• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.107-114
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• 2020

Extreme value distributions have often been used for the analysis (e.g., prediction of return level) of data which are observed from natural disaster. By the extreme value theory, the block maxima asymptotically follow the generalized extreme value distribution as sample size increases; however, this may not hold in a small sample case. For solving this problem, this paper proposes the use of a log-logistic (LLG) distribution whose validity is evaluated through goodness-of-fit test and model selection. The proposed method is illustrated with data from annual maximum earthquake magnitudes of China. Here, we present the predicted return level and confidence interval according to each return period using LLG distribution.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.899-907
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• 2022

Purpose: Although many studies on seismic fragility analysis of general bridges have been conducted using machine learning methods, studies on curved bridge structures are insignificant. Therefore, the purpose of this study is to analyze the seismic fragility of bridges with I-shaped curved girders based on the machine learning method considering the material property and geometric uncertainties. Method: Material properties and pier height were considered as uncertainty parameters. Parameters were sampled using the Latin hypercube technique and time history analysis was performed considering the seismic uncertainty. Machine learning data was created by applying artificial neural network and response surface analysis method to the original data. Finally, earthquake fragility analysis was performed using original data and learning data. Result: Parameters were sampled using the Latin hypercube technique, and a total of 160 time history analyzes were performed considering the uncertainty of the earthquake. The analysis result and the predicted value obtained through machine learning were compared, and the coefficient of determination was compared to compare the similarity between the two values. The coefficient of determination of the response surface method was 0.737, which was relatively similar to the observed value. The seismic fragility curve also showed that the predicted value through the response surface method was similar to the observed value. Conclusion: In this study, when the observed value through the finite element analysis and the predicted value through the machine learning method were compared, it was found that the response surface method predicted a result similar to the observed value. However, both machine learning methods were found to underestimate the observed values.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.27 no.3
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• pp.203-210
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• 2021

With the development of package printing technology, the package has expanded from the basic function of protecting products to the marketing function through package design. Color, the visual element that composes the package design, is delivered to the consumer most quickly and effectively. As color marketing of these package designs expands, accurate color reproduction that the product wants to express is becoming more important. The color of an object is transmitted by absorption and scattering of light. Spectral reflectance refers to the intensity of light reflected by an object at different wavelengths by the spectral effect. As a result, the color of the object is expressed in various colors. Packaged printing inks have their own absorption and scattering coefficients, and the Kubelka-Munk model for color reproduction and prediction defines the relationship between these correlation coefficients through reflectance. In the Kubelka-Munk model for color reproduction and prediction, the relationship between the absorption and scattering coefficients (K/S) of printed material is predicted as the sum of the K/S values according to the mixing ratio of all color ink used. In this study, the reflectance of the measured print is reversely calculated at the mixing ratio of print ink using the Kubelka-Munk model. Through this, the relationship value of the ink-specific absorption/scattering coefficient constituting the final printed material is predicted. Delta E is derived through the predicted reflectance, and the similarity between the measured value and the predicted value is confirmed.

• Tuberculosis and Respiratory Diseases
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• v.49 no.3
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• pp.290-297
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• 2000

Background : Current non-invasive methods for evaluating the mediastinum by computed tomographic (CT) scan have limited sensitivity and specificity. The recently introduced PET was reported to be a more sensitive and specific method for the mediastinal staging of NSCLC (sensitivity : 76-100 %, specificity : 81-100%) than CT or MRI. We assessed the usefulness of PET in the mediastinal staging of NSCLC. Methods : We reviewed the medical records of NSCLC patients that had undertaken staging work-up by both CT and PET before thoracotomy between January 1997 and December 1998. A total of 23 patients were enrolled in the study (14 males and 7 females) with a mean age of 61$\pm$9 years. By comparing the clinical(CT and PET) and pathologic stagings, we evaluated the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of PET in thoracic nodal staging. Results : Sensitivity, specificity, positive predicted value and negative predicted value were 38%, 40%, 25% and 50% respectively for computed tomography, and 50%, 60%, 30% and 69% for PET. The accuracy of FDG-PET in our study was lower than that reported by previous other studies. Conclusion : The addition of FDG-PET to CT scanning has limited benefit for the thoracic nodal staging of NSCLC, but its value in our study was lower than that observed by others.

• Journal of the Korean Geosynthetics Society
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• v.19 no.1
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• pp.93-101
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• 2020

In order to predict the nonlinear behaviour of the soil, the elasto-plastic hyperbolic model was selected, which was considered to be relatively simple and highly predictable. The soil parameter determination and the behavior analysis program were developed by formalizing the functions related to the constitutive model. Each soil parameter was determined from the results of the drained triaxial compression tests of Baekma river sand with the change of relative density. The stress-strain behavior was predicted using the soil parameters determined under each condition. As a result, the deviator stress for the axial strain is verified to have a good match between the measured value and predicted value at each relative density. In the relationship between the volumetric stain and the axial strain, when the relative density is loose, the measured value and predicted value tend to match, and when relative density is dense, the predicted value of the volumetric strain appears somewhat smaller than the measured value due to the limitation of the constitutive model.

