• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 춘계학술대회 논문집
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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.

• 동서간호학연구지
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• 제15권2호
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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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• 제14권2호
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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.

• 인터넷정보학회논문지
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• 제24권1호
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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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• 제7권1호
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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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.

• 교통기술과정책
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• 제12권3호
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• pp.7-13
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• 2015

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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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.

• 한국ITS학회 논문지
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• 제21권1호
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• pp.51-63
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• 2022

호송주행(convoy driving)은 군집주행(Platoon)과 함께 다중차량 협력주행 (Multi-vehicle cooperation) 기술 중 하나로써 국외에서는 호송주행시 차량의 대형유지를 위한 차량제어 메커니즘 연구가 활발히 진행되고 있으며, 유럽의 Autonet 2030연구에서는 고속도로를 대상으로 호송주행 서비스를 개발하고 실증한 바 있으나 국내에서는 아직까지 호송주행에 대한 개념정립조차 미흡한 실정이다. 이에 본 연구에서는 호송주행의 서비스 개념을 정립하고 도시부 도로에서의 서비스 적용을 위한 시나리오 및 통신 메시지 등을 개발하여 시뮬레이션 분석을 통해 그 실효성을 검증하고자 하였다. 도시부 도로의 대표적인 V2I 협력주행 서비스인 사각지대 운행지원 서비스 및 딜레마존 안전주행 서비스를 대상으로 개별차량 협력주행 시와 호송주행 시를 비교 분석한 결과 교통안전성 지표인 상충횟수와 교통효율성 지표인 지체시간 및 정지수가 개별차량 협력주행 시보다 호송주행 시 크게 개선되는 것을 알 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제20권2호
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• pp.55-62
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• 2015

본 논문에서는 디지털 운행기록장치를 활용하여 자동차 운전자에 대한 실시간 위험운전행동 분석 및 경고를 통한 운전습관 개선과 안전운전 지원이 가능한 방법을 제안한다. 대부분의 교통사고와 에코 드라이빙은 자동차 운전자의 운전습관과 밀접한 관련이 있으며 이러한 운전습관을 자동화된 방법으로 실시간 분석 및 경고로 잘못된 운전습관을 개선시킬 필요가 있다. 제안 방법에 대한 구현 및 실험을 통하여 본 논문에서 제안하는 방법으로 운전자의 위험운전행동에 대한 실시간 분석 및 경고를 해줌으로써 운전습관 개선 유도와 안전운전을 지원하여 에코 드라이빙에 실제로 도움이 될 수 있음을 증명하였다.