• Smart Media Journal
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• v.12 no.8
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• pp.60-68
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• 2023

After COVID-19, hotel industry is rapidly experiencing changes in the business environment, and under the influence of the Fourth Industrial Revolution, hotel industry is striving to secure competitive advantages through differentiation, including the use of big data and the IoT in service provision, as well as the introduction of artificial intelligence(AI) robot services. This study analyzed the perceived value of AI robot services and their impact on usage attitudes and behavioral intentions of customers who have used hotels that have introduced AI robot services. The results of the study showed that the value of robot services perceived by customers who have used robot services in hotels is categorized into three dimensions: social, experiential, and functional, and all of them have a positive effect on usage attitudes, with social, functional, and experiential values having a positive effect on usage attitudes in that order. Attitude toward use was also analyzed to have a positive effect on behavioral intention, which is consistent with the value-attitude-behavior model. Therefore, it is necessary for hotels to improve the satisfaction of hotel guests through diversified services of AI robot service.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.151-160
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• 2016

The purpose of this research is to investigate Korean popular attitudes and social perceptions of 'sharing economy' terminology at the current moment from a creative or socio-economic point of view. In Korea, this study discovers and interprets the objective and tangible annual changes and patterns of sociocultural collective intelligence that have taken place over the last five years by applying text mining in the big data analysis approach. By crawling and Googling, this study collected a significant amount of time series web meta-data with regard to the theme of the sharing economy on the world wide web from 2010 to 2014. Consequently, huge amounts of raw data concerning sharing economy are processed into the value-added meaningful 'word clouding' form of graphs or figures by using the function of word clouding with R programming. Till now, the lack of accumulated data or collective intelligence about sharing economy notwithstanding, it is worth nothing that this study carried out preliminary research on conducting a time-series big data analysis from the perspective of knowledge management and processing. Thus, the results of this study can be utilized as fundamental data to help understand the academic and industrial aspects of future sharing economy-related markets or consumer behavior.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.468-481
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• 2018

This study aims to empirically investigate how users' online engagement behaviors predict their uses of paid contents. To this end, the data from the 2016 Korean Media Panel Survey, which has been conducted annually by the Korea Information Society Development Institute(KISDI), were analyzed. Major findings(N=8.313) were as follows. First, the active type of online engagement(e.g., posting, commenting), which contributes to direct creation of online contents, was the most powerful predictor to explain the DV. On the other hand, relatively passive actions of user engagement(e.g., sharing, endorsing, voting) turned out to have no significant effects on the uses of paid contents, just as personality traits and online privacy concerns did. Based on these results, it is recommended that online contents or platform service providers should try to establish clearly-targeted marketing strategies, after thoroughly collecting and analyzing the data of users' various online behaviors.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.481-481
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• 2017

최근 기후변화로 인해 국내 기상특성이 변화하고 그에 따라 홍수, 가뭄(건천화) 및 폭염 등 물 관련 재해 발생 빈도가 증가하고 규모 또한 점점 커지고 있다. 또한, 세계적으로 태풍 및 가뭄발생 빈도도 꾸준히 증가하고 있어 정확한 예측 및 즉각적 대처능력 확보를 위한 방안이 필요한 실정이다. 아울러 급격한 도시화로 인한 빈번한 내수범람 및 유역차원의 홍수범람 등으로 인해 재난발생 시 그 피해가 극대화로 직결되고 있어 재난 시 발생할 수 있는 피해 현황을 정확하게 예 경보하기 위한 실시간 수재해 정보 서비스 및 모니터링이 가능한 통합 관리 기술이 필요하다. 이를 위해 현실에서 실시간으로 수재해 관련 부가정보를 영상으로 표출하고, 이를 종합적으로 분석하여 서비스할 수 있는 증강현실 모니터링 시스템을 개발 하고자 한다. 이를 통해 홍수, 가뭄(건천화) 및 태풍 등 물 관련 재해 현황을 실시간으로 감시하고 예측하여 대처 할 수 있는 정보 생산과 서비스 및 모니터링 등의 통합 관리가 가능할 것으로 판단된다. 이에 본 연구에서는 사용자가 손쉽게 소통할 수 있는 수재해 정보 서비스 구현을 위한 맞춤형 기술을 개발하고자 빅데이터 기반의 수재해 정보 증강현실(AR) 적용성 연구를 수행하였다. 이를 위해 재해 발생 시 빠르게 대처하기 위한 시스템을 구축하고자 관리자 및 사용자를 고려한 GUI 설계 및 수재해 정보의 Global 위성지도 기반 3D 시각화 적용을 위한 방안을 제시하고자 한다. 향후 스마트폰 기능을 적극적으로 활용하여 재해 대처 방안 및 행동 요령을 효과적으로 전달함으로써 재난 피해를 줄일 수 있는 애플리케이션(App) 개발을 진행할 예정이다. 개발된 증강현실 모니터링 시스템은 수재해 정보 서비스를 향상시키고 효율적인 예방 및 대처를 실현함으로써 국가 물 관련 재해를 혁신할 수 있는 기술을 확보하는 소중한 토대가 될 것으로 사료된다.

