• Journal of the Korean Society of Safety
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• v.31 no.1
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• pp.118-125
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• 2016

The purpose of this study is to identify issues for an effective safety culture assessment by conducting a case study of an electronics manufacturing plant in Korea. Cooper's safety culture model was used as the assessment method, and Fleming and Hudson's safety culture maturity models were applied as assessment criteria. The results of the safety culture assessment showed that there needs to be a design optimized for study purposes. For example, the correlation between the questionnaire survey and in-depth interview needs to be analyzed. The result of the behavior monitoring should show the relationships with other dimensions. A safety culture maturity model has to be developed to customize the study factors and questions.

• Journal of the Korean Society of Safety
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• v.26 no.6
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• pp.1-6
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• 2011

This study focuses on the control strategy for industrial safety in Korea. Specifically, the effect of safety budget such as the industrial accident prevention fund on the safety performance to prevent and reduce industrial accidents is statistically examined first and modeled as a second order system. The effectiveness of such a dynamic model is also explained with a simple PI control mechanism in a feedback loop. The simulated model, however, suggests that, without improving the efficiency of the safety system, extra safety budget needed to decrease the accident rate to a level in advanced countries is far beyond the social consensus. An efficient way of reducing industrial accidents based on such a dynamic model with more internal damping but with less elastic nature in a feedback loop framework needs to be implemented.

• 시스템엔지니어링워크숍
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• s.4
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• pp.151-154
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• 2004

This paper presents the general system theory and its applications to the safety analysis method that is a recent trend over the traditional event-driven model. This new model is known as STAMP(Systems Theory Accident Modeling and Process)proposed by Nancy G. Leveson in MIT. The new model has benefits of systemic approaches concerning the system safety as a whole including the context it is in, its stimulants and outcomes, and its parts as well as the relationships among them in a holistic manner. The method consists of a hierarchical control structure, a process model, and the safety constraints governing the control. This paper demonstrates an example the contrasts the differences between the approaches of STAMP and the traditional safety models.

• Journal of the Korean Society of Systems Engineering
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• v.1 no.1
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• pp.20-25
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• 2005

This paper presents the general system theory and its applications to the safety analysis method that is a recent trend over the traditional event-driven model. This new model is known as STAMP(Systems Theory Accident Modeling and Process) proposed by Nancy G. Leveson in MIT. The new model has benefits o f systemic approaches concerning the system safety as a whole including the context it is in, its stimulants and outcomes, and its parts as well as the relationships among them in a holistic manner. The method consists of a hierarchical control structure, a process model, and the safety constraints governing the control. This paper demonstrates an example that contrasts the differences between the approaches of STAMP and the traditional safety models.

• Journal of the Korean Society of Safety
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• v.25 no.6
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• pp.186-196
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• 2010

It has been well known that traffic accidents occur under combined functional contributions of drivers, vehicles and road facilities, and that evaluation of safety levels for a specific road section or point is generally much complicated. Additionally, most of traffic accidents occur randomly implicating it is necessary to be evaluated in terms of probability theory. Thus, the evaluation model which reflects various characteristics and probabilistic distributions of traffic accidents has been necessary. The present paper provides a reliability based model with variables of probabilistic operating speeds and design speeds together which have been individually explaining associated characteristics in traffic accidents. Consequently, the model made it possible for speed management and road improvement projects to be evaluated in a common index. Application studies were performed in three cases. Through the studies, couples of facts were identified that the model successfully considered the probabilistic operating speeds and design speeds together and that then, the model evaluated road safety alternatives relatively which are complicatedly characterized and differently located.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.92-96
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• 2015

Due to unique characteristics of small construction companies, safety management is comprised of complex problems (e.g., resources constraints, a lack of formalized management structures, low level of management safety commitment etc.). In order to understand causal interdependencies between safety factors at different system levels (regulation, organization, technical and individual), this paper aims to develop a system dynamics (SD) model of safety management in small construction companies. The purpose of the SD model is to better understand why small construction companies have low level of safety performance. A causal loop diagram (CLD) was developed based on literature, with an attempt to map causal relationships between variables. The CLD was then converted into stock and flow diagram for simulation. Various tests were conducted to build confidence in the model's ability to represent the reality. A number of policies were analyzed by changing the value of parameters. The value of a system dynamics approach to safety management in small construction companies is its ability to address joint effects of multiple safety risk factors on safety performance with a systems thinking perspective. By taking into account feedback loops and non-linear relationships, such a system dynamics model provides insights into the complex causes of relatively poor safety performance of small construction companies and improvement strategies.

