397 research outputs found

    RORฮณt-specific transcriptional interactomic inhibition suppresses autoimmunity associated with TH17 cells.

    Get PDF
    The nuclear hormone receptor retinoic acid-related orphan receptor gamma t (RORฮณt) is a transcription factor (TF) specific to TH17 cells that produce interleukin (IL)-17 and have been implicated in a wide range of autoimmunity. Here, we developed a novel therapeutic strategy to modulate the functions of RORฮณt using cell-transducible form of transcription modulation domain of RORฮณt (tRORฮณt-TMD), which can be delivered effectively into the nucleus of cells and into the central nerve system (CNS). tRORฮณt-TMD specifically inhibited TH17-related cytokines induced by RORฮณt, thereby suppressing the differentiation of naรฏve T cells into TH17, but not into TH1, TH2, or Treg cells. tRORฮณt-TMD injected into experimental autoimmune encephalomyelitis (EAE) animal model can be delivered effectively in the splenic CD4(+) T cells and spinal cord-infiltrating CD4(+) T cells, and suppress the functions of TH17 cells. The clinical severity and incidence of EAE were ameliorated by tRORฮณt-TMD in preventive and therapeutic manner, and significant reduction of both infiltrating CD4(+) IL-17(+) T cells and inflammatory cells into the CNS was observed. As a result, the number of spinal cord demyelination was also reduced after tRORฮณt-TMD treatment. With the same proof of concept, tTbet-TMD specifically blocking TH1 differentiation improved the clinical incidence of rheumatoid arthritis (RA). Therefore, tRORฮณt-TMD and tTbet-TMD can be novel therapeutic reagents with the natural specificity for the treatment of inflammatory diseases associated with TH17 or TH1. This strategy can be applied to treat various diseases where a specific transcription factor has a key role in pathogenesis.ope

    ๋น„๊ฐ€์šฐ์‹œ์•ˆ ์žก์Œ ์˜์ƒ ๋ณต์›์„ ์œ„ํ•œ ๊ทธ๋ฃน ํฌ์†Œ ํ‘œํ˜„

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ˆ˜๋ฆฌ๊ณผํ•™๋ถ€,2020. 2. ๊ฐ•๋ช…์ฃผ.For the image restoration problem, recent variational approaches exploiting nonlocal information of an image have demonstrated significant improvements compared with traditional methods utilizing local features. Hence, we propose two variational models based on the sparse representation of image groups, to recover images with non-Gaussian noise. The proposed models are designed to restore image with Cauchy noise and speckle noise, respectively. To achieve efficient and stable performance, an alternating optimization scheme with a novel initialization technique is used. Experimental results suggest that the proposed methods outperform other methods in terms of both visual perception and numerical indexes.์˜์ƒ ๋ณต์› ๋ฌธ์ œ์—์„œ, ์˜์ƒ์˜ ๋น„๊ตญ์ง€์ ์ธ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜๋Š” ์ตœ๊ทผ์˜ ๋‹ค์–‘ํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ ๊ตญ์ง€์ ์ธ ํŠน์„ฑ์„ ํ™œ์šฉํ•˜๋Š” ๊ธฐ์กด ๋ฐฉ๋ฒ•๊ณผ ๋น„๊ตํ•˜์—ฌ ํฌ๊ฒŒ ๊ฐœ์„ ๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ, ์šฐ๋ฆฌ๋Š” ๋น„๊ฐ€์šฐ์‹œ์•ˆ ์žก์Œ ์˜์ƒ์„ ๋ณต์›ํ•˜๊ธฐ ์œ„ํ•ด ์˜์ƒ ๊ทธ๋ฃน ํฌ์†Œ ํ‘œํ˜„์— ๊ธฐ๋ฐ˜ํ•œ ๋‘ ๊ฐ€์ง€ ๋ณ€๋ถ„๋ฒ•์  ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ชจ๋ธ์€ ๊ฐ๊ฐ ์ฝ”์‹œ ์žก์Œ๊ณผ ์ŠคํŽ™ํด ์žก์Œ ์˜์ƒ์„ ๋ณต์›ํ•˜๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. ํšจ์œจ์ ์ด๊ณ  ์•ˆ์ •์ ์ธ ์„ฑ๋Šฅ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด, ๊ต๋Œ€ ๋ฐฉํ–ฅ ์Šน์ˆ˜๋ฒ•๊ณผ ์ƒˆ๋กœ์šด ์ดˆ๊ธฐํ™” ๊ธฐ์ˆ ์ด ์‚ฌ์šฉ๋œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์ด ์‹œ๊ฐ์ ์ธ ์ธ์‹๊ณผ ์ˆ˜์น˜์ ์ธ ์ง€ํ‘œ ๋ชจ๋‘์—์„œ ๋‹ค๋ฅธ ๋ฐฉ๋ฒ•๋ณด๋‹ค ์šฐ์ˆ˜ํ•จ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค.1 Introduction 1 2 Preliminaries 5 2.1 Cauchy Noise 5 2.1.1 Introduction 6 2.1.2 Literature Review 7 2.2 Speckle Noise 9 2.2.1 Introduction 10 2.2.2 Literature Review 13 2.3 GSR 15 2.3.1 Group Construction 15 2.3.2 GSR Modeling 16 2.4 ADMM 17 3 Proposed Models 19 3.1 Proposed Model 1: GSRC 19 3.1.1 GSRC Modeling via MAP Estimator 20 3.1.2 Patch Distance for Cauchy Noise 22 3.1.3 The ADMM Algorithm for Solving (3.7) 22 3.1.4 Numerical Experiments 28 3.1.5 Discussion 45 3.2 Proposed Model 2: GSRS 48 3.2.1 GSRS Modeling via MAP Estimator 50 3.2.2 Patch Distance for Speckle Noise 52 3.2.3 The ADMM Algorithm for Solving (3.42) 53 3.2.4 Numerical Experiments 56 3.2.5 Discussion 69 4 Conclusion 74 Abstract (in Korean) 84Docto

