• Smart Media Journal
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• v.8 no.3
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• pp.95-103
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• 2019

The global manufacturing industry has reached the limit to growth due to a long-term recession, the rise of labor cost and raw material. As a solution to these difficulties, we promote the 4th Industry Revolution based on ICT and sensor technology. Following this trend, this paper proposes the design of a model using manufacturing data in the protection film process for smart manufacturing innovation. In the protective film process, the manufacturing data of temperature, pressure, humidity, and motion and thermal image are acquired by various sensors for the raw material blending, stirring, extrusion, and inspection processes. While the acquired manufacturing data is stored in mass storage, A.I. platform provides time-series image analysis and its visualization.

• The Research Journal of the Costume Culture
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• v.27 no.5
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• pp.449-460
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• 2019

Today, the fashion business environment of the 4.0 generation is changing based on fashion technology combined with advanced digital technologies such as AI (Artificial Intelligence), big data and IoT (Internet of Things). "Digital Transformation" means a fundamental change and innovation in a digital paradigm including corporate strategy, organization, communication, and business model, based on the utilization of digital technology. Thus, this study examines digital transformation strategies through the fashion brand Burberry. The study contents are as follows. First, it examines the theoretical concept of digital transformation and its utilization status. Second, it analyzes the characteristics of Burberry's digital transformation based on its strategies. For the research methodology, a literature review was performed on books and papers, aligning with case studies through websites, social media, and news articles. The result showed that first, Burberry has reset their main target to Millennials who actively use mobile and social media, and continues to communicate with them by utilizing digital strategy in the entire management. Second, Burberry is quickly delivering consistent brand identity to consumers by internally creating and providing social media-friendly content. Third, they have started real-time product sales and services by using IT to enhance access to brands and to lead consumers towards more active participation. In this study, Burberry's case shows that digital transformation can contribute to increased brand value and sales, keeping up with the changes in the digital paradigm. Therefore, the study suggests that digital transformation will serve as an important business strategy for fashion brands in the future.

• The Korean Journal of Archival Studies
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• no.54
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• pp.211-248
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• 2017

This study examined ways to improve records management by using the new technology of the Fourth Industrial Revolution. To do this, we selected four technologies that have a huge impact on the production and management of records such as cloud, big data, artificial intelligence, and the Internet of Things. We tested these technologies and summarized their concepts, characteristics, and applications. The characteristics of the changed production records were analyzed by each technology. Because of new technology, the production of records has rapidly increased and the types of records have become diverse. With this, there is also a need for solutions to explain the quality of data and the context of production. To effectively introduce each technology into records management, a roadmap should be designed by classifying which technology should be applied immediately and which should be applied later depending on the maturity of the technology. To cope with changes in the characteristics of production records, a flexible data structure must be produced in a standardized format. Public authorities should also be able to procure Software as a Service (SaaS) products and use digital technology to improve the quality of public services.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.149-162
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• 2021

This paper established a new smart factory based on manufacturing data for an introductory company focusing on the personalized cosmetics manufacturing industry. We build on an example of a system that collects, manages, and analyzes documents and data that were previously managed by CGMP-based analog for data-driven use. To this end, we have established a system that can collect all data in real time at the production site by introducing artificial intelligence smart factory platform LINK5 MOS and POP system, collecting PLC data, and introducing monitoring system and pin board. It also aims to create a new business cluster space based on this project.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.133-139
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• 2020

