• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.79-85
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• 2017

It is critical that passengers should be given timely and correct escape or evacuation guidance from captain and crews when there are hazardous situations in a ship. Otherwise the consequences could be disastrous as "SEWOL Ferry" the South Korean passenger ship which sank in southern coastal area on 16th April 2014. Due to the captain's delayed evacuation decision and lack of sufficient number of crews to guide passengers' evacuation, the accident recorded many casualties, most of whom were high school students (302 passengers sank down with the ship while 172 rescued). Building a passenger ship with well-designed physical escape routes is one thing and guiding passengers to those escape routes in real disaster situation is another. Passengers get panic and move to a wrong direction, bottleneck makes situation worse, and even crews get panic also - passive static escape route signage and small number of trained crews might not be enough to take care of them. SESS (Smart Escape Support System) is being developed sponsored by South Korea Ministry of Ocean and Fisheries starting from 2016 with 4 years of roadmap. SESS comprises multiple active dynamic signage devices which communicate with real-time escape routing server software via LoRa (Long Range) proprietary wireless network.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.613-621
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• 2021

This study conducted a network analysis to determine how COVID-19 vaccine-related issue spread on Twitter during the introduction stage of the COVID-19. Issue diffusion tendency is analyzed according to the time period: phase 1 (initiation of vaccine introduction: March 7 - April 3, 2021), phase 2 (stagnant period of vaccination: April 4 - April 22, 2021), and phase 3 (increase of vaccination: April 23 - May 5, 2021). NodeXL was used for data collection and analysis. Daily Twitter network data were collected by entering search terms highly related to the COVID-19 vaccine. This study found that side effects-related opinions were repeatedly formed throughout the analysis period. As the vaccination rate increased and death cases were reported on media, death-related issues also emerged on Twitter. On the other hand, vaccine safety did not receive much attention on Twitter. The results of this study highlight the role of social media as a channel of issue diffusion when a national disaster strikes. We emphasize the need for the government to monitor public opinions on social media and reflect them in crisis communication strategies.

• Journal of Digital Convergence
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• v.16 no.5
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• pp.43-53
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• 2018

Recently, public service for the improvement of quality of life and life support such as safety, aging, disaster, welfare, housing, economy, urban environment, traffic etc are actively developed based on open public data, and the spread of the network and the necessity of everyday life, smartphones are playing a role in providing public services. Currently, the development of science is changing the life expectancy of human beings and changing into social structure in which aged people become bigger due to various social conditions and low fertility and aging problems. However, the elderly who do not have easy access to information are very uncomfortable in dealing with mobile devices with very low accessibility and utilization of public services provided by mobile phones. Therefore, this study recategorizes the condition of the elderly presented in the previous study and identifies the problem through case analysis provided for the elderly. Also, we summarize the hierarchy of the core items of the existing interface design and derive it as an improvement index of the public service design for the improvement of the information accessibility of the elderly, and propose a design method to improve the utilization of the public service provided through the mobile device.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.11-27
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• 2017

The road network system of major domestic urban areas such as city of Seoul was rapidly developed and regionally expanded. In addition, many kinds of life-lines such as electrical cables, telephone cables, water&sewerage lines, heat&cold conduits and gas lines were needed in order for urban residents to live comfortably. Therefore, most of the life-lines were individually buried in underground and individually managed. The utility tunnel is defined as the urban planning facilities for commonly installing life-lines in the National Land Planning Act. Expectation effectiveness of urban utility tunnels is reducing repeated excavation of roads, improvement of urban landscape; road pavement durability; driving performance and traffic flow. It can also be expected that ensuring disaster safety for earthquakes and sinkholes, smart-grind and electric vehicle supply, rapid response to changes in future living environment and etc. Therefore, necessity of urban utility tunnels has recently increased. However, all of the constructed utility tunnels are cut-and-cover tunnels domestically, which is included in development of new-town areas. Since urban areas can not accommodate all buried life-lines, it is necessary to study the feasibility assessment system for utility tunnel by urban patterns and capacity optimization for urban utility tunnels. In this study, we break away from the new-town utility tunnels and suggest a quantitative assessment model based on the evaluation index for urban areas. In addition, we also develop a program that can implement a quantitative evaluation system by subdividing the feasibility assessment system of urban patterns. Ultimately, this study can contribute to be activated the urban utility tunnel.

• Korean Chemical Engineering Research
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• v.59 no.2
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• pp.209-218
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• 2021

First principle-based modeling studies have been performed to improve the heat exchange efficiency of ORV and optimize operation, but the heat transfer coefficient of ORV is an irregular system according to time and location, and it undergoes a complex modeling process. In this study, FNN, LSTM, and AutoML-based modeling were performed to confirm the effectiveness of data-based modeling for complex systems. The prediction accuracy indicated high performance in the order of LSTM > AutoML > FNN in MSE. The performance of AutoML, an automatic design method for machine learning models, was superior to developed FNN, and the total time required for model development was 1/15 compared to LSTM, showing the possibility of using AutoML. The prediction of NG and seawater discharged temperatures using LSTM and AutoML showed an error of less than 0.5K. Using the predictive model, real-time optimization of the amount of LNG vaporized that can be processed using ORV in winter is performed, confirming that up to 23.5% of LNG can be additionally processed, and an ORV optimal operation guideline based on the developed dynamic prediction model was presented.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1167-1181
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• 2021

In relation to the algae bloom, four types of blue-green algae that emit toxic substances are designated and managed as harmful Cyanobacteria, and prediction information using a physical model is being also published. However, as algae are living organisms, it is difficult to predict according to physical dynamics, and not easy to consider the effects of numerous factors such as weather, hydraulic, hydrology, and water quality. Therefore, a lot of researches on algal bloom prediction using machine learning have been recently conducted. In this study, the characteristic importance of water quality factors affecting the occurrence of Cyanobacteria harmful algal blooms (CyanoHABs) were analyzed using the random forest (RF) model for Bohyeonsan Dam and Yeongcheon Dam located in Yeongcheon-si, Gyeongsangbuk-do and also predicted the occurrence of harmful blue-green algae using the machine learning and deep learning models and evaluated their accuracy. The water temperature and total nitrogen (T-N) were found to be high in common, and the occurrence prediction of CyanoHABs using artificial neural network (ANN) also predicted the actual values closely, confirming that it can be used for the reservoirs that require the prediction of harmful cyanobacteria for algal management in the future.