• Journal of Radiation Protection and Research
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• v.30 no.1
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• pp.45-54
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• 2005

A dynamic compartment model was developed to appraise the level of the contamination of agricultural plants by accidentally released tritium from nuclear facility. The model consists of a set of inter-connected compartments representing atmosphere, soil and plant. In the model three categories of plant are considered: leafy vegetables, grain plants and tuber plants, of which each is modeled separately to account for the different transport pathways of tritium. The predictive accuracy of the model was tested through the analysis of the tritium exposure experiments for rice-plants. The predicted TFWT(tissue free water tritium) concentration of the rice ear at harvest was greatly affected by the absolute humidity of air, the ratio of root uptake, and the rate of rainfall, while its OBT(organically bound tritium) concentration the stowing period of the ear, the absolute humidity of air and the content of hydrogen in the organic phase. There was a good agreement between the model prediction and the experimental results lot the OBT concentration of the ear.

• Nuclear Engineering and Technology
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• v.21 no.2
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• pp.73-88
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• 1989

Ten methods of the total two-phase pressure drop prediction based on five existing models and correlations have been examined for their accuracy and applicability to pressurized water reactor conditions. These methods were tested against 209 experimental data of local and bulk boiling conditions : Each correlations were evaluated for different ranges of pressure, mass velocity and Quality, and best performing models were identified for each data subsets. A computer code entitled 'K-TWOPD' has been developed to calculate the total two-phase pressure drop using the best performing existing correlations for a specific property range and a correction factor to compensate for the predicted error of the selected correlations. Assessment of this code shows that the present method fits all the available data within $\pm$11% at a 95% confidence level compared with $\pm$25%, for the existing correlations.

• The Journal of Engineering Research
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• v.5 no.1
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• pp.89-107
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• 2004

This study has tried to develop the modified DRASTIC Model by supplying the parameters, such as structural lineament density and landuse, into conventional DRASTIC model, and to predict the potential of groundwater contamination using GIS in Whanam 2 Area, Gyeonggi Province, Korea. Since the aquifers in Korea is generally through the joints of rock-mass in hydrogeological environment, lineament density affects to the behavior of groundwater and contaminated plumes directly, and land-use reflect the effect of point or non-point source of contamination indirectly. For the statistical analysis, lattice layers of each parameter were generated, and then level of confidence was assessed by analyzing each correlation coefficient. Composite contamination map was achieved as a final result by comparing modified DRASTIC potential and the amount of generation load of several contaminant sources logically. The result could suggest the predictability of the area of contamination potential on the respects of hydrogeological aspect and water quality.

• Membrane and Water Treatment
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• v.9 no.2
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• pp.115-121
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• 2018

Artificial neural network (ANN) simulation is used to predict the dynamic change of permeate flux during wheat starch industry wastewater microfiltration with and without static turbulence promoter. The experimental program spans range of a sedimentation times from 2 to 4 h, for feed flow rates 50 to 150 L/h, at transmembrane pressures covering the range of $1{\times}10^5$ to $3{\times}10^5Pa$. ANN predictions of the wastewater microfiltration are compared with experimental results obtained using two different set of microfiltration experiments, with and without static turbulence promoter. The effects of the training algorithm, neural network architectures on the ANN performance are discussed. For the most of the cases considered, the ANN proved to be an adequate interpolation tool, where an excellent prediction was obtained using automated Bayesian regularization as training algorithm. The optimal ANN architecture was determined as 4-10-1 with hyperbolic tangent sigmoid transfer function transfer function for hidden and output layers. The error distributions of data revealed that experimental results are in very good agreement with computed ones with only 2% data points had absolute relative error greater than 20% for the microfiltration without static turbulence promoter whereas for the microfiltration with static turbulence promoter it was 1%. The contribution of filtration time variable to flux values provided by ANNs was determined in an important level at the range of 52-66% due to increased membrane fouling by the time. In the case of microfiltration with static turbulence promoter, relative importance of transmembrane pressure and feed flow rate increased for about 30%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.341-346
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• 2022

Due to global warming and abnormal climate, the frequency and damage of floods are increasing, and the number of people exposed to flood-prone areas has increased by 25% compared to 2000. Floods cause huge financial and human losses, and in order to reduce the losses caused by floods, it is necessary to predict the flood in advance and decide to evacuate quickly. This paper proposes a flood risk determination model using a CNN-based classification model so that timely evacuation decisions can be made using rainfall and water level data, which are key data for flood prediction. By comparing the results of the CNN-based classification model proposed in this paper and the DNN-based classification model, it was confirmed that it showed better performance. Through this, it is considered that it can be used as an initial study to determine the risk of flooding, determine whether to evacuate, and make an evacuation decision at the optimal time.

