• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.1
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• pp.118-130
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• 2013

With the advent of a new services environment based on high-speed mobile networks and high-performance mobile devices, users in real life require content-centric services that provide personalized information conveniently and efficiently. These services are defined as ambient services. To implement and support sustainable ambient services, there is a critical need to conduct research regarding practicable models and methodologies. This paper proposes an effective model for ambient services based on the personalization of real-life space. The model consists of Public Info-space, Universal Info-space and Private Info-space. We also show a methodology for implementing the model with currently available techniques in order to prove that the model and methodology constitute an applicable solution to developing true ambient services. Finally, a kind of role-playing game which is built on a real university campus is presented to show the model to be available, where the test bed infrastructure consists of wireless mesh networks and real-time location systems (RTLSes).

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.181-190
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• 2005

Developing wireless internet service and mobile devices, mechanisms for web service across are various. However, the existing web infrastructure and content were designed for desktop computers and arc not well-suited for other types of accesses, e.g. PDA or mobile Phone that have less processing power and memory, small screens, limited input facilities, or network bandwidth etc. Thus, there is a growing need for transcoding techniques that provide that ability to browse the web through mobile devices. However, previous researches on existing web contents transcoding are service provider centric, which does not accurately reflect the user's continuously changing interest. In this paper, we presents a transcoding technique involved in making existing news contents based on XML available via customized wireless service, mobile phone.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.47-64
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• 2021

The global spread of COVID-19 around the world has not only affected many parts of our daily life but also has a huge impact on many areas, including the economy and society. As the number of confirmed cases and deaths increases, medical staff and the public are said to be experiencing psychological problems such as anxiety, depression, and stress. The collective tragedy that accompanies the epidemic raises fear and anxiety, which is known to cause enormous disruptions to the behavior and psychological well-being of many. Long-term negative emotions can reduce people's immunity and destroy their physical balance, so it is essential to understand the psychological state of COVID-19. This study suggests a method of monitoring medial news reflecting current days which requires striving not only for physical but also for psychological quarantine in the prolonged COVID-19 situation. Moreover, it is presented how an easier method of analyzing social media networks applies to those cases. The aim of this study is to assist health policymakers in fast and complex decision-making processes. News plays a major role in setting the policy agenda. Among various major media, news headlines are considered important in the field of communication science as a summary of the core content that the media wants to convey to the audiences who read it. News data used in this study was easily collected using "Bigkinds" that is created by integrating big data technology. With the collected news data, keywords were classified through text mining, and the relationship between words was visualized through semantic network analysis between keywords. Using the KrKwic program, a Korean semantic network analysis tool, text mining was performed and the frequency of words was calculated to easily identify keywords. The frequency of words appearing in keywords of articles related to COVID-19 emotions was checked and visualized in word cloud 'China', 'anxiety', 'situation', 'mind', 'social', and 'health' appeared high in relation to the emotions of COVID-19. In addition, UCINET, a specialized social network analysis program, was used to analyze connection centrality and cluster analysis, and a method of visualizing a graph using Net Draw was performed. As a result of analyzing the connection centrality between each data, it was found that the most central keywords in the keyword-centric network were 'psychology', 'COVID-19', 'blue', and 'anxiety'. The network of frequency of co-occurrence among the keywords appearing in the headlines of the news was visualized as a graph. The thickness of the line on the graph is proportional to the frequency of co-occurrence, and if the frequency of two words appearing at the same time is high, it is indicated by a thick line. It can be seen that the 'COVID-blue' pair is displayed in the boldest, and the 'COVID-emotion' and 'COVID-anxiety' pairs are displayed with a relatively thick line. 'Blue' related to COVID-19 is a word that means depression, and it was confirmed that COVID-19 and depression are keywords that should be of interest now. The research methodology used in this study has the convenience of being able to quickly measure social phenomena and changes while reducing costs. In this study, by analyzing news headlines, we were able to identify people's feelings and perceptions on issues related to COVID-19 depression, and identify the main agendas to be analyzed by deriving important keywords. By presenting and visualizing the subject and important keywords related to the COVID-19 emotion at a time, medical policy managers will be able to be provided a variety of perspectives when identifying and researching the regarding phenomenon. It is expected that it can help to use it as basic data for support, treatment and service development for psychological quarantine issues related to COVID-19.