• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.1
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• pp.32-39
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• 2018

Repairing timing and the extended service life with repairing are very important for cost estimation during operation. Conventionally used model for repair cost shows a step-shaped cost elevation without consideration of variability of extended service life due to repairing. In the work, RC(Reinforced Concrete) Column is considered for probabilistic evaluation of repairing number and cost. Two mix proportions are prepared and chloride behavior is evaluated with quantitative exterior conditions. The repairing frequency and cost are investigated with varying service life and the extended service life with repairing which were derived from the chloride behavior analysis. The effect of COV(Coefficient of Variation) on repairing frequency is small but the 1st repairing timing is shown to be major parameter. The probabilistic model for repairing cost is capable of reducing the number of repairing with changing the intended service life unlike deterministic model of repairing cost since it can provide continuous repair cost with time.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.573-586
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• 2020

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow's PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 ㎍/㎥ and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.

• Journal of Preventive Medicine and Public Health
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• v.24 no.2 s.34
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• pp.161-170
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• 1991

The purpose of this study is to find the tracking of blood pressure in primary school-age children. A follow-up study was conducted from 1986 to 1990 on 330 first grade children attending primary schools in Kangwha County, Kyungki-Do. Basically we employed a linear regression model with random coefficients to figure out the relation between blood pressure changes and initial blood pressure. We obtained the following results ; 1. The mean blood pressures were increased grade went up in both sexs and were generally higher in female than male except for the systolic blood pressure at first grade. The size of difference was about 0.8 mmHg in mean systolic blood pressure and 1.5 mmHg in mean diastolic blood pressure. 2. The average annual increasing rates of systolic blood pressure were 2.5 mmHg in male and 3.1 mmHg in female respectively. For the diastolic blood pressure IV the average annual increasing rates were observed to be 3.0 mmHg in male and 2.9 mmHg in female respectively. Increasing rate of systolic blood pressure was significantly higher in female than male. 3. The adjusted regression coefficient of systolic blood pressure change on initial value was -0.11 in male and -0.13 in female and that coefficient of diastolic blood pressure change on initial value was -0.01 in male and -0.11 in female. This result shows that children with higher initial blood pressure do not pick up their blood pressure faster than others with lower initial blood pressure. There is no evidence of tracking of blood pressure in children. It is essential to find the earliest age having the tracking of blood pressure and we leave it for the further study.

• Steel and Composite Structures
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• v.43 no.5
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• pp.581-601
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• 2022

The main objective of this research work is to investigate the free vibration behavior of annular sandwich plates resting on the Kerr foundation at thermal conditions. This sandwich configuration is composed of two FGM face sheets as coating layer and a porous GPLRC (GPL reinforced composite) core. It is supposed that the GPL nanofillers and the porosity coefficient vary continuously along the core thickness direction. To model closed-cell FG porous material reinforced with GPLs, Halpin-Tsai micromechanical modeling in conjunction with Gaussian-Random field scheme is used, while the Poisson's ratio and density are computed by the rule of mixtures. Besides, the material properties of two FGM face sheets change continuously through the thickness according to the power-law distribution. To capture fundamental frequencies of the annular sandwich plate resting on the Kerr foundation in a thermal environment, the analysis procedure is with the aid of Reddy's shear-deformation plate theory based high-order shear deformation plate theory (HSDT) to derive and solve the equations of motion and boundary conditions. The governing equations together with related boundary conditions are discretized using the generalized differential quadrature (GDQ) method in the spatial domain. Numerical results are compared with those published in the literature to examine the accuracy and validity of the present approach. A parametric solution for temperature variation across the thickness of the sandwich plate is employed taking into account the thermal conductivity, the inhomogeneity parameter, and the sandwich schemes. The numerical results indicate the influence of volume fraction index, GPLs volume fraction, porosity coefficient, three independent coefficients of Kerr elastic foundation, and temperature difference on the free vibration behavior of annular sandwich plate. This study provides essential information to engineers seeking innovative ways to promote composite structures in a practical way.

