• Journal of Korea Water Resources Association
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• v.50 no.3
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• pp.169-179
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• 2017

Recently, due to global warming and climate change in Korea, local heavy storm occurs frequently. In this study, the risky areas for flooding in urban areas are analyzed for flood inundation based on two-dimensional urban flood runoff model (XP-SWMM) focusing on coastal high flood-risk urban areas. In addition, the MCDM (Multi-Criteria Decision Making) technique is utilized in order to establish the flood defense structural measures. The alternative flood reduction method are compared and the optimum flood defense measures are selected. A simulation model was used with three structural flood prevention measures (drainage pipe construction, water detention, flood pumping station). In order to decrease the flooding area, flood assessment criteria are suggested (flooded area, maximum inundation depth, damaged residential area, construction cost). Priorities of alternatives are determined by using compromise programming. As a result, the optimal flood defence alternative suggested for Janghang Zone 1 is flood pumping station and for Janghang Zone 2, 3 are drainage pipe construction.

• Journal of Construction Engineering and Project Management
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• v.8 no.4
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• pp.1-24
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• 2018

In the last decade, the use of Building Information Modeling (BIM) as a new technology has been applied with traditional Computer-aided design implementations in an increasing number of architecture, engineering, and construction projects and applications. Its employment alongside construction management, can be a valuable tool in helping move these activities and projects forward in a more efficient and time-effective manner. The traditional stakeholders, i.e., Owner, A/E and the Contractor are involved in this BIM system that is used in almost every activity of construction projects, such as design, cost estimate and scheduling. This article extracts major features of the application of BIM from perspective of participating BIM components, along with the different phrases, and applies to them a logistic analysis using a fuzzy performance tree, quantifying these phrases to judge the effectiveness of the BIM techniques employed. That is to say, these fuzzy performance trees with fuzzy logic concepts can properly translate the linguistic rating into numeric expressions, and are thus employed in evaluating the influence of BIM applications as a mathematical process. The rotational fuzzy models are used to represent the membership functions of the performance values and their corresponding weights. Illustrations of the use of this fuzzy BIM performance tree are presented in the study for the uninitiated users. The results of these processes are an evaluation of BIM project performance as highly positive. The quantification of the performance ratings for the individual factors is a significant contributor to this assessment, capable of parsing vernacular language into numerical data for a more accurate and precise use in performance analysis. It is hoped that fuzzy performance trees and fuzzy set analysis can be used as a tool for the quality and risk analysis for other construction techniques in the future. Baldwin's rotational models are used to represent the membership functions of the fuzzy sets. Three scenarios are presented using fuzzy MEAN, AND and OR gates from the lowest to intermediate levels of the tree, and fuzzy SUM gate to relate the intermediate level to the top component of the tree, i.e., BIM application final performance. The use of fuzzy MEAN for lower levels and fuzzy SUM gates to reach the top level suggests the most realistic and accurate results. The methodology (fuzzy performance tree) described in this paper is appropriate to implement in today's construction industry when limited objective data is presented and it is heavily relied on experts' subjective judgment.

• Journal of Environmental Health Sciences
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• v.45 no.1
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• pp.1-8
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• 2019

Objectives: The purpose of this study was to estimate the ventilation rate of residential homes in Korea through tracer gas methods using indoor and outdoor concentrations of carbon dioxide ($CO_2$) and $CO_2$ generation rates from breathing. Methods: In this study, we calculated the number of occupants in a home by time through data on the average number of people per household from the Korean National Statistical Office and also measured the amount of $CO_2$ generation by breathing to estimate the indoor $CO_2$ generation rate. To estimate the ventilation rate, several factors such as the $CO_2$ generation rate and average volume of residential house provided by the Korean National Statistical Office, indoor $CO_2$ concentrations measured by sensors, and outdoor $CO_2$ concentrations provided by the Korea Meteorological Administration, were applied to a mass balance model for residential indoor environments. Results: The average number of people were 2.53 per household and Koreans spend 61.0% of their day at home. The $CO_2$ generation rate from breathing was $13.9{\pm}5.3L/h$ during sleep and $15.1{\pm}5.7L/h$ in a sedentary state. Indoor and outdoor $CO_2$ concentrations were 849 ppm and 407 ppm, respectively. The ventilation rate in Korean residential houses calculated by the mass balance model were $42.1m^3/h$ and 0.71 air change per hour. Conclusions: The estimated ventilation rate tended to increase with an increase in the number of occupants. Since sensor devices were used to collect data, sustainable data could be collected to estimate the ventilation rate of Korean residential homes, which enables further studies such as on changes in the ventilation rate by season resulting from the activities of occupants. The results of this study could be used as a basis for exposure and risk assessment modeling.

