• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.268-277
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• 2022

In this study, each component of flow noise source of underwater propeller installed to the scale model of the KVLCC2 is investigated and the effect of each noise source on underwater-radiated noise is quantitatively analyzed. The computation domain is set to be the same as the test section of the large cavitation tunnel in the Korea Research Institute of Ship and Ocean Engineering. First, for the high-resolution computation of flow field which is noise source region, the incompressible multiphase Delayed Detached Eddy Simulation is performed. Based on flow simulation results, the Ffowcs Williams and Hawkings integral equation is used to predict underwater-radiated noise and its validity is confirmed through the comparison with the tunnel experiment result. For the quantitative comparison on the contribution of each noise source, the spectral levels of sound pressure and power levels predicted using propeller tip-vortex cavitation, blade surface and rudder surface as the integral region of noise sources are investigated. It is confirmed that the cavitation which is monopole noise source significantly contributed to the underwater-radiated noise than propeller blades and rudder which is dipole noise source, and the rudder have more contribution than propeller blades due to the influence of the propeller wake.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.299-308
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• 2022

This study was conducted to predict the tendency for heat exchange and boil-off gas (BOG) in a liquefied hydrogen tank under sloshing excitation. First, athe fluid domain excited by sloshing was modeled using a multiphase-thermal flow domain in which liquid hydrogen and hydrogen gas are in the saturated state. Both the the volume of fluid (VOF) and Eulerian-based multi-phase flow methods were applied to validate the accuracy of the pressure prediction. Second, it was indirectly shown that the fluid velocity prediction could be accurate by comparing the free surface and impact pressure from the computational fluid dynamics with those from the experimental results. Thereafter, the heat ingress from the external convective heat flux was reflected on the outer surfaces of the hydrogen tank. Eulerian-based multiphase-heat flow analysis was performed for a two-dimensional Type-C cylindrical hydrogen tank under rotational sloshing motion, and an inflation technique was applied to transform the fluid domain into a computational grid model. The heat exchange and heat flux in the hydrogen liquid-gas mixture were calculated throughout the analysis,, whereas the mass transfer and vaporization models were excluded to account for the pure heat exchange between the liquid and gas in the saturated state. In addition, forced convective heat transfer by sloshing on the inner wall of the tank was not reflected so that the heat exchange in the multiphase flow of liquid and gas could only be considered. Finally, the effect of sloshing on the amount of heat exchange between liquid and gas hydrogen was discussed. Considering the heat ingress into liquid hydrogen according to the presence/absence of a sloshing excitation, the amount of heat flux and BOG were discussed for each filling ratio.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.35 no.3
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• pp.273-281
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• 1990

Two rice varieties, Samkangbyeo and Sangpungbyeo, were transplanted on 1/2000a pots at 6 different dates beginning on May 11 with 10 day interval in 1987 and at 4 different dates beginning on May 21 with 10 day interval in a paddy field at the Chungbuk Provincial Rural Development Administration. Dry matter distributions to stem and leaf sheath, leaves and ear at different growth stages were analyzed to provide basic informations neccessary for the development of dynamic growth model. Dry matter production was reduced as transplanting was delayed and the degree of reduction was greater at the transplanting later than June 1. Dry matter distribution to stem and leaf sheath was increased up to 60-70 days after transplanting with the maximum ratio between 60-70%, which were decreased to 37-43% in pots and 27-33% in field at the end of ripening stage. On the other hand, dry matter distribution to leaf blade was decreased from 40-50% at transplanting to 11-17% at harvesting. Ear dry matter distribution increased rapidly after heading and the distribution ratio was 42-49% in pots and 52-62% in field. Although regression equations to predict dry matter distribution to different parts of rice plant were satisfactory for individual experiment, the application to different experiment was not appropriate.

• Smart Media Journal
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• v.11 no.10
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• pp.9-21
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• 2022

The recent expansion of the use of social media has served as an opportunity to express users' opinions in real time in various fields such as society, economy, politics, and culture, and brought many platforms that provide various information about companies. Among them, Glassdoor.com which started 2008 in US provides users with evaluations of the current and the former employees of their companies and also provides a outlooks for the company's growth Such a platform has the utility of providing necessary information to whom want to find a job or change jobs. In addition to this, variable studies have shown that the company information provided through these platforms is useful for investors as well. In this study, it was tested whether the corporate growth prospects of employees provided by Jobplanet, a platform with a typical function similar to Glassdoor.com in Korea, have predictive power to predict actual corporate growth. The forecast provided by Jobplanet and the company's financial indicator data received from FnGuide were collected and composed of panel data and analyzed using fixed effect model regression analysis. As a result, it was found that companies with positive prospects had higher employment growth than companies with negative prospects. When the outlook was neutral, the employment growth rate was higher than that of companies with a negative outlook.

