• 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.24 no.3
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• pp.247-262
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• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.