• Journal of Radiation Protection and Research
• /
• v.41 no.1
• /
• pp.49-55
• /
• 2016

Background: For the purpose of baseline data collection and enhancement of environmental monitoring the distribution studies of $^{137}Cs$ and $^{90}Sr$ in the soil of Uljin province was performed and the relation between surface soil activities and soil properties (pH, TOC and median of the surface soil) was analyzed. Materials and Methods: For 14 spots within 10 km from the NPP surface soil samples were collected and soils for depth profile were sampled for 3 spots in April 2011. Using ${\gamma}$-ray spectrometry with HPGe detector, the concentrations of $^{137}Cs$ were determined and the concentrations of $^{90}Sr$ were measured by counting ${\beta}$-activity of $^{90}Y$ (in equilibrium with $^{90}Sr$) in a gas flow proportional counter. Results and Discussion: The concentration ranges of $^{137}Cs$ and $^{90}Sr$ were $<0.479-39.6Bq{\cdot}(kg-dry)^{-1}$ (avg. $7.51Bq{\cdot}(kg-dry)^{-1}$) and $0.209-1.85Bq{\cdot}(kg-dry)^{-1}$ (avg. $0.74Bq{\cdot}(kg-dry)^{-1}$) which were similar to the reported values from other regions in Korea. The activity ratio of $^{137}Cs$ to $^{90}Sr$ in surface soils was around 9.67, which is much bigger than the initial value of 1.75 for worldwide fallouts because of faster downward movement of $^{90}Sr$ after fallout than that of $^{137}Cs$. For depth profile studies soils were collected down to 40 cm depth for the locations of Deokgu, Hujeong and Maehwa. The $^{137}Cs$ concentration distribution of the first two showed maximum values at top soils and decreased rapidly in exponential manner, while $^{90}Sr$ showed two local maximum values for soils near top and about 30 cm depth. Through linear fittings between the $^{137}Cs$ and $^{90}Sr$ concentrations of surface soil and pH, TOC and median of the surface soil, the only probable relationship obtained was between $^{137}Cs$ and TOC (determination coefficient $R^2=0.6$). Conclusion: The concentration ranges of $^{137}Cs$ and $^{90}Sr$ in Uljin were similar to the reported values from other regions in Korea. The only probable relationship obtained between activities and soil properties was between $^{137}Cs$ and TOC.

• Journal of the Korean Society for Marine Environment & Energy
• /
• v.16 no.3
• /
• pp.151-162
• /
• 2013

Studies of the distribution of micro-phytoplankton community and chlorophyll a concentration have focused on the vertical profiles of marine environmental factors such as water temperature, salinity, sigma-t, light intensity, and dissolved oxygen in the continental slope on the east parts of East China Sea in the early summer of 2009. Water temperature showed a gradual reduction according to the depth. While the salinity was low in the surface layer showing a mixed down to the relatively subsurface layer, it was increased with an increase in the depth at the middle and bottom layers showing a maximum value at 150~200 m followed by a decreasing aspect afterwards, although the change was not large. The change of sigma-t was governed by the water temperature, and gradually increased in the surface layer with an increase in the depth, showing a value higher than in the surface layer by about 3 $kg/m^3$ at the bottom layer. Although the intensity of light was exponential reduced in the surface layer, the compensation depth was located at the depth of about 80m. The vertical profiles of chlorophyll a concentration was governed by the intensity rather than the changes in water temperature or salinity, exhibiting a maximum value at the compensation depth corresponding to 1% in the surface light intensity. The micro-phytoplankton communities consisted of 56 genera 103 species showing a relatively variety, while the standing crop was also changed to 112.0~470.0 cells/L in the pelagic environment, showing a maximum chlorophyll a concentration. Although a variety of dominant species appear at low dominance without dominant species appearing with a right-wing point in the phytoplankton communities, the silicoflagellate, Otactis otonaris at the station A and the dominance of 26% due to Leptocylindrus mediterraneus at the station C have been judged to be unusual. For community analysis of infinitesimal creatures such as phytoplankton of oligotrophic waters through the present study, ecology studies through vertical sample collection agreeing with the results of continuous observation such as identification of vertical distribution in a marine environment or of maximum chlorophyll layers have been considered rather than a survey method with intervals of a given depth such as surface, subsurface, middle and bottom layers.

