• Journal of Soil and Groundwater Environment
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• v.13 no.1
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• pp.1-12
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• 2008

The purpose of this study is to characterize the hydrogeochemical characteristics of hot spring waters and to interpret the source of noble gases and the geochemical environment of the hot spring waters distributed along the eastern area of the Korean peninsula. For this purpose, We carried out the chemical, stable isotopic and noble gas isotopic analyses for eleven hot spring water and fourteen hot spring gas samples collected from six hot spring sites. The hot spring waters except the Osaek hot spring water show the pH range of 7.0 to 9.1. However, the Osaek $CO_2$-rich hot spring water shows a weak acid of pH 5.7. The temperature of hot spring waters in the study area ranges from $25.7^{\circ}C$ to $68.3^{\circ}C$. Electrical conductivity of hot spring waters varies widely from 202 to $7,130{\mu}S/cm$. High electrical conductivity (av., $3,890{\mu}S/sm$) by high Na and Cl contents of the Haeundae and the Dongrae hot spring waters indicates that the hot spring waters were mixed with seawater in the subsurface thermal system. The type of hot springs in the viewpoint of dissolved components can be grouped into three types: (1) alkaline Na-$HCO_3$ type including sulfur gas of the Osaek, Baekam, Dukgu and Chuksan hot springs, and (2) saline Na-Cl type of the Haeundae and Dongrae hot springs, and (3) weak acid $CO_2$-rich Na-$HCO_3$ type of Osaek hot spring. Tritium ratios of the Haeundae and the Dongrae hot springs indicate different residence time in their aquifers of older water of $0.0{\sim}0.3$ TU and younger water of $5.9{\sim}8.8$ TU. The ${\delta}^{18}O$ and ${\delta}D$ values of hot spring waters indicate that they originate from the meteoric water, and that the values also reflect a latitude effect according to their locations. $^3He/^4He$ ratios of the hot spring waters except Osaek $CO_2$-rich hot spring water range from $0.1{\times}10^{-6}$ to $1.1{\times}10^{-6}$ which are plotted above the mixing line between air and crustal components. It means that the He gas in hot spring waters was originated mainly from atmosphere and crust sources, and partly from mantle sources. The Osaek $CO_2$-rich hot spring water shows $3.3{\times}10^{-6}$ in $^3He/^4He$ ratio that is 2.4 times higher than those of atmosphere. It provides clearly a helium source from the deep mantle. $^{40}Ar/^{36}Ar$ ratios of hot spring water are in the range of an atmosphere source.

• Journal of Life Science
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• v.16 no.3 s.76
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• pp.534-539
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• 2006

In the ozone disinfection unit process of a piston type batch reactor with continuous ozone analysis using a flow injection analysis (FIA) system, the CT values for 1 log inactivation of Cryptosporidium parvum by viability assays of DAPI/PI and excystation were $1.8{\sim}2.2\;mg/L{\cdot}min$ at $25^{\circ}C$ and $9.1mg/L{\cdot}min$ at $5^{\circ}C$, respectively. At the low temperature, ozone requirement rises $4{\sim}5$ times higher in order to achieve the same level of disinfection at room temperature. In a 40 L scale pilot plant with continuous flow and constant 5 minutes retention time, disinfection effects were evaluated using excystation, DAPI/PI, and cell infection method at the same time. About 0.2 log inactivation of Cryptosporidium by DAPI/PI and excystation assay, and 1.2 log inactivation by cell infectivity assay were estimated, respectively, at the CT value of about $8mg/L{\cdot}min$. The difference between DAPI/PI and excystation assay was not significant in evaluating CT values of Cryptosporidium by ozone in both experiment of the piston and the pilot reactors. However, there was significant difference between viability assay based on the intact cell wall structure and function and infectivity assay based on the developing oocysts to sporozoites and merozoites in the pilot study. The stage of development should be more sensitive to ozone oxidation than cell wall intactness of oocysts. The difference of CT values estimated by viability assay between two studies may partly come from underestimation of the residual ozone concentration due to the manual monitoring in the pilot study, or the difference of the reactor scale (50 mL vs 40 L) and types (batch vs continuous). Adequate If value to disinfect 1 and 2 log scale of Cryptosporidium in UV irradiation process was 25 $mWs/cm^2$ and 50 $mWs/cm^2$, respectively, at $25^{\circ}C$ by DAPI/PI. At $5^{\circ}C$, 40 $mWs/cm^2$ was required for disinfecting 1 log Cryptosporidium, and 80 $mWs/cm^2$ for disinfecting 2 log Cryptosporidium. It was thought that about 60% increase of If value requirement to compensate for the $20^{\circ}C$ decrease in temperature was due to the low voltage low output lamp letting weaker UV rays occur at lower temperatures.

