• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.589-600
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• 2022

Investigations on the molten-pool sloshing behavior are of essential value for improving nuclear safety evaluation of Core Disruptive Accidents (CDA) that would be possibly encountered for Sodium-cooled Fast Reactors (SFR). This paper is aimed at synthesizing the knowledge from our recent studies on molten-pool sloshing behavior with solid particles conducted at the Sun Yat-sen University. To better visualize and clarify the mechanism and characteristics of sloshing induced by local Fuel-Coolant Interaction (FCI), experiments were performed with various parameters by injecting nitrogen gas into a 2-dimensional liquid pool with accumulated solid particles. It was confirmed that under different particle-bed conditions, three representative flow regimes (i.e. the bubble-impulsion dominant, transitional and bed-inertia dominant regimes) are identifiable. Aimed at predicting the regime transitions during sloshing process, a predictive empirical model along with a regime map was proposed on the basis of experiments using single-sized spherical solid particles, and then was extended for covering more complex particle conditions (e.g. non-spherical, mixed-sized and mixed-density spherical particle conditions). To obtain more comprehensive understandings and verify the applicability and reliability of the predictive model under more realistic conditions (e.g. large-scale 3-dimensional condition), further experimental and modeling studies are also being prepared under other more complicated actual conditions.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.167-167
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• 2023

Soil organic carbon (SOC) is a critical component of soil health and is crucial in mitigating climate change by sequestering carbon from the atmosphere. Accurate estimation of SOC storage is essential for understanding SOC dynamics and developing effective soil management strategies. This study aimed to investigate the factors influencing the spatial distribution of SOC storage in South Korea, using bulk density (BD) prediction to estimate SOC stock. The study utilized data from 393 soil series collected from various land uses across South Korea established by Korea Rural Development Administration from 1968-1999. The samples were analyzed for soil properties such as soil texture, pH, and BD, and SOC stock was estimated using a predictive model based on BD. The average SOC stock in South Korea at 30 cm topsoil was 49.1 Mg/ha. The study results revealed that soil texture and land use were the most significant factors influencing the spatial distribution of SOC storage in South Korea. Forested areas had significantly higher SOC storage than other land use types. Climate variables such as temperature and precipitation had a relative influence on SOC storage. The findings of this study provide valuable insights into the factors influencing the spatial distribution of SOC storage in South Korea.

• KSBB Journal
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• v.6 no.1
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• pp.15-25
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• 1991

The optimal glucose concentration for the high-density culture of recombinant yeasts was obtained using dynamic simulation. An adaptive and predictive algoritilm complimented by the rule base was proposed for the control of the fed-batch fermentation process. The measurement of process variables has relatively long sampling period and relatively long time delay characteristics. As one of the solution on these problems, prediction techniques and rule bases were added to a classical recursive identification and control algorithm. Rule bases were used in the determination of control input considering the difference between the predicted value and the measured value. A mathelnatical model was used in the estimation and interpretation of the changes of state variables and parameters. Better performances were obtained by employing the control algorithm proposed in the present study compared to the conventional adaptive control method.

• Tunnel and Underground Space
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• v.4 no.2
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• pp.144-155
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• 1994

This research is designed to assess current achievement levels for mechanized excavation systems in Korea adn suggest the model predictive of TBM performance using statistical approaches. A test section in the TBM construction sites is selected to measure and analyze TBM performance. The field records including operating data, time allocation into downtime catagories, and machine design are analyzed on a shift basis. There are a total of 240 shifts, with most days operating two shifts per day. Examples of the probability density functions produced from the test section are presented and discussed. Relationships between TBM penetration rate and rock physical properties are investigated and the empirical equations for TBM performance prediction are also assessed with the field data.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.15
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• pp.6407-6412
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• 2015

Purpose: To predict prostatic carcinoma using a logistic regression model on prebiopsy peripheral blood samples. Materials and Methods: Data of a total of 873 patients who consulted Urology Outpatient Clinics of Fatih Sultan Mehmet Training and Research Hospital between February 2008 and April 2014 scheduled for prostate biopsy were screened retrospectively. PSA levels, prostate volumes, prebiopsy whole blood cell counts, neutrophil and platelet counts, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), biopsy results and Gleason scores in patients who had established diagnosis of prostate cancer (PCa) were evaluated. Results: This study was performed on a total of 873 cases, with an age range 48-76 years, divided into three groups as for biopsy results. with diagnoses of benign prostatic hyperplasia (BPH) (n=304, 34.8 %), PCa (n=265, 30.4 %) and histological prostatitis (n=304; 34.8 %). Intra- and intergroup comparative evaluations were performed. White blood cell and neutrophil counts in the histological prostatitis group were significantly higher than those of the BPH and PCa groups (p=0.001; p=0.004; p<0.01). A statistically significant intergroup difference was found for PLR (p=0.041; p<0.05) but not lymphocyte count (p>0.05). According to pairwise comparisons, PLR were significantly higher in the PCa group relative to BPH group (p=0.018, p<0.05, respectively). Though not statistically significant, higher PLR in cases with PCa in comparison with the prostatitis group was remarkable (p=0.067, and p>0.05, respectively). Conclusions: Meta-analyses showed that in patients with PSA levels over 4 ng/ml, positive predictive value of PSA is only 25 percent. Therefore, novel markers which can both detect clinically significant prostate cancer, and also prevent unnecessary biopsies are needed. Relevant to this issue in addition to PSA density, velocity, and PCA3, various markers have been analyzed. In the present study, PLR were found to be the additional predictor of prostatic carcinoma.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.5 no.1
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• pp.49-58
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• 1987