• Journal of Food Hygiene and Safety
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• v.26 no.2
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• pp.120-124
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• 2011

Predictive mathematical models were developed for predicting the kinetics of growth of Listeria monocytogenes in smoked salmon, which is the popular ready-to-eat foods in the world, as a function of temperature (4, 10, 20 and $30^{\circ}C$). At these storage temperature, the primary growth curve fit well ($r^2$=0.989~0.996) to a Gompertz equation to obtain specific growth rate (SGR) and lag time (LT). The Polynomial model for natural logarithm transformation of the SGR and LT as a function of temperature was obtained by nonlinear regression (Prism, version 4.0, GraphPad Software). Results indicate L. monocytogenes growth was affected by temperature mainly, and SGR model equation is $365.3-31.94^*Temperature+0.6661^*Temperature^{\wedge^2}$ and LT model equation is $0.1162-0.01674^*Temperature+0.0009303^*Temperature{\wedge^2}$. As storage temperature decreased $30^{\circ}C$ to $4^{\circ}C$, SGR decreased and LT increased respectively. Polynomial model was identified as appropriate secondary model for SGR and LT on the basis of most statistical indices such as bias factor (1.01 by SGR, 1.55 by LT) and accuracy factor (1.03 by SGR, 1.58 by LT).

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.302-309
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• 2022

Occupational safety accidents are caused by various factors, and it is difficult to predict when and why they occur, and it is directly related to the lives of workers, so the interest in safety accidents is increasing every year. Therefore, in order to reduce safety accidents at industrial fields, workers are required to wear personal protective equipment. In this paper, we proposes a method to automatically check whether workers are wearing safety helmets among the protective equipment in the industrial field. It detects whether or not the helmet is worn using YOLOv5, a computer vision-based deep learning object detection algorithm. We transfer learning the s model among Yolov5 models with different learning rates and epochs, evaluate the performance, and select the optimal model. The selected model showed a performance of 0.959 mAP.

• Journal of the Korean Society of Safety
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• v.19 no.2
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• pp.34-40
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• 2004

This paper presents an analysis of SAR(Specific Absorption Rate) distribution characteristics in a head model using FDTD(Finite Difference Time Domain). In this study human head was modelled in four elements-layered structure, consisting of skin, fat, skull and brain. To calculate the electromagnetic fields wihtin the head model, FDTD method was used. In the FDTD method, the electromagnetic wave is analyzed by solving a Maxwell's equations repeatedly. For the calculation, distance between power source and head model increased by 10[m]. Power density and incident electric field intensity were calculated. Based on the incident electric field, the program which calaculated internal electric fields intensity and SAR calculation of the head model were developed. The results of developed program using FDTD were compared with those of a commericial programs, which showed the availability and usefulness of the suggested scheme in this paper.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.11a
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• pp.59-60
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• 2018

The purpose of this study is to predict and classify the accident types based on the KOSHA (Korea Occupational Safety & Health Agency) and weather data. We also have an effort to suggest an important management method according to accident types by deriving feature importance. We designed two models based on accident data and weather data (model(a)) and only weather data (model(b)). As a result of random forest method, the model(b) showed a lack of accuracy in prediction. However, the model(a) presented more accurate prediction results than the model(b). Thus we presented safety management plan based on the results. In the future, this study will continue to carry out real time prediction to occurrence types to prevent safety accidents by supplementing the real time accident data and weather data.