• Proceedings of the KSR Conference
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• 2011.05a
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• pp.690-696
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• 2011

A number of conventional railway bridge is more than 2600. Non-ballast plate girder bridge is about 700 and this is 27% of all bridge numbers. Non-ballast plate girder has advantages that self load is more lighter than moving load and construction cost is more inexpensive than concrete bridge. But non-ballast plate girder has disadvantages that vibration and noise is bigger than concrete bridge. This study had analyzed behavior of non-ballast plate girder according to the arrangement of supports and driving conditions to review the proper arrangement of support. Measurements were performed in single line and disel locomotive of 7400type were used as test vehicle. The vehicle's driving conditions are as follows; Change of driving direction, Constant speed driving, Deceleration driving, Acceleration driving. Main measurement contents were horizontal displacement and vertical vibration acceleration in girder of vicinity support. Results of measurement are as follows; In case that a vehicle drives from fixed support to movable support, vertical vibration acceleration of the girder was smaller than opposition case.

• Journal of East-West Nursing Research
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• v.15 no.2
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• pp.71-81
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• 2009

Purpose: This study was conducted to find out interrelation of health behavior and related variables to provide basic data for an effective health promotion for the taxi-divers. Methods: 293 male taxi-drivers from 2 cities in Korea participated in this study. The data were collected using questionnaires from April 17th to Jun 3rd, 2006, and analyzed by descriptive statistics, t-test, ANOVA, Pearson correlation, and multiple regression. Results: There were statistically significant differences according to monthly income, past illness or surgery, current disease or medication, frequency of fright on daily driving (FFDD), driving fatigue, working style, social support in health status; current disease or medication, FFDD, driving fatigue, duty shift, social support in health perception; body mass index (BMI), FFDD, driving fatigue, intention of changing job, social support in health behavior. Social support, health status, health perception, and health behavior were significantly correlated with one another. The multiple regression analysis showed that health perception (17.8%), BMI (6.8%), intention of changing job (5.7%), and driving fatigue (4.2%) explained the 34.5% variance of health behavior. And the 22.6% of variance of health perception was explained by social support (12.2%), health status (6.9%), and duty shift (3.2%). Conclusions: To promote the taxi-drivers' health, nursing intervention strategies unique for them should consider health behavior and affecting factors.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.41-44
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• 2020

In autonomous driving, object recognition based on machine learning is one of the core software technologies. In particular, the object recognition using deep learning becomes an essential element for autonomous driving software to operate. In this paper, we introduce a drivable area detection method based on Region-based CNN model to support autonomous driving. To effectively detect the drivable area, we used the BDD dataset for model training and demonstrated its effectiveness. As a result, our R-CNN model using BDD datasets showed interesting results in training and testing for detection of drivable areas.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.75-81
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• 2000

Vibration problems induced by an air cooled heat exchanger with axial flow fan were investigated during the operation of a petrochemical plant. Two different studies were done; one was experimental field test and the other was theoretical verification. To find main cause of the blade passing frequency of the fan after installing additional blockage board at the air inlet of the axial fan, the frequency spectrum was measured. The vibrations of the blade passing frequency became higher. The natural frequency of driving support of the heat exchanger was theoretically calculated. It was approximately equal to the blade passing frequency. During the normal operation of the plant, it was impossible to modify the structure of the driving support. Instead, the blade number was increased to reduce vibration level. It increased the ratio of the forcing frequency to the natural frequency of the driving support over the resonance region.

• Transportation Technology and Policy
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• v.12 no.3
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• pp.7-13
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• 2015

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.981-984
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• 1995

The feed system using ball screw is constructed by ball screw, support bering and LM guide, and servo system for driving ball screw. AC servo motr drives ball screw which was connected by coupling. In this study, a new axial direction dynamic modeling of ball screw driving system was developed, and forced vibraition test using the impact hammer was experimented. The simulation result is compared with experimental result, which defines the reliability of mathematical modeling.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.51-63
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• 2022

Convoy driving is one of the technologies of multi-vehicle cooperation driving along with platoon driving. All over the world, research on vehicle control mechanisms to maintain vehicle formation during convoy driving convoy driving has been actively conducted and in Europe's Autonet 2030 project has developed and demonstrated convoy driving services for highways. But, even the concept of convoy driving is still insufficient in Korea. Therefore, in this study, the concept of convoy driving service was established and scenarios and communication messages for service application on urban roads were developed. And its effectiveness was verified through simulation analysis. As a result of comparing and analyzing individual vehicle cooperative driving and convoy driving for the blind spot support service and dilemma zone safety support service, which are representative V2I cooperative driving services on urban roads, the number of conflicts(indicator of traffic safety) and delays and stops(indicator of traffic efficiency) are significantly improved in convoy driving compared to individual vehicle cooperative driving.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.55-62
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• 2015

In this paper, we proposed the method that enabling warning through real-time analysis of dangerous driving behavior, improving driving habits and safe driving using the digital tachograph. Most of traffic accidents and green drive are closely related of driving habits. These wrong driving habits need to be improved by the real-time analysis, warning and automated method of driving habits. We confirmed the proposed that the method will help support eco-driving, safe driving through real-time analysis of driving behavior and warning through the method implementation and experiment.