• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.6
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• pp.63-68
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• 2006

Recent statistical data regarding electric shock accidents have been analyzed to examine the electric shock accidents occurred at the voltage of 22,900[V], In order to demonstrate the mechanism of the 22,900[V] electric shock accident, a number of experiments to simulate electric shock accidents have been performed based on the analysis results. In the experiment, the current flowing through a human body model was measured to quantitatively analyze the hazards of the simulated electric shock accidents in various situations. As a result of the experiment, it was shown that once an electric shock accident occurred the accident proved fatal to the human body, regardless of electric shock situation.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• Journal of the Korean Society of Safety
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• v.33 no.6
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• pp.77-84
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• 2018

Report documents of industrial and occupational accidents have continuously been accumulated in private and public institutes. Amongst others, information on narrative-texts of accidents such as accident processes and risk factors contained in disaster report documents is gaining the useful value for accident analysis. Despite this increasingly potential value of analysis of text information, scientific and algorithmic text analytics for safety management has not been carried out yet. Thus, this study aims to develop data processing and visualization techniques that provide a systematic and structural view of text information contained in a disaster report document so that safety managers can effectively analyze accident risk factors. To this end, the risk factor map using text mining and self-organizing map is developed. Text mining is firstly used to extract risk keywords from disaster report documents and then, the Self-Organizing Map (SOM) algorithm is conducted to visualize the risk factor map based on the similarity of disaster report documents. As a result, it is expected that fruitful text information buried in a myriad of disaster report documents is analyzed, providing risk factors to safety managers.

• Safety and Health at Work
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• v.9 no.4
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• pp.427-433
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• 2018

Background: The purpose of this study was to identify the influence of workers' perceived workload, accident experiences, supervisors' safety leadership, and an organization's safety climate on the cognitive and emotional risk perception. Methods: Six hundred and twenty employees in a variety of manufacturing organizations were asked to complete to a questionnaire. Among them, a total of 376 employees provided valid data for analysis. To test the hypothesis, correlation analysis and hierarchical regression analysis were used. Statistical analyses were conducted using IBM SPSS program, version 23. Results: The results indicated that workload and accident experiences have a positive influence and safety leadership and safety climate have a negative influence on the cognitive and emotional risk perception. Workload, safety leadership, and the safety climate influence perceived risk more than accident experience, especially for the emotional risk perception. Conclusion: These results indicated that multilevel factors (organization, group, and individual) play a critical role in predicting individual risk perceptions. Based on these results, therefore, to reduce risk perception related with unsafe behaviors and accidents, organizations need to conduct a variety of safety programs that enhance their safety climate beyond simple safety-related education and training. Simultaneously, it needs to seek ways to promote supervisors' safety leadership behaviors (e.g., site visits, feedback, safety communication, etc.). In addition, it is necessary to adjust work speed and amount and allocate task considering employees' skill and ability to reduce the workload for reducing risk perception.

• Research in Community and Public Health Nursing
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• v.32 no.1
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• pp.64-72
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• 2021

Purpose: The purpose of this study is to analyze occupational accident compensation insurance coverage and occupational accidents incidence for special-type delivery workers. Methods: The data for occupational accident compensation insurance coverage and occupational accidents from 2012 to 2017 were analyzed through descriptive statistics. Results: Rates of occupational accident compensation insurance coverage of special-type delivery workers decreased gradually from 43.4% in 2012 to 28.5% in 2016, and 29.0% in 2017. Rates of occupational illnesses death per ten thousand workers increased gradually from 2.1‱ in 2013 to 3.1‱ in 2016, and 8.6‱ in 2017. All occupational illness deaths were due to cerebro-cardiovascular diseases. Road traffic accidents and slips accounted for the largest proportion of occupational accidents. Conclusion: Special-type delivery workers have a high risk of industrial accidents, so it is necessary to raise industrial accident insurance coverage and provide professional and systematic occupational safety and health services.

