• Journal of The Korean Astronomical Society
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• v.55 no.5
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• pp.149-172
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• 2022

We present a new method which constructs an Hɪ super-profile of a galaxy which is based on profile decomposition analysis. The decomposed velocity profiles of an Hɪ data cube with an optimal number of Gaussian components are co-added after being aligned in velocity with respect to their centroid velocities. This is compared to the previous approach where no prior profile decomposition is made for the velocity profiles being stacked. The S/N improved super-profile is useful for deriving the galaxy's global Hɪ properties like velocity dispersion and mass from observations which do not provide sufficient surface brightness sensitivity for the galaxy. As a practical test, we apply our new method to 64 high-resolution Hɪ data cubes of nearby galaxies in the local Universe which are taken from THINGS and LITTLE THINGS. In addition, we also construct two additional Hɪ super-profiles of the sample galaxies using symmetric and all velocity profiles of the cubes whose centroid velocities are determined from Hermite h3 polynomial fitting, respectively. We find that the Hɪ super-profiles constructed using the new method have narrower cores and broader wings in shape than the other two super-profiles. This is mainly due to the effect of either asymmetric velocity profiles' central velocity bias or the removal of asymmetric velocity profiles in the previous methods on the resulting Hɪ super-profiles. We discuss how the shapes (𝜎_n/𝜎_b, A_n/A_b, and A_n/A_tot) of the new Hɪ super-profiles which are measured from a double Gaussian fit are correlated with star formation rates of the sample galaxies and are compared with those of the other two super-profiles.

• Structural Engineering and Mechanics
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• v.85 no.2
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• pp.275-287
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• 2023

The use of environmental-friendly building materials is becoming increasingly popular worldwide. Compared to the normal concrete, rubber-based concrete is considered more durable, environmentally friendly, socially and economically viable. In this investigation, M20 grade concrete was designed and the fine aggregates were replaced with crumb rubber of two different micron sizes (0.221 mm and 0.350 mm). Fly ash (FA) and silica fume (SF) replaces the binder as supplementary cementitious materials at a rate of 0, 5, 10, 15, and 20% by weight. The mechanical properties of concrete including compressive strength, tensile, and flexural strength were determined. The polynomial work expectation validates the response surface approach (RSM) concept for optimizing SF and FA substitution. The maximum compressive strength (22.53 MPa) can be observed for the concrete containing 10% crumb rubber, 15% fly ash and 15% silica fume. The reduced unit weight of the rubberized concrete may be attributed to the lower specific gravity of the rubber particles. Two-way ANOVA with a significance criterion of less than 0.001 has been utilized with modest residual error from the lack of fit and the pure error. The predictive model accurately forecasts the variable-response relationship. Since, the crumb rubber is obtained from wasted tires incorporating FA and SF as a cementitious ingredient, it helps to significantly improve mechanical properties of concrete and reduce environmental degradation.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.9
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• pp.825-830
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• 2009

Pharmaceutical compounds enter the water environment through the diverse pathways. Because their concentration in the water environment was frequently detected in the level of ppt to ppb, the monitoring system should be optimized as much as possible for finding appropriate management policies and technical solutions. One Factor At a Time (OFAT) approach approximating the response with a single variable has been preferred for the optimization of LC-MS/MS operational conditions. However, it is common that variables in analytical instruments are interdependent. Therefore, the best condition could be found by using the statistical optimization method changing multiple variables at a time. In this research, response surface analysis (RSA) was applied to the LC-MS/MS analysis of emerging antibiotic compound, sulfamethoxazole, for the best sensitivity. In the screening test, fragmentation energy and collision voltage were selected as independent variables. They were changed simultaneously for the statistical optimization and a polynomial equation was fit to the data set. The correlation coefficient, $R^2$ valuerepresented 0.9947 and the error between the predicted and observed value showed only 3.41% at the random condition, fragmentation energy of 60 and collision voltage of 17 eV. Therefore, it was concluded that the model derived by RSA successfully predict the response. The optimal conditions identified by the model were fragmentation energy of 116.6 and collision voltage of 10.9 eV. This RSA can be extensively utilized for optimizing conditions of solid-phase extraction and liquid chromatography.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.417-430
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• 2014

We have found an error in the operational OMI HCHO columns, and corrected it by applying a background parameterization derived on a 4th order polynomial fit to the time series of monthly average OMI HCHO data. The corrected OMI HCHO agrees with this understanding as well as with the other sensors measurements and has no unrealistic trends. A new scientific approach, statistical analyses with EOF and SVD, was adapted to reanalyze the consistency of the corrected OMI HCHO with other satellite measurements of HCHO, CO, $NO_2$, and fire counts over Africa. The EOF and SVD analyses with MOPITT CO, OMI $NO_2$, SCIAMAHCY, and OMI HCHO show the overall spatial and temporal pattern consistent with those of biomass burning over these regions. However, some discrepancies were observed from OMI HCHO over northern equatorial Africa during the northern biomass burning seasons: The maximum HCHO was found further downwind from where maximum fire counts occur and the minimum was found in January when biomass burning is strongest. The statistical analysis revealed that the influence of biogenic activity on HCHO wasn't strong enough to cause the discrepancies, but it is caused by the error in OMI HCHO from using the wrong Air Mass Factor (AMF) associated with biomass burning aerosol. If the error is properly taken into consideration, the biomass burning is the strongest source of HCHO seasonality over the regions. This study suggested that the statistical tools are a very efficient method for evaluating satellite data.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.1
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• pp.98-107
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• 2016

