• The Journal of Korean Society for School & Community Health Education
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• v.13 no.2
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• pp.107-118
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• 2012

Backgrounds: In order to prevent dental injuries that often occur in child and adolescent, it is intended to investigate and assess actual state of the injury development, present epidemiological background, and consider and discuss for preparing preventive means against the injury development. Purpose: It was attempted to understand major features of dental injuries developing in child and adolescent and indentify high risk factors of dental injuries in child and adolescent. Methods: In this study, 523 cases of computerized data collected as disease entities of dental injuries among 1-18 years old patient visiting S university hospital located in Seoul in 2009 were analyzed and following results were obtained. Results: It was found that the ratio of dental injuries by genders in child and adolescent was 66.14% of male and 33.86% of female. It was shown also that causes of dental injuries by ages were more in order of falling, bumping, chewing, traffic accident, sports, violence, and crash. In addition places where dental injuries occur by ages were home in less than 5 year old group, park, playground, and play yard in 6-11 year old group, park, playground, and play yard also in 12-14 year old group, and stairs, road, and outdoor places such as mountain climbing, beach, and camping in 15-18 year old group. It was found that time rages when dental injuries in child and adolescent often develop were 15-19 o'clock for falling, 15-19 o'clock for crash, 15-19 o'clock for bumping, 19-03 o'clock for violence, 15-19 o'clock for traffic accident, 15-19 o'clock for sports activity, and 15-19 o'clock for chewing. Conclusion: Background of dental injury inducing factors are very complicated and diversified, so deep study and analysis are required for its prediction. Therefore, it seems necessary to identify risk factors by phases such as before, at, and after accident, establish strategies to reduce injury development, and develop and utilize necessary programs.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.45-52
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• 2022

In the field of UAM, which is attracting attention as a next-generation transportation system, technology developments for using UAVs have been actively conducted in recent years. Since UAVs adopted with these technologies are mainly operated in urban areas, it is imperative that accidents are prevented. However, it is not easy to predict the abnormal flight state of an UAV causing a crash, because of its strong non-linearity. In this paper, we propose a method for predicting a flight state of an UAV, based on a CNN-LSTM hybrid model. To predict flight state variables at a specific point in the future, the proposed model combines the CNN model extracting temporal and spatial features between flight data, with the LSTM model extracting a short and long-term temporal dependence of the extracted features. Simulation results show that the proposed method has better performance than the prediction methods, which are based on the existing artificial neural network model.

• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.55-62
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• 2023

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.

• Journal of Space Technology and Applications
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• v.1 no.1
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• pp.23-32
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• 2021

The high-level Space Situational Awareness (SSA) objective is to provide to the users dependable, accurate and timely information in order to support risk management on orbit and during re-entry and support safe and secure operation of space assets and related services. Therefore the risk assessment for the re-entry of space objects should be managed nationally. In this research, the Software for Re-Entry Prediction of space objects (SREP) was developed for national SSA system. In particular, the rate of change of the drag coefficient is estimated through a newly proposed Drag Scale Factor Estimation (DSFE), and is used for high-precision orbit propagator (HPOP) up to an altitude of 100 km to predict the re-entry time and position of the space object. The effectiveness of this re-entry prediction is shown through the re-entry time window and ground track of space objects falling in real events, Grace-1, Grace-2, Tiangong-1, and Chang Zheng-5B Rocket body. As a result, through analysis 12 hours before the final re-entry time, it is shown that the re-entry time window and crash time can be accurately predicted with an error of less than 20 minutes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.203-213
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• 2021

A vehicle crash occurs due to various factors such as the geometry of the road section, traffic, and driver characteristics. A safety performance function has been used in many studies to estimate the relationship between vehicle crash and road factors statistically. And depends on the purpose of the analysis, various characteristic variables have been used. And various characteristic variables have been used in the studies depending on the purpose of analysis. The existing domestic studies generally reflect the average characteristics of the sections by quantifying the traffic volume in macro aggregate units such as the ADT, but this has a limitation that it cannot reflect the real-time changing traffic characteristics. Therefore, the need for research on effective aggregation units that can flexibly reflect the characteristics of the traffic environment arises. In this paper, we develop a safety performance function that can reflect the traffic characteristics in detail with an aggregate unit for one hour in addition to the daily model used in the previous studies. As part of the present study, we also perform a comparison and evaluation between models. The safety performance function for daily and hourly units is developed using a negative binomial regression model with the number of accidents as a dependent variable. In addition, the optimal negative binomial regression model for each of the hourly and daily models was selected, and their prediction performances were compared. The model and evaluation results presented in this paper can be used to determine the risk factors for accidents in the highway section considering the dynamic characteristics. In addition, the model and evaluation results can also be used as the basis for evaluating the availability and transferability of the hourly model.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.10a
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• pp.223-226
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• 2009

The plastics are widely utilized in the inside of vehicles. The dynamic tensile characteristics of auto-body plastics are important in a prediction of deformation mode of the plastic component which undergoes the high speed deformation during car crash. This paper is concerned with the dynamic tensile characteristics of the auto-body plastics at intermediate strain rates. Quasi-static tensile tests were carried out at the strain rate ranged from 0.001/sec to 0.01/sec using the static tensile machine(Instron 5583). Dynamic tensile tests were carried out at the strain rate ranged from 0.1/sec to 100/sec using the high speed material testing machine developed. Conventional extensometry method is no longer available for plastics, since the deformation of plastic is accompanied with localized deformation. In this paper, quasi-static and dynamic tensile tests were performed using ASTM IV standard specimens with grids and images from a high speed camera were analyzed for strain measurement. True stress-strain relations and the actual strain rates at each deformation step were obtained by processing load data and deformation images, assuming the plastics to deform uniformly in each grid.

• 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.