• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.251-252
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• 2023

Compared to other industries the construction industry experiences more casualties and property damage due to safety accidents. One of the reasons is the increasing number of foreign workers. For this reason, past studies have found that foreign workers at construction sites are more exposed to safety accidents than non-foreign workers. Nevertheless the proportion of foreign workers involved in safety accidents at construction sites is increasing, and there has been a lack of research to predict the risk of safety accidents at construction sites. Additionally, realistic safety management is lacking due to a lack of safety accident risk prediction research. Therefore, in this study, we would like to propose basic research that proposes an AI-based safety accident prediction model framework for predicting safety accidents of foreign workers at construction sites. The framework and results of this study will contribute to reducing and preventing the risk of safety accidents for foreign workers through risk prediction for safety management of foreign workers at construction sites.

• Fire Science and Engineering
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• v.33 no.4
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• pp.50-58
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• 2019

The prediction performance of B-RISK was evaluated for the fire behaviors of combustibles in a compartment using Fire Dynamics Simulator (FDS). First of all, to predict the heat release rate (HRR) for two combustible sets, the HRR for one combustible set and the design fire curve were used as input values for B-RISK. Comparing results of B-RISK calculations with experimental data for two combustible sets, it was found that B-RISK results predicted insufficiently for fire growth rate of experimental data but there was good agreement for maximum HRR and total HRR with the experimental data. And the B-RISK results were used for input values of FDS to evaluate the fire behaviors of B-RISK results. Comparing results of FDS calculations with experimental data, the simulation results showed that the temperature and concentrations of O₂, CO₂ in the fire growth phase were different from the experimental data. However, when using the B-RISK result for percentile 70%, the simulation results sufficiently predicted the overall fire behaviors.

• Korean Circulation Journal
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• v.53 no.9
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• pp.581-593
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• 2023

In the catheterization laboratory, the measurement of physiological indexes can help identify functionally significant lesions and has become one of the standard methods to guide treatment decision-making. Plaque vulnerability refers to a coronary plaque susceptible to rupture, enabling risk prediction before coronary events, and it can be detected by defining a certain type of plaque morphology on coronary imaging modalities. Although coronary physiology and plaque vulnerability have been considered different attributes of coronary artery disease, the underlying pathophysiological basis and clinical data indicate a strong correlation between coronary hemodynamic properties and vulnerable plaque. In prediction of coronary events, emerging data have suggested independent and additional implications of a physiology-based approach to a plaque-based approach. This review covers the fundamental interplay between coronary physiology and plaque morphology during disease progression with clinical data supporting this relationship and examines the clinical relevance of physiological indexes in prediction of clinical outcomes and therapeutic decision-making along with plaque vulnerability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Genomics & Informatics
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• v.10 no.3
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• pp.175-183
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• 2012

Colorectal cancer (CRC) is among the leading causes of cancer deaths and can be caused by environmental factors as well as genetic factors. Therefore, we developed a prediction model of CRC using genetic risk scores (GRS) and evaluated the effects of conventional risk factors, including family history of CRC, in combination with GRS on the risk of CRC in Koreans. This study included 187 cases (men, 133; women, 54) and 976 controls (men, 554; women, 422). GRS were calculated with most significantly associated single-nucleotide polymorphism with CRC through a genomewide association study. The area under the curve (AUC) increased by 0.5% to 5.2% when either counted or weighted GRS was added to a prediction model consisting of age alone (AUC 0.687 for men, 0.598 for women) or age and family history of CRC (AUC 0.692 for men, 0.603 for women) for both men and women. Furthermore, the risk of CRC significantly increased for individuals with a family history of CRC in the highest quartile of GRS when compared to subjects without a family history of CRC in the lowest quartile of GRS (counted GRS odds ratio [OR], 47.9; 95% confidence interval [CI], 4.9 to 471.8 for men; OR, 22.3; 95% CI, 1.4 to 344.2 for women) (weighted GRS OR, 35.9; 95% CI, 5.9 to 218.2 for men; OR, 18.1, 95% CI, 3.7 to 88.1 for women). Our findings suggest that in Koreans, especially in Korean men, GRS improve the prediction of CRC when considered in conjunction with age and family history of CRC.

