• Korean Journal of Radiology
• /
• v.20 no.1
• /
• pp.148-157
• /
• 2019

Objective: To evaluate the current status of managing adverse reactions to iodinated contrast media (ICM) for computed tomography in referral hospitals in South Korea compared with hospitals in other countries. Materials and Methods: This survey investigation involved 59 Korean and 15 overseas hospitals using guideline-based questionnaires consisting of 24 items in 7 main categories related to managing adverse reactions to ICM. Results: Informed written consent with risk factor evaluation was appropriately performed in most of the Korean hospitals. There was considerable variability in assessing renal function across the hospitals; serum creatinine level was used as a reference in 76.4% of Korean hospitals. The Korean hospitals preferred a more stringent approach to determining normal renal function (p = 0.01), withholding metformin (p = 0.01), and fasting before ICM exposure (p < 0.001) compared with overseas hospitals. All the Korean hospitals had an emergency protocol and in-hospital system for adverse reactions to ICM. The Korean (87.7%) and overseas hospitals (100%) were similarly equipped with epinephrine (p = 0.332), but only 38.6% of Korean hospitals were equipped with a bronchodilator (p = 0.004). For patients with a previous hypersensitivity reaction to ICM, 62.3% of Korean hospitals pre-medicated with anti-histamine and corticosteroid according to the severity of the previous reaction, and changed the culprit ICM in 52.8%, while skin test was performed in 17%. Conclusion: In general, Korean referral hospitals were well-prepared regarding informed consent, protocol, and an in-hospital system for managing adverse reactions to ICM. Nevertheless, there was considerable variability in details and management, thus requiring standardization by reflecting current guidelines.

• International Journal of Automotive Technology
• /
• v.8 no.4
• /
• pp.493-504
• /
• 2007

This paper proposes a stereo vision-based forward obstacle detection and distance measurement method. In general, stereo vision-based obstacle detection methods in automotive applications can be classified into two categories: IPM (Inverse Perspective Mapping)-based and disparity histogram-based. The existing disparity histogram-based method was developed for stop-and-go applications. The proposed method extends the scope of the disparity histogram-based method to highway applications by 1) replacing the fixed rectangular ROI (Region Of Interest) with the traveling lane-based ROI, and 2) replacing the peak detection with a constant threshold with peak detection using the threshold-line and peakness evaluation. In order to increase the true positive rate while decreasing the false positive rate, multiple candidate peaks were generated and then verified by the edge feature correlation method. By testing the proposed method with images captured on the highway, it was shown that the proposed method was able to overcome problems in previous implementations while being applied successfully to highway collision warning/avoidance conditions, In addition, comparisons with laser radar showed that vision sensors with a wider FOV (Field Of View) provided faster responses to cutting-in vehicles. Finally, we integrated the proposed method into a longitudinal collision avoidance system. Experimental results showed that activated braking by risk assessment using the state of the ego-vehicle and measuring the distance to upcoming obstacles could successfully prevent collisions.

• International Journal of Contents
• /
• v.14 no.3
• /
• pp.53-60
• /
• 2018

Based on the previous studies on sinkholes and ground subsidence conducted until date, the factors affecting the occurrence of sinkholes can be divided into natural environmental factors and human environmental factors in accordance with the purpose of the study. Furthermore, to be more specific, the human environment can be classified into the artificial type and the social type. In this study, the assessment indices for assessing risks of sinkholes and ground subsidence were developed by performing AHP analysis based on the results of the study by Lee et al. (2016), who selected the risk factors for the occurrence of sinkholes by performing Delphi analysis targeting relevant experts. Analysis showed that the artificial environmental factors were of significance in affecting the occurrence of sinkholes. Explicitly, the underground factors were found to be of importance in the natural environment, and among them, the level of underground water turned out to be an imperative influencing factor. In the artificial environment, the underground and subterranean structures exhibited similar importance, and in the underground structures, the excessive use of the underground space was found to be an important influencing factor. In the subterranean ones, the level of water leakage and the erosion of the water supply and sewage piping system were the influential factors, and in the surface, compaction failure was observed as an imperative factor. In the social environment, the regional development, and above all, the groundwater overuse were found to be important factors. In the managemental and institutional environment, the improper construction management proved to be the most important influencing factor.