The use of digital terrain model has been enlarged in calculating the earthwork due to the development of aerial photogrammetry. The calculation of earthwork plays a major role in plan or design of many civil engineering projects, and thus it has become very important to advance the accuracy of earthwork calculation. In this study, I have made an analysis of influences which DTM makes on the height accuracy of data ; on the basis of the analysis, we can develop the predictive model formula of profile shape coefficients by which the accuracy of earthwork can he preestimated in practical design according to data density of terrain, making thereby good contribution to the calculation of both earthwork amount and its expenses. This study shows that the accuracy of earthwork is more affected by the distances of cross-sections than by data density and that the effects by the standard errors of height decrease in proportion as the distances of cross-sections are great It also shows that when the prediction model formula of profile shape coefficients is applied to ordinary cases, the differences between the predicted earthwork errors and the errors by ordinary est imation are at 0.8374~3.1437$cm^3$/m, on flat terrain and 1.5628~6.967$cm^3$/m, on mountainous terrain-so little as to be ignored ; thus it can be safely ascertained that the accurate earthwork errors can be predicted applying the prediction model formula made in this study.

• Journal of Korea Water Resources Association
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• v.39 no.1 s.162
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• pp.79-88
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• 2006

An accurate prediction of inflow water temperature is essentially required for real-time simulation and analysis of rainfall-induced turbidity 烈os in a reservoir. In this study, water temperature data were collected at every hour during the flood season of 2004 at the upstream of Daecheong Reservoir to justify its characteristics during rainfall event and model development. A significant drop of river water temperature by 5 to $10^{\circ}C$ was observed during rainfall events, and resulted in the development of density flow regimes in the reservoir by elevating the inflow density by 1.2 to 2.6 kg/$m^3$ Two types of statistical river water temperature models, a logistic model(DLG) and regression models(DMR-1, DMR-2, DMR-3) were developed using the field data. All models are shown to reasonably replicate the effect of rainfall events on the water temperature drop, but the regression models that include average daily air temperature, dew point temperature, and river flow as independent variables showed better predictive performance than DLG model that uses a logistic function to determine the air to water relation.

• Korean Journal of Environment and Ecology
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• v.29 no.3
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• pp.333-343
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• 2015

This research aims at identifying the goshawk's possible and replaceable breeding ground by using the MaxEnt prediction model which has so far been insufficiently used in Korea, and providing evidence to expand possible protection areas for the goshawk's breeding for the future. The field research identified 10 goshawk's nests, and 23 appearance points confirmed during the 3rd round of environmental research were used for analysis. 4 geomorphic, 3 environmental, 7 distance, and 9 weather factors were used as model variables. The final environmental variables were selected through non-parametric verification between appearance and non-appearance coordinates identified by random sampling. The final predictive model (MaxEnt) was structured using 10 factors related to breeding ground and 7 factors related to appearance area selected by statistics verification. According to the results of the study, the factor that affected breeding point structure model the most was temperature seasonality, followed by distance from mixforest, density-class on the forest map and relief energy. The factor that affected appearance point structure model the most was temperature seasonality, followed by distance from rivers and ponds, distance from agricultural land and gradient. The nature of the goshawk's breeding environment and habit to breed inside forests were reflected in this modeling that targets breeding points. The northern central area which is about $189.5 km^2$(2.55 %) is expected to be suitable breeding ground. Large cities such as Cheongju and Chungju are located in the southern part of Chungcheongbuk-do whereas the northern part of Chungcheongbuk-do has evenly distributed forests and farmlands, which helps goshawks have a scope of influence and food source to breed. Appearance point modeling predicted an area of $3,071 km^2$(41.38 %) showing a wider ranging habitat than that of the breeding point modeling due to some limitations such as limited moving observation and non-consideration of seasonal changes. When targeting the breeding points, a specific predictive area can be deduced but it is difficult to check the points of nests and it is impossible to reflect the goshawk's behavioral area. On the other hand, when targeting appearance points, a wider ranging area can be covered but it is less accurate compared to predictive breeding point since simple movements and constant use status are not reflected. However, with these results, the goshawk's habitat can be predicted with reasonable accuracy. In particular, it is necessary to apply precise predictive breeding area data based on habitat modeling results when enforcing an environmental evaluation or establishing a development plan.