    Multivariable index for assessing the activity and predicting all-cause mortality in antineutrophil cytoplasmic antibody-associated vasculitis

    Get PDF
    BACKGROUND: So far, there has been no tool to estimate activity at diagnosis and predict all-cause mortality in patients with ANCA-associated vasculitis (AAV). Hence, we determined the initial predictors of them in patients with AAV. METHODS: We retrospectively reviewed the medical records of 182 patients with AAV. Severe AAV was defined as Birmingham Vasculitis Activity Score (BVAS) โ‰ฅ 16. The cutoffs were extrapolated by the receiver operator characteristic (ROC) curve. The odds ratio (OR) and the relative risk (RR) were assessed using the multivariable logistic regression analysis and the chi-square test, respectively. RESULTS: In the comparison analysis, patients with severe AAV exhibited the higher neutrophil and platelet counts, creatinine, erythrocyte sedimentation rate and C-reactive protein, and the lower lymphocyte count, hemoglobin, and serum albumin than those without. In the multivariable logistic regression analysis, creatinine โ‰ฅ 0.9 mg/dL (OR 2.264), lymphocyte count โ‰ค 1430.0/mm3 (OR 1.856), and hemoglobin โ‰ค 10.8 g/dL (OR 2.085) were associated with severe AAV. We developed a new equation of a multivariable index for AAV (MVIA) = 0.6 ร— (Lymphocyte count โ‰ค 1430.0/mm3 ) + 0.7 ร— (Hemoglobin โ‰ค 10.8 g/dL) + 0.8 ร— (Creatinine โ‰ฅ 0.9 mg/dL). The optimal cutoff of MVIA for severe AAV was set as 1.35. Severe AAV was identified more frequently in patients with MVIA at diagnosis โ‰ฅ1.35 than those without (RR 4.432). Patients with MVIA at diagnosis โ‰ฅ1.35 exhibited the lower cumulative patient survival rate than those without. CONCLUSION: Multivariable index for AAV could assess the cross-sectional activity and predict all-cause mortality in patients with AAV.ope

    Antineutrophil Cytoplasmic Antibody-Associated Vasculitis in Korea: A Narrative Review

    Get PDF
    Antineutrophil cytoplasmic antibody-associated vasculitis (AAV) is a group of systemic necrotising vasculitides, which often involve small vessels, and which lead to few or no immune deposits in affected organs. According to clinical manifestations and pathological features, AAV is classified into three variants: microscopic polyangiitis, granulomatosis with polyangiitis (GPA), and eosinophilic GPA. The American College of Rheumatology 1990 criteria contributed to the classification of AAV, although currently the algorithm suggested by the European Medicines Agency in 2007 and the Chapel Hill Consensus Conference Nomenclature of Vasculitides proposed in 2012 have encouraged physicians to classify AAV patients properly. So far, there have been noticeable advancements in studies on the pathophysiology of AAV and the classification criteria for AAV in Western countries. However, studies analysing clinical features of Korean patients with AAV have only been conducted and reported since 2000. One year-, 5 year-, and 10 year-cumulative patient survival rates are reported as 96.1, 94.8, and 92.8%. Furthermore, initial vasculitis activity, prognostic factor score, age and specific organ-involvement have been found to be associated with either all-cause mortality or poor disease course. The rate of serious infection is 28.6%, and 1 year-, 5 year- and 10 year-cumulative hospitalised infection free survival rates range from 85.1% to 72.7%. The overall standardised incidence ratio of cancer in AAV patients was deemed 1.43 compared to the general Korean population.ope

