• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.27-28
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• 2022

There is a limit to preventing various types of safety accidents in advance at construction sites. Even for buildings of the same total floor area, it is expected that the more complex the building shape or the higher the number of floors, the higher the probability of a safety accident. Therefore, it is necessary to analyze the effect of the shape of a building on safety accidents using safety accident data generated during actual construction. The purpose of this study is to analyze the impact of building shape on safety accidents. As a result, the R2 value of shape factor and safety accident was 0.901, and the R2 value of construction difficulty and safety accident was 0.944. In the future, the results of this study will be used as basic data for improving safety management related systems.

• Nuclear Engineering and Technology
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• v.41 no.7
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• pp.945-952
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• 2009

The Three Mile Island Unit 2 (TMI-2) accident provides unique full scale data, thus providing opportunities to check the capability of codes to model overall plant behavior and to perform a spectrum of sensitivity and uncertainty calculations. As part of the TMI-2 analysis benchmark exercise sponsored by the Organization for Economic Cooperation and Development Nuclear Energy Agency (OECD NEA), several member countries are continuing to improve their system analysis codes using the TMI-2 data. The Republic of Korea joined this benchmark exercise in November 2005. Seoul National University has analyzed the TMI-2 accident as well as the currently proposed alternative scenario along with a sensitivity study using the Modular Accident Analysis Program Version 4.03 (MAAP4.03) code in collaboration with the Korea Hydro and Nuclear Power Company. Two input files are required to simulate the TMI-2 accident with MAAP4: the parameter file and an input deck. The user inputs various parameters, such as volumes or masses, for each component. The parameter file contains the information on TMI-2 relevant to the plant geometry, system performance, controls, and initial conditions used to perform these benchmark calculations. The input deck defines the operator actions and boundary conditions during the course of the accident. The TMI-2 accident analysis provided good estimates of the accident output data compared with the OECD TMI-2 standard reference. The alternative scenario has proposed the initial event as a loss of main feed water and a small break on the hot leg. Analysis is in progress along with a sensitivity study concerning the break size and elevation.

• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.98-104
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• 2018

Macroscopic accident analyses have been conducted to incorporate transportation safety into long-term transportation planning. In macro-level accident prediction model, exposure variable(e.g. a settled population) have been used as fundamental explanatory variable under the concept that each trip will be subjected to a probable risk of accident. However, a settled population may be embedded error by exclusion of active population concept. The objective of this research study is to develop macro-level accident prediction model using floating population variable(concept of including a settled population and active population) collected from mobile phone data. The concept of accident prediction models is introduced utilizing exposure variable as explanatory variable in a generalized linear regression with assumption of a negative binomial error structure. The goodness of fit of model using floating population variable is compared with that of the each models using population and the number of household variables. Also, log transformation models are additionally developed to improve the goodness of fit. The results show that the log transformation model using floating population variable is useful for capturing the relationships between accident and exposure variable and generally perform better than the models using other existing exposure variables. The developed model using floating population variable can be used to guide transportation safety policy decision makers to allocate resources more efficiently for the regions(or zones) with higher risk and improve urban transportation safety in transportation planning step.

• Journal of Navigation and Port Research
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• v.26 no.3
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• pp.329-336
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• 2002

Recently, in the whole fields of industry including shipping business, the conception and importance of Human Factor is very highly and frequently emphasized. But the domestic crew disaster including injury and illness in our shipping business shows higher accident rate than other shipping countries. Therefore the developing of measures to prevent/reduce the accident is highly required as the marine accident including crew disaster causes enormous loss of property and human life in size and scale. But, because the domestic data regarding the crew accident are relatively insufficient and rule, the developing of those measures has many difficulties. Therefore, this study is to make the concerning data for the useful reference by showing the trend and current situation of crew accidents, assorted by the crew's rank, type of the accident, injured part of the body, cause of the accident, type of ships and type of the works when the accident occurred, by using the each ocean-going shipping company's recent 5 years('95∼'99) data in korea.