Big data, artificial intelligence (AI), and machine learning are keywords that represent the Fourth industrial Revolution. In addition, as the development of science and technology, the Korean government, public institutions and industries want professionals who can collect, analyze, utilize and predict data. This means that data analysis and utilization education become more important. Education on data analysis and utilization is increasing with trends in other academy. However, it is true that not many academy run long-term and systematic education. Korea Institute of Science and Technology Information (KISTI) is a data ecosystem hub and one of its performance missions has been providing data utilization and analysis education to meet the needs of industries, institutions and governments since 1966. In this study, KISTI's data education was analyzed using the number of curriculum trainees per year from 2001 to 2019. With this data, the change of interest in education in information and data field was analyzed by reflecting social and historical situations. And we identified the characteristics of KISTI and trainees. It means that the identity, characteristics, infrastructure, and resources of the institution have a greater impact on the trainees' interest of data-use education.In particular, KISTI, as a research institute, conducts research in various fields, including bio, weather, traffic, disaster and so on. And it has various research data in science and technology field. The purpose of this study can provide direction forthe establishment of new curriculum using data that can represent KISTI's strengths and identity. One of the conclusions of this paper would be KISTI's greatest advantages if it could be used in education to analyze and visualize many research data. Finally, through this study, it can expect that KISTI will be able to present a new direction for designing data curricula with quality education that can fulfill its role and responsibilities and highlight its strengths.

• Journal of Integrative Natural Science
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• v.13 no.4
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• pp.132-140
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• 2020

With the recent introduction of artificial intelligence (AI) technology, the use of data is rapidly increasing, and newly generated data is also rapidly increasing. In order to obtain the results to be analyzed based on these data, the first thing to do is to classify the data well. However, when classifying data, if only one classification technique belonging to the machine learning technique is applied to classify and analyze it, an error of overfitting can be accompanied. In order to reduce or minimize the problems caused by misclassification of the classification system such as overfitting, it is necessary to derive an optimal classification by comparing the results of each classification by applying several classification techniques. If you try to interpret the data with only one classification technique, you will have poor reasoning and poor predictions of results. This study seeks to find a method for optimally classifying data by looking at data from various perspectives and applying various classification techniques such as LDA and QDA, such as linear or nonlinear classification, as a process before data analysis in data analysis. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable and the correlation between the variables. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified to suit the purpose of analysis. This is a process that must be performed before reaching the result by analyzing the data, and it may be a method of optimal data classification.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.24-38
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• 2021

This study aims to measure Korea's global competitiveness in intelligent information technology, which is the core technology of the 4th industrial revolution. For analysis, we collect patents of each field and prior patents cited by them, which are applied at the U.S. Patent Office (USPTO) between 2010 and 2018 from PATSTAT Online. A global knowledge transfer network was established by grouping citing- and cited-relationships at a national level. The in-degree centrality is used to evaluate technology acceptance, which indicates the process of absorbing existing technological knowledge to create new knowledge in each field. Second, to evaluate the impact of existing technological knowledge on the creation of new one, the out-degree centrality is investigated. Third, we apply the PageRank algorithm to qualitatively and quantitatively investigate the importance of the relationships between countries. As a result, it is confirmed through all the indicators that the AI sector is currently the least competitive.

• Journal of Internet of Things and Convergence
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• v.7 no.2
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• pp.41-45
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• 2021

This paper proposes a video content delivery scheme for vehicles. Today, we spend a lot of time commuting to work in vehicles such as trains and cars. In addition, the number of users who enjoy video content such as YouTube and Netflix in order to appease the boredom in the vehicle is increasing rapidly. Video content requires a larger amount of data usage than text-based content. Hence, the user's mobile communication data usage increases rapidly along with the cost. The proposed video content delivery scheme downloads a lot of video content in advance when the vehicle is in a free Wi-Fi area. In this way, it is possible to play video content in a vehicle at a low cost. It is expected that the proposed scheme can be applied to the Internet of Things(IoT) for moving objects.

• Convergence Security Journal
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• v.20 no.4
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• pp.113-123
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• 2020

The fourth industrial revolution can be called the hyper-connected-based intelligent revolution triggered by advanced information technology and intelligent technology, and the basis for implementing these technologies is 'data'. This study proposes a way to strategically use data in order to lead this intelligent revolution in the defense area. First of all, implications through analysis of domestic and international trends and prior research and current status of defense data management were analyzed, and four directions for development were presented. If the government composes conditions for building, releasing, sharing, distribution, and convergence of defense data considering the environment of national defense in the future, it is expected that it will serve as a foundation and a shortcut to be a digitalized strong military through smart defense innovation in the era of the fourth industrial revolution.