• Korean Circulation Journal
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• v.53 no.9
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• pp.621-631
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• 2023

Background and Objectives: The morphology-voltage-P-wave duration (MVP) electrocardiography (ECG) risk score is a newly defined scoring system that has recently been used for atrial fibrillation (AF) prediction. The aim of this study was to evaluate the ability of the MVP ECG risk score to predict AF in patients with an implantable cardioverter defibrillator (ICD) and heart failure with reduced ejection fraction in long-term follow-up. Methods: The study used a single-center, and retrospective design. The study included 328 patients who underwent ICD implantation in our hospital between January 2010 and April 2021, diagnosed with heart failure. The patients were divided into low, intermediate and high-risk categories according to the MVP ECG risk scores. The long-term development of atrial fibrillation was compared among these 3 groups. Results: The low-risk group included 191 patients, the intermediate-risk group 114 patients, and the high-risk group 23 patients. The long-term AF development rate was 12.0% in the low-risk group, 21.9% in the intermediate risk group, and 78.3% in the high-risk group. Patients in the high-risk group were found to have 5.2 times higher rates of long-term AF occurrence compared to low-risk group. Conclusions: The MVP ECG risk score, which is an inexpensive, simple and easily accessible tool, was found to be a significant predictor of the development of AF in the long-term follow-up of patients with an ICD with heart failure with reduced ejection fraction. This risk score may be used to identify patients who require close follow-up for development and management of AF.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.941-942
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.53-54
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• 2009

• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.334-343
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• 2023

Purpose: In this study, the cavity data found through ground cavity exploration was combined with underground facilities to derive a correlation, and the ground subsidence prediction map was verified based on the AI algorithm. Method: The study was conducted in three stages. The stage of data investigation and big data collection related to risk assessment. Data pre-processing steps for AI analysis. And it is the step of verifying the ground subsidence risk prediction map using the AI algorithm. Result: By analyzing the ground subsidence risk prediction map prepared, it was possible to confirm the distribution of risk grades in three stages of emergency, priority, and general for Busanjin-gu and Saha-gu. In addition, by arranging the predicted ground subsidence risk ratings for each section of the road route, it was confirmed that 3 out of 61 sections in Busanjin-gu and 7 out of 68 sections in Sahagu included roads with emergency ratings. Conclusion: Based on the verified ground subsidence risk prediction map, it is possible to provide citizens with a safe road environment by setting the exploration section according to the risk level and conducting investigation.

• Journal of the Ergonomics Society of Korea
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• v.33 no.4
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• pp.289-297
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• 2014

Objective: The purpose of this study is to identify the incidence risk of cardiovascular disease (CVD) in the workplace, and to suggest the prediction models for level of CVD incidence risk. Background: CVD can be caused by various factors related to personal habits such as diet and exercise, or genetics. However it can also be caused and aggravated by work, making the elimination of such risk factors at work crucial disease (KOSHA, 2013). Method: The distribution of CVD risk assessment levels of 162 workers was compared with the acquired medical examination data to discuss the necessity of assigning additional risk factors. Two alternative risk assessment models were given to enhance the accuracy of the evaluation; adjusting risk scores given in the KOSHA GUIDE H-1-2013 (alternative 1) and building a matrix of KOSHA GUIDE H-1-2013 and risk assessment results based on work condition levels (alternative 2). To verify the suggested models, medical examination results of 12 workers approved of convalescence were referred to. Results: The second alternative showed more relevance between the results and workers approved of convalescence in predicting the risk group when applied to actual heath examination data from the approved workers. The power of description of the new method for determining the risk of CVD incidence, 83.3%, is higher than that of KOSHA GUIDE H-1-2013, 25%. Conclusion: Results of this study imply that more approved workers had been from unmanaged normal groups than managed risk groups, raising the importance of CVD management. Application: The new prediction model considering working time and shift work developed in this study is expected to be a fundamental data for risk analysis and management of CVD in the workplace.