• 제어로봇시스템학회논문지
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• 제21권4호
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• pp.330-335
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• 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.

• 제어로봇시스템학회논문지
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• 제22권5호
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• pp.361-366
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• 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.

• 제어로봇시스템학회논문지
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• 제21권2호
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• pp.102-107
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• 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.

• 제어로봇시스템학회논문지
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• 제22권4호
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• pp.266-270
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• 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.

• 한국ITS학회 논문지
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• 제10권2호
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• pp.94-102
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• 2011

본 논문은 눈 깜박임 패턴을 이용한 새로운 졸음 검출 알고리즘을 제안하였다. 유한오토마타를 이용한 졸음 검출 모델을 제안하여 눈감은 상태를 나타내는 입력 심벌의 개수만을 체크함으로써 눈 깜박임, 졸음, 수면 검출을 용이하게 하였다. 또한 수평 투영 히스토그램의 특성을 이용하여 눈동자가 있는 영역만을 구해 수직 투영 히스토그램을 취함으로써 눈썹이나 안경테와 같은 외부 영향을 최소화 시켜 정확도를 높였다. ZJU 눈 깜박임 데이터베이스를 이용한 눈 깜박임 검출 실험 결과 93% 이상의 정확도를 얻음으로써 제안된 방법의 우수함을 보였다.

• 해양환경안전학회지
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• 제28권4호
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• pp.533-541
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• 2022

최근 5년간 발생한 해양사고를 분석한 결과, 소형선박은 전체 좌초사고의 77.0 %를 차지하고, 인명사상도 66.1%로서 매우 높았다. 목포지방해양안전심판원은 2021년 72건을 재결하였고, 이중 좌초사건은 10건이었다. 그리고 좌초사건 중 8건은 소형어선에서 발생하였다. 본 논문은 2021년 목포지방해양안전심판원에서 재결하였던 소형어선에서 발생한 8건의 좌초사고에 대해 분석하였다. 이 소형어선의 좌초사고는 맑은 날씨에 시정이 2~4마일로 양호하고, 파고 1미터 이내로 양호한 해상상태에서 발생한 것을 파악되었다. 그리고 좌초사고의 주요 원인은 피로에 따른 졸음 운항, 경계 소홀, 선위확인 소홀, GPS Plotter 과신, 해도도식 및 조석간만의 차에 대한 이해부족 등인 것으로 나타났다. 이에 소형어선의 좌초사고 저감을 위해 다음의 방안을 제시하였다. 첫째, 갑판부 항해당직 부원교육을 이수한 선원이 선장을 보좌하도록 하여야 한다. 둘째, 졸음 방지용 경보장치는 조타실에 설치하여야 한다. 셋째, GPS Plotter의 성능기준과 최신화를 위해 제도는 마련되어야 한다. 마지막으로, 소형어선 선장은 주기적으로 해도도식과 기초 지문항법을 숙지할 수 있도록 교육을 받아야 한다.

• 대한치과기공학회지
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• 제39권4호
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• pp.263-273
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• 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.