• Ocean Systems Engineering
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• v.9 no.3
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• pp.287-328
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• 2019

Comprehensive understanding of the flood risk assessments via frequency analysis often demands multivariate designs under the different notations of return periods. Flood is a tri-variate random consequence, which often pointing the unreliability of univariate return period and demands for the joint dependency construction by accounting its multiple intercorrelated flood vectors i.e., flood peak, volume & durations. Selecting the most parsimonious probability functions for demonstrating univariate flood marginals distributions is often a mandatory pre-processing desire before the establishment of joint dependency. Especially under copulas methodology, which often allows the practitioner to model univariate marginals separately from their joint constructions. Parametric density approximations often hypothesized that the random samples must follow some specific or predefine probability density functions, which usually defines different estimates especially in the tail of distributions. Concentrations of the upper tail often seem interesting during flood modelling also, no evidence exhibited in favours of any fixed distributions, which often characterized through the trial and error procedure based on goodness-of-fit measures. On another side, model performance evaluations and selections of best-fitted distributions often demand precise investigations via comparing the relative sample reproducing capabilities otherwise, inconsistencies might reveal uncertainty. Also, the strength & weakness of different fitness statistics usually vary and having different extent during demonstrating gaps and dispensary among fitted distributions. In this literature, selections efforts of marginal distributions of flood variables are incorporated by employing an interactive set of parametric functions for event-based (or Block annual maxima) samples over the 50-years continuously-distributed streamflow characteristics for the Kelantan River basin at Gulliemard Bridge, Malaysia. Model fitness criteria are examined based on the degree of agreements between cumulative empirical and theoretical probabilities. Both the analytical as well as graphically visual inspections are undertaken to strengthen much decisive evidence in favour of best-fitted probability density.

• Journal of the Korean Geotechnical Society
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• v.34 no.11
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• pp.43-55
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• 2018

AGF (Artificial Ground Freezing) method is a temporary ground improvement method which can apply to all types of soil with the purpose of high stiffness and low hydraulic conductivity. However, the groundwater flow and the heterogeneity of the ground increase the uncertainty of the ice-column formation which hinders the reliability of this method. The effects of groundwater flow and layered heterogeneity on ice-wall integrity by AGF method were analyzed using finite element analysis program for a coupled thermo-hydro phenomena in the freezing ground. Groundwater flow changes circular ice-column into elliptical shapes and increases the time required for the formation of ice walls. The previous theoretical formula overestimated the completion time of the ice wall and the critical groundwater velocity by neglecting the thermal interaction between adjacent ice-columns. Numerical results presented the corrected formula and verified the proposed equation for the dimensionless ice-wall completion time. In the layered heterogeneous ground, the thickness of the layer with higher hydraulic conductivity and its relative magnitude were found to be important factors in the ice-wall completion time and critical velocity.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.123-137
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• 2021

In this study, we performed cross calibration of KOMPSAT-3 AEISS imaging sensor with reference to normalized pixels in the Landsat 8 OLI scenes of homogenous ROI recorded by both sensors between January 2014 and December 2019 at the Libya 4 PICS. Cross calibration is using images from a stable and well-calibrated satellite sensor as references to harmonize measurements from other sensors and/or characterize other sensors. But cross calibration has two problems; RSR and temporal difference. The RSR of KOMPSAT-3 and Landsat 8 are similar at the blue and green bands. But the red and NIR bands have a large difference. So we calculate SBAF of each sensor. We compared the SBAF estimated from the TOA Radiance simulation with KOMPSAT-3 and Landsat 8, the results displayed a difference of about 2.07~2.92% and 0.96~1.21% in the VIS and NIR bands. Before SBAF, Reflectance and Radiance difference was 0.42~23.23%. Case of difference temporal, we simulated by 6S and Landsat 8 for alignment the same acquisition time. The SBAF-corrected cross calibration coefficients using KOMPSAT-3, 6S and simulated Landsat 8 compared to the initial cross calibration without correction demonstrated a percentage difference in the spectral bands of about 0.866~1.192%. KOMPSAT-3 maximum uncertainty was estimated at 3.26~3.89%; errors due to atmospheric condition minimized to less than 1% (via 6S); Maximum deviation of KOMPSAT-3 DN was less than 1%. As the result, the results affirm that SBAF and 6s simulation enhanced cross-calibration accuracy.

