• Korean Journal of Construction Engineering and Management
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• v.10 no.1
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• pp.78-90
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• 2009

Recently, operation of highway is the complex digital Infrastructure based on complicated IT. The application of IT is increasing more and more in digital Infrastructure. Though IT is very convenient, if unpredicted operating risk of highway occurs, widespread damage can be large. When operating risk of highway occurs, road users are out of smoothly-run service because of the operating interruption. This risk causes unpredicted operating management cost and additional maintenance cost. It will excess over the planned operating cost, which may leads to users's unsafety and operator's insolvency because of income loss. Until now, related studies to find out the risk are not sufficient. The purpose of this study is to suggest risk cost items and to estimate the reasonable risk cost by using simulation method in case of occurring the huge power failure at the operating digitalized highway. This study indicates the several plans to hedge against risk cost and the management of highway project. From now on, it will be used as basic data to confirm the soundness of operating system in Digital Infrastructure.

• YAKHAK HOEJI
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• v.55 no.4
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• pp.352-360
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• 2011

Standard combination chemotherapy including isoniazid, rifampin, pyrazinamide, and ethambutol is very effective against tuberculosis. But, these medicines can cause hepatotoxicity which is the main reason for treatment interruption or change in drug regimen. In order to identify risk factors associated with hepatotoxcity in Koreans and assess elevated baseline LFTs' contributions to hepatotoxicity, a retrospective case control study was performed. The medical records of 277 patients who diagnosed with tuberculosis at a community hospital from January 1st, 2007 to June 30th, 2010 were reviewed. Patients were categorized into 3 groups (non toxic group, patients without increase in LFT levels; mild to moderate hepatotoxic group and severe hepatotoxic group). And the correlation between risk factors and hepatotoxicity was analyzed by using SPSS program. The overall incidence of hepatotoxicity was 18% and 8.7% of patients developed severe toxicity. Patients in the severe toxic group had the longest treatment period among the three groups. In 75% of severe toxic group, hepatotoxicity occurred within 18.3 days after starting medication. Hypoalbuminemia (serum albumin <3 g/dl) was a significant risk factor for development of severe toxicity. Elevated baseline transaminase (except ALT), total bilirubin, and preexisting hepatitis were also risk factors which were more than twice as likely to increase risk of severe hepatotoxicity (p>0.05). In conclusion, hypoalbuminemia (serum albumin level <3 g/dl) was a significant risk factor for anti-tuberculosis druginduced severe toxicity. Therefore, before starting antituberculosis chemotherapy, serum albumin level should be assessed at baseline. In high-risk patients (hypoalbuminemia, elevated LFTs) for hepatotoxicty, liver function should be closely monitored up to at least 21 days after taking medication.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2283-2287
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• 2015

In order to explore the association between RASSF1A methylation and nasopharyngeal carcinoma (NPC) risk of Chinese, we carried out a meta-analysis with searches of PubMed, Web of Science, ProQest and Medline databases. Ultimately, 14 articles were identified and analysised using R Software (R version 3.1.2) including meta packages. Overall, we found a significant relationship between RASSF1A methylation and NPC risk (OR 30.7; 95 % CI, 16.71~56.23; z=11.0591; p<0.0001) in a fixed effects model and (OR 32.1; 95% CI, 14.27~72.01; z=8.3984; p<0.0001) in a random effects model pooled. In tissue and NP brushings groups, similar results were found. Hence, our study identified a strong association between RASSF1A methylation and NPC and highlighted a promising potential for RASSF1A methylation in NPC risk prediction of Chinese.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.444-450
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• 2013

For intelligent vehicle technology, it is very important to recognize the states of around vehicles and assess the collision risk for safety driving of the vehicle. Specifically, it is very fatal the collision with the vehicle coming from opposite direction. In this paper, a centerlane violation prediction method is proposed. Only radar signal based prediction makes lots of false alarm cause of measurement noise and the false alarm can make more danger situation than the non-prediction situation. We proposed the novel prediction method using IMM algorithm and fuzzy logic to increase accuracy and get rid of false positive. Fuzzy logic adjusts the radar signal and the IMM algorithm appropriately. It is verified by the computer simulation that shows stable prediction result and fewer number of false alarm.

