• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of the Society of Disaster Information
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• v.15 no.2
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• pp.223-238
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• 2019

Purpose: This study aims at exploring implications of discourse and social practice produced by various stakeholders in politics, economy and society to provide useful material for effective disaster response in South Korea. Method: Applying the Critical Discourse Analysis model of Fairclough, this study analyzes the newspaper articles of three domestic press companies mainly about the November 2017 Pohang earthquake. Results: As a result, first, the three media companies point out the low effectiveness of disaster response manuals and evacuation training. Second, strengthening shelter services and expanding support for the victims are important for recovery from the earthquake. Third, to prevent the future damages, they suggest the implementation efforts to improve the seismic design and short message service based disaster alert system. Conclusion: Based on the findings, this study suggests to improve the practicality and effectiveness of disaster prevention measures, establish an organic and integrated disaster response system, emphasize the roles and participation of citizens, check the responsibility of experts, and make the media to form sound discourse on disaster response.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.35-40
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• 2019

It is important to constantly provide service in real-time multimedia applications using multicast. Transmission path reconstruction occurs in hybrid multicast using Internet Protocol (IP) multicast and ALM in order to adapt the network status to things like congestion. So, there is a problem in which real-time QoS is reduced, caused by an increase in end-to-end delay. In this paper, we want to solve this problem through multi-layer transmission path construction. In the proposed method, we deploy the control server and application layer overlay host (ALOH) in each multicast domain (MD) for hybrid multicast construction. After the control server receives the control information from an ALOH that joins the MD, it makes a group based on the hop count and sends it to the ALOH in each MD. The ALOH in the MD performs the role of sending the packet to another ALOH and constructs the multi-layered transmission path in order of priority by using control information that is received from the control server and based on the delay between neighboring ALOHs. When congestion occurs in, or is absent from, the ALOH in the upper MD, the ALOH selects the path with the highest priority in order to reduce end-to-end delay. Simulation results show that the proposed method could reduce the end-to-end delay to less than 289 ms, on average, under congestion status.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.140-145
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• 2019

With the development of IT technology, many changes are taking place in the health service environment over the past. However, even if medical technology is converged with IT technology, the problem of medical costs and management of health services are still one of the things that needs to be addressed. In this paper, we propose a model for hospitals that have established the IoT system to efficiently analyze and manage the personal information of users who receive medical services. The proposed model aims to efficiently check and manage users' medical information through an in-house IoT system. The proposed model can be used in a variety of heterogeneous cloud environments, and users' medical information can be managed efficiently and quickly without additional human and physical resources. In particular, because users' medical information collected in the proposed model is stored on servers through the IoT gateway, medical staff can analyze users' medical information accurately regardless of time and place. As a result of performance evaluation, the proposed model achieved 19.6% improvement in the efficiency of health care services for occupational health care staff over traditional medical system models that did not use the IoT system, and 22.1% improvement in post-health care for users who received medical services. In addition, the burden on medical staff was 17.6 percent lower on average than the existing medical system models.

• Journal of Convergence for Information Technology
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• v.9 no.4
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• pp.1-7
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• 2019

The medical environment, combined with IT technology, is changing the paradigm for medical services from treatment to prevention. In particular, as ICT convergence digital healthcare technology is applied to hospital medical systems, infrastructure technologies such as big data, Internet of Things, and artificial intelligence are being used in conjunction with the cloud. In particular, as medical services are used with IT devices, the quality of medical services is increasingly improving to make them easier for users to access. Medical institutions seeking to incorporate IoT services into cloud health care environment services are trying to reduce hospital operating costs and improve service quality, but have not yet been fully supported. In this paper, a patient information collection model from hospital IoT system, which has established a cloud environment, is proposed. The proposed model prevents third parties from illegally eavesdropping and interfering with patients' biometric information through IoT devices attached to the patient's body at hospitals in cloud environments that have established hospital IoT systems. The proposed model allows clinicians to analyze patients' disease information so that they can collect and treat diseases associated with their eating habits through IoT devices. The analyzed disease information minimizes hospital work to facilitate the handling of prescriptions and care according to the patient's degree of illness.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.337-342
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• 2019

This letter describes the development of WRI (Wildfire Risk Index) using GDAPS (Global Data Assimilation and Prediction System) and satellite data, and its application to the Goseong-Sokcho and Gangneung-Donghae wildfires in April 4, 2019. We made sure that the proposed WRI represented the change of wildfire risk of around March 19 and April 4 very well. Our approach can be a viable option for wildfire risk monitoring, and future works will be necessary for the utilization of GK-2A products and the coupling with the wildfire prediction model of the Korea Forest Service.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.434-439
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• 2019