• Journal of Advanced Navigation Technology
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• v.23 no.4
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• pp.296-301
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• 2019

Artificial intelligence is closely linked to our real lives, leading innovation in various fields. Especially, as a means of transportation possessing artificial intelligence, autonomous unmanned vehicles are actively researched and are expected to be put into practical use soon. Autonomous cars and autonomous unmanned aerial vehicles are required to equip accurate navigation system so that they can find out their present position and move to their destination. At present, the navigation of transportation that we operate is mostly dependent on GPS. However, GPS is vulnerable to external intereference. In fact, since 2010, North Korea has jammed GPS several times, causing serious disruptions to mobile communications and aircraft operations. Therefore, in order to ensure safety in the operation of the autonomous unmanned vehicles and to prevent serious accidents caused by the intereference, rapid situation judgment and countermeasure are required. In this paper, based on big data and machine learning technology, we propose a countermeasure system for GPS interference that supports decision making by applying John Boyd's OODA loop cycle (detection - direction setting - determination - action).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.1-7
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• 2021

Investor mood from social media is gaining increasing attention for leading a price movement in stock market. Based on the behavioral finance theory, this study argues that sentiment extracted from social media using big data technique can predict a real-time (short-run) price momentum in Chinese stock market. Collecting Sina Weibo posts that related to COVID-19 using keyword method, a daily influential weighted sentiment factors is extracted from the sizable raw data of over 2 millions of posts. We examine one supervised and 4 unsupervised sentiment analysis model, and use the best performed word-frequency and BiLSTM mdoel. The test result shows a similar movement between stock price change and sentiment factor. It indicates that public mood extracted from social media can in some extent represent the investors' sentiment and make a difference in stock market fluctuation when people are concentrating on a special events that can cause effect on the stock market.

• The Journal of Bigdata
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• v.7 no.2
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• pp.235-244
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• 2022

The rental history data of public bicycles in Seoul were analyzed to examine how pandemic phenomena such as COVID-19 caused changes in people's micro mobility. Data for 2019 and 2021 were compared and analyzed by dividing them before and after COVID-19. Data were collected from public data portal sites, and data marts were created for in-depth analysis. In order to compare the changes in the two periods, the riding direction type dimension and the rental station type dimension were added, and the derived variables (rotation rate per unit, riding speed) were newly created. There is no significant difference in the average rental time before and after COVID-19, but the average rental distance and average usage speed decreased. Even in the mobility of Ttareungi, you can see the slow rhythm of daily life. On weekdays, the usage rate was the highest during commuting hours even before COVID-19, but it increased rapidly after COVID-19. It can be interpreted that people who are concerned about infection prefer Ttareungi to village buses as a means of micro-mobility. The results of data mart-based visualization and analysis proposed in this study will be able to provide insight into public bicycle operation and policy development. In future studies, it is necessary to combine SNS data such as Twitter and Instagram with public bicycle rental history data. It is expected that the value of related research can be improved by examining the behavior of bike users in various places.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.367-372
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• 2024

Although developmental disabilities account for a relatively low number of the total number of disabilities, they are generally classified as severe disabilities considering the degree of disability. If these developmental disorders are discovered early, adaptability and early treatment efficiency can be improved, but most parents do not detect any signs from their children or miss the right time for treatment. In this paper, we conducted development of the developmental disorder diagnosis algorithm that can recognize hand-flapping, one of the early unusual behaviors of developmental disorders, for parents and early childhood care workers who cannot recognize signs of early developmental disorders based on specific behavioral characteristics as a pilot study. It was confirmed that the recognition area and fingers were accurately recognized, and the number of hand flapping was accurately counted. It is expected that research on algorithms that can diagnose various behavioral patterns will continue to be conducted and expanded all through algorithms advancement and expansion of functional performance using big data.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.263-264
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• 2014

공간에 대한 정보는 시간과 함께 사람이 생활을 하는데 있어 반드시 알아야 하는 가장 근본적인 정보이며, 공간정보는 우리가 일상생활이나 특정한 상황에 처해 있을 때 행동이나 태도를 결정하는 중요한 기초정보와 기준을 제시한다. 미래의 공간정보사회에서는 지능 사물이 공간정보를 이용하고 인간은 간접적으로 활용하는 서비스로 진화한다. 미래의 공간정보는 상황정보, 실시간성, 다양성(풍부성), 정밀성을 바탕으로 빅데이터와 클라우드 기술이 융합되어 사용자 중심의 맞춤형 서비스가 제공될 것이다.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2016.11a
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• pp.435-438
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• 2016

재난에 대비하여 각 기관에서는 자체의 위기관리 매뉴얼을 보유하고 있고, 이를 기반으로 재난대응 훈련 등을 실시하고 있다. 그러나 이러한 매뉴얼은 개념적이어서 재난 발생 시 현장에서 즉각적으로 수행하기에는 구체성이 부족하다. 본 연구는 이러한 관점에서 위기관리 매뉴얼을 SOP 기반의 시스템으로 변환하여 운영할 수 있는 플랫폼 기술을 개발하는데 있다. 플랫폼은 단위 행동 모임인 SOP 관리 모듈, 위기관리 매뉴얼 기반 재난 대응 절차를 수행을 위한 시나리오 관리 모듈, 그리고 기관의 담당자가 직접 해당 SOP를 수행하기 위한 정보인 조직도 관리 모듈로 구성되어 있으며, 실제 재난 대응을 통하여 수행한 활동에 대한 정보를 기록하여 빅 데이터를 분석하여 보완할 수 있는 기반을 마련하였다.