• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.398-408
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• 2021

Since COVID-19, the world's food shortage population has more than doubled from 130 million to 270 million. In addition, as various issues related to the food industry such as climate change arise, the importance of agriculture and livestock is increasing. In particular, it is still difficult to utilize data generated in these field. Therefore, the objective of this study was to explain the limitations of using data based on fragmentary analysis and the necessity of Digital Twin. The additional objective was to propose an architecture and necessary technologies of a Digital Twin platform suitable for agricultural and livestock. It also proposed a Digital Twin-based service that could be used in the near future, such as labor reduction, productivity improvement, personalized consumption, transportation, and distribution by incorporating intelligent information convergence technology into facility horticulture and livestock farming.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.671-677
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• 2021

The purpose of this study is to investigate the effect of leader-member exchange (LMX) on envy and counterproductive behaviors (CWB) moderated by similarity. Specifically, we focused on the negative side of LMX to examine the relationship between LMX and envy, and the mediating role of envy on the relationship between LMX and CWB. Further, we also examined the moderating role of similarity on the relationship between envy and CWB. Given that CWB can be harmful to any organizational, it was worthwhile to find possible antecedents of CWB, envy and LMX. A total number of 238 employees participated in this study and the results supported our hypotheses. The results of this study can have managerial implications, showing the important role of manager's personalized treatment for each of his/her subordinates.

• Journal of the Korea Convergence Society
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• v.9 no.12
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• pp.39-45
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• 2018

Twitter users use 'Following', 'Retweet' and so on to find tweets that they are interested in. However, it is difficult for users to find tweets that are of interest to them on Twitter, which has more than 300 million users. In this paper, we developed a customized tweet recommendation system to resolve it. First, we gather current trends to collect tweets that are worth recommending to users and popular tweets that talk about trends. Later, to analyze users and recommend customized tweets, the users' tweets and the collected tweets are categorized. Finally, using Web service, we recommend tweets that match with user categorization and users whose interests match. Consequentially, we recommended 67.2% of proper tweet.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.730-737
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• 2018

With the increasing number of health consumers aiming for a happy quality of life, the O2O medical marketing market is activated by choosing reliable health care facilities and receiving high quality medical services based on the medical information distributed on web's blog. Because unstructured text data used on the Internet, mobile, and social networks directly or indirectly reflects authors' interests, preferences, and expectations in addition to their expertise, it is difficult to guarantee credibility of medical information. In this study, we propose a blog reading system that provides users with a higher quality medical information service by classifying medical information blogs (medical blog, ad blog) using bigdata and MLP processing. We collect and analyze many domestic medical information blogs on the Internet based on the proposed big data and machine learning technology, and develop a personalized health information recommendation system for each disease. It is expected that the user will be able to maintain his / her health condition by continuously checking his / her health problems and taking the most appropriate measures.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.21-31
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• 2018

In Hyper-Connected Society, IoT-based Lifelog data is used throughout the Internet and is an important component of customized services that reflect user requirements. Also, Users are using social network services to easily express their interests and feelings, and various life log data are being accumulated. In this paper, Intelligent characters using IoT based lifelog data have been developed and qualitative/quantitative data are collected and analyzed in order to systematically grasp emotions of users. For this, qualitative data through the social network service used by the user and quantitative data through the wearable device are collected. The collected data is verified for reliability by comparison with the persona through esnography. In the future, more intelligent characters will be developed to collect more user life log data to ensure data reliability and reduce errors in the analysis process to provide personalized services.

• Journal of Korea Game Society
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• v.19 no.1
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• pp.37-46
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• 2019

As the development of IT technology and Web services have changed recently, education environment combined with high-tech digital content is becoming a new trend with the demands of educational consumers. In Korea, however, there is no systematic discussion on the issue, and there is not enough evidence to verify the effectiveness of the research. Therefore, we will systematically analyze the educational effects of Digital Storytelling to create an educational environment that meets the new horizon of intelligent web and personalized service provision in the Web3.0 era, In this study, 12 domestic experimental studies that verify the educational effects of digital storytelling were systematically analyzed, compared with overseas cases, and proposed development plans in the Web3.0 era.

• Land and Housing Review
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• v.13 no.4
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• pp.91-103
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• 2022

Home has significant meaning in the real world. In contrast, there's a lack of interest in homes in the Metaverse compared to other architectural spaces. This study aims to establish the concept of a home in the Metaverse based on meaning, typology, and characteristics of real-world homes. For this purpose, previous research and existing models of real- world homes were analyzed and case studies and a survey were conducted of homes in the Metaverse. As a result of the research, a model of home in the Metaverse is proposed based on physical, social, and personal models. Modifying one's dwelling, a refuge from the outside world, and self-expression/personalized space was also identified as the most significant characteristic of a home in the Metaverse. The results of this study will be helpful in future Metaverse virtual world research and development of Metaverse service platforms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.537-542
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• 2021

Smart TVs provide a variety of services and information compared to existing TVs based on the Internet. In order to provide more personalized services or information, it is necessary to analyze users' viewing patterns and provide customized services or information based on them. The proposed system receives the user's TV viewing pattern, analyzes it, and recommends a TV program or movie as customized information to the user. For this, the system was constructed with a preprocessor and a deep learning model. The preprocessor refines the name of the TV program watched by the user, the date the TV program was watched, and the watched time. Then, the multi-attribute LSTM model trains the refined data and performs prediction.The proposed system is a system that provides customized information to users, and is believed to be a leading technology in digital convergence that combines existing IoT technology and deep learning technology.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.579-586
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• 2021

This study examines the application of big data and tasks of tourism based on the case of Jeju Special Self-Governing Province, which used big data for regional tourism policy. Through the use of big data, it is possible to understand rapidly changing tourism trends and trends in the tourism industry in a timely and detailed manner. and also could be used to elaborate existing tourism statistics. In addition, beyond the level of big data analysis to understand tourism phenomena, its scope has expanded to provide a platform for providing real-time customized services. This was made possible by the cooperative governance of industry, government, and academia for data building, analysis, infrastructure, and utilization. As a task, the limitation of budget dependence and institutional problems such as the infrastructure for building personal-level data for personalized services, which are the ultimate goal of smart tourism, and the Personal Information Protection Act remain. In addition, expertise and technical limitations for data analysis and data linkage remain.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.117-125
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• 2022

Recently, as users' interest for travel increases, research on a travel route recommendation service that replaces the cumbersome task of planning a travel itinerary with automatic scheduling has been actively conducted. The most important and common goal of the itinerary recommendations is to provide the shortest route including popular tour spots near the travel destination. A number of existing studies focused on providing personalized travel schedules, where there was a problem that a survey was required when there were no travel route histories or SNS reviews of users. In addition, implementation issues that need to be considered when calculating the shortest path were not clearly pointed out. Regarding this, this paper presents a quantified method to find out popular tourist destinations using social big data, and discusses problems that may occur when applying the shortest path algorithm and a heuristic algorithm to solve it. To verify the proposed method, 63,000 places information was collected from the Gyeongnam province and big data analysis was performed for the places, and it was confirmed through experiments that the proposed heuristic scheduling algorithm can provide a timely response over the real data.