    Controlling Nutritional Status Score is Associated with All-Cause Mortality in Patients with Antineutrophil Cytoplasmic Antibody-Associated Vasculitis

    Get PDF
    PURPOSE: The controlling nutritional status (CONUT) score was developed to detect undernutrition in patients. Here, we investigated whether the CONUT score estimated at diagnosis could help predict poor outcomes [all-cause mortality, relapse, and end-stage renal disease (ESRD)] of antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). MATERIALS AND METHODS: We retrospectively reviewed and collated data, including baseline characteristics, clinical manifestations (to calculate AAV-specific indices), and laboratory results, from 196 newly diagnosed AAV patients. Serum albumin, peripheral lymphocyte, and total cholesterol levels (at diagnosis) were used to calculate CONUT scores. RESULTS: In total, 111 patients had high CONUT scores (โ‰ฅ3), which showed higher frequency of myeloperoxidase-ANCA and ANCA positivity, and demonstrated higher AAV-specific indices. The optimal cut-offs of CONUT score (at diagnosis) for predicting all-cause mortality and ESRD were โ‰ฅ3.5 and โ‰ฅ2.5, respectively. Patients with CONUT scores higher than the cut-off at diagnosis exhibited lower cumulative and ESRD-free survival rates compared to those with lower scores than the cut-off. In multivariable analyses, diabetes mellitus [hazard ratio (HR): 4.394], five-factor score (HR: 3.051), and CONUT score โ‰ฅ3.5 (HR: 4.307) at diagnosis were independent predictors of all-cause mortality, while only serum creatinine (HR: 1.714) was an independent predictor of ESRD occurrence.ope

    Rituximab Biosimilar Prevents Poor Outcomes of Microscopic Polyangiitis and Granulomatosis with Polyangiitis as Effectively as Rituximab Originator

    Get PDF
    Purpose: There has been no extensive study to compare the efficacy between rituximab originator (Mabtheraยฎ) and its biosimilar (Truximaยฎ) for microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA). Here, we investigated the clinical effects of rituximab on poor outcomes of MPA and GPA in Korean patients, and compared those between Mabtheraยฎ and Truximaยฎ. Materials and methods: We retrospectively reviewed the medical records of a total of 139 patients, including 97 MPA patients and 42 GPA patients. At diagnosis, antineutrophil cytoplasmic antibody positivity and comorbidities were assessed. During follow-up, all-cause mortality, relapse, end-stage renal disease, cerebrovascular accident and acute coronary syndrome were evaluated as poor outcomes. In this study, rituximab was used as either Mabtheraยฎ or Truximaยฎ. Results: The median age at diagnosis was 60.1 years and 46 patients were men (97 MPA and 42 GPA patients). Among poor outcomes, patients receiving rituximab exhibited a significantly lower cumulative relapse-free survival rate compared to those not receiving rituximab (p=0.002). Nevertheless, rituximab use did not make any difference in other poor outcomes of MPA and GPA except for relapse, which might be a rebuttal to the fact that rituximab use after relapse eventually led to better prognosis. There were no significant differences in variables at diagnosis and during follow-up between patients receiving Mabtheraยฎ and those receiving Truximaยฎ. Patients receiving Truximaยฎ exhibited a similar pattern of the cumulative survival rates of each poor outcome to those receiving Mabtheraยฎ. Conclusion: Truximaยฎ prevents poor outcomes of MPA and GPA as effectively as does Mabtheraยฎ.ope

    Total Haemolytic Complement Activity at Diagnosis as an Indicator of the Baseline Activity of Antineutrophil Cytoplasmic Antibody-associated Vasculitis