• Journal of Radiation Protection and Research
• /
• v.40 no.4
• /
• pp.252-260
• /
• 2015

This paper describes the technical background for the Korean wildlife radiation dose assessment code, K-BIOTA, and the summary of its application. The K-BIOTA applies the graded approaches of 3 levels including the screening assessment (Level 1 & 2), and the detailed assessment based on the site specific data (Level 3). The screening level assessment is a preliminary step to determine whether the detailed assessment is needed, and calculates the dose rate for the grouped organisms, rather than an individual biota. In the Level 1 assessment, the risk quotient (RQ) is calculated by comparing the actual media concentration with the environmental media concentration limit (EMCL) derived from a bench-mark screening reference dose rate. If RQ for the Level 1 assessment is less than 1, it can be determined that the ecosystem would maintain its integrity, and the assessment is terminated. If the RQ is greater than 1, the Level 2 assessment, which calculates RQ using the average value of the concentration ratio (CR) and equilibrium distribution coefficient (Kd) for the grouped organisms, is carried out for the more realistic assessment. Thus, the Level 2 assessment is less conservative than the Level 1 assessment. If RQ for the Level 2 assessment is less than 1, it can be determined that the ecosystem would maintain its integrity, and the assessment is terminated. If the RQ is greater than 1, the Level 3 assessment is performed for the detailed assessment. In the Level 3 assessment, the radiation dose for the representative organism of a site is calculated by using the site specific data of occupancy factor, CR and Kd. In addition, the K-BIOTA allows the uncertainty analysis of the dose rate on CR, Kd and environmental medium concentration among input parameters optionally in the Level 3 assessment. The four probability density functions of normal, lognormal, uniform and exponential distribution can be applied.The applicability of the code was tested through the participation of IAEA EMRAS II (Environmental Modeling for Radiation Safety) for the comparison study of environmental models comparison, and as the result, it was proved that the K-BIOTA would be very useful to assess the radiation risk of the wildlife living in the various contaminated environment.

• Journal of Korean Society of Environmental Engineers
• /
• v.36 no.6
• /
• pp.421-428
• /
• 2014

For the analysis of water supply network, demand-driven and pressure-driven analysis methods have been proposed. Of the two methods, demand-driven analysis (DDA) can only be used in a normal operation condition to evaluate hydraulic status of a pipe network. Under abnormal conditions, i.e., unexpected pipe destruction, or abnormal low pressure conditions, pressure-driven analysis (PDA) method should be used to estimate the suppliable flowrate at each node in a network. In order to carry out the pressure-driven analysis, head-outflow relationship (HOR), which estimates flowrate at a certain pressure at each node, should be first determined. Most previous studies empirically suggested that each node possesses its own characteristic head-outflow relationship, which, therefore, requires verification by using actual field data for proper application in PDA modeling. In this study, a model pipe network was constructed, and various operation scenarios of normal and abnormal conditions, which cannot be realized in real pipe networks, were established. Using the model network, data on pressure and flowrate at each node were obtained at each operation condition. Using the data obtained, previously proposed HOR equations were evaluated. In addition, head-outflow relationship at each node was analyzed especially under multiple pipe destruction events. By analyzing the experimental data obtained from the model network, it was found that flowrate reduction corresponding to a certain pressure drop (by pipe destruction at one or multiple points on the network) followed intrinsic head-outflow relationship of each node. By comparing the experimentally obtained head-outflow relationship with various HOR equations proposed by previous studies, the one proposed by Wagner et al. showed the best agreement with the exponential parameter, m of 3.0.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.139-156
• /
• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• The Korean Journal of Nuclear Medicine
• /
• v.18 no.2
• /
• pp.17-23
• /
• 1984

