• Korean Journal of Psychosomatic Medicine
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• v.3 no.2
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• pp.166-173
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• 1995

The author compared the anxiety and depression level between glaucoma patents and normal controls by means of STAI(State-Trait Anxiety Inventory) and BDI(Beck Depression Inventory). The subject was the 38 glaucoma patients who visited ophthalmology clinic of Ewha Womans University Mokdong Hospital. The results were as follows : 1) There was no statistically significant difference in BDI and STAI according to age, education, religion, marital status. 2) There was no significant difference in STAI between the glaucoma patient group and the normal control group except that the female patients showed a tendency toward higher anxiety. 3) There was a significant difference in BDI between the female glaucoma patient group and the female control group(P<.01). But in the case of male there was no significant difference in BDI. 4) Eighteen point four percent of the glaucoma patients(10.5% of male patients, 26.4% of female patients) had suspected depressive disorder, and there was a tendency toward depression in 28.9% of glaucoma patients(10.5% of male patients, 47.4% of female patients). 5) There was no significant difference in BDI and STAI according to the use of beta-blocker or the other concommitant physical diseases. 6) The patients of glaucoma were generally concerned about the possibility of blindness, and felt annoyed with the fact that they should use eye drops or oral medication everyday.

• Journal of the Society of Disaster Information
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• v.18 no.4
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• pp.922-929
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• 2022

Purpose: The purpose of this paper is to develop a deep learning model to detect fire extinguishers in images taken from CCTVs in underground utility tunnels. Method: Various fire extinguisher images were collected for detection of fire extinguishers in the running-based underground utility tunnel, and a model applying the One-stage Detector method was developed based on the CNN algorithm. Result: The detection rate of fire extinguishers photographed within 10m through CCTV video in the underground common area is over 96%, showing excellent detection rate. However, it was confirmed that the fire extinguisher object detection rate drops sharply at a distance of 10m or more, in a state where it is difficult to see with the naked eye. Conclusion: This paper develops a model for detecting fire extinguisher objects in underground common areas, and the model shows high performance, and it is judged that it can be used for underground common area digital twin model synchronizing.