    Get PDF
    Objective. The total haemolytic complement activity (CH50) assay evaluates the functioning of the complement system. Accumulating evidence indicates that the activation of the complement system plays a critical role in the pathogenesis of antineutrophil cytoplasmic antibody-associated vasculitis (AAV). Therefore, this study aimed to investigate whether CH50 levels at diagnosis could reflect the baseline activity of AAV. Methods. This retrospective study included 101 immunosuppressive drug-naรฏve patients with AAV. At diagnosis, all patients underwent clinical assessments for disease activity, including measurement of the Birmingham Vasculitis Activity Score (BVAS) and Five Factor Score (FFS), and laboratory evaluations, such as tests for CH50, C3, and C4 levels. The association between CH50 levels and disease activity was determined. Results. The median BVAS and FFS at diagnosis were 12.0 and 1.0, respectively, whereas the median CH50 level was 60.4 U/mL. There was a negative correlation between the CH50 level and BVAS (r=โˆ’0.241; p=0.015). A CH50 cut-off value of 62.1 U/mL was used to classify the patients into two groups: patients with CH50 levels ๏ผœ62.1 U/mL (low-CH50 group) and those with CH50 levels โ‰ฅ 62.1 U/mL (high-CH50 group). The low-CH50 group had a higher proportion of patients with high disease activity, based on the BVAS, than the high-CH50 group (52.5% vs. 23.8%, p=0.004). Additionally, the low-CH50 group exhibited a lower relapse- free survival rate than the high-CH50 group; however, this difference was not statistically significant (p=0.082). Conclusion. Low CH50 levels at diagnosis may reflect high baseline activity of AAV.ope

    Novel mortality-predicting index at diagnosis can effectively predict all-cause mortality in patients with antineutrophil cytoplasmic antibody-associated vasculitis

    Get PDF
    Background: This study investigated whether the inflammation prognostic index (IPI) and the mortality predicting index (MPI) at diagnosis could predict all-cause mortality in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). Methods: We included 223 AAV patients and reviewed their medical records. Clinical and laboratory data and AAV-specific indices at diagnosis were assessed. The IPI was calculated as neutrophil-to-lymphocyte ratio (NLR) ร— C-reactive protein to albumin ratio (CAR). Here, we newly developed an MPI (NLR ร— CAR ร— monocyte counts). Results: The mean age of 223 patients (122 MPA, 57 GPA and 44 EGPA patients) was 59 years. The rate of mortality was 11.2%. Using the receiver operator characteristic curve for all-cause mortality, the cut-offs were calculated as NLR: 3.22, CAR: 3.25, IPI: 18.53 and MPI: 8367.82. In the univariable Cox hazard analysis, age, gender, smoking history, BVAS, FFS and over the cut-off of each index showed statistical significance. As the indices share at least two mutual variables, the multivariable analysis was conducted four times based on each index. An IPI โ‰ฅ18.53 (HR 3.162) and MPI โ‰ฅ8367.82 (HR 3.356) were significantly associated with all-cause mortality. Conclusions: This study developed a novel indicator, MPI, that uses the existing NLR and CAR indices and proved that it could predict all-cause mortality in AAV patients.ope