For nearly a century it has been known that chemical activity accompanies mental activity, but only recently has it been possible to begin to examine its exact nature. Positron-emitting radioactive tracers have made it possible to study the chemistry of the human mind in health and disease, using chiefly cyclotron-produced radionuclides, carbon-11, fluorine-18 and oxygen-15. It is now well established that measurable increases in regional cerebral blood flow, glucose and oxygen metabolism accompany the mental functions of perception, cognition, emotion and motion. On May 25, 1983 the first imaging of a neuroreceptor in the human brain was accomplished with carbon-11 methyl spiperone, a ligand that binds preferentially to dopamine-2 receptors, 80% of which are located in the caudate nucleus and putamen. Quantitative imaging of serotonin-2, opiate, benzodiazapine and muscarinic cholinergic receptors has subsequently been accomplished. In studies of normal men and women, it has been found that dopamine and serotonin receptor activity decreases dramatically with age, such a decrease being more pronounced in men than in women and greater in the case of dopamine receptors than serotonin-2 receptors. Preliminary studies in patients with neuropsychiatric disorders suggests that dopamine-2 receptor activity is diminished in the caudate nucleus of patients with Huntington's disease. Positron tomography permits quantitative assay of picomolar quantities of neuro-receptors within the living human brain. Studies of patients with Parkinson's disease, Alzheimer's disease, depression, anxiety, schizophrenia, acute and chronic pain states and drug addiction are now in progress. The growth of any scientific field is based on a paradigm or set of ideas that the community of scientists accepts. The unifying principle of nuclear medicine is the tracer principle applied to the study of human disease. Nineteen hundred and sixty-three was a landmark year in which technetium-99m and the Anger camera combined to move the field from its latent stage into a second stage characterized by exponential growth within the framework of the paradigm. The third stage, characterized by gradually declining growth, began in 1973. Faced with competing advances, such as computed tomography and ultrasonography, proponents and participants in the field of nuclear medicine began to search for greener pastures or to pursue narrow sub-specialties. Research became characterized by refinements of existing techniques. In 1983 nuclear medicine experienced what could be a profound change. A new paradigm was born when it was demonstrated that, despite their extremely low chemical concentrations, in the picomolar range, it was possible to image and quantify the distribution of receptors in the human body. Thus, nuclear medicine was able to move beyond physiology into biochemistry and pharmacology. Fundamental to the science of pharmacology is the concept that many drugs and endogenous substances, such as neurotransmitters, react with specific macromolecules that mediate their pharmacologic actions. Such receptors are usually identified in the study of excised tissues, cells or cell membranes, or in autoradiographic studies in animals. The first imaging and quantification of a neuroreceptor in a living human being was performed on May 25, 1983 and reported in the September 23, 1983 issue of SCIENCE. The study involved the development and use of carbon-11 N-methyl spiperone (NMSP), a drug with a high affinity for dopamine receptors. Since then, studies of dopamine and serotonin receptors have been carried out in over 100 normal persons or patients with various neuropsychiatric disorders. Exactly one year later, the first imaging of opitate receptors in a living human being was performed [1].

• Journal of Soil and Groundwater Environment
• /
• v.12 no.5
• /
• pp.19-32
• /
• 2007