• Journal of Korean Academy of Nursing
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• v.51 no.6
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• pp.689-702
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• 2021

Purpose: This study aimed to develop a situation-specific theory to explain nurses' experiences of the COVID-19 crisis. Methods: The participants were 16 hospital nurses who experienced the COVID-19 crisis. Data were collected through in-depth individual interviews from September 2, 2020 to January 20, 2021. Transcribed interview contents were analyzed using Corbin and Strauss's grounded theory method. Results: A total of 38 concepts and 13 categories were identified through an open coding process. The core category found was becoming a pioneering nurse who turns crises into opportunities. The causal conditions were the chaos of being exposed defenselessly to an unexpected pandemic, fear caused by a nursing care field reminiscent of a battlefield, and moral distress from failing to protect patients' human dignity. The contextual conditions were feeling like the scapegoat of the hospital organization, increasing uncertainty due to the unpredictable state of COVID-19, and relative deprivation due to inappropriate treatment. The central phenomenon was suffering alone while experiencing the dedication of the COVID-19 hero image. The action/interactional strategy were efforts to find a breakthrough and getting the nurse's mind right, and the intervening conditions were gratitude for those who care for broken hearts and getting used to myself with repetitive work. The Consequences were becoming an independent nurse and frustration with the unchanging reality. Conclusion: This study provides the foundation for the nurse's situation-specific theory of the COVID-19 crisis by defining the crisis perceived by nurses who cared for COVID-19 patients and suggesting types of coping with the crisis.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2733-2742
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• 2020

This paper presents an assessment of applicability of the multigroup cross sections generated with Monte Carlo tools to the fast reactor analysis based on transport calculations. 33-group cross section sets were generated for simple one- (1-D) and two-dimensional (2-D) sodium-cooled fast reactor problems using the SERPENT code and applied to deterministic steady-state and depletion calculations. Relative to the reference continuous-energy SERPENT results, with the transport corrected P₀ scattering cross section, the k-eff value was overestimated by 506 and 588 pcm for 1-D and 2-D problems, respectively, since anisotropic scattering is important in fast reactors. When the scattering order was increased to P₅, the 1-D and 2-D problem errors were increased to 577 and 643 pcm, respectively. A sensitivity and uncertainty analysis with the PERSENT code indicated that these large k-eff errors cannot be attributed to the statistical uncertainties of cross sections and they are likely due to the approximate anisotropic scattering matrices determined by scalar flux weighting. The anisotropic scattering cross sections were alternatively generated using the MC²-3 code and merged with the SERPENT cross sections. The mixed cross section set consistently reduced the errors in k-eff, assembly powers, and nuclide densities. For example, in the 2-D calculation with P3 scattering order, the k-eff error was reduced from 634 pcm to -223 pcm. The maximum error in assembly power was reduced from 2.8% to 0.8% and the RMS error was reduced from 1.4% to 0.4%. The maximum error in the nuclide densities at the end of 12-month depletion that occurred in ²³⁷Np was reduced from 3.4% to 1.5%. The errors of the other nuclides are also reduced consistently, for example, from 1.1% to 0.1% for ²³⁵U, from 2.2% to 0.7% for ²³⁸Pu, and from 1.6% to 0.2% for ²⁴¹Pu. These results indicate that the scalar flux weighted anisotropic scattering cross sections of SERPENT may not be adequate for application to fast reactors where anisotropic scattering is important.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.199-209
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• 2022

