• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.143-159
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• 2017

Due to the rapid development of IT in recent years, not only personal information but also the key technologies and information leakage that companies have are becoming important issues. For the enterprise, the core technology that the company possesses is a very important part for the survival of the enterprise and for the continuous competitive advantage. Recently, there have been many cases of technical infringement. Technology leaks not only cause tremendous financial losses such as falling stock prices for companies, but they also have a negative impact on corporate reputation and delays in corporate development. In the case of SMEs, where core technology is an important part of the enterprise, compared to large corporations, the preparation for technological leakage can be seen as an indispensable factor in the existence of the enterprise. As the necessity and importance of Information Security Management (ISM) is emerging, it is necessary to check and prepare for the threat of technology infringement early in the enterprise. Nevertheless, previous studies have shown that the majority of policy alternatives are represented by about 90%. As a research method, literature analysis accounted for 76% and empirical and statistical analysis accounted for a relatively low rate of 16%. For this reason, it is necessary to study the management model and prediction model to prevent leakage of technology to meet the characteristics of SMEs. In this study, before analyzing the empirical analysis, we divided the technical characteristics from the technology value perspective and the organizational factor from the technology control point based on many previous researches related to the factors affecting the technology leakage. A total of 12 related variables were selected for the two factors, and the analysis was performed with these variables. In this study, we use three - year data of "Small and Medium Enterprise Technical Statistics Survey" conducted by the Small and Medium Business Administration. Analysis data includes 30 industries based on KSIC-based 2-digit classification, and the number of companies affected by technology leakage is 415 over 3 years. Through this data, we conducted a randomized sampling in the same industry based on the KSIC in the same year, and compared with the companies (n = 415) and the unaffected firms (n = 415) 1:1 Corresponding samples were prepared and analyzed. In this research, we will conduct an empirical analysis to search for factors influencing technology leakage, and propose an early warning system through data mining. Specifically, in this study, based on the questionnaire survey of SMEs conducted by the Small and Medium Business Administration (SME), we classified the factors that affect the technology leakage of SMEs into two factors(Technology Characteristics, Organization Characteristics). And we propose a model that informs the possibility of technical infringement by using Support Vector Machine(SVM) which is one of the various techniques of data mining based on the proven factors through statistical analysis. Unlike previous studies, this study focused on the cases of various industries in many years, and it can be pointed out that the artificial intelligence model was developed through this study. In addition, since the factors are derived empirically according to the actual leakage of SME technology leakage, it will be possible to suggest to policy makers which companies should be managed from the viewpoint of technology protection. Finally, it is expected that the early warning model on the possibility of technology leakage proposed in this study will provide an opportunity to prevent technology Leakage from the viewpoint of enterprise and government in advance.

• Tuberculosis and Respiratory Diseases
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• v.62 no.5
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• pp.389-397
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• 2007

Background B-type natriuretic peptide (BNP) has been shown to be strong mortality predictors in a wide variety of cardiovascular syndromes. Little is known about BNP in patients with acute respiratory distress syndrome (ARDS). We studied whether BNP can predict mortality in patients with ARDS. Method Echocardiographic study was done to all patients with ARDS, and we excluded patient with low ejection fraction (less than 50%) or showing any features of diastolic dysfunction. 47 patients were enrolled between December, 2003 and February, 2006. Parameters including BNP were obtained within 24h hours at the time of enrollment. Result Mean BNP concentrations and APACHE II scores differed between the survivors and nonsurvivors (BNP, $219.5{\pm}57.7pg/mL$ vs $492.3{\pm}88.8pg/mL$; p=0.013, APACHE II score, $17.4{\pm}1.6$ vs $23.1{\pm}1.3$, p=0.009, respectively). With the use of the threshold value for BNP of 585 pg/mL, the specificity for the prediction of mortality was 94%. The threshold value for APACHE II of 15.5 showed sensitivity of 87%. 'APACHE II + $11{\times}logBNP$' showed sensitivity 63%, and specificity 82%, using threshold value for 46.14. Conclusion BNP concentrations and APCHE II scores were more elevated in nonsurvivors than survivors in patients with ARDS who have normal ejection fraction. BNP can predict mortality. Further study should be done.

