํ˜•์ƒ์— ์ ์šฉํ•˜์—ฌ ์„ฑ๋Šฅ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ ํ˜•์ƒ์ด ์™œ๊ณก๋œ ์ƒํƒœ์—์„œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋ฌธ์ œ์ ์„ ๊ฐœ์„ ํ•˜๊ธฐ ์œ„ํ•ด, ๋ถ€์—ฝ์˜ ํฌ๊ธฐ๋ฅผ ์ œ์•ฝํ•  ์ˆ˜ ์žˆ๋Š” ํ•ญ์„ ์ถ”๊ฐ€ํ•˜์—ฌ ์ƒˆ๋กœ์šด ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•˜์˜€๊ณ , ์•ž์˜ ๋‘ ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค.Maste

    ๋ฐœ์ „๊ธฐ ๊ณ ์ •์ž ๊ถŒ์„  ํก์Šต์— ๋Œ€ํ•œ ํ†ต๊ณ„์  ์ถ”๋ก  ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2014. 2. ์œค๋ณ‘๋™.The power generator, as one of the most critical components in a power plant, is typically maintained through use of a time- or usage-based strategy. Either strategy could result in a substantial waste of remaining useful life (RUL), high maintenance costs, and/or low plant availability due to excess, untimely, or missed maintenance. Recently, the field of prognostics and health management has offered new general diagnostic and prognostic techniques to precisely assess health conditions and robustly predict the RUL of engineered systems, with the aim of addressing the aforementioned deficiencies. This paper explores a smart health reasoning system that can be used to assess the health condition of power generator stator windings and their levels of water absorption. The system monitors health based on capacitance measurements of the winding insulations. In particular, a new relative health measure, namely the Directional Mahalanobis Distance (DMD), is proposed to quantify the health condition of stator windings. This paper also proposes an empirical health classification rule, based upon the DMD, which factors in maintenance history. The proposed smart health reasoning system is validated using eight years field data from eight generators, each of which contains forty-two windings.Abstract i List of Figures vii Nomenclature ix Abbreviations xi Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Overview 2 1.3 Thesis Layout 3 Chapter 2. Literature Review 4 2.1 Prognostics and Health Management Techniques used to Support the Health Reasoning Function 4 2.2 Existing Tests to Detect Leaks or Water Absorption 5 2.3 Summary and Discussion 8 Chapter 3. Description of the Sensing Function and Data Analysis 9 3.1 Fundamentals of Capacitance Measurements 9 3.2 Capacitance Data Acquisition 12 3.3 Statistical Characterization of the Capacitance Data 15 3.4 Data Grouping 19 Chapter 4. Statistical Health Reasoning System 21 4.1 Review of Mahalanobis Distance 21 4.2 A New Concept of Statistical Distance: Directional Mahalanobis Distance 23 4.2.1 Data Projection 23 4.2.2 Transformation 26 4.3 Comparison of Performance of Mahalanobis Distance (MD) and Directional Mahalanobis Distance (DMD) 26 Chapter 5. Health Classification 31 5.1 Maintenance History Related to Water Absorption 31 5.2 Review of Scaled Mahalanobis Distance 32 5.3 Health Grade System 34 5.4 Validation Study 36 Chapter 6. Conclusion 39 Bibliography 40 APPENDIX A. 46 ๊ตญ๋ฌธ ์ดˆ๋ก 47 ๊ฐ์‚ฌ์˜ ๊ธ€ 49Maste

    Neural basis of attributional style in schizophrenia.