• Journal of the Korean Recycled Construction Resources Institute
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• v.7 no.3
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• pp.279-286
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• 2019

In this study, an experimental study on the tensile properties of steel fiber-reinforced ultra high strength concrete(UHSC) with a standard compressive strength of 180MPa was performed. Steel fibers with a volume ratio of 1% were mixed to prepare direct tensile strength specimens and prism specimens for the three-point bending test. The fabricated specimens were set up in the middle section of the specimen to induce cracks, and the test was carried out according to each evaluation method. First, the stress-strain curves were analyzed by performing direct tensile strength tests to investigate the behavior characteristics of concrete after cracking. In addition, the load-CMOD curve was obtained through the three-point bending test, and the inverse analysis was performed to evaluate the stress-strain curve. Tensile behavior characteristics of the direct tensile test and the three-point bending test of the indirect test were similar. In addition, the tensile stress-strain curve modeling presented in the SC structural design guidelines was performed, and the comparative analysis of the measured and predicted values was performed. When the material reduction factor of 1.0 was applied, the predicted value was similar to the measured value up to the strain of 0.02, but when the material reduction factor of 0.8 was applied, the predicted value was close to the lower limit of the measured value. In addition, when the strain was greater than 0.02, the predicted value by SC structural design guideline to underestimated the measured value.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.55-58
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• 2000

To achieve high performance by exploiting instruction level parallelism(ILP) aggressively in superscalar processors, value prediction is used. Value prediction is a technique that breaks data dependences by predicting the outcome of an instruction and executes speculatively it's data dependent instruction based on the predicted outcome. In this paper, the performance of a hybrid value prediction scheme with dynamic classification mechanism is measured and analyzed by using execution-driven simulator for SPECint95 benchmark set.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.4
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• pp.327-332
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• 2014

In this paper, the temperature distribution according to the property change of the insulating oil of the power transformer and max temperature were predicted through the ductility interpretation which heat-flow is coupled. By using CFD (Computation Fluid Dynamics) for the interpretation, the temperature distribution of 154kV the class single phase power transformer was predicted. The power loss causing the temperature rise of the transformer was changed to the heat source and we used as the input value for the heat-flow analysis. The temperature distribution was predicted according to the change of the density, specific heat, thermal conductivity and viscosity, that is the ingredient having an effect on the temperature rise of the transformer oil. The mineral oil of 4 kinds used in domestic and international based on the interpreted result was selected and the temperature distribution according to each load and Hot Spot temperature was predicted.

• Journal of Biosystems Engineering
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• v.26 no.1
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• pp.39-46
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• 2001

This research conducted to investigate the variation of the moisture content, crack ratio, and hardness of the whole and cracked brown rice after conditioning at the initial moisture content of 13, 14, and 15% with time lapse. The conditioning was conducted by increasing the moisture content of the sample to 0.4 and 0.8%. For basic information and conditioning characteristics for the development of a conditioning machine for the brown rice, predicted models of above three properties were developed using a nonlinear regression analysis of SAS with Gauss-Newton, Gradient, and DUD methods. Results of this research could be summarized as follows. 1. No moisture variation occurred after 0.5 hour conditioning. 2. The increasement of the crack ratio was 7.6 and 17.5% with the sample increased the moisture content of 0.4 and 0.8%, respectively, after 8 hours conditioning. 3. The hardness of the conditioned whole grain of the brown rice decreased 0.82 and 1,000kg$\_$f/ with the sample increased moisture content 0.4 and 0.8%, respectively, after 8 hours conditioning with respect to the non-conditioned sample. 4. The hardness of the conditioned cracked grain of the brown rice decreased 0.54 and 0.81kg$\_$f/ with the sample increased moisture content 0.4 and 0.8%, respectively, after 8 hours conditioning with respect to the non-conditioned sample. The hardness of the broken grain was about 0.81∼1.88kg$\_$f/ lower than whole grain. 5. The moisture content variation, increasing rate of the crack ratio, and hardness of the cracked and whole grain was predicted as a negative exponential function. 6. Each predicted model with the nonlinear regression analysis, which was very accurate and had a very small amount of sum of square of error between experimental value and predicted value, which could be used for predicting the physical variation after conditioning.

• Journal of Microbiology and Biotechnology
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• v.17 no.12
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• pp.1996-2004
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• 2007

In this study, the optimization of culture medium using a Sterigmatomyces elviae mutant was investigated using statistical analysis to increase the cell mass and lactosucrose ($^4G-{\beta}$-D-galactosylsucrose) production. In basal medium, the cell mass and lactosucrose production were 4.12 g/l and 140.91 g/l, respectively. However, because of the low cell mass and lactosucrose production, optimization of culture medium was carried out to increase the cell mass and lactosucrose production. Culture media were optimized by the S. elviae mutant using analysis of variance (ANOVA) and response surface methodology (RSM). Central composite designs using RSM were utilized in this investigation. Quadratic models were obtained for cell mass and lactosucrose production. In the case of cell mass, optimal components of the medium were as follows: sucrose 1.13%, yeast extract 0.99%, bactopeptone 2.96%, and ammonium sulfate 0.40%. The predicted maximum value of cell mass was about 5.20 g/l and its experimental value was 5.08 g/l. In the case of lactosucrose production, optimal components of the medium were as follows: sucrose 0.96%, yeast extract 1.2%, bactopeptone 3.0%, and ammonium sulfate 0.48%. Then, the predicted maximum value of lactosucrose production was about 194.12 g/l and the corresponding experimental value was about 183.78 g/l. Therefore, by culturing using predicted conditions, the real cell mass and lactosucrose production increased to 23.3% and 30.42%, respectively.