• IE interfaces
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• v.20 no.1
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• pp.41-48
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• 2007

The main purpose of this paper is cost reduction in absurd pilot positive expense and human accident prevention which is caused by in the pilot selection process. We use classification models such as logistic regression, decision tree, and neural network based on aptitude test results of 505 ROK Air Force applicants in 2001~2004. First, we determine the reliability and propriety against the aptitude test system which has been improved. Based on this conference flight simulator test item was compared to the new aptitude test item in order to make additional yes or no decision from different models in terms of classification accuracy, ROC and Response Threshold side. Decision tree was selected as the most efficient for each sequential flight training result and the last flight training results predict excellent. Therefore, we propose that the standard of pilot selection be adopted by the decision tree and it presents in the aptitude test item which is new a conference flight simulator test.

• Nuclear Engineering and Technology
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• v.15 no.2
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• pp.123-134
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• 1983

Estimates of the rate of heat removal from the containment atmosphere following a loss of colant accident (LOCA) are important to the prediction of containment peak pressure and temperature which are essential parameters in designing the containment building. An overall survey and discussion of mechanisms, experimental results, empirical correlations and analytical models that are relevant to the heat transfer inside the containment have been made. As a result of this review, the current state of the knowledge about tile containment heat transfer can be understood and it is known that more investigations are needed to avoid the misuse of various correlations.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.725-733
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• 2024

This study aims to develop an artificial intelligence-based model for predicting the causes of chemical accidents, utilizing data on 865 chemical accident situations and cases provided by the Chemical Safety Agency under the Ministry of Environment from January 2014 to January 2024. The research involved training the data using six artificial intelligence models and compared evaluation metrics such as accuracy, precision, recall, and F1 score. Based on 356 chemical accident cases from 2020 to 2024, additional training data sets were applied using chemical accident cause investigations and similar accident prevention measures suggested by the Chemical Safety Agency from 2021 to 2022. Through this process, the Multi-Layer Perceptron (MLP) model showed an accuracy of 0.6590 and a precision of 0.6821. the Multi-Layer Perceptron (MLP) model showed an accuracy of 0.6590 and a precision of 0.6821. The Logistic Regression model improved its accuracy from 0.6647 to 0.7778 and its precision from 0.6790 to 0.7992, confirming that the Logistic Regression model is the most effective for predicting the causes of chemical accidents.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.3D
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• pp.343-353
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• 2011

The unsignalized intersections in a community road in the city of Seoul have 3,753 traffic accidents(9%) of total 41,702 cases in 2008, not high in the occurrence rate of traffic accidents, but seem to have a quite high potential of accidents due to the unreasonable and insufficient operation of systems and facilities in the part of traffic foundations. In particular, the un-signalized intersections in a community road have an insufficient measure for safety as compared to the crossroads with signals, and there are few analysis of traffic accidents and domestic researches on the model of affecting factors. Our country also has no concept of passing priority in operating a crossroad without signals, differently from foreign countries, so the researches and safety measures for improving the safety of a crossroad without signals in a community road are urgent. Therefore, This study set out to analyze the road conditions, traffic conditions, and traffic environment conditions on unsignalized intersection, to identify the elements that would impose obstructions in safety, and develop a traffic accident prediction model to evaluate the safety of an unsignalized intersection using the correlation between the elements and an accident. In addition, the focus was made on suggesting appropriate traffic safety policies by dealing with the danger elements in advance and on enhancing the safety on intersection in developing a traffic accident prediction model for an unsignalized intersection.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.662-674
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• 2014

In this study, accident frequency prediction models were constructed by collecting variables such as geometric structures, safety facilities, traffic volume and weather conditions, land use, highway design-satisfaction criteria along 780km (4,372 sections) of 4 lane-highways over 8 areas. As for models, a fixed parameter model and a random parameter model were employed. In the random parameter model, some influences were reversed as the range was expressed based on specific probability in the case of no fixed coefficients. In the fixed parameter model, the influences of independent variables on accident frequency were interpreted by using one coefficient, but in the random parameter model, more various interpretations were took place. In particular, curve radius, securement of shoulder lane, vertical grade design criteria satisfaction showed both positive and negative influence, according to specific probability. This means that there could be a reverse effect depending on the behavioral characteristics of drivers and the characteristics of highway sections. Rather, they influence the increase of accident frequency through the all sections.

