• Journal of the Korean Society of Food Science and Nutrition
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• v.30 no.5
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• pp.978-985
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• 2001

The purpose of this study was to investigate the effect of alcohol drinking on dietary behaviors and nutrient intakes among the university male students. The subjects were divided three group; no-alcohol group(n=83), alcohol group(n=78), and high-alcohol group(n=78).And they were observed general characteristics, life style, eating pattern, food frequency and nutrient intake using questionnaires. The mean age, height, weight and BMI of the subjects were 25.8$\pm$6.1 years old 171.5$\pm$5.4 cm, 63.4$\pm$9.7 kg and 2.3$\pm$2.8/kg/$m^2$, respectively. The types of residence and person who prepares meals were significantly different among the groups: the frequency of self-boarding and preparing meals oneself in high-alcohol group were higher than in other two groups. The frequency of physical exercise and cigarette smoking in high-alcohol group were higher than in other two groups. There were no significant differences in skipping meals among three groups. However, the most common reson why high-alcohol group skipped meals was due to a eating habit, while a lack of time in other two groups. The results show that the high-alcohol group tended to eat more often instant ramien, soybean sprout, anchovy, and coffee compared to the other two groups. The energy intakes in alcohol and high-alcohol groups were lower than those in no-alcohol group. In conclusion, high-alcohol students have unhealthy dietary behaviors in the light of high frequency of cigarette smoking, eating habit of skipping meals and instant foods, and therefore showing a strong need of proper education in alcohol withdrawal and meal management for them.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.