As the uncertainty of technology development and market needs increases due to changes in the global business environment, the interest and demand for R&D activities of individual companies are increasing. To respond to these environmental changes, technology commercialization players are paying great attention to enhancing the qualitative competitiveness of R&D. In particular, R&D companies in the marine and fishery sector face many difficulties compared to other industries. For example, the R&D environment is barren, it is challenging to secure R&D human resources, and it is facing a somewhat more difficult environment compared to other sectors, such as the difficulty in maintaining R&D continuity due to the turnover rate of researchers. In this study, based on the empirical data and patent status of private companies closely related to the R&D technology status, big data analysis, and simulation analysis methods were used to identify the relative position of individual companies' R&D capabilities and industrial perspectives. In this study, based on industrial evidence and patent applications closely related to the R&D technology status, the R&D capabilities of individual companies were evaluated using extensive data analysis and simulation analysis methods, and a statistical test was performed to analyze if there were differences in capabilities from an industrial point of view. At this time, the industries to be analyzed were based on all sectors, the maritime industry, the fisheries industry, and the maritime industry integration sector. In conclusion, it was analyzed that there was a certain level of difference in the R&D capabilities of individual companies in each industry sector, Therefore when developing a future R&D capability system, it was confirmed that it was necessary to separate the population for each industry and establish a strategy.

• Progress in Medical Physics
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• v.33 no.4
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• pp.80-87
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• 2022

Purpose: Proton therapy has different relative biological effectiveness (RBE) compared with X-ray treatment, which is the standard in radiation therapy, and the fixed RBE value of 1.1 is widely used. However, RBE depends on a charged particle's linear energy transfer (LET); therefore, measuring LET is important. We have developed a LET measurement method using the inefficiency characteristic of an EBT3 film on a proton beam's Bragg peak (BP) region. Methods: A Gafchromic EBT3 film was used to measure the proton beam LET. It measured the dose at a 10-cm pristine BP proton beam in water to determine the quenching factor of the EBT3 film as a reference beam condition. Monte Carlo (MC) calculations of dose-averaged LET (LET_d) were used to determine the quenching factor and validation. The dose-averaged LETs at the 12-, 16-, and 20-cm pristine BP proton beam in water were calculated with the quenching factor. Results: Using the passive scattering proton beam nozzle of the National Cancer Center in Korea, the LET_d was measured for each beam range. The quenching factor was determined to be 26.15 with 0.3% uncertainty under the reference beam condition. The dose-averaged LETs were measured for each test beam condition. Conclusions: We developed a method for measuring the proton beam LET using an EBT3 film. This study showed that the magnitude of the quenching effect can be estimated using only one beam range, and the quenching factor determined under the reference condition can be applied to any therapeutic proton beam range.

• Analytical Science and Technology
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• v.21 no.1
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• pp.14-19
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• 2008

Among the many odor materials, aldehyde has bad influences not only on human nose, but also on human health. Aldehyde has a strong odor at infinitesimal level, such as down to ppt concentration. Also aldehydes are highly reactive and have poor stability. Therefore, manufacture of standard gases, analysis of aldehydes are very difficult compare to other air pollution analysis. Aldehyde CRM containing 4 different aldehydes at $10{\mu}mol/mol$ (ppm) is developed by using gravimetry method according to ISO 6142 guide. The standard gases were reproduced again, and examined its reproducibility of preparation by GC-FID. The developed aldehyde CRM's certified value showed a relative expanded uncertainties of 2.11 % (95 % of confidence level, k=2).

• Analytical Science and Technology
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• v.22 no.4
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• pp.302-306
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• 2009

A trace amount of boron in steel significantly influences its mechanical and physical properties. A prompt gamma ray activation analysis (PGAA) method is used to measure boron in low alloy steel samples of KRISS 101-01-C21~C26. NIST SRMs of 362, 364, 1761 and 1767 serve as the control standards to validate the measurement method. The measured values of the NIST SRMs are consistent with their certified values within the expected uncertainties, except for that of NIST SRM 362. Experimental uncertainties are evaluated according to the guidelines given by the International Organization for Standardization (ISO). The expanded uncertainties are calculated with a coverage factor of 2, at approximately 95% confidence level. The calculated relative expanded uncertainties of boron mass fractions are between 3% and 7% at the mg/kg level. The results are compared with the results measured by the solvent extraction-inductively coupled optical emission spectrometry (ICP/OES) method.