• The Journal of Engineering Geology
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• v.30 no.4
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• pp.663-671
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• 2020

Recently, there have been many natural disasters in Korea, not only in forest areas but also in urban areas, and the national requirements for them are increasing. In particular, there is no pre-disaster information system that can systematically manage the collapse of the slope of the national highway. In this study, big data analysis was conducted on the factors causing slope collapse based on the detailed investigation report on the slope collapse of national roads in Gangwon-do and Gyeongsang-do areas managed by the Cut Slope Management System (CSMS) and the basic survey of slope failures. Based on the analysis results, a slope collapse risk prediction model was established through Adaboost, a classification-based machine learning model, reflecting the collapse slope location and weather information. It also developed a visualization map for the risk of slope collapse, which is a visualization program, to show that it can be used for preemptive disaster prevention measures by identifying the risk of slope due to changes in weather conditions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.8
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• pp.661-673
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• 2014

In this paper, a precise orbit determination process was carried out based on KARISMA(KARI Collision Risk Management System) developed by KARI(Korea Aerospace Research Institute), in which optical tracking data of a geostationary satellite was used. The real optical tracking data provided by ESA(European Space Agency) for the ARTEMIS geostationary satellite was used. And orbit determination error was approximately 420 m compared to that of the ESA's orbit determination result from the same optical tracking data. In addition, orbit prediction was conducted based on the orbit determination result with optical tracking data for 4 days, and the position error for the orbit prediction during 3 days was approximately 500~600 m compared to that of ESA's result. These results imply that the performance of the KARISMA's orbit determination function is suitable to apply to the collision risk assessment for the space debris.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.13-22
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• 2007

The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.

• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.96-103
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• 2023

Generally, human stampedes and crowd collapses occur when people press against each other, causing falls that may result in death or injury. Particularly, crowd accidents have become increasingly common since the 1990s, with an average of 380 deaths annually. For instance, in Korea, a stampede occurred during the Itaewon Halloween festival on October 29, 2022, when several people crowded onto a narrow, downhill road, which was 45 meters long and between 3.2 and 4 meters wide. Precisely, this stampede was primarily due to the excessive number of people relative to the road size. Essentially, stampedes can occur anywhere and at any time, not just at events, but also in other places where large crowds gather. More specifically, the likelihood of accidents increases when the crowd density exceeds a turbulence threshold of 5-6 /m². Meanwhile, festivals and events, which have become more frequent and are promoted through social media, garner people from near and far to a specific location. Besides, as cities grow, the number of people gathering in one place increases. While stampedes are rare, their impact is significant, and the uncertainty associated with them is high. Currently, there is no scientific system to analyze the risk of stampedes due to crowd concentration. Consequently, to prevent such accidents, it is essential to prepare for crowd disasters that reflect social changes and regional characteristics. Hence, this study proposes using digital topographic maps and crowd-density risk simulations to develop a 3D model of the region. Specifically, the crowd density simulation allows for an analysis of the density of people walking along specific paths, which enables the prediction of danger areas and the risk of crowding. By using the simulation method in this study, it is anticipated that safety measures can be rationally established for specific situations, such as local festivals, and preparations may be made for crowd accidents in downtown areas.

• Journal of the Korean Society of Radiology
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• v.13 no.2
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• pp.201-207
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• 2019

This study has evaluated whether the method of using the combination of different risk group, according to K-TIRADS classification and K-TIRADS classification in thyroid ultrasonography is useful in a differential diagnosis of benign and malignant nodules. The subject was patients underwent thyroid ultrasonography and retrospective analysis were performed based on the results of fine needle aspiration cytology. A chi-square test was performed for the difference analysis of the score system in K-TIRADS and different risk group according to the benign and malignant of thyroid nodule. The optimized cut off value was determined by the K-TIRADS score and different risk group to predict malignant nodule through ROC curve analysis. In the differential verification result of K-TIRADS and different risk group, according to the classification of benign and malignant nodule group each showed significant difference statistically(p=.001). In the point classification according to K-TIRADS for the prediction of benign and malignant in ROC curve analysis showed AUC 0.786, Cut-off value>2(p=.001), and in the different risk group, it was decided as AUC 0.640, Cut-off value>2(p=.001). When discovering the nodule in thyroid ultrasound, it is considered that the K-TIRADAS which helps in identifying benign and malignant thyroid nodules, it is considered to be helpful in the differential diagnosis of thyroid nodules, than the classification system according to Different risk group, and when applying the classification system according to K-TIRADS, it is considered that it can reduce unnecessary fine needle aspiration cytology and could be helpful in finding the malignant nodules early.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.649-661
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• 2020