• Korea Journal of Hospital Management
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• v.26 no.1
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• pp.17-25
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• 2021

This study aimed to analysis factors related to in-hospital death of injured patients by patient safety accident. A total of 1,529 inpatients were selected from Korea Centers for Disease Control and Prevention database(2013-2017). Frequency, Fisher's exact test, t-test, ANOVA, logistic regression analyses by using STATA 12.0 were performed. Analysis results show that the mortality rate was lower for female than male but the mortality rate was higher for the older age, the higher the CCI, head (or neck), multiple, systemic damage sites, internal and others, metropolitan cities based on Seoul and 300-499 based on the bed size of 100-299. Based on these findings, the possibility of using the in-depth investigation of discharge damage from the Korea Centers for Disease Control and Prevention as a data source for the patient safety survey conducted to understand the actual status of patient safety accident types, frequency, and trends should be reviewed. Also, it is necessary to prevent injury and minimize death by identifying factors that affect death after injury by patient safety accident.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1508-1515
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• 2022

In the present study, the cavity module of the MELCOR code is used for the simulation of molten corium concrete interaction (MCCI) during the late phase of postulated large break loss of coolant (LB-LOCA) accident in the APR1400 reactor design. Using the molten corium composition data from previous MELCOR Simulation of APR1400 under LB-LOCA accident, the ex-vessel phases of the accident sequences with long-term MCCI are recalculated with stand-alone cavity package of the MELCOR code to investigate the impact of water ingression and melt eruption models which were hitherto absent in MELCOR code. Significant changes in the MCCI behaviors in terms of the heat transfer rates, amount of gases released, and maximum cavity ablation depths are observed and reported in this study. Most especially, the incorporation of these models in the new release of MELCOR code has led to the reduction of the maximum ablation depth in radial and axial directions by ~38% and ~32%, respectively. These impacts are substantial enough to change the conclusions earlier reached by researchers who had used the older versions of the MELCOR code for their studies. and it could also impact the estimated cost of the severe accident mitigation system in the APR1400 reactor.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.41-47
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• 2022

A reliable prediction model of national occupational accident fatality rate can be used to evaluate level of safety and health protection for workers in a country. Moreover, the socio-economic aspects of occupational accidents can be identified through interpretation of a well-organized prediction model. In this paper, we propose a machine learning based relative comparison methods to predict and interpret a national occupational accident fatality rate based on socio-economic indicators. First, we collected 29 years of the relevant data from 11 developed countries. Second, we applied 4 types of machine learning regression models and evaluate their performance. Third, we interpret the contribution of each input variable using Shapley Additive Explanations(SHAP). As a result, Gradient Boosting Regressor showed the best predictive performance. We found that different patterns exist across countries in accordance with different socio-economic variables and occupational accident fatality rate.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.814-826
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• 2023

Sensor faults in nuclear power plant instrumentation have the potential to spread negative effects from wrong signals that can cause an accident misdiagnosis by plant operators. To detect sensor faults and make accurate accident diagnoses, prior studies have developed a supervised learning-based sensor fault detection model and an accident diagnosis model with faulty sensor isolation. Even though the developed neural network models demonstrated satisfactory performance, their diagnosis performance should be reevaluated considering real-time connection. When operating in real-time, the diagnosis model is expected to indiscriminately accept fault data before receiving delayed fault information transferred from the previous fault detection model. The uncertainty of neural networks can also have a significant impact following the sensor fault features. In the present work, a pilot study was conducted to connect two models and observe actual outcomes from a real-time application with an integrated system. While the initial results showed an overall successful diagnosis, some issues were observed. To recover the diagnosis performance degradations, additive logics were applied to minimize the diagnosis failures that were not observed in the previous validations of the separate models. The results of a case study were then analyzed in terms of the real-time diagnosis outputs that plant operators would actually face in an emergency situation.

• Journal of the Korean Society of Safety
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• v.38 no.5
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• pp.51-57
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• 2023

Numerous studies have been conducted to analyze the causal relationships of maritime accidents using natural language processing techniques. However, when multiple causes and effects are associated with a single accident, the effectiveness of extracting these causal relations diminishes. To address this challenge, we compiled a dataset using verdicts from maritime accident cases in this study, analyzed their causal relations, and applied labeling considering the association information of various causes and effects. In addition, to validate the efficacy of our proposed methodology, we fine-tuned the KoELECTRA Korean language model. The results of our validation process demonstrated the ability of our approach to successfully extract multiple causal relationships from maritime accident cases.