• Journal of Ecology and Environment
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• v.45 no.4
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• pp.264-276
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• 2021

Background: Salt marshes provide a variety of ecosystem services; however, they are vulnerable to human activity, water level fluctuations, and climate change. Analyses of the relationships between plant communities and environmental conditions in salt marshes are expected to provide useful information for the prediction of changes during climate change. In this study, relationships between the current vegetation structure and environmental factors were evaluated in the tidal flat at the southern tip of Ganghwa, Korea, where salt marshes are well-developed. Results: The vegetation structure in Ganghwa salt marshes was divided into three groups by cluster analysis: group A, dominated by Phragmites communis; group B, dominated by Suaeda japonica; and group C, dominated by other taxa. As determined by PERMANOVA, the groups showed significant differences with respect to altitude, soil moisture, soil organic matter, salinity, sand, clay, and silt ratios. A canonical correspondence analysis based on the percent cover of each species in the quadrats showed that the proportion of sand increased as the altitude increased and S. japonica appeared in soil with a relatively high silt proportion, while P. communis was distributed in soil with low salinity. Conclusions: The distributions of three halophyte groups differed depending on the altitude, soil moisture, salinity, and soil organic matter, sand, silt, and clay contents. Pioneer species, such as S. japonica, appeared in soil with a relatively high silt content. The P. communis community survived under a wider range of soil textures than previously reported in the literature; the species was distributed in soils with relatively low salinity, with a range expansion toward the sea in areas with freshwater influx. The observed spatial distribution patterns may provide a basis for conservation under declining salt marshes.

• Journal of the Korean Society of Safety
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• v.37 no.5
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• pp.80-88
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• 2022

The Korean nuclear industry had developed the SPACE (Safety and Performance Analysis Code for nuclear power plants) code, which adopts a two-fluid, three-field model that is comprised of gas, continuous liquid and droplet fields and has the capability to simulate three-dimensional models. According to the revised law by the Nuclear Safety and Security Commission (NSSC) in Korea, the multiple failure accidents that must be considered for the accident management plan of a nuclear power plant was determined based on the lessons learned from the Fukushima accident. Generally, to improve the reliability of the calculation results of a safety analysis code, verification is required for the separate and integral effect experiments. Therefore, the goal of this work is to verify the calculation capability of the SPACE code for multiple failure accidents. For this purpose, an experiment was conducted to simulate a Control Element Drive Mechanism (CEDM) break with a safety injection failure using the ATLAS test facility, which is operated by Korea Atomic Energy Research Institute (KAERI). This experiment focused on the comparison between the experiment results and code calculation results to verify the performance of the SPACE code. The results of the overall system transient response using the SPACE code showed similar trends with the experimental results for parameters such as the system pressure, mass flow rate, and collapsed water level in component. In conclusion, it can be concluded that the SPACE code has sufficient capability to simulate a CEDM break with a safety injection failure accident.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.335-337
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• 2021

Flood damage is occurring all over the world, and the number of people living in flood-prone areas reached 86 million, a 25% increase compared to 2000. These floods cause enormous damage to life and property, and it is essential to decide on an appropriate evacuation in order to reduce the damage. Evacuation in anticipation of a flood also incurs a lot of cost, and if an evacuation is not performed due to an error in the flood prediction, a greater cost is incurred. Therefore, in this paper, we propose a flood risk determination model using the CNN model to enable evacuation at an appropriate time by using the time series data of precipitation and water level. Through this, it is thought that it can be utilized as an initial study to determine the time of flood evacuation to prevent unnecessary evacuation and to ensure that evacuation can be carried out at an appropriate time.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.2B
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• pp.131-137
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• 2006

We have investigated the properties of the Singular Spectrum Analysis (SSA) coupled with the Linear Recurrent Formula which made it possible to complement the parametric time series model. The SSA has been applied to extract the underlying properties of the principal component of hydrologic time series, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. Generally, the prediction by the SSA method can be applied to hydrologic time series governed (may be approximately) by the linear recurrent formulae. This study has examined the forecasting ability of the SSA-LRF model. These methods were applied to monthly discharge and water surface level data. These models indicated that two of the time series have good abilities of forecasting, particularly showing promising results during the period of one year. Thus, the method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.208-216
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• 2023

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.