Cut-slope management system (CSMS) has been investigated all slopes on the road of the whole country to evaluate risk rating of each slope. Based on this evaluation, the decision-making for maintenance can be conducted, and this procedure will be helpful to establish a consistent and efficient policy of safe road. CSMS has updated the database of all slopes annually, and this database is constructed based on a basic and detailed investigation. In the database, there are two type of data: first one is an objective data such as slopes' location, height, width, length, and information about underground and bedrock, etc; second one is subjective data, which is decided by experts based on those objective data, e.g., degree of emergency and risk, maintenance solution, etc. The purpose of this study is identifying an data application plan to utilize those CSMS data. For this purpose, logistic regression, which is a basic machine-learning method to construct a prediction model, is performed to predict a judging-type variable (i.e., subjective data) based on objective data. The constructed logistic model shows the accurate prediction, and this model can be used to judge a priority of slopes for detailed investigation. Also, it is anticipated that the prediction model can filter unusual data by comparing with a prediction value.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.8
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• pp.3527-3531
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• 2015

Background: Serbia is one of the countries with highest incidence and mortality rates for cervical cancer in Central and South Eastern Europe. Introducing a risk index could provide a powerful means for targeting groups at high likelihood of having an abnormal cervical smear and increase efficiency of screening. The aim of the present study was to create and assess validity ofa index for prediction of an abnormal Pap test result. Materials and Methods: The study population was drawn from patients attending Departments for Women's Health in two primary health care centers in Serbia. Out of 525 respondents 350 were randomly selected and data obtained from them were used as the index creation dataset. Data obtained from the remaining 175 were used as an index validation data set. Results: Age at first intercourse under 18, more than 4 sexual partners, history of STD and multiparity were attributed statistical weights 16, 15, 14 and 13, respectively. The distribution of index scores in index-creation data set showed that most respondents had a score 0 (54.9%). In the index-creation dataset mean index score was 10.3 (SD-13.8), and in the validation dataset the mean was 9.1 (SD=13.2). Conclusions: The advantage of such scoring system is that it is simple, consisting of only four elements, so it could be applied to identify women with high risk for cervical cancer that would be referred for further examination.

• Journal of the Korean Applied Science and Technology
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• v.35 no.3
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• pp.606-611
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• 2018

City natural gas is classified flammable hazardous gas and should be secured according to explosion risk assessment determined by Industrial Standard KS C IEC. In this study, leak size, ventilation grade and effectiveness were adopted to the KS C IEC for risk assessment in natural gas supply system. To evaluate the applicability of the computational fluid dynamics (CFD), the risk assessment was studied for four different conditions using hypothetical volume($V_z$) valuesfrom gas leak experiments, KS C IEC calculation, and CFD simulation.

• Journal of Korean Public Health Nursing
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• v.16 no.1
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• pp.201-212
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• 2002

This study was designed to identify the risk factors of unplanned readmission in a university hospital. The six-month discharge information from January to June, 2000 in a tertiary university hospital was used as a source of data through the medical record and hospital information system. To increase the effect of comparison. the data were collected by sampling 192 couples (384 patients) of unplanned readmission group through the matching by its disease groups, sex, and age. The accuracy of prediction for unplanned readmission was analyzed by constructing the predicted model of unplanned readmission through the logistic regression. The study results are as follows. The conditional logistic regression analysis was performed with nine variables at the significance level 0.05 through univariate analysis including residence, days after discharge, initial admission route, previous admission, transfer to special care unite, hospital stay days, medical care expenses, special cares, and laboratory and imaging services. As a result, the closer the patients live in Seoul and Gyeong-in area (Odds ratio=2.529, p=0.003), the shorter the days after discharge was (Odds ratio=0.600, p=0.000), and the more frequent admission rate was (Odds ratio=2.317, p=0.004), the more unplanned readmission was resulted. Also, the accuracy of prediction for data classification of this regression model showed $70.3\%$(032+83/306).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.25-30
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• 2024

Train derailment accidents have been increasing over the past five years. The causes of these railroad derailments were found to be mainly defective track switches that change train tracks, use of old parts, and poor maintenance issues. In this paper, to solve these problems, an intelligent sensor-based automatic railway risk prediction algorithm and hypothesis were established and computer simulation experiments were performed. In particular, research on RFID technology and IoT sensor technology was conducted on a WEB basis. In addition, in this paper, in order to prevent country of origin counterfeiting accidents, a blockchain-based computer simulation to prevent forgery of railway parts was performed using open source.