• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.4
• /
• pp.330-335
• /
• 2015

Approximately 23% of traffic accidents appear to be caused by drowsiness while driving. This fact shows that drowsy driving is a big factor in many traffic accidents. Therefore, the development of a drowsiness checking system is necessary to prevent drowsy driving. In this paper, we analyse the changes of the histogram of eye region images which are acquired using a CCD camera. We develop a drowsiness checking system using this histogram change information. The experimental results show that our proposed method enhances the accuracy of checking drowsiness by nearly 98%, and can be used to prevent vehicle accidents due to the drowsiness of a driver.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.5
• /
• pp.361-366
• /
• 2016

In this paper, we propose a technique for drowsiness detection using the histogram and edge information of eyes. The drowsiness of vehicle drivers is the main cause of many vehicle accidents. Therefore, the checking of eye images in order to detect the drowsiness status of a driver is very important for preventing accidents. In our suggested method, we analyze the changes of the histograms and edges of eye region images, which are acquired using a CCD camera. The experimental results show that our proposed method enhances the accuracy of detecting drowsiness to nearly 99%, and can be used for preventing vehicle accidents caused by the driver's drowsiness.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.2
• /
• pp.102-107
• /
• 2015

In this paper, we propose a technique of drowsiness detection using a histogram for vehicle safety. The drowsiness of vehicle drivers is often the main cause of many vehicle accidents. Therefore, the checking of eye images in order to detect the drowsiness status of a driver is very important for preventing accidents. In our suggested method, we analyse the changes of a histogram of eye region images which are acquired using a CCD camera. We develop a drowsiness detection system using this histogram change information. The experimental results show that the proposed method enhances the accuracy of detecting drowsiness to nearly 97%, and can be used to prevent accidents due to driver drowsiness.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.4
• /
• pp.266-270
• /
• 2016

In this paper, we propose a technique of drowsiness detection using machine vision. The drowsiness of vehicle driver is often the primary cause of motor vehicle accidents. Therefore, the checking of eye images for detecting drowsiness status of driver is critical for preventing these accidents. In our suggested method, we analyze the changes of histogram and edge of eye region images which are acquired using CCD camera. We developed a drowsiness detection system using the histogram and edge change information. The experimental results show that our proposed method enhances the accuracy of detecting drowsiness nearly to 98%, and can be used for preventing vehicle accidents due to the drowsiness of drivers.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.10 no.2
• /
• pp.94-102
• /
• 2011

In this paper, a novel drowsiness detection algorithm using eye-blink pattern is proposed. The proposed drowsiness detection model using finite automata makes it easy to detect eye-blink, drowsiness and sleep by checking the number of input symbols standing for closed eye state only. Also it increases the accuracy by taking vertical projection histogram after locating the eye region using the feature of horizontal projection histogram, and minimizes the external effects such as eyebrows or black-framed glasses. Experimental results in eye-blinks detection using the JZU eye-blink database show that our approach achieves more than 93% precision and high performance.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.4
• /
• pp.533-541
• /
• 2022

An analysis of marine accidents that occurred in the last five years, revealed that 77.0 % of all grounding accidents and 66.1% of all marine casualties involved small vessels, which was a very high level relatively. The Mokpo Regional Maritime Safety Tribunal (Mokpo-KMST) inquired on 72 cases of marine accidents in 2021, of which 10 cases were grounding accidents. Furthermore, eight cases of grounding accidents occurred in small fishing vessels. This study analyzed eight cases of grounding accidents on small fishing vessels that inquired in the jurisdictional area of Mokpo-KMST in 2021. I found out that this grounding occurred in clear weather with good visibility (2-4 miles) and good sea conditions with a wave height of less than 1 meter. Furthermore, I found that the main causes of grounding were drowsy navigation due to fatigue, neglect of vigilance, neglect of checking ship's position, overconfidence in GPS plotter, and lack of understanding of chart symbols and tidal differences. To reduce grounding accidents of small fishing vessels, I suggested the following measures. First, crew members who have completed the able seafarer training course on bridge watchkeeping should assist to the master. Second, alarm systems to prevent drowsiness should be installed in the bridge. Third, the regulation should be prepared for the performance standards and updating GPS plotter. Finally, the skipper of small vessels should be trained periodically to be familiar with chart symbols and basic terrestrial navigation.

• Journal of Technologic Dentistry
• /
• v.39 no.4
• /
• pp.263-273
• /
• 2017

Purpose: This study aimed to explore the relations between class distracting factors and class satisfaction of the dental technology students and then provide a primary data to help further related studies and develop educational programs with which instructors can efficiently manage their classroom. Methods: For this study we have conducted a survey started from the beginning of May 2017 to the end of June. The subjects of the survey were Dental Technology students of D-city, K-city, W-city, selected by random sampling method. The questionnaire was self-administrated and 437 valid results were chosen for our analysis among 450 distributed questionnaires. Results: The results of the research was as follows. Firstly, The overall average point of class distracting factors was 2.5 point. The environmental factors were the highest point as 2.59 and as for the subcategories tiredness and drowsiness was the highest point as 2.76. Secondly, The overall average point of class satisfaction turned out 3,88 point and compliance with class and attitude factors gained the highest point as 4.06. Of the subcategories strict roll checking was the highest point as 4.17. Thirdly, As for class distracting factors from general characteristics a statistical significance was shown as follows; 'instructor factor'(p<.01), 'learner factor'(p<.05), 'total class distracting factor'(p<.05) in the area of gender, 'environmental factor'(p<.001), 'total class distracting factor'(p<.01), 'learner factor'(p<.05), 'instructor factor'(p<.05) in the area of gender 'learner factor'(p<.001), 'instructor factor'(p<.001), 'environmental factor'(p<.001), 'total class distracting factor'(p<.01) in the area of class grade, 'environmental factor'(p<.05) in GPA. Fourthly, A statistical significance, a negative correlation (p<.01) were shown between class distracting factors and class satisfaction. Class distracting factor that especially affects the class satisfaction was instructor factor(p<.001) and the explanatory power of the model turned out 14.7%, which was statistically meaningful (p<.001). Conclusion : Results of this study reveal that instructor factor is the key to class satisfaction of the students. So it is crucial that the instructor faithfully prepare for the class to reinforce the students' learning. Additionally further studies should be followed with more subjects and newer perspectives to develop innovative teaching methodology.