• Korean Chemical Engineering Research
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• v.55 no.1
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• pp.48-53
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• 2017

Since the number and the amount of toxic substances handled by domestic companies have been increased, the possibility of serious chemical accidents has become severe. According to Chemistry Safety Clearing-house (CSC), the number of chemical accidents for the last five years has been rapidly raised. A representative example which shows the serious impact of a chemical accident is HF (Hydrogen Fluoride) accident generated in Gumi in 2012. In order to make effective responses for mitigating losses of accidents, the most suitable consequence model has to be selected and implemented throughout the considerations of chemical properties and environments. Even if each consequence model has been verified by the results of experiments, it is necessary to analyze and compare the usability of them according to various scenarios. In this study, the Gumi HF accident is simulated by HGSYSTEM, which is the most specialized model for the release and dispersion of HF. It is found that the ending point of ERPG-2 is about 1 km from the accident point. In order to investigate the usability of the most representative consequence models (ALOHA and CARIS), the results of them are compared with one of HGSYSTEM.

• Journal of the Korea Institute of Building Construction
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• v.18 no.2
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• pp.151-159
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• 2018

Accident rate in the construction industry of South Korea is increasing every year, and it represents the highest percentage among industries. This shows that activities performed to prevent safety accidents in the country are not efficient when it comes to reduce the accident rate. In order to resolve this issue, a model for the prediction of human accidents should be established. In addition, it is required a quantification study based on pattern of human accidents. Therefore, the objective of this study is to quantify uncertainty of human accidents risk and predict how to change in various circumstances by using Monte Carlo Simulation. To achieve the objective, first, pattern of human accidents was defined. Second, insurance claim payout and information of human accidents during 14 years in construction site were collected. Third, descriptive analysis is conducted to determine the characteristics of the accident pattern. Fourth, to quantitatively analyze the pattern of the human accidents, the population of each accident occurrence and payout were estimated. Finally, estimated populations was analyzed according to characteristics of distribution by using Monte carlo simulation. In the future, this study can be used as a reference for developing the safety management checklist in construction site and development of prediction models of human accident.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.155-166
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• 2009

Road design safety assessment by existing tools and methods have normally been examined by expert judgements using design documents and on-site inspections. The existing methods, however, have two main problems such as insufficiency of objectiveness and inability to measure effects of accident countermeasures. This paper studies ways to develop a road safety assessment system through reviewing the IHSDM developed in USA. The crash prediction module of IHSDM calculate accident frequency and rate of roadway segments using accident prediction models and accident modification factors for safety evaluation. The methodology of evaluation and development of accident modification factors somewhat overcome the problems of the existing methods. In spite of these advantages, IHSDM could not relevantly reflect characteristics of domestic rural roadways since it overestimate the number of accidents and rate of korean rural roadways. Especially, IHSDM may not evaluate or consider land use patterns of Korean roadways, and futhermore, original environment on base conditions used to develop IHSDM may not be different from ours. The user requirements being developed for a road safety assessment system for Korean roadways include enhanced flexibility and diversity of data input-output processes.

• Journal of the Korean Society of Systems Engineering
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• v.19 no.2
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• pp.18-31
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• 2023

Exploring artificial intelligence and machine learning for nuclear safety has witnessed increased interest in recent years. To contribute to this area of research, a machine learning model capable of accurately predicting nuclear power plant response with minimal computational cost is proposed. To develop a robust machine learning model, the Best Estimate Plus Uncertainty (BEPU) approach was used to generate a database to train three models and select the best of the three. The BEPU analysis was performed by coupling Dakota platform with the best estimate thermal hydraulics code RELAP/SCDAPSIM/MOD 3.4. The Code Scaling Applicability and Uncertainty approach was adopted, along with Wilks' theorem to obtain a statistically representative sample that satisfies the USNRC 95/95 rule with 95% probability and 95% confidence level. The generated database was used to train three models based on Recurrent Neural Networks; specifically, Long Short-Term Memory, Gated Recurrent Unit, and a hybrid model with Long Short-Term Memory coupled to Convolutional Neural Network. In this paper, the System Engineering approach was utilized to identify requirements, stakeholders, and functional and physical architecture to develop this project and ensure success in verification and validation activities necessary to ensure the efficient development of ML meta-models capable of predicting of the nuclear power plant response.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.79-88
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• 2019

This study analyzes the influential factors and patterns associated with death from aircraft accidents and incidents using data mining techniques. To this end, we used two datasets for aircraft accidents and incidents, one from the National Transportation Safety Board (NTSB) and the other from the Federal Aviation Administration (FAA). We developed our prediction models using the decision tree classifier to predict death from aircraft accidents or aircraft incidents and thereby derive the main cause factors and patterns that can cause death based on these prediction models. In the NTSB data, deaths occurred frequently when the aircraft was destroyed or people were performing dangerous missions or maneuver. In the FAA data, deaths were mainly caused by pilots who were less skilled or less qualified when their aircraft were partially destroyed. Several death-related patterns were also found for parachute jumping and aircraft ascending and descending phases. Using the derived patterns, we proposed helpful strategies to prevent death from the aircraft accidents or incidents.