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    Attributional style means how people typically infer the causes of emotional behaviors. No study has shown neural basis of attributional style in schizophrenia, although it was suggested as a major area of social cognition research of schizophrenia. Fifteen patients with schizophrenia and 16 healthy controls underwent functional magnetic resonance imaging while performing three (happy, angry, and neutral) conditions of attribution task. Each condition included inferring situational causes of an avatar' (virtual character) emotional or neutral behavior. In the between-groups contrast maps of the happy conditions, the patient group compared to the control group showed decreased activations in the inferior frontal (BA 44) and the ventral premotor cortex (BA 6), in which the % signal changes were associated with negative symptoms. In the angry conditions, the patient group compared to the control group exhibited increased activations in the precuneus/posterior cingulate cortex (Pcu/PCC) (BA 7/31), in which the % signal changes were related to positive symptoms. In conclusion, patients with schizophrenia may have functional deficits in mirror neuron system when attributing positive behaviors, which may be related to a lack of inner simulation and empathy and negative symptoms. In contrast, patients may have increased activation in the Pcu/PCC related to self-representations while attributing negative behaviors, which may be related to failures in self- and source-monitoring and positive symptoms.ope

    ๊ต์ˆ˜์  ๋‚ด์šฉ ์ง€์‹์— ๋Œ€ํ•œ ์ค‘๋“ฑ ๊ณผํ•™ ์˜ˆ๋น„๊ต์‚ฌ์˜ ์ธ์‹ ์กฐ์‚ฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณผํ•™๊ต์œก๊ณผ ์ง€๊ตฌ๊ณผํ•™์ „๊ณต,2001.Maste

    Study on Preprocessing Performance of Convolutional Neural Networks for Vessel Classification

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    The problem of vessel classification has been actively studied for vessel design, underwater target detection and military use. Various acoustic signal processing methods have been presented for the target detection and classification, to overcome strong ambient noise, transmission path complexity and transmission loss of underwater acoustic. Meanwhile, with the rapid development of artificial intelligence algorithms, problems in various fields are being solved by utilizing them in various fields, and among them, convolutional neural network algorithms show excellent performance in the fields of image classification. Recently, studies on the application of various artificial intelligence algorithms for vessel classification have also been actively conducted, and many studies on the application of convolutional neural networks have also been suggested for this field. However, studies on the performance of the