• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.705-712
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• 2022

The pandemic over the past three years has brought drastic changes in our lives, and those changes are now becoming a natural part of our daily lives. Daily life and economic activities based on online, such as video conferencing, remote classes, telecommuting, and online streaming services, have become daily routines after Corona. And the result of rapid development of communication and graphic technology is the metaverse. Therefore, the purpose of this study is to study the possibility from the perspective of public design for the correct use of the metaverse space of local governments. To this end, in this study, big data analysis was performed on 'local government', 'public design', and 'metaverse'. As a result of this study, we should actively use metaverse with high topic and potential as a space for local governments' promotion or discussion as a means to restore the reliability of local governments and overcome negative perceptions. In addition, it is necessary to actively reflect public design in order to increase the public reliability of local governments' metaverse.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.209-222
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• 2020

The purpose of this paper is to predict the future changes of housing through the Delphi technique. The targets to predict were set by housing type, housing space, housing demand, and architectural technology. The results were as follows: ① The influences of social and value perspectives on the change of housing type, space, and demand would be high, on the other hands, the influence of political perspective would be low. ② In terms of housing type, the increase in demand for downsizing housing for high-rise buildings and the possibility of realizing remote medical support services and homecare using big data are highly predicted. That is, ③ it is anticipated that IoTs will have a significant influences on future housing changes, and ④ enactment of co-housing and related laws by the sharing economy, services for maintenance through the supply of high-rise and high-density homes, housing support for residents, and advanced lease markets by developed architectural technology are expected as anticipated forms of future housing.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.31-37
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• 2021

Recently, in the market of home appliances, the technical differentiation of products using convergence technology has been receiving a lot of response to satisfy consumer demand. However, air-conditioner products are an area that requires research and development in the early stages of convergence technology. In this paper, it is developed that a non-contact bio-signal(respiration, movement) collection technology using IR-UWB(Impulse-Radio Ultra Wideband) technology, which controls the air-conditioner direction according to the user's location and also monitors sleep to provide an optimal sleep environment. In addition, emotional lighting and ASMR are developed to provide a comfortable and emotional place of life. Finally, based on the developed convergence technology, we develop intelligent smart air-conditioning services for the convenience of daily life and a comfortable resting space.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.310-318
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• 2018

Recently, the importance of big data analysis is increasing as a large amount of data is generated by various devices connected to the Internet with the advent of Internet of Things (IoT). Especially, it is necessary to analyze various large-scale IoT streaming sensor data generated in real time and provide various services through new meaningful prediction. This paper proposes a real-time indoor PM10 concentration prediction LSTM model based on streaming data generated from IoT sensor using AWS. We also construct a real-time indoor PM10 concentration prediction service based on the proposed model. Data used in the paper is streaming data collected from the PM10 IoT sensor for 24 hours. This time series data is converted into sequence data consisting of 30 consecutive values from time series data for use as input data of LSTM. The LSTM model is learned through a sliding window process of moving to the immediately adjacent dataset. In order to improve the performance of the model, incremental learning method is applied to the streaming data collected every 24 hours. The linear regression and recurrent neural networks (RNN) models are compared to evaluate the performance of LSTM model. Experimental results show that the proposed LSTM prediction model has 700% improvement over linear regression and 140% improvement over RNN model for its performance level.

• Land and Housing Review
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• v.13 no.4
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• pp.55-71
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• 2022

Recently, the domestic department store industry is growing into a complex shopping cultural space, which is advanced and differentiated by changes in consumption patterns. In addition, competition is intensifying across 70 places operated by five large companies. This study investigates the determinants of the visits to department stores using the big data concept's automatic vehicle access system (pos) and proposes how to strengthen the competitiveness of the department store industry. We use a negative binomial regression test to predict the frequency of visits to 67 branches, except for three branches whose annual sales were incomplete due to the new opening in 2021. The results show that the demand for visiting department stores is positively associated with airport, terminal, and train stations, land areas, parking lots, VIP lounge numbers, luxury store ratio, F&B store numbers, non-commercial areas, and hotels. We suggest four strategies to enhance the competitiveness of domestic department stores. First, department store consumers have a high preference for luxury brands. Therefore, department stores need to form their own overseas buyer teams to discover and attract new luxury brands and attract customers who have a high demand for luxury brands. In addition, to attract consumers with high purchasing power and loyalty, it is necessary to provide more differentiated products and services for VIP customers than before. Second, it is desirable to focus on transportation hub areas such as train stations, airports, and terminals in Gyeonggi and Incheon. Third, department stores should attract tenants who can satisfy customers, given that key tenants are an important component of advanced shopping centers for department stores. Finally, the department store, a top-end shopping center, should be developed as a space with differentiated shopping, culture, dining out, and leisure services, such as "The Hyundai", which opened in 2021, to ensure future growth potential.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.324-327
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• 2013