• The Journal of Engineering Geology
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• v.31 no.3
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• pp.395-432
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• 2021

Groundwater recharge rates vary widely by location and with time. They are difficult to measure directly and are thus often estimated using simulations. This study employed frequency and regression analysis and a classification and regression tree (CART) algorithm in a machine learning method to estimate groundwater recharge. CART algorithms are considered for the distribution of precipitation by subbasin (PCP), geomorphological data, indices of the relationship between vegetation and landuse, and soil type. The considered geomorphological data were digital elevaion model (DEM), surface slope (SLOP), surface aspect (ASPT), and indices were the perpendicular vegetation index (PVI), normalized difference vegetation index (NDVI), normalized difference tillage index (NDTI), normalized difference residue index (NDRI). The spatio-temperal distribution of groundwater recharge in the SWAT-MOD-FLOW program, was classified as group 4, run in R, sampled for random and a model trained its groundwater recharge was predicted by CART condidering modified PVI, NDVI, NDTI, NDRI, PCP, and geomorphological data. To assess inter-rater reliability for group 4 groundwater recharge, the Kappa coefficient and overall accuracy and confusion matrix using K-fold cross-validation were calculated. The model obtained a Kappa coefficient of 0.3-0.6 and an overall accuracy of 0.5-0.7, indicating that the proposed model for estimating groundwater recharge with respect to soil type and vegetation cover is quite reliable.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.363-373
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• 2024

Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. Materials and Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set. Results: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test. Conclusion: The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.

• KSBB Journal
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• v.4 no.2
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• pp.110-117
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• 1989

The fiffusion characteristics of substrate of varing biomass concentrations into and from Ca- alginate gel beads in well-stirred solutions were investigated. Ca-alginate gel beads were immobilized by Zymomonas mobilis or free from cells. The values of the diffusion coefficient of substrate were calculated by means of the method of Least squares and Random pore model. Reaction rates are expressed by the Michaelis-Menten type equation, and the results are compared with experimental data. Intraparticle effective diffusivity of substrate resistance on reaction by using immobilized Z.mobilis entrapped by Ca-alginated gel seemed to be restricted by cell density. The experimental data also indicated relationship between the effective diffusivity and the cell concentration used in the gel preparation.

• Journal of the Korean Society of Visualization
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• v.7 no.1
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• pp.9-13
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• 2009

Fluorescence recovery after photobleaching (FRAP) has been widely used for the measurement of molecular diffusion in living cells and tissues. We developed an image-based FRAP (iFRAP) technique using a modified real-time microscope and a 488 nm Ar-ion laser. A fractional intensity curve was obtained from the time-lapse images of fluorescence recovery in the bleached spot to determine the diffusion coefficient of fluorescently labeled macromolecules in porous medium. We validated iFRAP through experiments with agar gels (0.5% and 1.5% w/v) containing FITC-Dextrans (10, 70 and 500 kDa MW). Further validation was performed by a Monte Carlo approach, where we simulated the three-dimensional random walk of macromolecules in agar gel model. Diffusion coefficients were deduced from the mean square displacement curves and showed good agreements with those measured by iFRAP.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.383-390
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• 2016

Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

• Journal of Electrical Engineering and Technology
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• v.6 no.5
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• pp.677-683
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• 2011

The present paper describes the setup of standard partial discharge calibrator for measuring partial discharge and estimating uncertainties. The standard PD calibrator was designed and set up, consisting of a digital pulse generator, capacitor modules, and a digital oscilloscope controlled by software developed in the laboratory. Using this software, averages of charges and rising times and their standard deviations in the measured pulses can also be calculated. The standard PD calibrator generates five types of pulses: single, double, random, oscillating, and long-rising. The coefficient sensitivities to estimate the uncertainties of pulses were extracted in the model circuit of the standard PD calibrator. The uncertainties of charges and rising times in pulses of the standard PD calibrator were estimated with single pulses. These values were 0.3%-1.4% in charges and 1.9%-7.0% in rising time; however, these values are lower than the limit values in IEC 60270.