• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.618-626
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• 2017

This study was conducted to investigate the factors affecting depression in elementary school teachers. The research design was a descriptive study. Methods:Data were collected by questionnaires from 283 elementary school teachers. Data were analyzed using a t-test, ANOVA, a $Scheff{\acute{e}}$ test, correlation analysis, and multiple regression analysis. Results: The mean scores were 2.44, 3.07 and 3.68 out of 5 on Likert scales for burnout, job stress and ego-resiliency, respectively. The mean scores were 1.47 and 2.95 out of 4 on Likert scales for items of depression and job satisfaction, respectively. Teaching experience and class size affected depression significantly. There was a positive correlation between depression and burnout(r=0.465, p<.001), and between depression and job stress(r =.220, p<.001),while a negative correlation was observed between depression and job satisfaction(r=-.249, p<.001), and depression and ego-resiliency(r=-.643, p<.001). Multiple regression analysis showed that ego-resiliency(${\beta}=0.639$), job satisfaction(${\beta}=.141$), burnout(${\beta}=.094$), and job stress(${\beta}=.067$) affected depression in order and the four research variables led to a 42.7% prediction for depression among elementary school teachers. Conclusion: Based on the results of this study, a systematic plan for decreasing job stress and increasing eco-resilience is needed to improve depression among elementary school teachers.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.135-144
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• 2021

Recently, data-driven decision-making technology has become a key technology leading the data industry, and machine learning technology for this requires high-quality training datasets. However, real-world data contains missing values for various reasons, which degrades the performance of prediction models learned from the poor training data. Therefore, in order to build a high-performance model from real-world datasets, many studies on automatically imputing missing values in initial training data have been actively conducted. Many of conventional machine learning-based imputation techniques for handling missing data involve very time-consuming and cumbersome work because they are applied only to numeric type of columns or create individual predictive models for each columns. Therefore, this paper proposes a new data imputation technique called 'Denoising Self-Attention Network (DSAN)', which can be applied to mixed-type dataset containing both numerical and categorical columns. DSAN can learn robust feature expression vectors by combining self-attention and denoising techniques, and can automatically interpolate multiple missing variables in parallel through multi-task learning. To verify the validity of the proposed technique, data imputation experiments has been performed after arbitrarily generating missing values for several mixed-type training data. Then we show the validity of the proposed technique by comparing the performance of the binary classification models trained on imputed data together with the errors between the original and imputed values.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1037-1051
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• 2020

The accurate monitoring and forecasting of the intensity of tropical cyclones (TCs) are able to effectively reduce the overall costs of disaster management. In this study, we proposed a multi-task learning (MTL) based deep learning model for real-time TC intensity estimation and forecasting with the lead time of 6-12 hours following the event, based on the fusion of geostationary satellite images and numerical forecast model output. A total of 142 TCs which developed in the Northwest Pacific from 2011 to 2016 were used in this study. The Communications system, the Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) data were used to extract the images of typhoons, and the Climate Forecast System version 2 (CFSv2) provided by the National Center of Environmental Prediction (NCEP) was employed to extract air and ocean forecasting data. This study suggested two schemes with different input variables to the MTL models. Scheme 1 used only satellite-based input data while scheme 2 used both satellite images and numerical forecast modeling. As a result of real-time TC intensity estimation, Both schemes exhibited similar performance. For TC intensity forecasting with the lead time of 6 and 12 hours, scheme 2 improved the performance by 13% and 16%, respectively, in terms of the root mean squared error (RMSE) when compared to scheme 1. Relative root mean squared errors(rRMSE) for most intensity levels were lessthan 30%. The lower mean absolute error (MAE) and RMSE were found for the lower intensity levels of TCs. In the test results of the typhoon HALONG in 2014, scheme 1 tended to overestimate the intensity by about 20 kts at the early development stage. Scheme 2 slightly reduced the error, resulting in an overestimation by about 5 kts. The MTL models reduced the computational cost about 300% when compared to the single-tasking model, which suggested the feasibility of the rapid production of TC intensity forecasts.

• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.154-163
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• 2021

This study was carried out to present the habitat distribution status and the habitat distribution prediction of Sophora koreensis, which is the Korean Endemic Plant included in the EN (Endangered) class of the IUCN Red List. The habit distribution survey of Sophora koreensis confirmed 19 habitats in Gangwon Province, including 13 habitats in Yanggu-gun, 3 habitats in Inje-gun, 2 habitats in Chuncheon-si, and 1 habitat in Hongcheon-gun. The northernmost habitat of Sophora koreensis in Korea was in Imdang-ri, Yanggu-gun; the easternmost habitat in Hangye-ri, Inje-gun; the westernmost habitat in Jinae-ri, Chuncheon-si; and the southernmost habitat in Sungdong-ri, Hongcheon-gun. The altitude of the Sophora koreensis habitats ranged from 169 to 711 m, with an average altitude of 375m. The area of the habitats was 8,000-734,000 m², with an average area of 202,789 m². Most habitats were the managed forests, such as thinning and pruning forests. The MaxEnt program analysis for the potential habitat of Sophora koreensis showed the AUC value of 0.9762. The predictive habitat distribution was Yanggu-gun, Inje-gun, Hwacheon-gun, and Chuncheon-si in Gangwon Province. The variables that influence the prediction of the habitat distribution were the annual precipitation, soil carbon content, and maximum monthly temperature. This study confirmed that habitats of Sophora koreensis were mostly found in the ridge area with rich light intensity. They can be used as basic data for the designation of protected areas of Sophora koreensis habitat.

• Korean Journal of Ecology and Environment
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• v.48 no.4
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• pp.229-237
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• 2015

This study aims to offer basic data to effectively preserve and manage pine forests using more precise pine forests' distribution status. In this regard, this study predicts the geographical distribution change of pine forests growing in South Korea, due to climate change, and evaluates the spatial distribution characteristics of pine forests by age. To this end, this study predicts the potential distribution change of pine forests by applying the MaxEnt model useful for species distribution change to the present and future climate change scenarios, and analyzes the effects of bioclimatic variables on the distribution area and change by age. Concerning the potential distribution regions of pine forests, the pine forests, aged 10 to 30 years in South Korea, relatively decreased more. As the area of the region suitable for pine forest by age was bigger, the decreased regions tend to become bigger, and the expanded regions tend to become smaller. Such phenomena is conjectured to be derived from changing of the interaction of pine forests by age from mutual promotional relations to competitive relations in the similar climate environment, while the regions suitable for pine forests' growth are mostly overlap regions. This study has found that precipitation affects more on the distribution of pine forests, compared to temperature change, and that pine trees' geographical distribution change is more affected by climate's extremities including precipitation of driest season and temperature of the coldest season than average climate characteristics. Especially, the effects of precipitation during the driest season on the distribution change of pine forests are irrelevant of pine forest's age class. Such results are expected to result in a reduction of the pine forest as the regions with the increase of moisture deficiency, where climate environment influencing growth and physiological responses related with drought is shaped, gradually increase according to future temperature rise. The findings in this study can be applied as a useful method for the prediction of geographical change according to climate change by using various biological resources information already accumulated. In addition, those findings are expected to be utilized as basic data for the establishment of climate change adaptation policies related to forest vegetation preservation in the natural ecosystem field.

• Tuberculosis and Respiratory Diseases
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• v.43 no.5
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• pp.755-762
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• 1996

Background : Continuous positive airway pressure(CPAP) is doubtlessly using as a medical treatment of choice for patients with obstructive sleep apnea (OSA) syndrome. CPAP is effective in OSA patients as a physical "pneumatic pressure splint" mechanism. We have done this study for two purposes, first to seek for the factors to determine the optimal CPAP titer, second to predict the minimal CPAP titer using the determined factors. Methods: We studied a 72 OSA patients who were treated with CPAP. All of them were studied by using a two nights polysomnographic rests in hospital. We compared the patients requiring CPAP over $10cmH_2O$ with those who required CPAP under 5cm $H_2O$ to determine the factors affecting the minimal CPAP titer. Results : The high CPAP group is characterized by a significantly higher body mass index(BMI), apnea index(AI) and apnea and hyponea index(AHI) and significantly lower lowest $SaO_2$. Regression analysis using the optimal four variables resulted in the following prediction equation for CPAP titer. CPAPtiter=8.382 + 0.064 ${\times}$ BMI + 0.077 ${\times}$ AI - 0.004 ${\times}$ AHI - 0.077 ${\times}$ lowest $SaO_2$ When this regression equation was applied to the 72 patients, the mean CPAP titer as predicted by the above equation was $7.80{\pm}2.96$ mmHg. Compared this value with actually determined CPAPtiter, $7.93{\pm}4.00$mmHg, there was no significant difference between the two values. Conclusion: Obesity, apnea severity and lowest Sa02 were strongly correlated with CPAP titer. Linear regression equation for CPAP titer using these indices predicted very closely the actually measured values in the sleep laboratory.

