• Journal of Periodontal and Implant Science
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• v.27 no.3
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• pp.533-547
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• 1997

In periodontics, much progress was made in the understanding of periodontal disease from 1960s to 1980s and in prevention and management of periodontal disease since the end of 1980s. This presentation will discuss about the prevalence of periodontal disease, treatment need, and provision of periodontal treatment in Korea, and how we could manage the periodontal disease efficiently in the future. According to an epidemiological study in Korea, periodontal disease(including gingivitis) was present in 82% of general population and periodontitis in 30-40% in adult population over 30y and juvenile periodontitis in 0.1% of adolescents. If we consider that at least 17% of these patients may have recurrent or refractory forms, there is obviously an abundance of disease that needs treatment, As a result of increase in life expectancy, senile population over 65 y will be increased from 6% in 1996 to 6.9% in 2000, and tooth retention rate and periodontal treatment need are expected to increase. Periodontists need all the help they can get from the general dentists to control periodontal disease. As for provision, postgraduate course in periodontics started in 1957 in Korea and produced over 700 specialized dentists in periodontics. One report indicated that the periodontists as well as general practitioners did periodontal therapy on only a few periodontal patients, because of specific control by current medical insurance system in Korea. Comprehensive periodontal examination is rarely done in local dental clinic. Therefore, enhancement of periodontal care in medical insurance system and education of simplified periodontal examination such as Periodontal Screening & Recording will make dentists diagnose and manage the management of adult patients is based on the recognition that there are multiple diseases, including gingivitis, chronic adlt periodontitis, and other more aggressive forms of periodontitis, and requires the earliest possible recognition of these three disease categories. In this presentation, we discuss practical approach using PSR to diagnose, manage and refer the patients, to facilitate the separation of the simple from the complex and the predictable from the unpredictable form of periodontal diseases and to integrate diagnostic and therapeutic techniques into private practice today.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.83-98
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• 2021

The purpose of this study to find the factors influencing the fire alarms using IoT firefighting facility management system data of Seoul Fire & Disaster Headquarters, and to present academic implications for establishing an effective prevention system of fire situation. As the number of high and complex buildings increases and former bulidings are advanced, the fire detection facilities that can quickly respond to emergency situations are also increasing. However, if the accuracy of the fire situation is incorrectly detected and the accuracy is lowered, the inconvenience of the residents increases and the reliability decreases. Therefore, it is necessary to improve accuracy of the system through efficient inspection and the internal environment investigation of buildings. The purpose of this study is to find out that false detection may occur due to building characteristics such as usage or time, and to aim of emphasizing the need for efficient system inspection and controlling the internal environment. As a result, it is found that the size(total area) of the building had the greatest effect on the fire alarms, and the fire alarms increased as private buildings, R-type receivers, and a large number of failure or shutoff days. In addition, factors that influencing fire alarms were different depending on the main usage of the building. In terms of time, it was found to follow people's daily patterns during weekdays(9 am to 6 pm), and each peaked around 10 am and 2 pm. This study was claimed that it is necessary to investigate the building environment that caused the fire alarms, along with the system internal inspection. Also, it propose additional recording of building environment data in real-time for follow-up research and system enhancement.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.25-32
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• 2021

In line with future changes in the marine environment, Aids to Navigation has been used in various fields and their use is increasing. The term "Aids to Navigation" means an aid to navigation prescribed by Ordinance of the Ministry of Oceans and Fisheries which shows navigating ships the position and direction of the ships, position of obstacles, etc. through lights, shapes, colors, sound, radio waves, etc. Also now the use of Aids to Navigation is transforming into a means of identifying and recording the marine weather environment by mounting various sensors and cameras. However, Aids to Navigation are mainly lost due to collisions with ships, and in particular, safety accidents occur because of poor observation visibility due to sea fog. The inflow of sea fog poses risks to ports and sea transportation, and it is not easy to predict sea fog because of the large difference in the possibility of occurrence depending on time and region. In addition, it is difficult to manage individually due to the features of Aids to Navigation distributed throughout the sea. To solve this problem, this paper aims to identify the marine weather environment by estimating sea fog level approximately with images taken by cameras mounted on Aids to Navigation and to resolve safety accidents caused by weather. Instead of optical and temperature sensors that are difficult to install and expensive to measure sea fog level, sea fog level is measured through the use of general images of cameras mounted on Aids to Navigation. Furthermore, as a prior study for real-time sea fog level estimation in various seas, the sea fog level criteria are presented using the Haze Model and Dark Channel Prior. A specific threshold value is set in the image through Dark Channel Prior(DCP), and based on this, the number of pixels without sea fog is found in the entire image to estimate the sea fog level. Experimental results demonstrate the possibility of estimating the sea fog level using synthetic haze image dataset and real haze image dataset.