In Korea, there have been various methods of estimating groundwater recharge which generally can be subdivided into three types: baseflow separation method by means of groundwater recession curve, water budget analysis based on lumped conceptual model in watershed, and water table fluctuation method (WTF) by using the data from groundwater monitoring wells. However, groundwater recharge rate shows the spatial-temporal variability due to climatic condition, land use and hydrogeological heterogeneity, so these methods have various limits to deal with these characteristics. To overcome these limitations, we present a new method of estimating recharge based on water balance components from the SWAT-MODFLOW which is an integrated surface-ground water model. Groundwater levels in the interest area close to the stream have dynamics similar to stream flow, whereas levels further upslope respond to precipitation with a delay. As these behaviours are related to the physical process of recharge, it is needed to account for the time delay in aquifer recharge once the water exits the soil profile to represent these features. In SWAT, a single linear reservoir storage module with an exponential decay weighting function is used to compute the recharge from soil to aquifer on a given day. However, this module has some limitations expressing recharge variation when the delay time is too long and transient recharge trend does not match to the groundwater table time series, the multi-reservoir storage routing module which represents more realistic time delay through vadose zone is newly suggested in this study. In this module, the parameter related to the delay time should be optimized by checking the correlation between simulated recharge and observed groundwater levels. The final step of this procedure is to compare simulated groundwater table with observed one as well as to compare simulated watershed runoff with observed one. This method is applied to Mihocheon watershed in Korea for the purpose of testing the procedure of proper estimation of spatio-temporal groundwater recharge distribution. As the newly suggested method of estimating recharge has the advantages of effectiveness of watershed model as well as the accuracy of WTF method, the estimated daily recharge rate would be an advanced quantity reflecting the heterogeneity of hydrogeology, climatic condition, land use as well as physical behaviour of water in soil layers and aquifers.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.30 no.3
• /
• pp.393-407
• /
• 1997

A survey of biological and chemical characteristics in the middle-northern East Sea of Korea was carried out at 28 stations in October, 1995 on board R/V Tam-Yang. On the basis of the vertical profiles of temperature, salinity and dissolved oxygen, water masses in the study area were divided into 5 major groups; (1) Low Saline Surface Water (LSSW), (2) Tsushima Surface Water (TSW), (3) Tsushima Middle Water (TMW), (4) North Korean Cold Water (NKCW), (5) last Sea Porper Water (ESPW). Other 4 mixed water masses were also observed. It is highly possible that the LSSW which occured at depths of $0\~30m$ in the most southern part of the study area is originated from the Yangtze River (Kiang) of China due to very low salinity $(<32.0\%_{\circ})$ relatively high concentration of dissolved silicate and no sources of freshwater input into that area. Oxygen maximum layer in the vertical profile was located near surface at northern cold waters and became deeper at the warm southern area. Oxygen minimum layer af depths $50\~100m$, which is TMW, were found in only southern area. In the vortical profiles of nutrients, the concentrations were very low in the surface layer and increased drammatically near the thermocline. The highest concentration occurred in the ESPW. The relatively low value of Si/P ratio in the ESPW (13.63) compared to other reports in the East Sea was due to continuous increase of P with depth as well as Si. The N : P ratio was about 6.92, showing that nitrogenous nutrient is the limiting factor for phytoplankton growth. The exponential relationship between Si and P, compared to the linear relationship between N and P, indicates that nitrate and phosphate have approximately the same regenerative pattern, but silicate has delayed regenerative pattern.

• The Journal of the Petrological Society of Korea
• /
• v.26 no.2
• /
• pp.127-141
• /
• 2017