Recently, interest in urban temperature change and ground surface temperature change has been increasing due to weather phenomenon due to global warming, heat island phenomenon caused by urbanization in urban areas. In Korea, weather data such as temperature and precipitation have been collected since 1904. In recent years, there are 96 ASOS stations and 494 AWS weather observation stations. However, in the case of terrestrial networks, terrestrial meteorological data except measurement points are predicted through interpolation because they provide point data for each installation point. In this study, to improve the resolution of ground surface temperature measurement, the surface temperature using satellite image was calculated and its applicability was analyzed. For this purpose, the satellite images of Landsat 8 OLI TIRS were obtained for Seoul Metropolitan City by seasons and transformed to surface temperature by applying NASA equation to the thermal bands. The ground measurement data was based on the temperature data measured by AWS. Since the AWS temperature data is station based point data, interpolation is performed by Kriging interpolation method for comparison with Landsat image. As a result of comparing the satellite image base surface temperature with the AWS temperature data, the temperature difference according to the season was calculated as fall, winter, summer, based on the RMSE value, Spring, in order of applicability of Landsat satellite image. The use of that attribute and AWS support starts at $2.11^{\circ}C$ and RMSE ${\pm}3.84^{\circ}C$, which reflects information from the extended NASA.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.836-854
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• 2018

This study is analyzing the effects of energy conservation consciousness on Korean energy saving behavior through the mediating effects of energy-related broadcasting media, such as TV. The study is carried out through the survey with structured questionnaire for each factors, using a mediating analysis based on PROCESS Macro proposed by Hayes (2013). The results of the study have been found that people with higher energy conservation consciousness displayed energy saving behavior more frequently and more actively for energy saving behavior as they were more exposed to energy-related broadcasting media. By energy-related broadcasting media the effect of energy public service ad and campaign was the largest at 15.3%, followed by energy news at 12.1%. But energy documentary has no effect on energy saving behavior. Based on the results of this analysis, it is necessary to establish a broadcasting policy that enhances the frequency of energy-related broadcasting media and energy documentary that can induce energy saving behavior. It is also necessary to make efforts to increase the reliability of analysis through empirical data such as electricity bills and fuel cost which show the actual saving level by energy saving behavior.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.601-609
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• 2018

The Smart City Plan specifies that it should reflect the content of the Smart City Comprehensive Plan, which is the upper plan while presenting the basic direction, promotion strategy, service establishment and operation plans of a smart city construction project. However, there are not enough empirical studies on whether plan contents are systematically established according to smart city planning hierarchy. In this study, we are to use the social network analysis to find out the local government's smart city plan is actually highly connected to the Smart City Comprehensive Plan, a master plan at national level. To this end, we conducted a social network analysis on Gwangmyeong and Chuncheon, which were recently approved for smart city planning. First, 108 keywords were derived from the 2nd Smart City Comprehensive Plan, and the connectivity between these keywords and Smart City Planning was analyzed. The results of the social network analysis showed that the total connections of Smart City Planning in Gwangmyeong was 371, which was higher than 307 in Chuncheon, and the average degree of connection per keyword and network density were also higher in the Gwangmyeong Smart City Planning than the Chuncheon Smart City Planning. The results of the study showed that the Smart City Planning actually had a high connectivity with the Smart City Comprehensive Plan, and the keywords with high connection centrality were different for each local government. The result of this study can be used as a basis for judging whether there is a high correlation between plans.

• Journal of Energy Engineering
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• v.28 no.2
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• pp.1-17
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• 2019

This article analyzes causal relationships among gross domestic product(GDP), electricity consumption, carbon dioxide($CO_2$) emission and foreign direct investments(FDI) inflow of Korea over the period from 1976 to 2014, using unit root test, cointegration test, and vector error correction model(VECM). As the results, this article found (1) a long-run bi-directional causality between GDP and electricity consumption, which may imply a negative impact of electricity consumption-saving policy on economic growth, (2) uni-directional short- and long-run causalities running from $CO_2$ emission to GDP, and a uni-directional long-run causality running from $CO_2$ emission to electricity consumption, which can result in a negative impact of $CO_2$ emission reduction policy on economic growth and electricity consumption, (3) a uni-directional long-run causality running from FDI to GDP, and uni-directional short- and long-run causalities running from FDI to electricity consumption, which may result from relatively lower electricity prices than investing countries, (4) no causality between FDI and $CO_2$ emission, which is based on the characteristics of FDI composed of service industries. Considering the above causal relationships among the four variables, the policy implication needs to focus on the electricity demand management based on the relevant R&Ds, and on the gradual transition from fossil fuel- to renewable-energy. Adaptive policy to increase the FDI inflow is also needed.