• Applied Chemistry for Engineering
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• v.17 no.4
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• pp.349-356
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• 2006

We have investigated adsorption and desorption properties of CO adsorption on silica supported Ru/Ni alloys at various Ru/Ni mole content ratio as well as CO partial pressures using Fourier transform infrared spectrometer (FT-IR). For Ru-$SiO_{2}$ sample, four bands were observed at $2080.0cm^{-1}$, $2021.0{\sim}2030.7cm^{-1}$, $1778.9{\sim}1799.3cm^{-1}$, $1623.8cm^{-1}$ on adsorption and three bands were observed at $2138.7cm^{-1}$, $2069.3cm^{-1}$, $1988.3{\sim}2030.7cm^{-1}$ on vacumn desorption. For Ni-$SiO_{2}$ sample, four bands were observed at $2057.7cm^{-1}$, $2019.1{\sim}2040.3cm^{-1}$, $1862.9{\sim}1868.7cm^{-1}$, $1625.7cm^{-1}$ on adsorption and two bands were observed at $2009.5{\sim}2040.3cm^{-1}$, $1828.4{\sim}1868.7cm^{-1}$ on vacumn desorption. These absorption bands correspond with those of the previous reports approximately. For Ru/Ni(9/1, 8/2, 7/3, 6/4, 5/5; mole content ratio)-$SiO_{2}$ samples, three bands were observed at $2001.8{\sim}2057.7cm^{-1}$, $1812.8{\sim}1926.5cm^{-1}$, $1623.8{\sim}1625.7cm^{-1}$ on adsorption and three bands were observed at $2140.6cm^{-1}$, $2073.1cm^{-1}$, $1969.0{\sim}2057.7cm^{-1}$ on vacumn desorption. The spectrum pattern observed for Ru/Ni-$SiO_{2}$ sample at 9/1 Ru/Ni mole content ratio on CO adsorption and on vacumn desorption is almost like the spectrum pattern observed for Ru-$SiO_{2}$ sample. But the spectrum patterns observed for Ru/Ni-$SiO_{2}$ samples under 8/2 Ru/Ni mole content ratio on CO adsorption and vacumn desorption are almost like the pattern observed for $Ni-SiO_{2}$ sample. It may be suggested surfaces of alloy clusters on the Ru/Ni-$SiO_{2}$ samples contain more Ni components than the mole content ratio of the sample considering the above phenomena. With Ru/Ni-$SiO_{2}$ samples the absorption band shifts may be ascribed to variations of surface concentration, strain variation due to atomic size difference, variation of bonding energy and electronic densities, and changes of surface geometries according to surface concentration variation. Studies for CO adsorption on Ru/Ni alloy cluster surface by LEED and Auger spectroscopy, interation between Ru/Ni alloy cluster and $SiO_{2}$, and MO calculation for the system would be needed to look into the phenomena.

• Journal of Soil and Groundwater Environment
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• v.14 no.3
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• pp.40-46
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• 2009

The applicability of a well-type autotrophic sulfur-oxidizing reactive barrier (L $\times$ W $\times$ D = $3m\;{\times}\;4\;m\;{\times}\;2\;m$) as a long-term treatment option for nitrate removal in groundwater was evaluated. Pilot-scale (L $\times$ W $\times$ D = $8m\;{\times}\;4\;m\;{\times}\;2\;m$) flow-tank experiments were conducted to examine remedial efficacy of the well-type reactive barrier. A total of 80 kg sulfur granules as an electron donor and Thiobacillus denitrificans as an active bacterial species were prepared. Thiobacillus denitrificans was successfully colonized on the surface of the sulfur granules and the microflora transformed nitrate with removal efficiency of ~12% (0.07 mM) for 11 days, ~24% (1.3 mM) for 18 days, ~45% (2.4 mM) for 32 days, and ~52% (2.8 mM) for 60 days. Sulfur granules attached to Thiobacillus denitrificans were used to construct the well-type reactive barrier comprising three discrete barriers installed at 1-m interval downstream. Average initial nitrate concentrations were 181 mg/L for the first 28 days and 281 mg/L for the next 14 days. For the 181 mg/L (2.9 mM) plume, nitrate concentrations decreased by ~2% (0.06 mM), ~9% (0.27 mM), and ~15% (0.44 mM) after $1^{st}$, $2^{nd}$, and $3^{rd}$ barriers, respectively. For the 281 mg/L (4.5 mM) plume, nitrate concentrations decreased by ~1% (0.02 mM), ~6% (0.27 mM), and ~8% (0.37 mM) after $1^{st}$, $2^{nd}$, and $3^{rd}$ barriers, respectively. Nitrate plume was flowed through the flow-tank for 49 days by supplying $1.24\;m^3/d$ of nitrate solution. During nitrate treatment, flow velocity (0.44 m/d), pH (6.7 to 8.3), and DO (0.9~2.8 mg/L) showed little variations. Incomplete destruction of nitrate plume was attributed to the lack of retention time, rarely transverse dispersion, and inhibiting the activity of denitrification enzymes caused by relatively high DO concentrations. For field applications, it should be considered increments of retention time, modification of well placements, and intrinsic DO concentration.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

