• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2000.11a
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• pp.87-90
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• 2000

The hydrogeochemical and isotopic studies on deep groundwater in the Munkyeong area, Kyeongbuk province were carried out. $CO_2$-rich groundwater (Ca-HC $O_3$ type) is characterized by low pH (5.8~6.5) and high TDS (up to 2,682 mg/L), while alkali groundwater (Na-HC $O_3$ type) shows a high pH (9.I~10.4) and relatively low TBS (72~116 mg/L). $CO_2$-rich water may have evolved by $CO_2$ added at depth during groundwater circulation. This process leads to the dissolution of surrounding rocks and Ca, Na, Mg, K and HC $O_3$ concentrations are enriched. The low Pc $o_2$ (10$^{-6.4}$atm) of alkali groundwaters seems to result from the dissolution of silicate minerals without a supply of $CO_2$. The $\delta$$^{18}$ O and $\delta$D values and tritium data indicate that two types of deep groundwater were both derived from pre-thermonuclear meteoric water. The carbon Isotope data show that dissolved carbon in the $CO_2$-rich water was possibly derived from deep-seated $CO_2$ gas. The $\delta$$^{18}$ S values of dissolved sulfate show that sulfate reduction occurred at great depths. The application of various chemical geothermometers on $CO_2$-rich groundwater shows that the calculated deep reservoir temperature is about 130~175$^{\circ}C$. Based on the geological setting, water chemistry and environmental isotope data, each of the two types of deep groundwater represent distinct hydrologic and hydrogeochemical evolution at depth and their movement is controlled by the local fracture system.m.

• The Journal of Korean Physical Therapy
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• v.12 no.2
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• pp.57-67
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• 2000

The purpose of this study was carried out in five rabbits of 3kg to investigate the change of superficial and deep heats in ultrasound application by coupling media. Temperature measured with thermistor needle at skin. subcutaneous, muscle in before coupling media application, after coupling media application. 2minutes. 5minutes, 7minutes, 10minutes. Coupling media was used gel, glycerin, distilled water. The data was analyzed using spss/pc+and t-test The results were as fallow : 1. With skin. gel was significant temperature change in 2minutes(p<.05). glycerin was significant temperature change in 2minutes(p<.05), 5minutes(p<.05), 7minutes(p<.01), 10minutes(p<.01). distilled water was significant temperature change in post coupling media(p<.05), 2minutes(p<.01). 5minutes(p<.05). 7minutes(p<.01). 10minutes(p<.01). With subcutaneous. gel was no temperature change. glycerin was significant temperature change in 2minutes(p<.05), 5minutes(p<.05), 10minutes(p<.01). distilled water was no temperature change. With muscle. gel was no temperature change. glycerin was significant temperature change in 2minutes(p<.05). 5minutes(p<.05). 7minutes(p<.05). 10minutes( p<.05). distilled water was significant temperature change in 10minutes(p<.05). 2. Superficial heats of skin and subcutaneous was higher temperature change than Deep heats of muscle. 3. Gel. glycerin. distilled Water required minimum treatment 10minutes fur thermal effect. 4. Gel was low temperature change superficial and deep heats. and glycerin was high temperature change superficial and deep heats. This results show that gel is high transmissiveness in the coupling media and glycerin is low transmissiveness in the coupling media.

• Journal of Korean Society on Water Environment
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• v.37 no.4
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• pp.275-285
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• 2021

Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

• Journal of the Korean Society for Marine Environment & Energy
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• v.18 no.3
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• pp.143-156
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• 2015

In order to understand the dynamic characteristics of water column environments in the Western Pacific seamount area (approximately $150.2^{\circ}E$, $20^{\circ}N$), we investigated the water mass and the behavior of water column parameters such as dissolved oxygen, inorganic nutrients (N, P), and chlorophyll-a. Physico-chemical properties of water column were obtained by CTD system at the nine stations which were selected along the east-west and south-north direction around the seamount (OSM14-2) in October 2014. From the temperature-salinity diagram, the main water masses were separated into North Pacific Tropical Water and Thermocline Water in the surface layer, North Pacific Intermediate Water in the intermediate layer, and North Pacific Deep Water in the bottom layer, respectively. Oxygen minimum zone (OMZ, mean $O_2$ $73.26{\mu}M$), known as dysoxic condition ($O_2<90{\mu}M$), was distributed in the depth range of 700~1,200 m throughout the study area. Inorganic nutrients typified by nitrite + nitrate and phosphate showed the lowest concentration in the surface mixed layer and then gradually increased downward with representing the maximum concentration in the OMZ, with lower N:P ratio (13.7), indicating that the nitrogen is regarded as limiting factor for primary production. Vertical distribution of water column parameters along the east-west and south-north station line around the seamount showed the effect of bottom water inflowing at around 500 m deep in the western and southern region, and concentrations of water column parameters in the bottom layer (below 2,500 m deep) of the western and southern region were differently distributed comparing to those of the other side regions (eastern and northern). The value of Excess N calculated from Redfield ratio (N:P=16:1) represented the negative value throughout the study area, which indicated the nitrogen sink dominant environments, and relative higher value of Excess N observed in the bottom layer of western and southern region. These observations suggest that the topographic features of a seamount influence the circulation of bottom current and its effects play a significant role in determining the behavior of water column environmental parameters.