• Journal of Life Science
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• v.33 no.6
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• pp.490-497
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• 2023

Pregnant women may need to take medications to treat preexisting diseases or diseases that develop during pregnancy. However, some drugs may be fetotoxic and lead to, for example, teratogenicity and growth retardation. Predicting the fetotoxicity of drugs is thus important for the health of the mother and fetus. The fetotoxicity of many drugs has not been established because various challenges hinder the ability of researchers to determine their fetotoxicity. The need exists for in silico-based fetotoxicity assessment models, as they can modernize the testing paradigm, improve predictability, and reduce the use of animals and the costs of fetotoxicity testing. In this study, we collected data on the fetotoxicity of drugs and constructed fetotoxicity prediction models based on various machine learning algorithms. We optimized the models for more precise predictions by tuning the hyperparameters. We then performed quantitative performance evaluations. The results indicated that the constructed machine learning-based models had high performance (AUROC >0.85, AUPR >0.9) in fetotoxicity prediction. We also analyzed the feature importance of our model's predictions, which could be leveraged to identify the specific features of drugs that are strongly associated with fetotoxicity. The proposed model can be used to prescreen drugs and drug candidates at a lower cost and in less time. It provides a predictive score for fetotoxicity risk, which may be beneficial in the design of studies on fetotoxicity in human pregnancy.

• Journal of the Society of Disaster Information
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• v.9 no.4
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• pp.449-461
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• 2013

As the hydrofluoric acid leak in Gumi-si, Gyeongsangbuk-do or hydrochloric acid leak in Ulsan, Gyeongsangnam-do demonstrated, chemical related accidents are mostly caused by large amounts of volatile toxic substances leaking due to the damages of storage tank or pipe lines of transporter. Safety assessment is the most important concern because such toxic material accidents cause human and material damages to the environment and atmosphere of the surrounding area. Therefore, in this study, a hydrofluoric acid leaked from a storage tank was selected as the study example to simulate the leaked substance diffusing into the atmosphere and result analysis was performed through the numerical Analysis and diffusion simulation of ALOHA(Areal Location of Hazardous Atmospheres). the results of a qualitative evaluation of HAZOP (Hazard Operability)was looked at to find that the flange leak, operation delay due to leakage of the valve and the hose, and toxic gas leak were danger factors. Possibility of fire from temperature, pressure and corrosion, nitrogen supply overpressure and toxic leak from internal corrosion of tank or pipe joints were also found to be high. ALOHA resulting effects were a little different depending on the input data of Dense Gas Model, however, the wind direction and speed, rather than atmospheric stability, played bigger role. Higher wind speed affected the diffusion of contaminant. In term of the diffusion concentration, both liquid and gas leaks resulted in almost the same $LC_{50}$ and ALOHA AEGL-3(Acute Exposure Guidline Level) values. Each scenarios showed almost identical results in ALOHA model. Therefore, a buffer distance of toxic gas can be determined by comparing the numerical analysis and the diffusion concentration to the IDLH(Immediately Dangerous to Life and Health). Such study will help perform the risk assessment of toxic leak more efficiently and be utilized in establishing community emergency response system properly.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.281-293
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• 2020

Recently, large and small fires have been happening more often in Korea. Fire is one of the most frequent disasters along with traffic accidents in korean cities, and this frequency is closely related to the land use and the type of facilities. Therefore, in this study, the significance of fires was analyzed by considering land use, facility types, human and social factors and using 10 years of fire data in Jinju city. Based on this, OLS (Ordinary Least Square) regression analysis, SLM (Spatial Lag Model) and SEM (Spatial Error Model) using space weights, were compared and analyzed considering the location of the fire and each factor, then a statistical model with high suitability was presented. As a result, LISA analysis of spatial distribution patterns of fires in Jinju city was conducted, and it was proved that the frequency of fires was high in the order as follow, central commercial area, industrial area and residential area. Multiple regression analysis was performed by integrating demographic, social, and physical variables. Therefore, the three models were compared and analyzed by applying spatial weighting to the derived factors. As a result of the significance test, the spatial error model was analyzed to be the most significant. The facilities that have the highest correlation with fire occurrence were second type neighborhood facilities, followed by detached house, first type neighborhood facilities, number of households, and sales facilities. The results of this study are expected to be used as significant data to identify factors and manage fire safety in urban areas. Also, through the analysis of the standard deviation ellipsoid, the distribution characteristics of each facility in the residential area, industrial area, and central commercial area among the use areas were analyzed. In, the second type neighborhood facility with the highest fire risk was concentrated in the center. The results of these studies are expected to be used as useful data for identifying factors and managing fire safety in urban areas.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.802-811
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• 2023

