• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1130-1135
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• 2022

In Seoul, about 75,000 CCTVs are installed in 25 district offices. Each ward office has built a control center for CCTV control and is performing 24-hour CCTV video control for the safety of citizens. Seoul Metropolitan Government is building a smart city integrated platform that is safe for citizens by providing CCTV images of the ward office to enable rapid response to emergency/emergency situations by signing an MOU with related organizations. In this paper, when an incident occurs at the Seoul Metropolitan Government Office, the escape route is predicted by discriminating people and vehicles using the AI DNN-based Template Matching technology, MLP algorithm and CNN-based YOLO SPP DNN model for CCTV images. In addition, it is designed to automatically disseminate image information and situation information to adjacent ward offices when vehicles and people escape from the competent ward office. The escape route prediction and tracking system using artificial intelligence can expand the smart city integrated platform nationwide.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.277-280
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• 2021

Recently as various contents are mass produced based on high accessibility, the media contents market is more active. Users want to find content that suits their taste, and each platform is competing for personalized recommendations for content. For an efficient recommendation system, high-quality metadata is required. Existing platforms take a method in which the user directly inputs the metadata of an image. This will waste time and money processing large amounts of data. In this paper, for media hyperpersonalization recommendation, keyframes are extracted based on the YCrCb color model of the video based on movie trailers, movie genres are distinguished through supervised learning of artificial intelligence and In the future, we would like to propose a utilization plan for generating metadata.

• Journal of Korean Public Health Nursing
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• v.16 no.2
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• pp.304-314
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• 2002

A survey was conducted from September 15 to December 15 2001. Structured questionnaires were mailed to school nurses in 607 elementary schools around the country in order to determine the current status of anti-smoking education in elementary schools. The result were as followers 1. Of the 607 schools, 535 $(88.1\%)$ offered smoking-prevention education. By region, Jeju-do $(100\%)$ topped the list, followed by Seoul $(90.3\%)$, Gyeongsang-do$(90.2\%)$, Jeolla-do$(88.9\%)$, Gangwon-do $(87.8\%)$, Chungcheong-do $(84.6\%)$, and Gyeonggi-do $(81.4\%)$. 'Recognition of the need for anti-smoking program $(86\%)$' was a major motivation for initiating the program, while 'too much workload $(46.4\%)$' was cited as a main reason for the failure to do so. 2. The classes were offered mostly for 6th-grade students $(87.8\%)$, while $9.0\%$ and $2.0\%$ were implemented at 5th- and 4th- grades, respectively. 3. $49.1\%$ of the classes offered lasted one hour, while $31.8\%$ involved a two-hour program. 4. Programs were mainly about smoking-related diseases, habitual nature of smoking, impediment to growth and development, etc. 5. Audio-visual lecture $(46.5\%)$ was most frequently used as a method of education, followed by lecture. 6. $72.7\%$ of the programs used classroom as a unit of education, while collective education by sex or by grade accounted for $22.6\%$. 7. Video $(51.0\%)$ was the most popular medium for education, while computer ranked second with $26.5\%$. 8. $92.5\%$ of the education was offered by school nurses. 9. $99.2\%$ of school nurses responded in favor of anti-smoking programs. with $60.1\%$ of them answering that such education is a must. 5th grade was the most commonly cited grade for the initiation of the programs, followed by 4th grade and 6th grade. $33.2\%$ picked two hours as the most appropriate length of the program at the elementary school level. while $25.1\%$ chose 3 hours out of the range of 1-11 hour(s). 10. With regard to the evaluation by school nurses on smoking-prevention program, more than $30\%$ felt that hours of education, education materials, medium of education, interests of other teachers, interests of school authorities, etc. were inadequate or insufficient.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.665-672
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• 2023

Purpose: Underground utility tunnel is facility that is jointly house infrastructure such as electricity, water and gas in city, causing condensation problems due to lack of airflow. This paper aims to prevent electricity leakage fires caused by condensation by detecting whether the control panel door in the underground utility tunnel is open using a deep learning model. Method: YOLO, a deep learning object recognition model, is trained to recognize the opening and closing of the control panel door using video data taken by a robot patrolling the underground utility tunnel. To improve the recognition rate, image augmentation is used. Result: Among the image enhancement techniques, we compared the performance of the YOLO model trained using mosaic with that of the YOLO model without mosaic, and found that the mosaic technique performed better. The mAP for all classes were 0.994, which is high evaluation result. Conclusion: It was able to detect the control panel even when there were lights off or other objects in the underground cavity. This allows you to effectively manage the underground utility tunnel and prevent disasters.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.114-123
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• 2023

With the development of computer vision technology, video sensors such as CCTV are detecting incident. However, most of the current incident have been detected based on existing fixed imaging equipment. Accordingly, there has been a limit to the detection of incident in shaded areas where the image range of fixed equipment is not reached. With the recent development of edge-computing technology, real-time analysis of mobile image information has become possible. The purpose of this study is to evaluate the possibility of detecting expressway emergencies by introducing computer vision technology to dash cam. To this end, annotation data was constructed based on 4,388 dash cam still frame data collected by the Korea Expressway Corporation and analyzed using the YOLO algorithm. As a result of the analysis, the prediction accuracy of all objects was over 70%, and the precision of traffic accidents was about 85%. In addition, in the case of mAP(mean Average Precision), it was 0.769, and when looking at AP(Average Precision) for each object, traffic accidents were the highest at 0.904, and debris were the lowest at 0.629.