• 대한공업교육학회지
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• v.42 no.1
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• pp.1-29
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• 2017

The purpose of this study was to investigate the vocational identity through the reflection of the major experience as the teacher through the life history of the technical high school. In order to achieve the purpose of this study, two technical high school mechanical and career teachers were selected as research participants and in-depth interviews were conducted with them. The data obtained through the in-depth interviews were analyzed through six steps. Six major experiences of the participants were identified as results of the research: (1) the experience of trying to train the specialist of precision machining in the beginning of teacher's life, (2) experience as a skill competition team teacher, (3) experience of innovating public education by introducing new industry field, (4) experience of constant learning new field and sharing with colleagues, (5) experience in the rapid change of the status of technical high school, (6) experience in the prevention of students' safety accidents and maintenance of the practice field. Through these educational experiences, each research participant was forming one's vocational identity as a mechanical teacher. The vocational identity of the research participants were identified as follows: (1) identity drifting phase, (2) identity stability stage, (3) transition stage of the teacher role, (4) suspended stage to preserve identity, (5) identification sublimation stage, (6) identification of the true meaning of the teacher, and integration of the identity. Through these six steps, their identities were formed, strengthened and changed at each stage.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.2
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• pp.216-232
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• 2016

In present, the Seoul City is undergoing traffic congestion problems caused by rapid urbanization and population growth. Thus the City government has reorganized the mass transportation system since 2004 and the subway has become a very important means for public transit. Since the subway system is typically a closed environment, the indoor air quality issues have often raised by the public. Especially since a huge amount of PM (particulate matter) is emitted from ground tunnels passing through the subway train, it is now necessary to assess the characteristics and behaviors of fine PM inside the tunnel. In this study, the concentration patterns of $PM_1$, $PM_{2.5}$, and $PM_{10}$ in the Seoul subway line-2 were analyzed by real-time measurement during winter (Jan 13, 2015) and summer (Aug 7, 2015). The line-2 consisting of 51 stations is the most busy circular line in Seoul having the railway of 60.2 km length. The the one-day average $PM_{10}$ concentrations were $148{\mu}g/m^3$ in winter and $66.3{\mu}g/m^3$ in summer and $PM_{2.5}$ concentrations were $118{\mu}g/m^3$ and $58.5{\mu}g/m^3$, respectively. The $PM_{2.5}/PM_{10}$ ratio in the underground tunnel was lower than the outdoor ratio and also the ratio in summer is higher than in winter. Further the study examined structural types of underground subsections to explain the patterns of elevated PM concentrations in the line-2. The subsections showing high PM concentration have longer track, shorter curvature radius, and farther from the outdoor stations. We also estimated the outdoor PM concentrations near each station by a spatial statistical analysis using the $PM_{10}$ data obtained from the 40 Seoul Monitoring Sites, and further we calculated $PM_{2.5}/PM_{10}$ and $PM_1/PM_{10}$ mass ratios near the outdoor subway stations by using our observed outdoor $PM_1$, $PM_{2.5}$, and $PM_{10}$ data. Finally, we could develop pollution maps for outdoor $PM_1$ and $PM_{2.5}$ near the line-2 by using the kriging method in spatial analysis. This methodology may help to utilize existing $PM_{10}$ database when managing and control fine particle problems in Korea.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1709-1722
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• 2022

The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model's input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.69-76
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• 2013

The incidence of globally infectious and pathogenic diseases such as H1N1 (swine flu) and Avian Influenza (AI) has recently increased. An infectious disease is a pathogen-caused disease, which can be passed from the infected person to the susceptible host. Pathogens of infectious diseases, which are bacillus, spirochaeta, rickettsia, virus, fungus, and parasite, etc., cause various symptoms such as respiratory disease, gastrointestinal disease, liver disease, and acute febrile illness. They can be spread through various means such as food, water, insect, breathing and contact with other persons. Recently, most countries around the world use a mathematical model to predict and prepare for the spread of infectious diseases. In a modern society, however, infectious diseases are spread in a fast and complicated manner because of rapid development of transportation (both ground and underground). Therefore, we do not have enough time to predict the fast spreading and complicated infectious diseases. Therefore, new system, which can prevent the spread of infectious diseases by predicting its pathway, needs to be developed. In this study, to solve this kind of problem, an integrated monitoring system, which can track and predict the pathway of infectious diseases for its realtime monitoring and control, is developed. This system is implemented based on the conventional mathematical model called by 'Susceptible-Infectious-Recovered (SIR) Model.' The proposed model has characteristics that both inter- and intra-city modes of transportation to express interpersonal contact (i.e., migration flow) are considered. They include the means of transportation such as bus, train, car and airplane. Also, modified real data according to the geographical characteristics of Korea are employed to reflect realistic circumstances of possible disease spreading in Korea. We can predict where and when vaccination needs to be performed by parameters control in this model. The simulation includes several assumptions and scenarios. Using the data of Statistics Korea, five major cities, which are assumed to have the most population migration have been chosen; Seoul, Incheon (Incheon International Airport), Gangneung, Pyeongchang and Wonju. It was assumed that the cities were connected in one network, and infectious disease was spread through denoted transportation methods only. In terms of traffic volume, daily traffic volume was obtained from Korean Statistical Information Service (KOSIS). In addition, the population of each city was acquired from Statistics Korea. Moreover, data on H1N1 (swine flu) were provided by Korea Centers for Disease Control and Prevention, and air transport statistics were obtained from Aeronautical Information Portal System. As mentioned above, daily traffic volume, population statistics, H1N1 (swine flu) and air transport statistics data have been adjusted in consideration of the current conditions in Korea and several realistic assumptions and scenarios. Three scenarios (occurrence of H1N1 in Incheon International Airport, not-vaccinated in all cities and vaccinated in Seoul and Pyeongchang respectively) were simulated, and the number of days taken for the number of the infected to reach its peak and proportion of Infectious (I) were compared. According to the simulation, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days when vaccination was not considered. In terms of the proportion of I, Seoul was the highest while Pyeongchang was the lowest. When they were vaccinated in Seoul, the number of days taken for the number of the infected to reach at its peak was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. When they were vaccinated in Pyeongchang, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. Based on the results above, it has been confirmed that H1N1, upon the first occurrence, is proportionally spread by the traffic volume in each city. Because the infection pathway is different by the traffic volume in each city, therefore, it is possible to come up with a preventive measurement against infectious disease by tracking and predicting its pathway through the analysis of traffic volume.