convolutional neural network according to preprocessing in the vessel classification problem have not been confirmed. In this dissertation, it presents the performance by preprocessing in a convolutional neural network based classifier. First, it considered the vessel acoustic datasets and mentioned the preprocessing in terms of data mining and convolutional neural network that is currently widely used. Experiments began with the classification, transformation, and preprocessing of vessel acoustic data that was actually collected and released. After that, the raw data were preprocessed with feature extraction and scaling precess. In the detailed preprocessing techniques, feature extractions were applied with Mel Spectrogram and Log Mel Spectrogram techniques, and the scalers was applied with standard, min-max, max-abs, and robust techniques, respectively. In addition, exploratory data analysis was performed on the feature extraction result to understand the characteristics of the vessel acoustic data. For the experiment, a simple convolutional neural network was designed, and the classification performance of convolutional neural networks was tested for each preprocessing case by combining feature extraction and scaler techniques. For each preprocessing combination, the performance of the convolutional neural network was shown with learning curve and evaluation measures. As a result of this experiment, the performance of all scalers was improved when features were extracted by the log mel spectrogram technique. In particular, the performance of standard scaler was rapidly improved compared to when features were extracted with mel spectrogram. The combination of log mel spectrogram and robust scaler preprocessing methods showed the best classification performance. Experiment shows that preprocessing techniques affect the classification performance of convolutional neural networks, such as properly adjusting the distribution of data values while suppressing the low frequency components that act as outliers in vessel acoustic data.์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1 1.2 ์—ฐ๊ตฌ ๋ชฉ์  2 1.3 ๋…ผ๋ฌธ์˜ ๊ตฌ์„ฑ 3 ์ œ 2 ์žฅ ์„ ๋ฐ• ์Œํ–ฅ ๋ฐ์ดํ„ฐ์…‹ 6 2.1 ์„ ๋ฐ• ์Œํ–ฅ ๋ฐ์ดํ„ฐ์…‹ ์‚ฌ๋ก€ 6 2.2 ๊ณต๊ฐœ๋œ ์„ ๋ฐ• ์Œํ–ฅ ๋ฐ์ดํ„ฐ์…‹(โ€œShipsEarโ€, โ€œDeepShipโ€) 7 ์ œ 3 ์žฅ ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ ๋ฐ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง 12 3.1 ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ 12 3.2 ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง 13 ์ œ 4 ์žฅ ์„ ๋ฐ• ๋ถ„๋ฅ˜๋ฅผ ์œ„ํ•œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๋ฐ ์ „์ฒ˜๋ฆฌ 17 4.1 ์‹คํ—˜ ๊ณผ์ • 17 4.2 ๋ฐ์ดํ„ฐ์…‹ ๋ฐ ๋ฐ์ดํ„ฐ ๋ถ„ํ•  18 4.3 ๋ฐ์ดํ„ฐ ๋ณ€ํ™˜ 19 4.4 ํŠน์ง• ์ถ”์ถœ 20 4.4.1 ๋ฉœ ์ŠคํŽ™ํŠธ๋กœ๊ทธ๋žจ ๋ณ€ํ™˜ 20 4.4.2 ๋กœ๊ทธ ๋ฉœ ์ŠคํŽ™ํŠธ๋กœ๊ทธ๋žจ ๋ณ€ํ™˜ 23 