• Journal of Korean Association for Spatial Structures
• /
• v.13 no.3
• /
• pp.73-81
• /
• 2013

In paper after the strong earthquake of recently the Korea neighborhood, the Korean government survey show that the 86% of school buildings in Korea are in potential damage risk and only 14% of them are designed as earthquake-resistance buildings. Earthquake Reinforcing projects of school have been a leading by the ministry of education, however their reinforcing methods done by not proved a engineering by experiment which results in uneconomical and uneffective rehabilitation for the future earthquake. An experimental and analytical study have been conducted for the shear reinforcing method of column by axis and horizontal axis load using attaching composite beam. Based on the previous research, in this study, Design examples are given to show the performance evaluation for the column reinforcing of old school buildings using nonlinear analysis is going to be conducted and strengthening method is going to be on the market after their performance is proved by the test.

• Journal of Advanced Navigation Technology
• /
• v.15 no.4
• /
• pp.609-615
• /
• 2011

In recent public institutions and banks to improve competitiveness in the integration of information systems projects are larger in size and may be the project's ever-growing need appropriate software project management plan. However, between the effect of the project management area for systematic research was scarce. Through this study, previous research in the important area of project management is developed, and "scope, schedule, quality, human resources, risk" of each area of impact analysis and the interaction between the progress of each area, the planning, execution, completion Performance levels were analyzed separately. Affected and Performance Analysis of the results to assess the level of the project management model is presented.

• Journal of the Korean Institute of Educational Facilities
• /
• v.20 no.1
• /
• pp.45-52
• /
• 2013

In paper after the strong earthquake of recently the Korea neighborhood, the Korean government survey show that the 86% of school buildings in Korea are in potential damage risk and only 14% of them are designed as earthquake-resistance buildings. Reinforcing projects of school have been conducting by the ministry of education, however their reinforcing methods done by not proved a engineering by experiment which results in uneconomical and uneffective rehabilitation for the future earthquake. An experimental and analytical study have been conducted for the shear and flexural reinforcing method of RC beam using composite beam. Based on the previous research, in this study, performance evaluation for the column reinforcing of old school buildings using nonlinear analysis is going to be conducted and strengthening method is going to be on the market after their performance is proved by the test.

• Journal of the Korean Society of Safety
• /
• v.34 no.6
• /
• pp.22-28
• /
• 2019

In South Korea, the enact of Korean Coast Guard Act-1 manages physical and chemical oil-dispersants. Oil snare, which is made of polypropylene, is newly added to the aforementioned act, and it has advantage on the ease of recovery compare to other adsorbents. This study synthesized bunker B-oil with diesel-oil and bunker C-oil to perform an adsorption test based on three samples which were manufactured in South Korea. As a result, adsorption test revealed 5.2 g/g more adsorption than the previous results from the act. Additional toluene test revealed that all the samples satisfied 90.0%, however coloured samples could release its pigment on the marine environment. Thus, colorless samples are recommended on the risk management of marine accidents. The study on the basic direction of the calculation of the test items and the standard value for the quality control of the oil snare was also carried out.

• Journal of Welding and Joining
• /
• v.23 no.6
• /
• pp.61-66
• /
• 2005

A probabilistic approach for evaluating failure risk is suggested in this paper. Probabilistic fracture analyses were performed for a pressurized pipe of a Cr-Mo steel reflecting variation of material properties at high temperature. A crack was assumed to be located along the weld fusion line. Probability density functions of major variables were determined by statistical analyses of material creep and creep crack growth data measured by the previous experimental studies by authors. Distributions of these variables were implemented in Monte Carlo simulation of this study. As a fracture parameter for characterizing growth of a fusion line crack between two materials with different creep properties, $C_t$ normalized with $C^*$ was employed. And the elapsed time was also normalized with tT, Resultingly, failure probability as a function of operating time was evaluated fur various cases. Conventional deterministic life assessment result was turned out to be conservative compared with that of probabilistic result. Sensitivity analysis for each input variable was conducted to understand the most influencing variable to the analysis results. Internal pressure, creep crack growth coefficient and creep coefficient were more sensitive to failure probability than other variables.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2022.11a
• /
• pp.207-208
• /
• 2022

Since 1960, the green area has decreased due to rapid urbanization and the artificial surface has increased, and the repair and water function of the previous surface has decreased due to the decrease in rainwater absorption capacity. In addition, the risk of carbon dioxide and fine dust is emerging due to the use of fossil fuels due to urbanization. As a result, permeable blocks, an eco-friendly product, are in the spotlight. Therefore, this study was conducted to examine the strength properties of the permeable block using a hydroball. As a result of the experiment, the flexural strength and compressive strength tended to decrease as the hydroball replacement rate increased. It is judged that the hydroball absorbs a large amount of moisture during the mixing process and lacks moisture required for curing, resulting in a decrease in strength. According to KS F 4419, since the hydroball replacement rate is satisfied up to 20%, further research is needed to analyze the adsorption performance of air pollutants in the future and evaluate their utilization as a permeable block in the future.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.105-129
• /
• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.