Marine spatial planning (MSP) is a crucial element for rational allocation and sustainable use of marine areas. Particularly, Fishing Activity Protected Areas constitute essential zones accounting for 45.6% designated for sustainable fishing activities. However, the current assessment of these zones does not adequately consider future demands and potential values, necessitating appropriate evaluation methods and predictive tools for long-term planning. In this study, we selected key fish species (Scomber japonicus, Trichiurus lepturus, Engraulis japonicus, and Larimichthys polyactis) within the Fishing Activity Protected Area to predict their distribution and compare it with the current designated zones for evaluating the ability of the prediction tool. Employing the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report scenarios (SSP1-2.6 and SSP5-8.5), we used species distribution models (such as MaxEnt) to assess the movement and distribution changes of these species owing to future variations. The results indicated a 30-50% increase in the distribution area of S. japonicus, T. lepturus, and L. polyactis, whereas the distribution area of E. japonicus decreased by approximately 6-11%. Based on these results, a species richness map for the four key species was created. Within the marine spatial planning boundaries, the overlap between areas rated "high" in species richness and the Fishing Activity Protected Area was approximately 15%, increasing to 21% under the RCP 2.6 scenario and 34% under the RCP 8.5 scenario. These findings can serve as scientific evidence for future evaluations of use zones or changes in reserve areas. The current and predicted distributions of species owing to climate change can address the limitations of current use zone evaluations and contribute to the development of plans for sustainable and beneficial use of marine resources.

• Information Systems Review
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• v.17 no.2
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• pp.77-100
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• 2015

This study examined the factors that influence the smartphone user's decision to accept the personalized mobile advertisement. As a theoretical basis, we applied the privacy calculus model (PCM) that illustrates how consumers are engaged in a dynamic adjustment process in which privacy risks are weighted against benefits of information disclosure. In particular, we investigated how smartphone users make a risk-benefit assessment under which personalized service as benefit-side factor and information privacy risks as a risk-side factor accompanying their acceptance of advertisements. Further, we extend the current PCM by considering advertisement fatigue as a new factor that may influence the user's acceptance. The research model with five (5) hypotheses was tested using data gathered from 215 respondents through a quasi-experimental survey method. During the survey, each participant was asked to navigate the website where the experimental simulation of a mobile advertisement service was provided. The results showed that three (3) out of five (5) hypotheses were supported. First, we found that the intention to accept advertisements is positively and significantly influenced by the perceived value of personalization. Second, perceived advertisement fatigue was also found to be a strong predictor of the intention to accept advertisements. However, we did not find any evidence of direct influence of privacy risks. Finally, we found that the significant moderating effect between the perceived value of personalization and advertisement fatigue. This suggests that the firms should provide effective tailored advertisement that can increase the perceived value of personalization to mitigate the negative impacts of advertisement fatigue.

• Journal of the Korean Child Neurology Society
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• v.26 no.4
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• pp.227-232
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• 2018

Purpose: Febrile seizure (FS) is the most common type of seizure in children between 6 months to 5 years of age. A family history of febrile seizures can increase the risk a child will have a FS. Yet, prevalence of FS regarding external environment has not been clearly proved. This study attempts to determine the association between prevalence of FS and weather. Methods: This study included medical records from the Korea National Health Insurance Review and Assessment Service. Data were collected from 29,240 children, born after 2004, diagnosed with FS who were admitted to one of the hospitals in Seoul, Korea, between January 2009 and December 2013. During the corresponding time period, data from the Korea Meteorological Administration on daily monitoring of four meteorological factors (sea-level pressure, amount of precipitation, humidity and temperature) were collected. The relationships of FS prevalence and each meteorological factor will be designed using Poisson generalized additive model (GAM). Also, the contributory effect of viral infections on FS prevalence and weather will be discussed. Results: The amount of precipitation was divided into two groups for comparison: one with less than 5 mm and the other with equal to or more than 5 mm. As a result of Poisson GAM, higher prevalence of FS showed a correlation with smaller amount of precipitation. Smoothing function was used to classify the relationships between three variables (sea-level pressure, humidity, and temperature) and prevalence of FS. FS prevalence was correlated with lower sea-level pressure and lower humidity. FS prevalence was high in two temperature ranges (-7 to $-1^{\circ}C$ and $18-21^{\circ}C$). Conclusion: Low sea-level pressure, small amount of precipitation, and low relative air humidity may increase FS prevalence risk.