4.5 ํƒ์ƒ‰์  ๋ฐ์ดํ„ฐ ๋ถ„์„๊ณผ ์„ ๋ฐ• ์Œํ–ฅ ์‹ ํ˜ธ์˜ ํŠน์„ฑ 24 4.5.1 ํƒ์ƒ‰์  ๋ฐ์ดํ„ฐ ๋ถ„์„(EDA) 24 4.5.2 ์„ ๋ฐ• ์Œํ–ฅ ์‹ ํ˜ธ์˜ ํŠน์„ฑ 27 4.6 ์Šค์ผ€์ผ๋ง 30 4.6.1 ์Šค์ผ€์ผ๋ง ๊ณผ์ • 31 4.6.2 Standard scaler 32 4.6.3 Min-Max scaler 34 4.6.4 Max-abs scaler 34 4.6.5 Robust Scaler 37 4.7 ์„ ๋ฐ• ๋ถ„๋ฅ˜๋ฅผ ์œ„ํ•œ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ ์„ค๊ณ„ 39 ์ œ 5 ์žฅ ์ „์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•๋ณ„ ํ•ฉ์„ฑ๊ณฑ ์‹ ๊ฒฝ๋ง์˜ ์„ ๋ฐ• ๋ถ„๋ฅ˜ ์„ฑ๋Šฅ 43 5.1 ํ•™์Šต ๊ณก์„ (Learning Curve) 43 5.2 ๋ถ„๋ฅ˜ ์„ฑ๋Šฅ 45 5.2.1 ๋ถ„๋ฅ˜ ์„ฑ๋Šฅ ํ‰๊ฐ€์ง€ํ‘œ 45 5.2.2 ํ‰๊ฐ€ ๊ฒฐ๊ณผ 48 ์ œ 6 ์žฅ ๊ฒฐ ๋ก  51 ์ฐธ๊ณ ๋ฌธํ—Œ 53Docto

    ์‹ค๋‚ด ์ „ํŒŒ ํ™˜๊ฒฝ์„ ๊ณ ๋ คํ•œ ์œ„์น˜ ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค๋ฅผ ์œ„ํ•œ ์„ ํ–‰ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2022. 8. ๊น€์„ฑ์ฒ .Because of intelligence of mobile devices which are serviced by wireless communications systems, core technologies for personalized services are into the limelight. Especially, location-based services which utilizes user's geographic data are widely discussed and offered to users in various form. The global positioning system (GPS) that is generally exploited for localization has not supported indoor users due to the signal distortion. Even though WiFi has been preferred a lot for indoor users, two issues have to be addressed exist. The first one is that too much time is required for a prior site survey. The other one is a performance degradation due to a complexity of an indoor propagation channel. In this dissertation, I propose technologies which relieve above issues. In this dissertation, I propose a fully-automated site survey technique. Any WiFi-based localization technique requires a site survey process which absorbs too much labor. Aided by the GPS, pedestrian dead reckoning (PDR), fine timing measurement (FTM), which are state-of-the-art technologies for a high accurate localization, make the proposed technique have a fully-automatic manner. Two trajectories respectively recorded the GPS and the PDR are integrated by a cost function designed for measuring similarity between two trajectories. The trajectory fusion of two systems makes a manual calibration of coordinates be not required. The positions of the WiFi access point (AP) deployed in an indoor are estimated based on highly accurate ranging results given by the FTM protocol. Next, followed by the above research, I propose a fingerprints database construction technique which has a automatic manner. Parallel with the AP positioning, ranging calibration parameters and path loss parameters are estimated based on the trajectory fusion. The received signal strength (RSS) values of unknown domain of an indoor are estimated based on the Gaussian process regression. From RSS values of known domain, path loss parameters