Jurassic granite from Geochang was analysed with respect to the characteristics of the rock cleavage. The multicriteria evaluation for the six directions of rock cleavages was performed using the microcrack spacing-related parameters derived from the enlarged photomicrographs (${\times}6.7$) of the thin section and the spacing-cumulative frequency diagrams. The results of analysis for the representative values of these spacing parameters with respect to the rock cleavage are summarized as follows. First, the analysis for deriving the main parameter indicating the order of arrangement among six diagrams was performed. The values of five parameters with respect to six directions of the rock cleavages were arranged in increasing or decreasing order for the above analysis. The decreasing order of the values of main parameter(mean spacing-median spacing, $S_{mean}-S_{median}$) and mean spacing are consistent with the order of H1, H2, G1, G2, R1 and R2 directions. These sequential arrangements of six directions of the rock cleavages can provide a basis for those of the six diagrams related to spacing. Second, the nine correlation charts between the above main parameter and various parameters were arranged in decreasing order of correlation coefficient ($R^2$). These related charts shows a high correlation of power-law function in common. The values of mean spacing, density (${\rho}$) and length of line oa are directly proportional to the value of main parameter, while the values of constant (a), exponent (${\lambda}$), spacing frequency (N), length of line oa', slope of exponential straight line (${\theta}$) and total length ($1mm{\geq}$) are inverse proportional. Third, the results of correlation analysis between the values of parameters for three planes and those for three rock cleavages are as follows. The values of frequency, total spacing, constant, exponent, slope and length of line oa' for three planes and three rock cleavages show an order of R' < G' < H' and H < G < R, respectively. On the other hand, the values of mean spacing, (mean spacing-median spacing), density and length of line oa show an order of H' < G' < R' and R < G < H, respectively. The correlation of the mutually reverse order of the values of parameters between three planes and three rock cleavages can be drawn. This type of correlation analysis is useful for discriminating three quarrying planes.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.69-88
• /
• 2022

One of the characteristics of South Korea's economic structure is that it is highly dependent on exports. Thus, many businesses are closely related to the global economy and diplomatic situation. In addition, small and medium-sized enterprises(SMEs) specialized in exporting are struggling due to the spread of COVID-19. Therefore, this study aimed to develop a model to forecast exports for next year to support SMEs' export strategy and decision making. Also, this study proposed a strategy to recommend promising export countries of each item based on the forecasting model. We analyzed important variables used in previous studies such as country-specific, item-specific, and macro-economic variables and collected those variables to train our prediction model. Next, through the exploratory data analysis(EDA) it was found that exports, which is a target variable, have a highly skewed distribution. To deal with this issue and improve predictive performance, we suggest a separated learning method. In a separated learning method, the whole dataset is divided into homogeneous subgroups and a prediction algorithm is applied to each group. Thus, characteristics of each group can be more precisely trained using different input variables and algorithms. In this study, we divided the dataset into five subgroups based on the exports to decrease skewness of the target variable. After the separation, we found that each group has different characteristics in countries and goods. For example, In Group 1, most of the exporting countries are developing countries and the majority of exporting goods are low value products such as glass and prints. On the other hand, major exporting countries of South Korea such as China, USA, and Vietnam are included in Group 4 and Group 5 and most exporting goods in these groups are high value products. Then we used LightGBM(LGBM) and Exponential Moving Average(EMA) for prediction. Considering the characteristics of each group, models were built using LGBM for Group 1 to 4 and EMA for Group 5. To evaluate the performance of the model, we compare different model structures and algorithms. As a result, it was found that the separated learning model had best performance compared to other models. After the model was built, we also provided variable importance of each group using SHAP-value to add explainability of our model. Based on the prediction model, we proposed a second-stage recommendation strategy for potential export countries. In the first phase, BCG matrix was used to find Star and Question Mark markets that are expected to grow rapidly. In the second phase, we calculated scores for each country and recommendations were made according to ranking. Using this recommendation framework, potential export countries were selected and information about those countries for each item was presented. There are several implications of this study. First of all, most of the preceding studies have conducted research on the specific situation or country. However, this study use various variables and develops a machine learning model for a wide range of countries and items. Second, as to our knowledge, it is the first attempt to adopt a separated learning method for exports prediction. By separating the dataset into 5 homogeneous subgroups, we could enhance the predictive performance of the model. Also, more detailed explanation of models by group is provided using SHAP values. Lastly, this study has several practical implications. There are some platforms which serve trade information including KOTRA, but most of them are based on past data. Therefore, it is not easy for companies to predict future trends. By utilizing the model and recommendation strategy in this research, trade related services in each platform can be improved so that companies including SMEs can fully utilize the service when making strategies and decisions for exports.