• Clinical and Experimental Reproductive Medicine
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• v.23 no.1
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• pp.51-59
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• 1996

Controlled Ovarian hyperstimulation(COH) is generally used to obtain synchronous high quality oocytes in in vitro fertilization-embryo transfer(IVF-ET). Many investigators have studied the relationship between serum hormone levels and outcomes of IVF-ET because there is no accurate estimation method of oocyte quality. Early premature luteinization of follicles before oocyte retrieval is the most troublesome problem in COH for IVF-ET. Gonadotropin-releasing hormone agonists(GnRH-a) are used as adjuncts with gonadotropins for COH in patients undergoing in IVF. The possible benefits of GnRH-a pretreatment include improving oocyte quality, allowing a more synchronous cohort of follicles to be recruited, and preventing premature lueinization hormone surges. In COH of IVF cycles, we investigated whether an elevated progesterone(P4) level on the day of human chorionic gonadotropin(hCG) administration indicates premature luteinization and is associated with a lower fertilization rate. Many investigators have studied that the lower fertilization rates seen in patients with elevated P4 levels might result from an adverse effect of P4 on the oocytes. We hypothesizes that serum P4 levels around the day of hCG may be helpful prediction of out come in IVF-ET cycles. Success rates after COH of IVF-ET cycles are dependent upon many variable factors. Follicular factors including the number of follicles, follicular diameters and especially serum estradiol(E2) levels as an indirect measurement of follicular function and guality have been thought to influence the outcomes of IVF-ET. To assess whether serum P4 and E2 levels affect the fertilization and pregnancy rate, we reviewed the stimulation cycles of 113 patients (119 cycles) undergoing IVF-ET with short protocol with GnRH-a, from March 1993 to August 1994 retrospectively. The serum P4 and E2 levels were compared on the day of hCG in the pregnant group, 45 patients(47 cycles) and in the non-pregnant group, 68 patients (72 cycles) respectively. The serum E2 level in non-pregnant group was $1367{\pm}875.8$ pg/ml which was significantly lower than that of pregnant group, $1643{\pm}987.9$ pg/ml( p< 0.01 ). And the serum P4 level in non-pregnant group was $2.1{\pm}1.4$ ng/ml which was significantly higher than that of pregnant group, $1.0{\pm}0.7$ ng/ml( p< 0.001 ). The fertilization rate was $61.3{\pm}21.3%$ in pregnant group which was higher than that of non-pregnant group, $41.1{\pm}20.2%$ (p< 0.01). We suggest that the serum levels of P4 and E2 on the day of hCG administration are additional parameters that predict the outcomes of IVF-ET cycles.

• Korean Journal of Biological Psychiatry
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• v.9 no.1
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• pp.25-33
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• 2002

Serotonin transporter(5-HTT) is one of the major action site of antidepressants in neuronal cells. According to the recent studies, it is known that the functional polymorphism in the promoter region of the 5-HTT gene(5-HTT linked polymorphism repetitive element in promoter region, 5-HTTLPR) is associated with antidepressant responsiveness, and the distributions of 5-HTTLPR is various among the different populations. Our preliminary study suggested that it is possible to measure the endophenotype of 5-HTTLPR genotype by examining the pharmacodynamic research of the 5-HTT in platelet membranes. However, there are limitations to predicting the antidepressant responsiveness only from the endophenotypic characteristics of 5-HTT gene promoter polymorphism, and therefore we propose to use the pharmacogenomic methods for overcoming these limitations. We found that the significant correlations existed among the genetic polymorphisms of biogenic amine transporters whose structure and characteristics are similar to the 5-HTT, and the predictable odds ratio of antidepressant responsiveness are increased significantly by combining the effect with other associated polymorphisms, compared to the effect of 5-HTT promoter polymorphism only. These results support the hypothesis that antidepressant treatment has to be individualized according to the genetic and ethnic background of depressed patients. It would be possible to develope the evaluation tools to predict the antidepressant responsiveness and its side effect profile, if scientists reveal the genes related to the action mechanism as well as the metabolism of antidepressants so as to discover the interaction of those genes and contribution of endogenotypes toward antidepressant responsiveness.

• Journal of Soil and Groundwater Environment
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• v.13 no.6
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• pp.72-84
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• 2008

This study was carried out to provide proper management plans for small portable water supply system in the Nonsan area through water quality monitoring, hydrogeochemical investigation and multivariate statistical analyses. Nonsan area is a typical rural area heavily depending on small water supply system for portable usage. Geology of the area is composed of granite dominantly along with metasedimentary rocks, gneiss and volcanic rocks. The monitoring results of small portable water supply system showed that 13-21% of groundwaters have exceeded the groundwater standard for drinking water, which is 5 to 8 times higher than the results from the whole country survey (2.5% in average). The major components exceeding the standard limits are nitrate-nitrogen, turbidity, total coliform, bacteria, fluoride and arsenic. High nitrate contamination observed at southern and northern parts of the study area seems to be caused by cultivation practices such as greenhouses. Although Ca and $HCO_3$ are dominant species in groundwater, concentrations of Na, Cl and $NO_3$ have increased at the granitic area indicating anthropogenic contamination. The groundwaters are divided into 2 groups, granite and metasedimentary rock/gneiss areas, with the second principal component presenting anthropogenic pollution by cultivation and residence from the principal components analysis. The discriminant analysis, with an error of 5.56% between initial classification and prediction on geology, can explain more clearly the geochemical characteristics of groundwaters by geology than the principal components analysis. Based on the obtained results, it is considered that the multivariate statistical analysis can be used as an effective method to analyze the integrated hydrogeochemical characteristics and to clearly discriminate variations of the groundwater quality. The research results of small potable water supply system in the study area showed that the groundwater chemistry is determined by the mixed influence of land use, soil properties, and topography which are controlled by geology. To properly control and manage small water supply systems for central and local governments, it is recommended to construct a total database system for groundwater environment including geology, land use, and topography.