• Journal of Wetlands Research
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• v.25 no.3
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• pp.167-175
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• 2023

Precipitation data is one of the essential input datasets used in various fields such as wetland management, hydrological simulation, and water resource management. In order to efficiently manage water resources using precipitation data, it is essential to secure as much data as possible by minimizing the missing rate of data. In addition, more efficient hydrological simulation is possible if precipitation data for ungauged areas are secured. However, missing precipitation data have been estimated mainly by statistical equations. The purpose of this study is to propose a new method to restore missing precipitation data using machine learning algorithms that can predict new data based on correlations between data. Moreover, compared to existing statistical methods, the applicability of machine learning techniques for restoring missing precipitation data is evaluated. Representative machine learning algorithms, Artificial Neural Network (ANN) and Random Forest (RF), were applied. For the performance of classifying the occurrence of precipitation, the RF algorithm has higher accuracy in classifying the occurrence of precipitation than the ANN algorithm. The F1-score and Accuracy values, which are evaluation indicators of the classification model, were calculated as 0.80 and 0.77, while the ANN was calculated as 0.76 and 0.71. In addition, the performance of estimating precipitation also showed higher accuracy in RF than in ANN algorithm. The RMSE of the RF and ANN algorithms was 2.8 mm/day and 2.9 mm/day, and the values were calculated as 0.68 and 0.73.

• Journal of Korean Port Research
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• v.11 no.2
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• pp.231-240
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• 1997

In the United States, a number of ocean outfalls discharge primary treated effluent into deep sea water and contribute for more efficient wastewater treatment. The long multiport diffuser connected by long pipe from a treatment plant discharge wastewater into deep water due to the steep slope of the sea bed. However, Plume discharged from the diffuser can have significant impacts on coastal communities and possibly immediate consequence on public health. Therefore, there have been growing interests about the dynamics of plume in the vicinity of the ocean outfalls. It is expected that the ocean outfall should be considered for more efficient and reliable wastewater treatments as soon as possible around coastal area in South Korea. A number of studies of plume ynamics have used various models to predict plume behavior. However, in many cases, the calculated values of plume behavior are in significantly poor agreement with realistic values. Therefore, in this study, it is recommended that improvements should be made in the application of the plume model to more simulate the actual discharge characteristics and ocean conditions. It should be noted that input parameters in plume models reflect realistic ocean conditions like waves as well as currents. In this study, as one of the new parameters, current and long wave-influenced plume rise and initial dilution have been taken into account by using simple linear wave theory under some specific assumptions for more reliable plume behavior description. Among the improved plume models approved by EPA (Environmental Protection Agency), the RSB(Roberts-Snyder-Baurngartner) and UM(Updated Merge) models were chosen for the calculation of plume behavior, and the variation calculated by both models on the basis of long period wave was compared in terms of plume rise and initial dilution.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.285-292
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• 2023

Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.61-68
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• 2023

As global growth has gradually declined, the Customer to Customer (C2C) market has expanded. And the growth potential of the C2C market is getting higher than in the past. Therefore, in this study, we examined what factors affect the price of used products within the C2C market. In order to examine the factors, we used data provided by Kaggle, which is a data science platform, and Mercari, Japan's largest C2C community marketplace platform. In research methods, the characteristics of the products were selected such as product categories, product status, shipping costs, product brands, and the data were analyzed using a linear mixing model to predict the price of C2C used goods. As a result, the variable that most affected the price was the shipping cost. When the seller paid for the shipping cost, the price would drop more than if the buyer had to pay. This study has been shown that the shipping costs is also an important factor in the used market, which can provide practical implications for customers of real transactions.

• Journal of the Korean Geotechnical Society
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• v.26 no.2
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• pp.45-54
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• 2010

The SCW numerical model described in the companion paper was used to carry out a comprehensive parametric study to evaluate the performance of the SCW. The five ground related parameters, which are porosity, hydraulic conductivity, thermal conductivity, specific heat, geothermal gradient, and five SCW design parameters, which are pumping rate, well depth, well diameter, dip tube diameter, bleeding rate, were used in the study. Two types of numerical simulations were performed. The first type was used to perform short-term (24-hour) simulation, while the second type 14 day simulation. The study results indicate that the parameters that have important influence on the performance of SCW were hydraulic conductivity, thermal conductivity, geothermal gradient, pumping rate, and bleeding rate. The thermal conductivity had the most important influence on the performance of the SCW. With the increase in the geothermal gradient, the performance increased in the heat mode, but decreased in the cooling mode. The hydraulic conductivity influenced the performance when the value was larger than $10^{-4}m/s$. The depth of the well increased the performance, but at the cost of increased cost of boring. The bleeding had an important influence on SCW, greatly enhancing the performance at a limited increased cost of operation. Overall, this study showed that various factors had a cumulative influence on the performance of the SCW, and a numerical simulation can be used to accurately predict the performance of the SCW.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.