• Transactions of Materials Processing
• /
• v.25 no.2
• /
• pp.124-129
• /
• 2016

Recently, global railway car makers are competing desperately in developing high-speed railway vehicles. Ensuring passenger safety during a crash is essential. The design and the manufacturing of energy absorbing components are becoming more and more important. A deformation tube is a typical passive energy absorbing component for railway cars. In the current study the slab method was used to predict the energy absorbing capability of a deformation tube during the early design stage. The usefulness of the prediction method is verified through the comparisons between the results of FE simulations and those of the prediction method.

• Journal of Auto-vehicle Safety Association
• /
• v.6 no.2
• /
• pp.36-42
• /
• 2014

In order to prevent the sharp edge during the side impact, a cause analysis and CAE based risk prediction were carried out in this study. It was found that sharp edge occurs mainly because of stiffness difference between the major parts and structural stress concentration. It could be improved by directly reinforcing the crack initiation region or by weakening the joints connecting the parts. The fracture criterion based on major in-plain strain was suggested and the risk prediction process for sharp edge prevention was established.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.20 no.2
• /
• pp.1-7
• /
• 2012

A Failure data from operating condition includes various failures. Reliability evaluation by operating condition is more correct than test condition. Additional, the evaluation result by operating condition is widely used for quality assurance, forecasting amount of manufacturing at EOL. To discover valuable things from the failure data, arrangement of the failure data and information technique to handle data is needed among many failure data. This paper introduces a reliability prediction program to solve this problem based on the failure. And new technologies for parameters estimation with method of Graphic-Wizard-Parameters-Estimation and Genetic Algorithm are introduced.

• Journal of the Korean Society of Safety
• /
• v.16 no.3
• /
• pp.117-124
• /
• 2001

This paper introduces m analysis framework and procedure for the support of the cognitive error analysis of emergency tasks in nuclear poler plants. The framework provides a new perspective in the utilization of influencing factors into error prediction. The framework can be characterized by two features. First, influencing factors that affect the occurrence of human error me classified into three groups, i.e., task characteristic factors(TCF), situation factors(SF), and performance assisting factors(PAF). This classification aims to support error prediction from the viewpoint of assessing the adequacy of PAF under given TCF and SF. Second, the assessment of influencing factors is made by each cognitive function. Through this, influencing factors assessment and error prediction can be made in an integrative way according to each cognitive function. In addition, it helps analysts identify vulnerable cognitive functions and error factors, and obtain specific nor reduction strategies. The proposed framework was applied to the error analysis of the bleed and feed operation of nuclear emergency tasks.

• Journal of the Korea Safety Management & Science
• /
• v.14 no.1
• /
• pp.87-93
• /
• 2012

The purpose of this study suggests a personalized algorithm of physical activity energy expenditure prediction through comparison and analysis of individual physical activity. The research for a 3-axial accelerometer sensor has increased the role of physical activity in promoting health and preventing chronic disease has long been established. Estimating algorithm of physical activity energy expenditure was implemented by using a tri-axial accelerometer motion detector of the SVM(Signal Vector Magnitude) of 3-axis(x, y, z). A total of 10 participants(5 males and 5 females aged between 20 and 30 years). The activities protocol consisted of three types on treadmill; participants performed three treadmill activity at three speeds(3, 5, 8 km/h). These activities were repeated four weeks.

• Journal of the Korean Society of Safety
• /
• v.38 no.4
• /
• pp.1-7
• /
• 2023

The steel wheels of urban railway vehicles gather a lot of data through regular measurements during maintenance. However, limited research has been carried out utilizing this data, resulting in difficulties predicting the maintenance period. This paper studied a machine learning model suitable for mileage prediction by studying the characteristics of mileage change according to diameter and flange thickness changes. The results of this study indicate that the larger the diameter, the longer the travel distance, and the longest flange thickness is at 30 mm, which gradually shortened at other times. As a result of research on the machine learning prediction model, it was confirmed that the random forest model is the optimal model with a high coefficient of determination and a low root mean square error.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.24 no.5
• /
• pp.519-526
• /
• 2018

In this study, an algorithm to predict evacuation routes in support of shipboard lifesaving activities is presented. As the first step of algorithm development, the feasibility and necessity of an evacuation route prediction algorithm are shown numerically. The proposed algorithm can be explained in brief as follows. This system continuously obtains and analyzes passenger movement data from the ship's monitoring system during non-disaster conditions. In case of a disaster, evacuation route prediction information is derived using the previously acquired data and a prediction tool, with the results provided to rescuers to minimize casualties. In this study, evacuation-related data obtained through fire evacuation trials was filtered and analyzed using a statistical method. In a simulation using the conventional evacuation prediction tool, it was found that reliable prediction results were obtained only in the SN1 trial because of the conceptual and structural nature of the tool itself. In order to verify the validity of the algorithm proposed in this study, an industrial engineering tool was adapted for evacuation characteristics prediction. When the proposed algorithm was implemented, the predicted values for average evacuation time and route were very similar to the measured values with error ranges of 0.6-6.9 % and 0.6-3.6 %, respectively. In the future, development of a high-performance evacuation route prediction algorithm is planned based on shipboard data monitoring and analysis.

• Journal of Digital Convergence
• /
• v.10 no.9
• /
• pp.319-324
• /
• 2012

Vehicle safety communications is one among the important technologies in order to protect a car accident. For this, many protocols forwarding a safe message have studied to protect a chain-reaction collision when a car accident occurs. most of these protocols assume that the time of generating a safe message is the same as an accident's. If a node predicts some traffic hazard and forwards a safe message, a driver can response some action quickly. So, In this paper, we proposes a traffic hazard prediction algorithm using the communication technique. As a result, we show that the frame reception success rate of using our algorithm to the previous protocol improved about 4~5%.

• Nuclear Engineering and Technology
• /
• v.56 no.1
• /
• pp.48-55
• /
• 2024

The safety-related raw water system's strong operational condition supports the radiation defense and biological shield of nuclear plant containment structures. Gaps and failures in maintaining proper working condition of main equipment like pump were among the most common causes of unavailability of safety related raw water systems. We integrated the advanced data analytics tools to evaluate the maintenance records of water systems and gave special consideration to deficiencies related to pump. We utilized maintenance data over a three-and-a-half-year period to produce metrics like MTBF, MTTF, MTTR, and failure rate. The visual analytic platform using tableau identified the efficacy of maintenance & deficiency in the safety raw water systems. When the number of water quality violation was compared to the other O&M deficiencies, it was discovered that water quality violations account for roughly 15% of the system's deficiencies. The pumps were substantial contributors to the deficit. Pump availability was predicted and optimized with real time data using response surface method. The prediction model was significant with r-squared value of 0.98. This prediction model can be used to predict forth coming pump failures in nuclear plant.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.5
• /
• pp.444-450
• /
• 2013

For intelligent vehicle technology, it is very important to recognize the states of around vehicles and assess the collision risk for safety driving of the vehicle. Specifically, it is very fatal the collision with the vehicle coming from opposite direction. In this paper, a centerlane violation prediction method is proposed. Only radar signal based prediction makes lots of false alarm cause of measurement noise and the false alarm can make more danger situation than the non-prediction situation. We proposed the novel prediction method using IMM algorithm and fuzzy logic to increase accuracy and get rid of false positive. Fuzzy logic adjusts the radar signal and the IMM algorithm appropriately. It is verified by the computer simulation that shows stable prediction result and fewer number of false alarm.