• Korean Journal of Environment and Ecology
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• v.15 no.4
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• pp.361-368
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• 2002

The valley forests located at the east-facing slope and the west facing slope in Hambaeksan area were studied to investigate forest structure in relation to aspect and altitude of the slope. There was little difference in density. mean DBH and basal area of the tree layer between east-facing slope and west-facing slope. The importance percentages of Tilia amurensis and Betula costata in west-facing slope were higher than those in east-facing slope. However, the importance percentages of Quercus mongilica and Fraxinus rhynchophylla in the west facing slope were lower than those in east-facing slope. Species diversity of the west-facing slope was 1.415 and that of the east-facing slope was 1.328. Elevation trends were also found for forest structure. As elevation Increased basal area and mean height of the tree layer decreased in both of east-facing slope and west-facing slope. There was a tendency that number of species, species diversity and evenness decreased with increasing elevation. The importance percentage of Quercus mongolica increased with increasing elevation while those of Betula costata and Maackia amurensis decreased. The result of cluster analysis for the tree and subtree layer indicated that the studied forests were classified into the mixed forest community of broad-leaved tree species at west-facing slope and the low and middle elevation belts of east-facing slope and Quercus mongolica community at the high elevation belt of east-facing slope. Quercus mongolica was significantly and positively correlated with Symplocos chinensis for. pilosa, Acer tschonoskii var. rubripes and deutzia glabrata. Betula costata was significantly and negatively correlated with Quercus mongolica and Acer pseudo-sieboldianum.

• Journal of the Korean Vacuum Society
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• v.18 no.1
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• pp.66-72
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• 2009

The detection methods are required to monitor and diagnose the abnormality on the insulation condition inside a gas-insulated switchgear (GIS). Due to a good sensitivity to the products decomposed by partial discharges (PDs) in $SF_6$ gas, the development of a SWNT gas sensor is actively in progress. However, a few numerical studies on the diffusion mechanism of the $SF_6$ decomposition products by PD have been reported. In this study, we modeled $SF_6$ decomposition process in a chamber by calculating temperature, pressure and concentration of the decomposition products by using a commercial CFD program in conjunction with experimental data. It was assumed that the mass production rate and the generation temperature of the decomposition products were $5.04{\times}10^{-10}$ [g/s] and over 773 K respectively. To calculate the concentration equation, the Schmidt number was specified to get the diffusion coefficient functioned by viscosity and density of $SF_6$ gas instead rather than setting it directly. The results showed that the drive potential is governed mainly by the gradient of the decomposition concentration. A lower concentration of the decomposition products was observed as the sensors were placed more away from the discharge region. Also, the concentration increased by increasing the discharge time. By installing multiple sensors the location of PD is expected to be identified by monitoring the response time of the sensors, and the information should be very useful for the diagnosis and maintenance of GIS.

• Food Science and Preservation
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• v.15 no.1
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• pp.15-20
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• 2008

By considering the storage temperatures of agricultural products, three types of PCMs $(K_1$, $K_2$, $K_3$) were developed to be used in temperature ranges of $0{\sim}5^{\circ}C$, $5{\sim}10^{\circ}C$ and $10{\sim}15^{\circ}C$, $K_1$ PCM for $0{\sim}5^{\circ}C$ was developed by mixture of $C_{14}H_{30}$ and soduim polyacrylate, and $K_2$ PCM for $5{\sim}10^{\circ}C$ and $K_3$ PCM for $10{\sim}15^{\circ}C$ were mixture of $C_{14}H_{30}$, $C_{18}H_{38}$ and soduim polyacrylate with different composition ratio. 'The target temperatures of cold chain system were set at $7^{\circ}C$, $13^{\circ}C$, and $17^{\circ}C$ with $K_{1-3}$, $K_{2-3}$ and $K_{3-1}$ PCMs, respectively. The times to reach the target temperatures in the storage chamber were 21 hours, 18 hours, and 61 hours with $K_1$, $K_2$, and $K_3$ PCMs, respectively. The performances of natural convection type and forced convection of the temperature controlled portable container were analyzed Apples were stored in the portable container of $5^{\circ}C$, and temperatures at surface and center were measured. The initial temperature of the apple was $25^{\circ}C$. The temperatures of apple at the surface and the center were $15^{\circ}C$ and $16^{\circ}C$, respectively, after 5 hours with natural convection type. However, the temperatures at the surface and the center were already reached to $7^{\circ}C$ within 1 hour with forced convection type. The forced convection type showed the better performance and the temperatures of portable container were maintained more than 15 hours.

• Journal of the Korean Geographical Society
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• v.28 no.4
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• pp.285-297
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• 1993

For the application of the diffusion equation, slope height and maximum slope angle are calculated from the plotted slope profile. Using denudation rate as a solution for the diffusion equation, an apparent age index can be calculated, which is the total amount of denudation through total time. Plots of slope angle versus slope height and apparent age index versus slope height are useful for determining relative or absolute ages and denudation rates. Mathematical simulation plots of slope angle versus slope height can generate equal denudation-rate lines for a given age. Mathematical simulations of slope angle versus age for a given slope height, for equal denudation-rate at a particular profile site, and for comparing to other sites having controlled ages.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.