and uncertainties are estimated. Additionally, I proposes an improved ranging scheme by analyzing a complex indoor propagation channel. In this dissertation, a propagation model considers an indoor hallway as a slab waveguide is established. Waveguide structures has two main energy loss mechanisms : penetration loss and boundary scattering. Multi-mode waveguide theory and surface scattering theory respectively quantify them. Proposed model based on two theories is validated with channel sounding measurements in real sites. Finally, I proposes an improved ray-tracing simulator based on the surface scattering theory. Indoor ray-tracing simulators hugely relieve a labor which is required for a channel sounding. In addition to, it has often been exploited for fingerprints database constructions. In this dissertation, the permittivity for the ray-tracing simulator is improved to be roughness-dependent. The simulator is validated with real site measurements.๋ฌด์„  ํ†ต์‹  ์„œ๋น„์Šค๋ฅผ ์ œ๊ณต ๋ฐ›๋Š” ๋‹จ๋ง๋“ค์˜ ์ ์ง„์ ์ธ ์ง€๋Šฅํ™”๋กœ ์ธํ•˜์—ฌ ๊ฐœ์ธ ๋งž์ถค ์„œ๋น„์Šค ์ œ๊ณต์„ ์œ„ํ•œ ๊ธฐ๋ฐ˜ ๊ธฐ์ˆ ๋“ค์ด ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ๋‹จ๋ง๋“ค์˜ ์ง€๋ฆฌ์  ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋งž์ถค ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๋Š” ์œ„์น˜๊ธฐ๋ฐ˜ ์„œ๋น„์Šค๋Š” ๋‹ค์–‘ํ•œ ํ˜•ํƒœ๋กœ์จ ์—ฐ๊ตฌ๋˜์–ด ์‚ฌ์šฉ์ž๋“ค์—๊ฒŒ ์ œ๊ณต๋˜๊ณ  ์žˆ๋‹ค. ์‹ค์™ธ์—์„œ ์ผ๋ฐ˜์ ์œผ๋กœ ์ด์šฉ๋˜๋Š” GPS๋Š” ์‹ค๋‚ด ํ™˜๊ฒฝ์—์„œ ์‹ ํ˜ธ๊ฐ€ ์™œ๊ณก๋˜์–ด ์‚ฌ์šฉ์ž์—๊ฒŒ ์ •ํ™•ํ•œ ์œ„์น˜ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์ง€ ๋ชป ํ•œ๋‹ค. ์‹ค๋‚ด ํ™˜๊ฒฝ์—์„œ๋Š” ๋ฒ”์šฉ์„ฑ๊ณผ ์ ‘๊ทผ์„ฑ ๊ด€์ ์—์„œ ์žฅ์ ์„ ๊ฐ€์ง€๋Š” WiFi ์‹œ์Šคํ…œ์ด ์ž์ฃผ ํ™œ์šฉ๋˜์ง€๋งŒ, ์ •ํ™•ํ•œ ์œ„์น˜ ์ •๋ณด์˜ ์ œ๊ณต์„ ์œ„ํ•ด์„œ ๋‹ค๋ฃจ์–ด์•ผ ํ•  ๋‘ ๊ฐ€์ง€ ๋ฌธ์ œ์ ์ด ์กด์žฌํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š” ์‚ฌ์ „ ํ˜„์žฅ ์กฐ์‚ฌ์— ์ˆ˜๋ฐ˜๋˜๋Š” ์‹œ๊ฐ„ ๋ฐ ๋…ธ๋™๋ ฅ์ด๋ฉฐ, ๋‘ ๋ฒˆ์งธ๋Š” ์‹ค๋‚ด ์ „ํŒŒ ์ฑ„๋„์˜ ๋ณต์žก์„ฑ์œผ๋กœ ์ธํ•œ ์„ฑ๋Šฅ์˜ ์ €ํ•˜์ด๋‹ค. ๋ณธ ๋ฐ•์‚ฌ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ์ƒ๊ธฐ ๋ฌธ์ œ์ ์„ ์™„ํ™”ํ•˜๋Š” ๊ธฐ๋ฐ˜ ๊ธฐ์ˆ ๋“ค์„ ์—ฐ๊ตฌํ•˜๊ณ  ์ œ์•ˆํ•˜์˜€๋‹ค. ๋ณธ ๋ฐ•์‚ฌ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ์ž๋™ํ™”๋œ ํ˜„์žฅ ์กฐ์‚ฌ ๊ธฐ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. WiFi ๊ธฐ๋ฐ˜์˜ ๋ชจ๋“  ์ธก์œ„ ๊ธฐ๋ฒ•์€ ํ˜„์žฅ์กฐ์‚ฌ๊ฐ€ ํ•„์ˆ˜์ ์œผ๋กœ ์„ ํ–‰๋˜์–ด์•ผ ํ•˜๋ฉฐ, ์ด๋Š” ๋งŽ์€ ๋…ธ๋™๋ ฅ์„ ์š”ํ•œ๋‹ค. GPS ๋ฐ ์ธก์œ„๋ฅผ ์œ„ํ•œ ๊ณ ์„ฑ๋Šฅ ๊ธฐ์ˆ ์ธ Pedestrian dead reckoning (PDR), Fine timing measurement (FTM) ์ •๋ณด๋ฅผ ์œตํ•ฉํ•˜์—ฌ ์ œ์•ˆํ•œ ํ˜„์žฅ ์กฐ์‚ฌ ๊ธฐ์ˆ ์ด ์ž๋™ํ™” ํŠน์„ฑ์„ ์ง€๋‹ˆ๋„๋ก ์„ค๊ณ„ ๋˜์—ˆ๋‹ค. ์ ํ•ฉํ•œ ๋น„์šฉํ•จ์ˆ˜ ์„ค๊ณ„๋ฅผ ํ†ตํ•œ GPS์™€ PDR ์ขŒํ‘œ์˜ ์œตํ•ฉ์€ PDR์— ๋Œ€ํ•œ ์ˆ˜๋™ ๊ต์ •์ด ์š”๊ตฌ๋˜์ง€ ์•Š๊ฒŒ ํ•˜๋ฉฐ, FTM์„ ํ†ตํ•˜์—ฌ ์ œ๊ณต ๋ฐ›์€ ๊ณ ์ •๋ฐ€์˜ ๊ฑฐ๋ฆฌ ์ถ”์ • ๊ฒฐ๊ณผ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์‹ค๋‚ด์— ๋ถ„ํฌํ•˜๋Š” WiFi