• Economic and Environmental Geology
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• v.44 no.3
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• pp.217-228
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• 2011

The feasibility study for the stabilization process using 5 amendments was performed to quantify As-immobilization efficiency in farmland soils around Samkwang abandoned mine, Korea. For the batch experiments, with 2% and 3% of granular lime(2-5 mm in diameter), leaching concentration of As from the soil decreased by 86% and 95% respectively, compared to that without the amendment. When 5% and 10% of granular limestone was added in the soil, As concentration decreased by 82% and 95%, showing that lime and limestone has a great capability to immobilize As in the soil. From the results of batch experiments, continuous column(15 cm in dimeter and 100 cm in length) tests using granular lime and limestone as amendments was performed. Without the amendment, As concentration from the effluent of the column ranged from 167 ${\mu}g$/L to 845 ${\mu}g$/L, which were higher than Korea Drinking Water Limit(50 ${\mu}g$/L). However, only with 1% and 2% of lime, As concentration from the column dramatically decreased by 97% for 9 years rainfall and maintained below 50 ${\mu}g$/L. With 5% of limestone and the mixed amendment(1% of lime + 2% of limestone), more than 95% diminution of As leaching from the column occurred within I year rainfall and maintained below 20 ${\mu}g$/L, suggesting that the capability of limestone to immobilize As in the farmland soil was outstanding and similar to that of lime. Results of experiments suggested that As stabilization process using limestone could be more available to immobilize As from the soil than using lime because of low pH increase and thus less harmful side effect.

• Journal of Korean Society on Water Environment
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• v.23 no.4
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• pp.467-473
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• 2007

Although the dominant land use at the Imha-dam watershed is forest areas, soil erosion has been increasing because of intensive agricultural activities performed at the fields located along the stream for easy-access to water supply and relatively favorable topography. In addition, steep topography at the Imha-dam watershed is also contributing increased soil erosion and sediment loads. At the Imha-dam watershed, outflow has increased sharply by the typhoons Rusa and Maemi in 2002, 2003 respectively. In this study, the Soil and Water Assessment Tool (SWAT) model was evaluated for simulation of flow and sediment behaviors with long-term temporal and spatial conditions. The precipitation data from eight precipitation observatories, located at Ilwol, Subi and etc., were used. There was no significant difference in monthly rainfall for 8 locations. However, there was slight differences in rainfall amounts and patterns in 2003 and 2004. The topographical map at 1:5000 scale from the National Geographic Information Institute was used to define watershed boundaries, the detailed soil map at 1:25,000 scale from the National Institute of Highland Agriculture and the land cover data from the Korea Institute of Water and Environment were used to simulate the hydrologic response and soil erosion and sediment behaviors. To evaluate hydrologic component of the SWAT model, calibration was performed for the period from Jan. 2002 to Dec. 2003, and validation for Jan. 2004 to Apr. 2005. The $R^2$ value and El value were 0.93 and 0.90 respectively for calibration period, and the $R^2$ value and El value for validation were 0.73 and 0.68 respectively. The $R^2$ value and El value of sediment yield data with the calibrated parameters was 0.89 and 0.84 respectively. The comparisons with the measured data showed that the SWAT model is applicable to simulate hydrology and sediment behaviors at Imha dam watershed. With proper representation of the Best Management Practices (BM Ps) in the SWAT model, the SWAT can be used for pre-evaluation of the cost-effective and sustainable soil erosion BMPs to solve sediment issues at the Imha-dam watershed. In Korea, the Universal Soil Loss Equation (USLE) has been used to estimate the soil loss for over 30 years. However, there are limitations in the field scale mdel, USLE when applied for watershed. Also, the soil loss changes temporarily and spatially, for example, the Imha-dam watershed. Thus, the SW AT model, capable of simulating hydrologic and soil erosion/sediment behaviors temporarily and spatially at watershed scale, should be used to solve the muddy water issues at the Imha-dam watershed to establish more effective muddy water reduction countermeasure.

• Pediatric Infection and Vaccine
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• v.18 no.2
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• pp.109-116
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• 2011