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

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Korean Journal of Ecology and Environment
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• v.34 no.1 s.93
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• pp.1-8
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• 2001

During April 1993 to November 1994, cations, anions, and conductivity were analyzed to examine how summer monsoon influences the ionic content of Taechung Reservoir, Korea. Interannual variability of ionic content reflected hydrological characteristics between the two years(high-flood year in 1993 vs. draught year in 1994). Cations, anions and conductivity were lowest during peak inflow in 1993 and highest during a drought in 1994. Floods in 1993 markedly decreased total salinity as a result of reduced Ca$^{2+}$ and HCO$_{3}\;^{-}$ and produced extreme spatial heterogeneity (i.e., longitudinal, vertical, and horizontal variation) in ionic concentrations. The dominant process modifying the longitudinal (the headwaters-to-downlake) and vertical (top-to-bottom) patterns in salinity was an interflow current during the 1993 monsoon. The interflow water plunged near a 27${\sim}$37 km-location (from the dam) of the mid-lake and passed through the 10${\sim}$30m stratum of the reservoir, resulting in an isolation of epilimnetic high conductivity water (>100 ${\mu}$S/cm) from advected river water with low conductivity (65${\sim}$75 ${\mu}$S/cm), During postmonsoon 1993, the factors regulating salinity differed spatially; salinity of downlake markedly declined as a result of dilution through the mixing of lake water with river water, whereas in the headwaters it increased due to enhanced CaCO$_{3}$ (originated from limestone/metamorphic rock) of groundwaters entering the reservoir. This result suggests an importance of the basin geology on ion compositions with hydrological characteristics. In 1994, salinity was markedly greater (p<0.001) relative to 1993 and ionic dilution did not occur during the monsoon due to reduced inflow. Overall data suggest that the primary factor influencing seasonal ionic concentrations and compositions in this system is the dilution process depending on the intensity of monsoon rainfall.

• Journal of the Korean Society for Marine Environment & Energy
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• v.16 no.1
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• pp.9-16
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• 2013

An experiment was conducted to evaluate the biochemical adverse effect of increased carbon dioxide in seawater on marine polychaete, Perinereis aibuhitensis. We measured the available energy reserves, Ea (total carbohydrate, protein, and lipid content) and the energy consumption, Ec (electron transport activity) of Perinereis aibuhitensis exposed for 7-d to a range of $CO_2$ concentration such as 0.39 (control =390 ppmv), 3.03 (=3,030 ppmv), 10.3 (=10,300 ppmv), and 30.1 (=30,100 ppmv) $CO_2$ mM, respectively. The cellular energy allocation (CEA) methodology was used to assess the adverse effects of toxic stress on the energy budget of the test organisms. The results of a decrease in CEA effect of increased carbon dioxide in seawater from all individual in Ea and Ec. Increase of carbon dioxide reduced pH in seawater, significantly. The chemical changes in sea- water caused by increasing $pCO_2$ might cause stresses to test organisms and changes in the cellular energy allocations. Results of this study can be used to understand the possible influence of $CO_2$ concentration increased by the leakage from sub-sea bed storage sites as well as fossil fuel combustion on marine organisms.

• Explosives and Blasting
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• v.30 no.2
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• pp.29-42
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• 2012

Variables influencing the free face movement due to rock blasting include the physical and mechanical properties, in particular the discontinuity characteristics, explosive type, charge weight, burden, blast-hole spacing, delay time between blast-holes or rows, stemming conditions. These variables also affects the blast vibration, air blast and size of fragmentation. For the design of surface blasting, the priority is given to the safety of nearby buildings. Therefore, blast vibration has to be controlled by analyzing the free face movement at the surface blasting sites and also blasting operation needs to be optimized to improve the fragmentation size. High-speed digital image analysis enables the analyses of the initial movement of free face of rock, stemming optimality, fragment trajectory, face movement direction and velocity as well as the optimal detonator initiation system. Even though The high-speed image analysis technique has been widely used in foreign countries, its applications can hardly be found in Korea. This thesis aims at carrying out a fundamental study for optimizing the blast design and evaluation using the high-speed digital image analysis. A series of experimentation were performed at two large surface blasting sites with the rock type of shale and granite, respectively. Emulsion and ANFO were the explosives used for the study. Based on the digital images analysis, displacement and velocity of the free face were scrutinized along with the analysis fragment size distribution. In addition, AUTODYN, 2-D FEM model, was applied to simulate detonation pressure, detonation velocity, response time for the initiation of the free face movement and face movement shape. The result show that regardless of the rock type, due to the displacement and the movement velocity have the maximum near the center of charged section the free face becomes curved like a bow. Compared with ANFO, the cases with Emulsion result in larger detonation pressure and velocity and faster reaction for the displacement initiation.