• 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.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• Korean Journal of Ecology and Environment
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• v.49 no.3
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• pp.197-207
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• 2016

This study aims at providing basic data to objectively evaluate the areas suitable for reintroduction of the species of Asiatic black bear (Ursus thibetanus) in order to effectively preserve the Asiatic black bears in the Korean protection areas including national parks, and for the species restoration success. To this end, this study predicted the potential habitats in East Asia, Southeast Asia and India, where there are the records of Asiatic black bears' appearances using the Maxent model and environmental variables related with climate, topography, road and land use. In addition, this study evaluated the effects of the relevant climate and environmental variables. This study also analyzed inhabitation range area suitable for Asiatic black and geographic change according to future climate change. As for the judgment accuracy of the Maxent model widely utilized for habitat distribution research of wildlife for preservation, AUC value was calculated as 0.893 (sd=0.121). This was useful in predicting Asiatic black bears' potential habitat and evaluate the habitat change characteristics according to future climate change. Compare to the distribution map of Asiatic black bears evaluated by IUCN, Habitat suitability by the Maxent model were regionally diverse in extant areas and low in the extinct areas from IUCN map. This can be the result reflecting the regional difference in the environmental conditions where Asiatic black bears inhabit. As for the environment affecting the potential habitat distribution of Asiatic black bears, inhabitation rate was the highest, according to land coverage type, compared to climate, topography and artificial factors like distance from road. Especially, the area of deciduous broadleaf forest was predicted to be preferred, in comparison with other land coverage types. Annual mean precipitation and the precipitation during the driest period were projected to affect more than temperature's annual range, and the inhabitation possibility was higher, as distance was farther from road. The reason is that Asiatic black bears are conjectured to prefer more stable area without human's intervention, as well as prey resource. The inhabitation range was predicted to be expanded gradually to the southern part of India, China's southeast coast and adjacent inland area, and Vietnam, Laos and Malaysia in the eastern coastal areas of Southeast Asia. The following areas are forecast to be the core areas, where Asiatic black bears can inhabit in the Asian region: Jeonnam, Jeonbuk and Gangwon areas in South Korea, Kyushu, Chugoku, Shikoku, Chubu, Kanto and Tohoku's border area in Japan, and Jiangxi, Zhejiang and Fujian border area in China. This study is expected to be used as basic data for the preservation and efficient management of Asiatic black bear's habitat, artificially introduced individual bear's release area selection, and the management of collision zones with humans.

• Clinical and Experimental Pediatrics
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• v.50 no.4
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• pp.369-375
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• 2007

Purpose : There have been numerous researches on urine ${\beta}_2$-microglobulin (${\beta}_2$-M) concerned with primary nephrotic syndrome and other glomerular diseases, but not much has been done in relation to pediatric age groups. Thus, our hospital decided to study the relations between the analysis of the test results we have conducted on pediatric patients and renal functions. Methods : Retrospective data analysis was done to 102 patients of ages 0 to 4 with renal diseases with symptoms such as hematuria, edema, and proteinuria who were admitted to Chung-Ang Yongsan Hospital and who participated in 24-hour urine and urine ${\beta}_2$-M excretion test between January of 2003 and January of 2006. Each disease was differentiated as independent variables, and the statistical difference of the results of urine ${\beta}_2$-M excretion of several groups of renal diseases was analyzed with student T-test by using test results as dependent variables. Results : Levels of urine ${\beta}_2$-M excretion of the 102 patients were as follows : 52 had primary nephrotic syndrome [MCNS (n=45, $72{\pm}45{\mu}g/g$ creatinine, ${\mu}g/g-Cr$), MPGN (n=3, $154{\pm}415{\mu}g/g-Cr$), FSGS (n=4, $188{\pm}46{\mu}g/-Cr$], six had APSGN ($93{\pm}404{\mu}g/g-Cr$), seven had IgA nephropathy ($3,414{\pm}106{\mu}g/g-Cr$), 9 had APN ($742{\pm}160{\mu}g/g-Cr$), 16 had cystitis ($179{\pm}168{\mu}g/g-Cr$), and 12 had HSP nephritis ($109{\pm}898{\mu}g/g-Cr$). IgA nephropathy (P<0.05) and APN (P<0.05) were significantly higher than in other renal diseases. Among primary nephrotic syndrome, FSGS with higher results of ${\beta}_2$-microglobulin test had longer treatment period (P<0.01) when compared to the lower groups, but no significant differences in Ccr, BUN, or Cr were observed. Conclusion : IgA nephropathy and APN groups showed significantly higher level of ${\beta}_2$-M excretion value than other groups. Although ${\beta}_2$-microglobulin value is not appropriate as an indicator of general renal function and pathology, it seems to be sufficient in the differential diagnosis of the UTI and in the prediction of the treat-ment period of nephrotic syndrome patients.