    A Study on the Measured Data Analysis of Berthing Velocity for Safe Berthing

    Get PDF
    When designing mooring facilities for harbor and dock facilities, the port facility manager should consider the berthing energy of the approaching ship. However, it is difficult to directly estimate the shipโ€™s berthing velocity, which has the greatest effect on the berthing energy. Therefore, it is analyzed statistically based on the measured data. The โ€˜Brolsma curveโ€™, which is widely referred to worldwide as the data on the berthing velocity, is the data collected in the 1970s. However, it does not correspond to the current situation because it is not reflected in the enlargement of the ship and the development of the mooring ability in the port facility. Therefore, it is necessary to determine the criteria of the new design berthing velocity by analyzing the actually measured ship berthing velocity recently. In this study, I try to derive a new criterion by analyzing the berthing velocity data observed from a tanker terminal in Korea. The comparison and analysis of the domestic and international reference data related to the berthing velocity complements the โ€˜Port and Fishing Design Standards (2014)โ€™and the improvement plan is prepared. The purpose of this study is to produce basic data that can be referenced in the design of mooring facilities by numerical analysis of the actual data analysis results. The actual data of the berthing velocity used in this study is measured at a tanker terminal operated by three jetties located in Korea for about 17 months and the total number of data is 207. As a result of analyzing the actual data by jetty and ship size, it was confirmed that most of the data are within the design berthing velocity of each jetty. In order to apply the actual data of berthing velocity to the probability distribution function, the frequency of berthing velocity was converted into histogram and compared with the three probability distributions of normal distribution, lognormal distribution and Weibull distribution. To find the most suitable probability distribution function, I conduct the goodness of fit test such as K-S test, A-D test and Q-Q plot. As a result, lognormal distribution was most suitable