access point (AP)๋“ค์˜ ์œ„์น˜๋ฅผ ์ถ”์ •ํ•˜์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์˜ ์‘์šฉ์œผ๋กœ์จ ์‹ค๋‚ด ํ™˜๊ฒฝ์˜ Fingerprints ๋ฐ์ดํ„ฐ ๋ฒ ์ด์Šค๋ฅผ ์ž๋™์œผ๋กœ ๊ตฌ์ถ•ํ•˜๋Š” ๊ธฐ์ˆ ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. WiFi AP๋“ค์˜ ์œ„์น˜๋ฅผ ์ถ”์ •ํ•˜๋Š” ๋™์‹œ์—, ์„ผ์„œ ์œตํ•ฉ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ถ”์ •๋œ ๋‹จ๋ง์˜ ๊ฒฝ๋กœ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ AP ๋ณ„๋กœ ๊ฑฐ๋ฆฌ ์ถ”์ • ๊ต์ • ํŒŒ๋ผ๋ฏธํ„ฐ, ๊ฒฝ๋กœ ์†์‹ค ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์ถ”์ •ํ•œ๋‹ค. ์‹ค๋‚ด ํ™˜๊ฒฝ์—์„œ ๋‹จ๋ง์ด ์ด๋™ํ•˜์ง€ ์•Š์€ ์ง€์ ์€ Gaussian process regression์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ง€์ ๋‹น Received signal strength (RSS) ๊ฐ’์ด ์ถ”์ •๋œ๋‹ค. ์‹ค์ œ ๋‹จ๋ง์ด ์ด๋™ํ•˜๋ฉด์„œ ์ธก์ •ํ•œ RSS ๊ฐ’์„ ๋ฐ”ํƒ•์œผ๋กœ ๊ฒฝ๋กœ ์†์‹ค ํŒŒ๋ผ๋ฏธํ„ฐ์™€ ์‹ ๋ขฐ๋„๊ฐ€ ์ถ”์ •๋˜๋ฉฐ ์ด๋ฅผ ์ข…ํ•ฉํ•˜์—ฌ Fingerprints ๋ฐ์ดํ„ฐ ๋ฒ ์ด์Šค๋ฅผ ๊ตฌ์ถ•ํ•œ๋‹ค. ๋ณต์žกํ•œ ์‹ค๋‚ด ์ „ํŒŒ ์ฑ„๋„์„ ๋ถ„์„ํ•จ์œผ๋กœ์จ ๊ฑฐ๋ฆฌ ์ถ”์ • ์„ฑ๋Šฅ์„ ๊ฐœ์„ ํ•œ ๊ธฐ์ˆ  ๋˜ํ•œ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹ค๋‚ด ๋ณต๋„ ๊ตฌ์กฐ๋ฅผ ๋„ํŒŒ๊ด€ ํ˜•ํƒœ๋กœ ๋ชจ๋ธ๋งํ•˜๋Š” ์ „ํŒŒ ๋ชจ๋ธ์„ ์ˆ˜๋ฆฝํ•˜์˜€๋‹ค. ๋„ํŒŒ๊ด€๊ตฌ์กฐ์—์„œ๋Š” ๋‘ ๊ฐ€์ง€์˜ ์—๋„ˆ์ง€ ์†์‹ค ์š”์†Œ๊ฐ€ ๊ณ ๋ ค๋œ๋‹ค : ํˆฌ๊ณผ ์†์‹ค ๋ฐ ๊ฒฝ๊ณ„๋ฉด ์‚ฐ๋ž€. ํ‘œ๋ฉด ์‚ฐ๋ž€ ์ด๋ก ๊ณผ ๋‹ค์ค‘ ๋ชจ๋“œ ๋„ํŒŒ๊ด€ ์ด๋ก ์ด ๋‘ ๊ฐ€์ง€ ์š”์†Œ๋ฅผ ๊ฐ๊ฐ ์ •๋Ÿ‰ํ™”ํ•œ๋‹ค. ์ด์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ˆ˜๋ฆฝ๋œ ์ „ํŒŒ ๋ชจ๋ธ์€ ์‹ค์ œ ํ™˜๊ฒฝ์—์„œ์˜ ์ฑ„๋„ ์‚ฌ์šด๋”ฉ ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ ๊ฒ€์ฆ๋œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ํ‘œ๋ฉด ์‚ฐ๋ž€ ์ด๋ก ์„ ์‘์šฉํ•œ ๋ ˆ์ดํŠธ๋ ˆ์ด์‹ฑ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ ๊ฐœ์„  ๋ฐฉ์•ˆ์— ๋Œ€ํ•˜์—ฌ ์ œ์•ˆํ•˜์˜€๋‹ค. ์‹ค๋‚ด ๋ ˆ์ดํŠธ๋ ˆ์ด์‹ฑ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ๋Š” ์ฑ„๋„ ์‚ฌ์šด๋”ฉ์— ์†Œ์š”๋˜๋Š” ๋…ธ๋™๋ ฅ์„ ํฌ๊ฒŒ ๊ฒฝ๊ฐํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, Fingerprinting ๋ฐ์ดํ„ฐ ๋ฒ ์ด์Šค ๊ตฌ์ถ•์— ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ํ‘œ๋ฉด ์‚ฐ๋ž€ ์ด๋ก ์„ ์ด์šฉํ•˜์—ฌ ๋ ˆ์ดํŠธ๋ ˆ์ด์‹ฑ ์‹œ๋ฎฌ๋ ˆ์ดํ„ฐ์— ์ž…๋ ฅ๋˜๋Š” ์œ ์ „์œจ์ด ํ‘œ๋ฉด์˜ ๊ฑฐ์นจ ์ •๋„๋ฅผ ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ๊ฐœ์„ ํ•˜์˜€์œผ๋ฉฐ ์‹ค์ œ ํ™˜๊ฒฝ์—์„œ์˜ ์ฑ„๋„ ์‚ฌ์šด๋”ฉ์„ ํ†ตํ•˜์—ฌ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.1. Introduction 1 2. GPS-aided Site Survey Technique for Indoor Location Based Services with a Stand-alone Mobile device 4 3. Automated Survey Technique for Fingerprints Database in Fully-Blind Indoor Environments 36 4. Fine-resolution Ranging based on Signal Strength in Indoor Hallway with Rough-surface Slab Waveguide 49 5. Ray-tracing based Channel Modeling in Harsh Environments with Surface Scattering Theory 78 6. Conclusion 95๋ฐ•
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