Recently, due to evolving cloud computing technology, we can easily and transparently utilize both local computing resource and remote computing resource in real life. Especially, enhancing smart device technologies and network infrastructures promote an increase of needs to share files between local smart devices and cloud storages. However, since smart devices have a limited storage space, storing files on cloud storage causes a starvation problem of local storage. It means that users can face a storage-lack problem even a cloud storage service provide a huge file storing space. In this research, we propose a method to manage files between smart devices and cloud storages. Our approach calculate file usage pattern based on recently used date, and then this approach determines local files being migrated. As a result, our approach is sufficient for handling data synchronization between big data storage farm and local thin client which contains limited storage space.

• Journal of Korean Society of Forest Science
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• v.108 no.3
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• pp.382-391
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• 2019

This study was conducted to verify whether smartphone hiking apps, which generate social network data including location information, are useful tools for analyzing the use characteristics of a forest recreation area. For this purpose, the study identified the functions and service characteristics of smartphone hiking apps. Also, the use characteristics of the area of Daegwallyoung were analyzed, compared with the results of the field survey, and the applicability of hiking apps was reviewed. As a result, the service types of hiking apps were analyzed in terms of three categories: "information offering," "hiking record," and "information sharing." This study focused on an app that is one of the "hiking record" types with the greatest number of users. Analysis of the data from hiking apps and a field survey in the Daegwallyoung area showed that both hiking apps and the field survey can be used to identify the movement patterns, but hiking apps based on a global positioning system (GPS) are more efficient and objective tools for understanding the use patterns in a forest recreation area, as well as for extracting user-generated photos. Second, although it is advantageous to analyze the patterns objectively through the walking-speed data generated, field surveys and observation are needed as complements for understanding the types of activities in each space. The hiking apps are based on cellphone use and are specific to "hiking" use, so user bias can limit the usefulness of the data. It is significant that this research shows the applicability of hiking apps for analyzing the use patterns of forest recreation areas through the location-based social network data of app users who record their hiking information voluntarily.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.3
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• pp.67-93
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• 2017

This study aims to investigate research trends on disaster safety Information based on the language network analysis methods. To accomplish it, we collected 312 Korean thesis and scholarly articles on disaster safety information published between 2008 and 2017 from RISS (Research Information Sharing Service) site. With the collected data, this study performed the statistical analysis based on bibliographic data. Also, this study performed the analysis of frequency and language network on keyword extracted from titles on the collected scholarly articles and thesis. This study found out that researches recently on Bigdata related to disaster safety information have been rapidly increased. Also, the needs of sharing and utilizing disaster safety information have increased. Also the various types of disaster safety information such as spatial data, real-time information, geographic information has been used for the disaster response.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.453-462
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• 2016

Since the Internet provides a way of expressing and sharing Internet users' mindsets, corporate marketers want to acquire measurable and actionable insights from web data. In the past, companies used to analyze the attitude, satisfaction, and loyalty of consumers toward their brands using survey data, whereas nowadays this is done using the big data extracted from Social Network Services. In this study, we propose a framework for clustering brand names using the social metrics gathered on social media. We also conduct a case study of the automobile industry to verify the feasibility of the proposed framework. We calculate the brand name distance for each pair of brand names based on the total number of times that they are mentioned together. These distances are used to project the brand name onto a 3-dimensional space using multidimensional scaling. After the projection, we found the clusters of brand names and identified the characteristics of each cluster. Furthermore, we concluded this paper with a discussion of the limitations and future directions of this research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.4
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• pp.43-48
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• 2017

Today our society is approaching new intelligence information society, which has been caused by the Fourth Industrial Revolution along with the development of information and communication technology(ICT). And this has just opened a new era of Internet of Things(IoT) that connects between human and objects and between objects through network, allowing transmission and reception of information beyond the limits of space. However, many crises occurred in the existing communication environment may threaten the security of Internet of Things, by violating the three components of information security. In this paper, this study aims to analyze security technology to achieve advanced security by dividing IoT security technology for coping with security vulnerability found in different components into three groups.