• Journal of the Korea Safety Management & Science
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• v.17 no.1
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• pp.1-11
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• 2015

The efficient safety estimation for a business should analyze an accident data by considering every possible and potential factor. Thus, we consider several factors to build the safety estimation model to meet fairness and rationality. This paper present the yearly statistic data of accident from KOSHA analyze the data by industry, scale, year of service of a employee, age and other factors; build the safety estimation model for the business based on the accident report derived the analysis. The estimation model is established by the weights for accident type, degree, scale, industry, year of service, and age of the employee derived from ANP(Analytic Network Process).

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.11a
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• pp.122-123
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• 2018

Although the disaster rate of the industry as a whole is on a downward trend, the disaster rate of the construction industry is on an ongoing trend. Therefore, in this study, we analyzed safety accident statistical data of the construction site over the past three years. As a result of the analysis, the incidence of disasters at small construction sites was very high. And the proportion of disaster occurred for workers who worked in less than 6 months even roughly 92.6%. In addition, as a result of analyzing the form of disaster occurrence, the crash was 34.1% and the fall was 15.1%. The analysis results of these construction safety accidents are to provide as a basic material for developing a policy that can prevent safety accidents and a safety accident prediction model.

• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.1-7
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• 2010

The efficient safety estimation for a business should analyze an accident data by considering every possible and potential factor. Thus, we consider several factors to build the safety estimation model to meet fairness and rationality. This paper present the yearly statistic data of accident from KOSHA analyze the data by industry, scale, year of service of a employee, age and other factors; build the safety estimation model for the business based on the accident report derived the analysis. The estimation model is established by the weights for accident type, degree, scale, industry, year of service, and age of the employee derived from AHP(Analytic Hierarchy Process).

• Journal of Auto-vehicle Safety Association
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• v.12 no.4
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• pp.70-77
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• 2020

In this study, we estimate the vehicle speed by analyzing the acoustic data recorded in a single microphone of a surveillance camera. The frequency analysis of the acoustic data corrects the Doppler effect, which is a characteristic of the moving sound source, and reflects the geometric relationship according to the location of the sound source and the microphone on the two-dimensional plane. The acoustic data is selected from the horn sound that is mainly observed in an urgent situation among various sound sources that may occur in a traffic accident, and the characteristics of the monotone source are considered. We verified the reliability of the proposed method by time domain acoustic analysis and actual vehicle evaluation. This method is effective and can be used for traffic accident analysis in the blind spot of the camera using a single microphone built into the existing surveillance camera.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.87-94
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• 2017

Many industrial accidents have occurred continuously in the manufacturing industries, construction industries, and service industries of Korea. Fatal accidents have occurred most frequently in the construction industries of Korea. Especially, the trend analysis of the accident rate and fatal accident rate is very important in order to prevent industrial accidents in the construction industries systematically. This paper considers forecasting of the accident rate and fatal accident rate with static and dynamic time series analysis methods in the construction industries. Therefore, this paper describes the optimal accident rate and fatal accident rate by minimization of the sum of square errors (SSE) among regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, proposed analytic function model (PAFM), and kalman filtering model (KFM) with existing accident data in construction industries. In this paper, microsoft foundation class (MFC) soft of Visual Studio 2008 was used to predict the accident rate and fatal accident rate. Zero Accident Program developed in this paper is defined as the predicted accident rate and fatal accident rate, the zero accident target time, and the zero accident time based on the achievement probability calculated rationally and practically. The minimum value for minimizing SSE in the construction industries was found in 0.1666 and 1.4579 in the accident rate and fatal accident rate, respectively. Accordingly, RAM and ARIMA model are ideally applied in the accident rate and fatal accident rate, respectively. Finally, the trend analysis of this paper provides decisive information in order to prevent industrial accidents in construction industries very systematically.

• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.77-82
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• 2015

This paper proposed an accident prediction model in order to prevent accidents in mountain areas using a big data analysis. Data of accidents in mountain areas are shown as graphs. We have analyzed cases: the number of accidents per year, day of week, time of day to find patterns of the negligent accident in mountain areas. The proposed prediction model consists of weighted variables of the accident in mountain through visualized big data analysis. The model of danger index performance is demonstrated by showing accident-prone areas with weighted variables.