Purpose : Pneumococcus is one of the most important causes of invasive infection through the childhood period. In January 2008, the Clinical and Laboratory Standards Institute (CLSI) published revised penicillin breakpoints for Streptococcus pneumoniae and penicillin susceptibility rates of S. pneumoniae increased in Korea. This study was performed to determine the probability of oral amoxicillin for the empirical treatment achieving bactericidal exposure against pneumococcus using pharmacodynamics model. Methods : Twenty-three isolates of pneumococci were subjected to determine minimum inhibitory concentration (MIC) for ${\beta}$-lactams and macrolide. For the ${\beta}$-lactams, exposure of fT >MIC (time that free drug concentrations remain above the MIC) for 50% of the administration interval have determined the probability of target attainment (PTA), and regimens that had a PTA >90% were considered optimal. An analysis was performed by applying MIC of 23 isolates to a 5000-patient Monte Carlo simulation model. Results : Among 23 isolates from healthy children, 7 (30.4%) isolates were MIC ${\leq}$1.0 ${\mu}g$/mL and 19 (82.6%) were MIC ${\leq}$2 ${\mu}g$/mL for amoxicillin. Amoxicillin 40 mg/kg/day achieved PTA >90% at MIC ${\leq}$1.0 ${\mu}g$/mL but PTA decreased to 52% at MIC 2 ${\mu}g$/mL, whereas amoxicillin 90 mg/kg/day can predict 97% of PTA at MIC 2 ${\mu}g$/mL. Overall, oral amoxicillin 90 mg/ kg/day for the empirical treatment against pneumococcus can expect more successful response in Korean children. Conclusion : Considering the resistantce pattern of pneumococci in Korean children, we estimate that oral amoxicillin 90 mg/kg/day will provide a pharmacodynamic advantage for the empirical treatment against pneumococcus. And low dose amoxicillin or macrolide are expected to have higher chance of treatment failure than high dose oral amoxicillin.

• Journal of Preventive Medicine and Public Health
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• v.26 no.2 s.42
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• pp.222-230
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• 1993

Industrial glues, known as 'Bonds' in Korea, contain many kinds of organic solvents, and glue sniffing of youths became one of the social problems in Korea. Mixed exposures to solvents by glue sniffing may induce chronic toxicities different from those by exposures to solvents of single component. To test effects of the glue sniffing on weight gain or central nervous system, two groups of 20 male Sprague-Dawley rats were exposed to air(control group) or vapors of the glues to narcotic status(exposed group), and weight check, tail flick test, hot plate test, rotarod treadmill test were done on the 14th,24th, 36th, 45th, 53rd, 86th, 102nd, 117th, 134th and 151st days after the first exposure. On the 188th day, their brains were excised and examined by a pathologist. Weight gain, controlled against time change, showed significant difference between the groups, but response times in tail flick test, hot plate tests, and rotarod treadmill test didn't. In pathological examination with blind method, no macroscopic or microscopic differences were found between the two groups. These results suggests that organic lesion in central nervous system may not ensue glue sniffing, but, before firm conclusion, more studies in various exposure conditions should be followed.

• Journal of Nutrition and Health
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• v.50 no.3
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• pp.284-293
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• 2017

Purpose: This study was conducted to examine the behavioral intentions of breastfeeding in single women using the theory of planned behavior. Methods: The questionnaires were distributed to 350 single women in her 20~30s, and 316 respondents were analyzed by descriptive statistics, Spearman's correlation, and multiple regression analysis. Results: The subjects showed strong intentions and favorable attitudes toward breastfeeding. The subjects were more favorably influenced by their mothers, siblings, friends, and coworkers who previously experienced breastfeeding than ones with no breastfeeding experiences. There were significant correlations between breastfeeding intention and attitudes (r = 0.321, p < 0.0001), subjective norms (r = 0.434, p < 0.0001), and perceived control (r = 0.307, p < 0.0001). However, regression analysis with two different age groups revealed that subjective norms (p < 0.0001) and perceived control (p < 0.001) contributed to the model of explaining breastfeeding intentions in subjects who were 25 years old or younger, whereas attitudes did not. In addition, subjects who were more than 25 years old showed that attitudes (p < 0.003) and subjective norms (p = 0.002) contributed to the model of explaining breastfeeding intentions while perceived control (p < 0.070) showed less contribution. Conclusion: These results suggest that the theory of planned behavior can be a useful tool to increase the rate of breastfeeding intentions in single women when designing educational materials, which requires consideration of age differences.

• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.73-82
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• 2019

Recently, as the occurrence frequency of sudden floods due to climate change increased, the flood damage on riverside social infrastructures was extended so that there has been a threat of overflow. Therefore, a rapid prediction of potential flooding in riverside social infrastructure is necessary for administrators. However, most current flood forecasting models including hydraulic model have limitations which are the high accuracy of numerical results but longer simulation time. To alleviate such limitation, data driven models using artificial neural network have been widely used. However, there is a limitation that the existing models can not consider the time-series parameters. In this study the water surface elevation of the Hangang River bridge was predicted using the NARX model considering the time-series parameter. And the results of the ANN and RNN models are compared with the NARX model to determine the suitability of NARX model. Using the 10-year hydrological data from 2009 to 2018, 70% of the hydrological data were used for learning and 15% was used for testing and evaluation respectively. As a result of predicting the water surface elevation after 3 hours from the Hangang River bridge in 2018, the ANN, RNN and NARX models for RMSE were 0.20 m, 0.11 m, and 0.09 m, respectively, and 0.12 m, 0.06 m, and 0.05 m for MAE, and 1.56 m, 0.55 m and 0.10 m for peak errors respectively. By analyzing the error of the prediction results considering the time-series parameters, the NARX model is most suitable for predicting water surface elevation. This is because the NARX model can learn the trend of the time series data and also can derive the accurate prediction value even in the high water surface elevation prediction by using the hyperbolic tangent and Rectified Linear Unit function as an activation function. However, the NARX model has a limit to generate a vanishing gradient as the sequence length becomes longer. In the future, the accuracy of the water surface elevation prediction will be examined by using the LSTM model.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.199-230
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• 2021