• Tunnel and Underground Space
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• v.28 no.5
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• pp.400-425
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• 2018

This study presents the research results and current status of the DECOVALEX-2019 project Task B. Task B named 'Fault slip modelling' is aiming at developing a numerical method to simulate the coupled hydro-mechanical behavior of fault, including slip or reactivation, induced by water injection. The first research step of Task B is a benchmark simulation which is designed for the modelling teams to familiarize themselves with the problem and to set up their own codes to reproduce the hydro-mechanical coupling between the fault hydraulic transmissivity and the mechanically-induced displacement. We reproduced the coupled hydro-mechanical process of fault slip using TOUGH-FLAC simulator. The fluid flow along a fault was modelled with solid elements and governed by Darcy's law with the cubic law in TOUGH2, whereas the mechanical behavior of a single fault was represented by creating interface elements between two separating rock blocks in FLAC3D. A methodology to formulate the hydro-mechanical coupling relations of two different hydraulic aperture models and link the solid element of TOUGH2 and the interface element of FLAC3D was suggested. In addition, we developed a coupling module to update the changes in geometric features (mesh) and hydrological properties of fault caused by water injection at every calculation step for TOUGH-FLAC simulator. Then, the transient responses of the fault, including elastic deformation, reactivation, progressive evolutions of pathway, pressure distribution and water injection rate, to stepwise pressurization were examined during the simulations. The results of the simulations suggest that the developed model can provide a reasonable prediction of the hydro-mechanical behavior related to fault reactivation. The numerical model will be enhanced by continuing collaboration and interaction with other research teams of DECOLVAEX-2019 Task B and validated using the field data from fault activation experiments in a further study.

• The Journal of Engineering Geology
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• v.15 no.2 s.42
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• pp.213-227
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• 2005

This study aims to assess the problems with investigation method and to suggest the complementary solutions by comparing the predicted data from surface investigation with the outcome data from underground cavern. In the study area, one(NE-1) of 6 fracture zones predicted during the surface investigation was only confirmed in underground caverns. Therefore, it is necessary to improve the confidence level for prediction. In this study, the fracture classification criteria was quantitatively suggested on the basis of the BHTV images of NE-1 fracture zone. The major orientation of background fractures in rock mass was changed at the depth of the storage cavern, the length and intensity were decreased. These characteristics result in the deviation of predieted predicted fracture properties and generate the investigation bias depending on the bore hole directions and investigated scales. The evaluation of hydraulic connectivity in the surface investigation stage needs to be analyze by the groundwater pressures and hydrochemical properties from the monitoring bore hole(s) equipped with a double completion or multi-packer system during the test bore hole is pumping or injecting. The hydraulic conductivities in geometric mean measured in the underground caverns are 2-3 times lower than those from the surface and furthermore the horizontal hydraulic conductivity in geometric mean is six times lower than the vertical one. To improve confidence level of the hydraulic conductivity, the orientation of test hole should be considered during the analysis of the hydraulic conductivity and the methodology of hydro-testing and interpretation should be based on the characteristics of rock mass and investigation purposes.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.11
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• pp.1041-1050
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• 2009

The ambient temperature and concentration of carbon dioxide in Gwangju and the reducing method of temperature, air pollutants were investigated using the atmospheric data in Gwangju. Average ambient temperature ($T_{a-ave}$ was $13.5^{\circ}C$ during 1961 to 2008. The temperature was predicted as increasing of about $2.7^{\circ}C$ in 2108 after 100 years using the trend line of regression equation. Carbon dioxide was 370.7 and 391.4 ppm at Anmyundo, in 1999 and 2008, respectively, showing proportionally increased as ambient temperature. The temperature at Gwangju, $14.2^{\circ}C$ during 1997 to 2008, was a little higher than at neighboring counties as Naju, Damyang, Hwasoon, and Jangsung. In Gwangju, Spring will start in mid-January of 2108, Summer in mid-May, Autumn in mid-October, and Winter in last-December. The average relative humidity in the air ($RH_{a-ave}$) was gradually decreased as the temperature inversely increased. The average $CO_2$ was 457 ppm, which is 65.6 ppm higher than that in Anmyundo, korean background area of $CO_2$ in 2008. Carbon dioxide showed positive correlation, both of them, with carbon monoxide (0.87) and relative humidity (0.48).

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.