• Journal of the Korean Solar Energy Society
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• v.29 no.2
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• pp.31-38
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• 2009

This study was conducted to forecast inner water temperature strata change by extracting deep water from a dam. For the methodology, the scope wherein the balance between the volume of low-temperature water intake through the virtual water intake opening as installed within the stored water area and the volume of water intake from the surrounding area is not destroyed was calculated through the CFD simulation technique using the computational fluid dynamics(CFD) interpretation method. This study suggested a supplementary method(diffuser) to avoid destroying the water temperature strata, and the effect was reviewed. In case of intake of the same volume, when the velocity of flow of water intake is reduced by increasing the pipe diameter, the destruction of water temperature strata can be minimized. When the area(height) where the intake of water is possible is low, a diffuser for interrupting the vertical direction inflow should be installed to secure favorable water intake conditions in case of water intake on the upper part. This study showed that there was no problem if the intake-enabled, low-temperature area was secured approximately 10m from the bottom when the scope that does not destroy the water temperature strata in case of water intake was forecast using the regression formula.

• Journal of Korean Society of Water Science and Technology
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• v.26 no.6
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• pp.89-97
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• 2018

The purpose of this reaserch is to analyze the charateristics of water quality in space through the operation of ARA River in Artificial Rivers of the Brackish Water Zone. The spatial distribution measured water temperature and salt levels for the surface, middle and deep layers by dividing the four areas of Incheon, Sicheon, Gyeyang and Gimpo. Water temperature did not vary much by water depth and branch, and its purpose is to maintain stable water environment through correlation analysis and operation. To examine the temporal and spatial distribution patterns of the Arachon, we measured DO on the Incheon branch, Sicheon, Gyeyang and Gimpo branch twice a month, and on the surface, the temperature level, The water temperature did not vary much by depth and location, and the water temperature in January and March tended to rise from Incheon to Gimpo, with the average difference of 1.1 degrees during the same period. The salinity difference between Incheon and Gimpo sites was 3.3 psu deep and 5.4 psu deep. In particular, floodgates from July to September are found to be less than 10psu overall, which is considered to be a gas due to the effects of floods and the inflow of Gulpo Stream. D.O. is located in some areas due to summer rains. The hypoxic layer has been identified.Analysis of seasonal data shows that water temperature and DO are strongly correlated in autumn. It was found that the water temperature and salt levels in the fall showed a weak correlation.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.347-356
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• 2023

In Water Distribution Systems (WDSs), can abnormal hydraulic and water quality conditions such as red-water phenomenon and leakage occur. To restore them, data is generated through various meters data to predict and detect. However, in the case of leakage if difficult to detect unless direct exploration is performed. Among them, unreported leakage, are not seen visually and account for the most considerable volumes of leakage, which leads to economic loss. Bur direct exploration is limited through on site conditions such as securing professional manpower. In this paper, leakage volumes and location were randomly generated for the WDS, which was assumed to be calibrated, and it was detected through a deep learning model. For abnormal data generation, the leakage was simulated using the emitter coefficient, and leakage detection was successfully performed through the generated abnormal data and normal data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.423-430
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• 2017

Recently, data analysis research has been carried out using the deep learning technique in various fields such as image interpretation and/or classification. Various types of algorithms are being developed for many applications. In this paper, we propose a precipitation prediction algorithm based on deep learning with high accuracy in order to take care of the possible severe damage caused by climate change. Since the geographical and seasonal characteristics of Korea are clearly distinct, the meteorological factors have repetitive patterns in a time series. Since the LSTM (Long Short-Term Memory) is a powerful algorithm for consecutive data, it was used to predict precipitation in this study. For the numerical test, we calculated the PWV (Precipitable Water Vapor) based on the tropospheric delay of the GNSS (Global Navigation Satellite System) signals, and then applied the deep learning technique to the precipitation prediction. The GNSS data was processed by scientific software with the troposphere model of Saastamoinen and the Niell mapping function. The RMSE (Root Mean Squared Error) of the precipitation prediction based on LSTM performs better than that of ANN (Artificial Neural Network). By adding GNSS-based PWV as a feature, the over-fitting that is a latent problem of deep learning was prevented considerably as discussed in this study.

• Journal of Plant Biotechnology
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• v.43 no.4
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• pp.444-449
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• 2016

Deep rooting, which enables plants to extract water from greater soil depths, is a critical strategy for improving plant survival under water-deficient conditions. However, as it is difficult to observe intact root systems belowground, several techniques have been developed to screen deep- and shallow-rooting phenotypes in rice. Here, we introduce a simple and convenient method for deep- and shallow-rooting phenotyping using a unique combination of sand, soil, and plastic mesh netting. Vandana, a drought-tolerant rice variety, and Dongjin, a Korean japonica rice variety, were used to analyze root phenotypes. No significant differences in root length were observed in rice grown under irrigated conditions regardless of net position, whereas roots were significantly longer, and ratio of deep root (RDR) values were significantly higher in Vandana rice grown under semi-drought conditions. In summary, this simple and useful method represents a low-cost means of phenotyping the roots of rice and other crops grown in various-sized pots and at multiple plant growth stages.