• Korean Chemical Engineering Research
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• v.47 no.2
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• pp.248-257
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• 2009

In this paper, human exposure and risk of environmental pollutants were predicted using an emission assessment model and multimedia fate model. Eight environmental pollutants, acetaldehyde, acrylonitrile, aniline, benzene, carbon tetrachloride, dichloromethane, formaldehyde and vinyl chloride, were selected for the risk assessment in an urban and industrial area in Korea. The emission rate of target pollutants were estimated after considering a variety of point and non-point emission sources including geographical information. A spatially refined multimedia fate model was applied to predict the environmental concentration and fate of pollutants. Hazard data of target materials were obtained from the IRIS(Integrated Risk Information System) database. Using the modeling results with hazard data, the human risks were assessed. Modeling results demonstrate that the considerable risks were observed for several pollutants.

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.368-378
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• 2021

Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required. Materials and Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set. Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%. Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.3
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• pp.228-234
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• 2016

As a college dormitory has the features of high dwelling density and a floating population that becomes crowded during particular times, when a disaster such as a fire occurs, it has the risk of causing much loss of life. In this study, the fire simulation program Fire Dynamics Simulator (FDS), is used to predict the risk when a fire occurs, to analyze the problem, and to suggest an improvement plan for a new cadet dormitory at an university in Korea. The research results are as follows. When a fire occurred in the ironing room inside the cadet dormitory, a smoke detector operated after 65 seconds. Thirteen seconds later, a sprinkler started to operate. The temperature and carbon monoxide density reached the limit value at 241 and 248 seconds, respectively. Because the limit visibility value was reached within 66 seconds after the occurrence of a fire, it is predicted that preparation must be finished and evacuation should begin within 1 minute after the fire occurs, in order to have no casualties. Synthesizing this dormitory fire risk prediction result, the visibility value is considered to be the most dangerous factor for personal safety. Because of this, installing a smoke extraction system is suggested to secure visibility. After the installation of a smoke extraction system, the problem of smoke diffusion in the corridors improved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.277-283
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• 2019

Advances in technology have allowed the development and commercialization of various parts; however this has shortened the discontinuation cycle of the components. This means that repair and logistic support of weapon system which is applied to thousands of part components and operated over the long-term is difficult, which is the one of main causes of the decrease in the availability of weapon system. To improve this problem, the United States has created a special organization for this problem, whereas in Korea, commercial tools are used to predict and manage DMSMS. However, there is rarely a method to predict life cycle of parts that are not presented DMSMS information at the commercial tools. In this study, the structured and unstructured data of parts of a commercial tool were gathered, preprocessed, and embedded using neural network algorithm. Then, a method is suggested to predict the life cycle risk (LC Risk) and year to end of life (YTEOL). In addition, to validate the prediction performance of LC Risk and YTEOL, the prediction value is compared with descriptive statistics.

• Smart Media Journal
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• v.9 no.1
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• pp.38-44
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• 2020

Due to the high dependence on electric energy, electric fires make up a significant portion of fires in Korea. Electric arcs by short circuits or poor contact cause three of four electrical fires. An electric arc is a discharge phenomenon of electrical current between the insulators, which instantaneously produces high temperature. In order to reduce the fire due to electric arc, this study aims to predict the electric arc risk. We collected arc data from the arc detectors and converted into graphs based on temporal arc data. We used machine learning for training converted graph with different number of temporal arc data. To measure the performance of the learning model, we use the test data. In the results, when the number of temporal arc data was 20, the prediction rate was high as 86%.

• Journal of the Korea Safety Management & Science
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• v.10 no.2
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• pp.71-83
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• 2008

Most researches on the corporate credit rating are generally classified into the area of bankruptcy prediction and bond rating. The studies on bankruptcy prediction have focused on improving the performance in binary classification problem, since the criterion variable is categorical, bankrupt or non-bankrupt. The other studies on bond rating have predicted the credit ratings, which was already evaluated by bond rating experts. The financial institute, however, should perform effective loan evaluation and risk management by employing the corporate credit rating model, which is able to determine the credit of corporations. Therefore, in this study we present a medium sized corporate credit rating system by using Artificial Neural Network(ANN) and Analytical Hierarchy Process(AHP). Also, we developed AHP model for credit rating using non-financial information. For the purpose of completed credit rating model, we integrated the ANN and AHP model using both financial information and non-financial information. Finally, the credit ratings of each firm are assigned by the proposed method.