• Journal of Nutrition and Health
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• v.50 no.5
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• pp.415-425
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• 2017

Purpose: Many studies have suggested that neuronal cells protect against oxidative stress-induced apoptotic cell death by polyphenolic compounds. We investigated the neuroprotective effects and the mechanism of action of Momordica charantia ethanol extract (MCE) against $H_2O_2-induced$ cell death of human neuroblastoma SK-N-MC cells. Methods: The antioxidant activity of MCE was measured by the quantity of total phenolic acid compounds (TPC), quantity of total flavonoid compounds (TFC), and 2,2-Diphenyl-1-pycrylhydrazyl (DPPH) radical scavenging activity. Cytotoxicity and cell viability were determined by CCK-8 assay. The formation of reactive oxygen species (ROS) was measured using 2,7-dichlorofluorescein diacetate (DCF-DA) assay. Antioxidant enzyme (SOD-1,2 and GPx-1) expression was determined by real-time PCR. Mitogen-activated protein kinases (MAPK) pathway and apoptosis signal expression was measured by Western blotting. Results: The TPC and TFC quantities of MCE were 28.51 mg gallic acid equivalents/extract g and 3.95 mg catechin equivalents/extract g, respectively. The $IC_{50}$ value for DPPH radical scavenging activity was $506.95{\mu}g/ml$ for MCE. Pre-treatment with MCE showed protective effects against $H_2O_2-induced$ cell death and inhibited ROS generation by oxidative stress. SOD-1,2 and GPx-1 mRNA expression was recovered by pre-treatment with MCE compared with the presence of $H_2O_2$. Pre-treatment with MCE inhibited phosphorylation of p38 and the JNK pathway and down-regulated cleaved caspase-3 and cleaved PARP by $H_2O_2$. Conclusion: The neuroprotective effects of MCE in terms of recovery of antioxidant enzyme gene expression, down-regulation of MAPK pathways, and inhibition apoptosis is associated with reduced oxidative stress in SK-N-MC cells.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Restorative Dentistry and Endodontics
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• v.34 no.3
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• pp.177-183
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• 2009

The aim of this study was to evaluate the marginal and internal gaps in CEREC3 CAD/CAM inlays of three different preparation designs. CEREC3 Inlays of three different preparation designs (n=10) were fabricated according to Group I-conventional functional cusp capping/shoulder preparation, Group II-horizontal reduction of cusps and Group III-complete reduction of cusps/shoulder preparation. After cementation of inlays. the bucco-lingual cross section was performed through the center of tooth. Cross section images of 20 magnifications were obtained through the stereomicroscope. The gaps were measured using the Leica application suite software at each reference point. Statistical analysis was performed using one-way ANOVA and Tukey's test (${\alpha}<0.05$). The marginal gaps ranged from 80.0 to $97.8{\mu}m$ for Group I, 42.0 to $194.8{\mu}m$ for Group II, 51.0 to $80.2{\mu}m$ for Group III. The internal gaps ranged from 90.5 to $304.1{\mu}m$ for Group I, 80.0 to $274.8{\mu}m$ for Group II, 79.7 to $296.7{\mu}m$ for Group III. The gaps of each group were the smallest on the margin and the largest on the horizontal wall. For the CEREC3 CAD/CAM inlays, the simplified designs (groups II and III) did not demonstrate superior results compared to the traditional cusp capping design (group I).

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.323-334
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• 2007

We analyzed the ocean environmental data from water sample and automatic measurement instruments with the Incheon-Jeju passenger ship for 18 times during 4 years from 2001 to 2004. The objectives of this study are to monitor the spatial and temporal variations of ocean environmental parameters in coastal waters of the Yellow Sea using water sample analysis, and to compare and analyze the reliability of automatic measurement sensors for chlorophyll and turbidity using in situ measurements. The chlorophyll concentration showed the ranges between 0.1 to $6.0mg/m^3$. High concentrations occurred in the Gyeonggi Bay through all the cruises. The maximum value of chlorophyll concentration was $16.5mg/m^3$ in this area during September 2004. The absorption coefficients of dissolve organic matter at 400 nm showed below $0.5m^{-1}$ except those in August 2001 During 2002-2003, it did not distinctly change the seasonal variations with the ranges 0.1 to $0.4m^{-1}$. In the case of suspended sediment (SS) concentration, most of the area showed below $20g/m^3$ through all seasons except the Gyeonggi Bay and around Mokpo area. In general SS concentration of autumn and winter season was higher than that of summer. The central area of the Yellow Sea appeared to have lower value $10g/m^3$. The YSI fluorometer for chlorophyll concentration had a very low reliability and turbidity sensor had a $R^2$ value of 0.77 through the 4 times measurements comparing with water sampling method. For the automatic measurement using instruments for chlorphlyll and suspended sediment concentration, McVan and Choses sensor was greater than YSI multisensor. The SeaWiFS SS distribution map was well spatially matched with in situ measurement, however, there was a little difference in quantitative concentration.