• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.27-34
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• 2024

The direct and indirect damages caused by fires in underground utility tunnels have a great impact on society as a whole, so efforts are needed to prevent and manage them in advance. To this end, research is ongoing to prevent disasters such as fire flooding by applying digital twin technology to underground utility tunnels. A network is required to transmit the sensed signals from each sensor to the platform. In essence, it is necessary to analyze the application of wireless networks in the underground utility tunnel environments because the tunnel lacks the reception range of external wireless communication systems. Within the underground utility tunnels, electromagnetic interference caused by transmission and distribution cables, and diffuse reflection of signals from internal structures, obstacles, and metallic pipes such as water pipes can cause distortion or size reduction of wireless signals. To ensure real-time connectivity for remote surveillance and monitoring tasks through sensing, it is necessary to measure and analyze the wireless coverage in underground utility tunnels. Therefore, in order to build a wireless network environment in the underground utility tunnels. this study minimized the shaded area and measured the actual cavity environment so that there is no problem in connecting to the wireless environment inside the underground utility tunnels. We analyzed the data transmission rate, signal strength, and signal-to-noise ratio for each section of the terrain of the underground utility tunnels. The obtained results provide an appropriate wireless planning approach for installing wireless networks in underground utility tunnels.

• Journal of the Korea Society for Simulation
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• v.25 no.2
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• pp.31-40
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• 2016

Wireless sensor networks have been used in many applications such as marine environment, army installation, etc. The sensor data is very important, because all these applications depend on sensor data. The possibility of communication failures becomes high since the surrounding environment of a wireless sense network has an sensitive effect on its communications. In particular, communication failures in underwater communications occur more frequently because of a narrow bandwidth, slow transmission speed, noises from the surrounding environments and so on. In cases of communication failures, the sensor data can be lost in the sensor data delivery process and these kinds of sensor data losses can make critical huge physical damages on human or environments in applications such as fire surveillance systems. For this reason, although a few of studies for storing and compressing sensor data have been proposed, there are lots of difficulties in actual realization of the studies due to none-existence of the framework using network communications. In this paper, we propose a framework for reducing loss of the sensor data and analyze its performance. The our analyzed results in non-framework application show a decreasing data recovery rate, T/t, as t time passes after a network failure, where T is a time period to fill the storage with sensor data after the network failure. Moreover, all the sensor data generated after a network failure are the errors impossible to recover. But, on the other hand, the analyzed results in framework application show 100% data recovery rate with 2~6% data error rate after data recovery.

• Journal of Preventive Medicine and Public Health
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• v.38 no.2
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• pp.125-131
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• 2005

Injury has recently become a major world-wide health problem. Injury related deaths occur in many actively working young people and produce major social and economical losses. However health related specialists do not recognize the importance of injury and there have not been many studies related to this problem. This research studied the trends of injury related research in Korea, mortality rate and prevalence rate, socio-economical losses and control in Korea and out of the country, based on literature from Korea and without such as statistical yearly reports on causes of deaths and OECD health reports, as well as WHO web sites. Studies in Korea about injury were very few, with 9 in the 1960's, 5 in the 1980's, 4 in the 1990's and 5 in 2000's. Mortality rate of injury was higher in Korea than in England, USA or Japan, especially in car accidents, suicide and falls. In Korea, the yearly trends in mortality rates were highest in car accidents but those rates are falling, suicide is steadily rising, with highest rate in 2003. Falls is in second rank with no change in rates. In 2003, the ten causes of death in Korea were suicide in 5th rank, transport accidents in 7th rank, and falls in 10th rank. Considering age groups, in the teens, transport accidents were 1st rank, in the 20's and 30's, suicide was 1st rank, and although there were some differences, falls, drowning, assault, fire were in the top 10. Prevalence rates of injury could not be known, but in 2001, according to the National Health and Nutrition Survey, lifelong injury was 10%, and yearly major injury was 1.3%, major injury for two weeks was 0.1%, and minor injury was 10%. In other foreign countries, injury has become to be recognized as a major health related problem, and much programs are being set up to reduce injury related deaths and injuries. WHO is putting much effort in prevention of violence and transport accidents, and in the USA, Canada and Europe, there are injury surveillance systems. Recently, as suicide is increasing rapidly and providing much problems, each country are managing suicide prevention programs. In Korea, Ministry of Construction and Transportation is managing and guiding the policies for prevention of transport accidents. In September of 2004, the Ministry of Health and Welfare has set up a 5 year plan of suicide prevention.

• Convergence Security Journal
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• v.21 no.1
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• pp.191-200
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• 2021

Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.