for the ship which was in laden condition and Weibull ditribution was most suitable in the case of ballast condition. Based on the results, I developed a method to calculate and use the predicted value of berthing velocity derived from the concept of probability of exceedance. The relationship curve between the berthing velocity and the DWT was derived by using the relation between the shipโ€™s specification and ship size. Using the linear regression analysis, the relational expressions corresponding to the confidence intervals of 50, 75, 90, 95, 98, and 99% were derived and rearranged into graphs. This study suggests a method to estimate the proper berthing velocity according to ship size through analysis of actual data. It is necessary to revise old reference paper for berthing velocity that do not correspond to the reality such as the data of 'Harbor and Fishing Design Standards'. However, since the actual data used in this study is limited to domestic tanker terminal, further studies are needed to collect and analyze the data collected from various types of vessels and ports. The ultimate goal is to develop the new relationship curve between the vessel size and the berthing velocity that can replace Brolsma curve.|ํ˜„์žฌ ํ•ญ๋งŒ ๋ฐ ๋ถ€๋‘ ์‹œ์„ค์„ ์„ค๊ณ„ํ•จ์— ์žˆ์–ด ํ•ญ๊ตฌ์— ์ ‘๊ทผํ•˜๋Š” ์„ ๋ฐ•์˜ ์ ‘์•ˆ์—๋„ˆ์ง€๋ฅผ ๊ณ ๋ คํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ด ์ ‘์•ˆ์—๋„ˆ์ง€์— ๊ฐ€์žฅ ํฐ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์„ ๋ฐ• ์ ‘์•ˆ์†๋„๋ฅผ ์ง์ ‘ ์ถ”์ •ํ•˜๊ธฐ๋Š” ์–ด๋ ค์šฐ๋ฏ€๋กœ ์‹ค์ธกํ•œ ๋ฐ์ดํ„ฐ์— ๊ทผ๊ฑฐํ•˜์—ฌ ํ†ต๊ณ„์ ์œผ๋กœ ๋ถ„์„ํ•˜๊ณ  ์žˆ๋Š” ์‹ค์ •์ด๋‹ค. ์ ‘์•ˆ์†๋„์— ๋Œ€ํ•œ ์ž๋ฃŒ๋กœ ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ๋งŽ์ด ์ฐธ์กฐํ•˜๊ณ  ์žˆ๋Š” โ€˜Brolsma curveโ€™๋Š” ๋ฐ์ดํ„ฐ ์ž์ฒด๊ฐ€ 1970๋…„๋Œ€์— ์ˆ˜์ง‘๋œ ์ž๋ฃŒ๋กœ์จ ์„ ๋ฐ•์˜ ๋Œ€ํ˜•ํ™” ๋ฐ ๊ณ„๋ฅ˜์‹œ์„ค์˜ ๋ฐœ์ „์— ๋Œ€ํ•œ ๋ฐ˜์˜์ด ๋˜์–ด์žˆ์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— ํ˜„์žฌ์˜ ์‹ค์ •์— ๋งž์ง€ ์•Š๋Š” ์ƒํ™ฉ์ด๋‹ค. ๋”ฐ๋ผ์„œ ์ตœ๊ทผ์— ์‹ค์ œ๋กœ ์ธก์ •๋œ ์„ ๋ฐ• ์ ‘์•ˆ์†๋„๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์ƒˆ๋กœ์šด ์„ค๊ณ„ ์ ‘์•ˆ์†๋„์˜ ๊ธฐ์ค€์„ ์ •ํ•˜๋Š” ๊ฒƒ์ด ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ตญ๋‚ด์˜ ํƒฑ์ปค๋ถ€๋‘์—์„œ ์‹ค์ธก๋œ ์ ‘์•ˆ์†๋„ ๋ฐ์ดํ„ฐ๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์ ‘์•ˆ์†๋„์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ๊ธฐ์ค€์„ ๋„์ถœํ•˜๊ณ ์ž ํ•œ๋‹ค. ์ ‘์•ˆ์†๋„์™€ ๊ด€๋ จํ•œ ๊ตญ๋‚ด์™ธ ์ฐธ๊ณ ๋ฌธํ—Œ์„ ๋น„๊ต, ๋ถ„์„ํ•˜์—ฌ ํ˜„์žฌ ๊ตญ๋‚ด์˜ ํ•ญ๋งŒ ๋ฐ ์–ดํ•ญ ์„ค๊ณ„๊ธฐ์ค€(2014)์— ๋Œ€ํ•œ ๋ณด์™„์ ์„ ๋„์ถœํ•˜๊ณ  ๊ฐœ์„ ๋ฐฉ์•ˆ์„ ๋งˆ๋ จํ•˜๊ณ ์ž ํ•˜๋ฉฐ, ์‹ค์ธก ๋ฐ์ดํ„ฐ ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ์ˆ˜์น˜ํ™” ํ•˜์—ฌ ๊ณ„๋ฅ˜ ์‹œ์„ค์˜ ์„ค๊ณ„์— ์ฐธ๊ณ ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ดˆ ์ž๋ฃŒ๋ฅผ ์ œ์ž‘ํ•˜๋Š” ๋ฐ ๋ชฉ์ ์„ ๋‘”๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ํ™œ์šฉํ•œ ์ ‘์•ˆ์†๋„์˜ ์‹ค์ธก๋ฐ์ดํ„ฐ๋Š” 3๊ฐœ์˜ Jetty๋กœ ๊ตฌ๋ถ„๋˜์–ด ์šด์˜๋˜๋Š” ๊ตญ๋‚ด์˜ ํ•œ ํƒฑ์ปค๋ถ€๋‘์—์„œ ์•ฝ 17๊ฐœ์›”๊ฐ„ ์ธก์ •ํ•œ ๊ฒƒ์œผ๋กœ์จ ์ด ๋ฐ์ดํ„ฐ ์ˆ˜๋Š” 207๊ฐœ์ด๋‹ค. ์ด ์‹ค์ธก๋ฐ์ดํ„ฐ๋ฅผ Jetty๋ณ„, ์„ ๋ฐ• ๊ทœ๋ชจ๋ณ„๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๋ถ„์„ํ•ด ๋ณธ ๊ฒฐ๊ณผ, ๋Œ€๋ถ€๋ถ„์˜ ๋ฐ์ดํ„ฐ๊ฐ€ ๊ฐ Jetty์˜ ์„ค๊ณ„์ ‘์•ˆ์†๋„ ๋ฒ”์ฃผ ์•ˆ์— ์žˆ๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ์‹ค์ธก๋ฐ์ดํ„ฐ์˜ ์ตœ๋Œ“๊ฐ’์€ ์„ค๊ณ„์ ‘์•ˆ์†๋„์˜ ์•ฝ 2๋ฐฐ๋ฅผ ์ดˆ๊ณผํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋ ‡๊ฒŒ ์‹ค์ธก ์ ‘์•ˆ์†๋„๊ฐ€ ์„ค๊ณ„์ ‘์•ˆ์†๋„๋ฅผ ์ดˆ๊ณผํ•˜๋Š” ๊ฒฝ์šฐ, ์ ‘์•ˆ์—๋„ˆ์ง€๊ฐ€ ๊ณผ๋„ํ•˜๊ฒŒ ์‚ฐ์ถœ๋˜์–ด ๋ถ€๋‘๊ฐ€ ์†์ƒ๋˜๋Š” ๋“ฑ์˜ ์‚ฌ๊ณ ๋กœ ์ด์–ด์งˆ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ทธ์— ๋Œ€ํ•œ ๋Œ€์ฑ…๋งˆ๋ จ์ด ํ•„์š”ํ•˜๋‹ค. ๋˜ํ•œ, ์ ‘์•ˆ์†๋„ ์‹ค์ธก๋ฐ์ดํ„ฐ๋ฅผ ํ™•๋ฅ ๋ถ„ํฌํ•จ์ˆ˜์— ์ ์šฉ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ์ ‘์•ˆ์†๋„์˜ ๋นˆ๋„์ˆ˜๋ฅผ Histogramํ™” ํ•˜์˜€์œผ๋ฉฐ, ์ด๋ฅผ ์ •๊ทœ๋ถ„ํฌ, ๋Œ€์ˆ˜์ •๊ทœ๋ถ„ํฌ, Weibull ๋ถ„ํฌ ์„ธ ๊ฐ€์ง€ ํ™•๋ฅ ๋ถ„ํฌ๋„์™€ ๋น„๊ตํ•˜์˜€๋‹ค. ๊ฐ€์žฅ ์ ํ•ฉํ•œ ํ™•๋ฅ ๋ถ„ํฌํ•จ์ˆ˜๋ฅผ ์ฐพ๊ธฐ ์œ„ํ•ด K-S ๊ฒ€์ •, A-D ๊ฒ€์ •, Q-Q Plot ๋“ฑ์˜ ์ ํ•ฉ๋„ ๊ฒ€์ •์„ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ๋งŒ์žฌ์ƒํƒœ๋กœ ์ ‘์•ˆํ•˜์˜€๋˜ ์„ ๋ฐ•์€ ๋Œ€์ˆ˜์ •๊ทœ๋ถ„ํฌ, ๊ฒฝํ•˜์ƒํƒœ์˜ ๊ฒฝ์šฐ์—๋Š” Weibull ๋ถ„ํฌ๊ฐ€ ๊ฐ€์žฅ ์ ํ•ฉํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ดˆ๊ณผํ™•๋ฅ  ๊ฐœ๋…์—์„œ ๋„์ถœ๋œ ์ ‘์•ˆ์†๋„ ์˜ˆ์ธก์น˜๋ฅผ ๊ณ„์‚ฐํ•˜์—ฌ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์„ ๋ฐ•์˜ ์ œ์›๊ณผ ์„ ๋ฐ• ๊ทœ๋ชจ ๊ฐ„์˜ ๊ด€๊ณ„์‹์„ ํ™œ์šฉํ•˜์—ฌ ์ ‘์•ˆ์†๋„์™€ ์„ ๋ฐ• ๊ทœ๋ชจ์™€์˜ ๊ด€๊ณ„์‹์„ ๋„์ถœํ•˜์˜€๋‹ค. ์„ ํ˜•ํšŒ๊ท€๋ถ„์„์„ ํ™œ์šฉํ•˜์—ฌ 50, 75, 90, 95, 98, 99 %์˜ ์‹ ๋ขฐ๊ตฌ๊ฐ„์— ํ•ด๋‹น๋˜๋Š” ๊ด€๊ณ„์‹์„ ๋„์ถœํ•˜๊ณ  ์ด๋ฅผ ๊ทธ๋ž˜ํ”„๋กœ ์žฌ์ •๋ฆฌ ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹ค์ธก ๋ฐ์ดํ„ฐ ๋ถ„์„์„ ํ†ตํ•œ ์„ ๋ฐ• ๊ทœ๋ชจ์— ๋”ฐ๋ฅธ ์ ์ • ์ ‘์•ˆ์†๋„ ๊ฐ’์„ ์‚ฐ์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•˜์—ฌ ํ˜„์žฌ ๊ตญ๋‚ด์—์„œ ์ฐธ๊ณ  ์ค‘์ธโ€˜ํ•ญ๋งŒ ๋ฐ ์–ดํ•ญ์„ค๊ณ„ ๊ธฐ์ค€โ€™์˜ ์ ‘์•ˆ์†๋„ ๊ด€๋ จ ๋ฐ์ดํ„ฐ์™€ ๊ฐ™์ด ํ˜„์‹ค๊ณผ ๋งž์ง€ ์•Š๋Š” ๋‚ด์šฉ์„ ๊ฐœ์ •ํ•  ํ•„์š”๊ฐ€ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ•˜์ง€๋งŒ ๋ณธ ์—ฐ๊ตฌ์—์„œ ์‚ฌ์šฉํ•œ ์‹ค์ธก ๋ฐ์ดํ„ฐ๊ฐ€ ๊ตญ๋‚ด์˜ ํƒฑ์ปค์„  ๋ถ€๋‘์— ํ•œ์ •๋˜์–ด ์žˆ์œผ๋ฏ€๋กœ, ๋‹ค์–‘ํ•œ ์„ ์ข… ๋ฐ ๋ถ€๋‘์—์„œ ์ทจํ•ฉํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์ทจํ•ฉ, ๋ถ„์„ํ•˜๋Š” ์ถ”๊ฐ€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋ฉฐ ์ด๋ฅผ ํ†ตํ•ด Brolsma curve๋ฅผ ๋Œ€์ฒดํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ์„ ๋ฐ•๊ทœ๋ชจ์™€ ์ ‘์•ˆ์†๋„์˜ ๊ด€๊ณ„์‹์„ ๊ฐœ๋ฐœํ•˜๊ณ ์ž ํ•œ๋‹ค.์ œ 1 ์žฅ ์„œ ๋ก  1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1.2 ์—ฐ๊ตฌ์˜ ๋‚ด์šฉ ๋ฐ ๋ฐฉ๋ฒ• 3 ์ œ 2 ์žฅ ์„ ๋ฐ• ์ ‘์•ˆ์—๋„ˆ์ง€์™€ ์ ‘์•ˆ์†๋„ 2.1 ๊ตญ๋‚ด์™ธ ์ ‘์•ˆ์†๋„ ๊ด€๋ จ ์—ฐ๊ตฌ ๋ฐ ๊ธฐ์ค€ ๊ฒ€ํ†  6 2.1.1 ๊ตญ์ œ ์ ‘์•ˆ์†๋„ ๊ด€๋ จ ์—ฐ๊ตฌ ๋ฐ ๊ธฐ์ค€ 6 2.1.2 ๊ตญ๋‚ด ์ ‘์•ˆ์†๋„ ๊ด€๋ จ ์—ฐ๊ตฌ ๋ฐ ๊ธฐ์ค€ 13 2.2 ์„ ๋ฐ• ์ ‘์•ˆ ์—๋„ˆ์ง€์˜ ์‚ฐ์ • 18 2.2.1 ์ด๋ก ์ ์ธ ๋ฐฉ๋ฒ•์˜ ์ ‘์•ˆ ์—๋„ˆ์ง€ ์‚ฐ์ • 18 2.2.2 ํ†ต๊ณ„์ ์ธ ๋ฐฉ๋ฒ•์˜ ์ ‘์•ˆ ์—๋„ˆ์ง€ ์‚ฐ์ • 24 2.2.3 ์ ‘์•ˆ ์—๋„ˆ์ง€์™€ ๋ฐฉ์ถฉ์žฌ 25 2.3 DAS(Dock mounted docking aid systems) 32 ์ œ 3 ์žฅ ์‹ค์ธก ์ ‘์•ˆ์†๋„์— ๋Œ€ํ•œ ํ™•๋ฅ ๋ถ„ํฌํ•จ์ˆ˜์˜ ์ ์šฉ 3.1 ์ ‘์•ˆ์†๋„ ์‹ค์ธก ๋ฐ์ดํ„ฐ ๋ถ„์„์˜ ๊ฐœ์š” 35 3.1.1 ์‹ค์ธก ๋ฐ์ดํ„ฐ์˜ ๊ฐœ์š” 35 3.1.2 ์‹ค์ธก ์ ‘์•ˆ์†๋„์˜ ํŠน์„ฑ ๋ถ„์„ 38 3.2 ํ™•๋ฅ ๋ถ„ํฌํ•จ์ˆ˜์˜ ์ข…๋ฅ˜ 49 3.3 ํ™•๋ฅ ๋ถ„ํฌํ•จ์ˆ˜์˜ ์ ์šฉ 51 3.3.1 ์‹ค์ธก ์ ‘์•ˆ์†๋„ ๋นˆ๋„์ˆ˜์™€ ํ™•๋ฅ ๋ถ„ํฌ๋„ 51 3.3.2 ํ™•๋ฅ ๋ถ„ํฌ๋„ ์ ํ•ฉ๋„ ๊ฒ€์ • 55 3.3.3 ์ ํ•ฉ๋„ ๊ฒ€์ • ๊ฒฐ๊ณผ ์š”์•ฝ 65 3.4 ์ดˆ๊ณผํ™•๋ฅ  ๊ฐœ๋…์—์„œ์˜ ์ ์šฉ 67 ์ œ 4 ์žฅ ์‹ ๋ขฐ์ˆ˜์ค€๋ณ„ ์ ‘์•ˆ์†๋„์™€ ์„ ๋ฐ•๊ทœ๋ชจ ๊ด€๊ณ„ 4.1 ์„ ๋ฐ•์˜ ๊ทœ๋ชจ์™€ ์„ ๋ฐ• ์ฃผ์š” ๋ฐ์ดํ„ฐ์˜ ๊ด€๊ณ„ 74 4.2 ์„ ํ˜•ํšŒ๊ท€๋ถ„์„ 75 4.3 ์„ ๋ฐ•๊ทœ๋ชจ์™€ ์ ‘์•ˆ์†๋„์˜ ๊ด€๊ณ„์‹ 76 4.3.1 ์‹ ๋ขฐ์ˆ˜์ค€๋ณ„ ์ ‘์•ˆ์†๋„์™€ ์„ ๋ฐ•๊ทœ๋ชจ์˜ ๊ด€๊ณ„์‹ ๋„์ถœ ๋ฐฉ๋ฒ• 76 4.3.2 ์ ‘์•ˆ์†๋„์™€ ์„ ๋ฐ•๊ทœ๋ชจ์˜ ๊ด€๊ณ„์‹ ๋„์ถœ ๊ฒฐ๊ณผ 80 ์ œ 5 ์žฅ ๊ฒฐ๋ก  ๋ฐ ์ œ์–ธ 5.1 ๊ฒฐ ๋ก  88 5.2 ์ œ ์–ธ 90 ์ฐธ๊ณ ๋ฌธํ—Œ 92Maste
    • โ€ฆ
    corecore