Innovative new products and services are being launched through the convergence between heterogeneous industries, and social interest and investment in convergence industries such as AI, big data-based future cars, and robots are continuously increasing. However, in the process of commercialization of convergence new products and services, there are many cases where they do not conform to the existing regulatory and legal system, which causes many difficulties in companies launching their products and services into the market. In response to these industrial changes, the current government is promoting the improvement of existing regulatory mechanisms applied to the relevant industry along with the expansion of investment in new industries. This study, in these convergence industry trends, aimed to analysis the existing regulatory system that is an obstacle to market entry of innovative new products and services in order to preemptively predict regulatory issues that will arise in emerging industries. In addition, it was intended to establish a regulatory impact analysis system to evaluate adequacy and prepare improvement measures. The flow of this study is divided into three parts. In the first part, previous studies on regulatory impact analysis and evaluation systems are investigated. This was used as basic data for the development direction of the regulatory impact framework, indicators and items. In the second regulatory impact analysis framework development part, indicators and items are developed based on the previously investigated data, and these are applied to each stage of the framework. In the last part, a case study was presented to solve the regulatory issues faced by actual companies by applying the developed regulatory impact analysis framework. The case study included the autonomous/electric vehicle industry and the Internet of Things (IoT) industry, because it is one of the emerging industries that the Korean government is most interested in recently, and is judged to be most relevant to the realization of an intelligent information society. Specifically, the regulatory impact analysis framework proposed in this study consists of a total of five steps. The first step is to identify the industrial size of the target products and services, related policies, and regulatory issues. In the second stage, regulatory issues are discovered through review of regulatory improvement items for each stage of commercialization (planning, production, commercialization). In the next step, factors related to regulatory compliance costs are derived and costs incurred for existing regulatory compliance are calculated. In the fourth stage, an alternative is prepared by gathering opinions of the relevant industry and experts in the field, and the necessity, validity, and adequacy of the alternative are reviewed. Finally, in the final stage, the adopted alternatives are formulated so that they can be applied to the legislation, and the alternatives are reviewed by legal experts. The implications of this study are summarized as follows. From a theoretical point of view, it is meaningful in that it clearly presents a series of procedures for regulatory impact analysis as a framework. Although previous studies mainly discussed the importance and necessity of regulatory impact analysis, this study presented a systematic framework in consideration of the various factors required for regulatory impact analysis suggested by prior studies. From a practical point of view, this study has significance in that it was applied to actual regulatory issues based on the regulatory impact analysis framework proposed above. The results of this study show that proposals related to regulatory issues were submitted to government departments and finally the current law was revised, suggesting that the framework proposed in this study can be an effective way to resolve regulatory issues. It is expected that the regulatory impact analysis framework proposed in this study will be a meaningful guideline for technology policy researchers and policy makers in the future.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1827-1836
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• 2022

Disasters that occur unexpectedly are difficult to predict. In addition, the scale and damage are increasing compared to the past. Sometimes one disaster can develop into another disaster. Among the four stages of disaster management, search and rescue are carried out in the response stage when an emergency occurs. Therefore, personnel such as firefighters who are put into the scene are put in at a lot of risk. In this respect, in the initial response process at the disaster site, robots are a technology with high potential to reduce damage to human life and property. In addition, Light Detection And Ranging (LiDAR) can acquire a relatively wide range of 3D information using a laser. Due to its high accuracy and precision, it is a very useful sensor when considering the characteristics of a disaster site. Therefore, in this study, development and experiments were conducted so that the robot could perform real-time monitoring at the disaster site. Multi-sensor module was developed by combining LiDAR, Inertial Measurement Unit (IMU) sensor, and computing board. Then, this module was mounted on the robot, and a customized Simultaneous Localization and Mapping (SLAM) algorithm was developed. A method for stably mounting a multi-sensor module to a robot to maintain optimal accuracy at disaster sites was studied. And to check the performance of the module, SLAM was tested inside the disaster building, and various SLAM algorithms and distance comparisons were performed. As a result, PackSLAM developed in this study showed lower error compared to other algorithms, showing the possibility of application in disaster sites. In the future, in order to further enhance usability at disaster sites, various experiments will be conducted by establishing a rough terrain environment with many obstacles.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.