The purpose of this study is for Human error prevention to acquire Collision Risk Perception Index (CRPI) sensed by the Officer Of the Watch (OOW) when the approaching distances are reduced in six types of ship encountering situations (Head on, $045^{\circ}$, $090^{\circ}$, $135^{\circ}$, Overtaking, Overtaken) between own ship and a target ship and then to predict CRPI fitting coefficients with polynomials in the curve-fitting process. CRPI acquisition experiments are carried out on two coast-guard ships and with the total of 30 crew members. Analysing results shows that CRPI data have goodness of fit to the six types of encountering situation. Futhermore, the One-Way ANOVA results show that CRPI has a negative affect to the OOW's age, career and license grade as the approaching distances is reduced. The availability of CRPI curve fitting with 3 degrees of polynomial was testified through the RMSE as 1.19 to Head on, 0.87 to $045^{\circ}$, 0.81 to $090^{\circ}$, 0.71 to $135^{\circ}$, 1.29 to Overtaking and 0.87 to Overtaken.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.40-48
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• 2012

This paper models the hue shift phenomenon and proposes a hue correction method to give perceptual matching between projector with and without additional white channel. To quantify the hue shift phenomenon for whole hue angle, 24 color patches with the same lightness are frist created along equally-spaced hue angle, and these are displayed one by one both displays with different luminance levels. Next, each hue value of the patches appeared on the projector with additional white channel is adjusted by observers until the hue values of patches on both displays appear the same visually. After obtaining the hue shift values from the color matching experiment, these values are piecewise fit into six polynomial functions, which approximately determine shifted hue amounts for an arbitrary hue values of each pixel in projector with additional white channel and are utilized to correct them. Actually, an input RGB image is converted to CIELAB LCH color space to get hue values of each pixel and this hue value is shifted as much as the amount calculated by the functions of hue shift model for correction. Finally, corrected image is inversely converted to an output RGB image. For an evaluation, the matching experiment with several test images and the z-score comparisons were performed.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.4
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• pp.567-571
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• 2005

In this study, the effects of varying the amount of water, soaking time at $25^{\circ}C$ and drying time after soaking at $45^{\circ}C$ on the induction of resveratrol were evaluated to optimize the soaking treatment by response surface methodology (RSM). After response surface regression (RSREG), the second-order polynomial equation was fitted to the experimental data. The analysis of variance showed that the model appeared to be adequate $(R^2=0.9547)$ with no significant lack of fit (p>0.1). From statistical analysis, amount of water and soaking time were found to be significant factors. On the other hand, drying time was not significant. Ridge analysis showed that the optimized parameters were $67.15\%$ for amount of water, 19.58 hr for soaking time, 65.56 hr for drying time. It was confirmed that resveratrol content was increased from $0.1\;{\mu}g/g$ to $4.55\;{\mu}g/g$ under the optimized conditions. In addition, the experimental values at the optimized condition agreed with values predicted by ridge analysis. The analytical method validation parameters such as accuracy, precision, and specificity were calculated to ensure the method's validity.

• Korean Journal of Food Science and Technology
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• v.35 no.5
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• pp.884-892
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• 2003

This study was conducted to optimize the water homogenization process of mandarin orange peel for colored rice. Four variables were used to determine the optimum conditions for homogenization speed, time, temperature, and water volume with a five level central composite design and response surface methodology. The process was optimized using the combination of EI and b values of rice coated with water extract of the mandarin orange peel. The effect of water volume was the most significant compared to the other variables on the quality of water homogenate. The regression polynomial model was a suitable (p>0.05) model by lack-of-fit analysis showing high significance. To optimize the process, based on surface response and contour plots, individual contour plots for the response variables were superimposed. The optimum conditions for manufacturing water extract from mandarin orange was with 8,500 rpm homogenization speed, 2.8 min time, $53^{\circ}C$ temperature, and 42 mL water volume with the maximum of restricted variables of EI above 400 and h value above 24.

• Progress in Medical Physics
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• v.5 no.1
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• pp.87-99
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• 1994

Purpose : The determination of electron beam output factor was investigated from individual applicator for various energy of ML-15MDX linear accelerator. The output factor of electron beam was extended from square to rectangular field in individual applicator size through with a least-square fit to a polynomial expression. Materials : In this experiments. the measurement of output was obtained from 2${\times}$cm$^2$ to 20${\times}$20cm$^2$ of field size in different applicator size for 4 to 15 MaV electron beam energy. The output factor was defined as the ratio of maximum dose output on the central axis of the field of individual applicator size to that of a given field size. Applicator factors were derived from comparing with the output dose of reference field size 10${\times}$10cm$^2$. The thickness of block was specially designed as 10mm in thickness of Lipowitz metal for field shaping in all electron energy. Two types of output curves are included as output factors versus side of square fields and that of variable side length for X and Y in one-dimensional to compare the expected values to that of experiments. Results : Expected output factors of rectangular which was derived from that of square fields in individual applicator size from 2${\times}$2cm$^2$ to 20${\times}$20cm$^2$ in different electron energy was very closed to that of experimental measurements within 2% uncertainty. However 1D method showed a 3% discrepancy in small rectangular field for low energy electron beam. Conclusion : Emperical non-linear polynomial regressions of square root and 1D method were performed to determin the output factor in various field size and electron energy. The expected output of electron beam of square root method for square field and 1D method for rectangular field were very closed to that of measurement in all selected electron beam energy.