• Journal of Practical Engineering Education
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• v.13 no.3
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• pp.483-490
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• 2021

In this paper, we proposed the development of a smart factory intergrated technology education platform using smart factory based CPPS (Cyber Physical Production System) and VR (Vitrual Reality) technology and educational methods using the platform. A platform has been developed to learn how to integrate 3D digital twin and BOP (Bill of Process)-based manufacturing processes. In addition, Digital Twin established a smart factory-based integrated education platform by linking mechanical systems, digital twins, and virtual reality through the OPC-UA server. Based on this platform, the smart factory integration platform is proposed to have individual elements of the smart factory integration platform through BOP-based digital twin simulation, OPC-UA integration, MES system, SCADA system, and VR interworking.

• Journal of Information Technology Applications and Management
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• v.27 no.1
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• pp.75-95
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• 2020

Smart Factory is the decisive factor of the Fourth Industrial Revolution and is a key field for national competitiveness. Until now, most smart factory research has focused on policy and technology. In order to spread more technology, it is necessary to study what factors influence the adoption of smart factory technology in the enterprise. Nevertheless, little research has been done. In this study, based on the UTAUT (Unified Theory of Acceptance and Use of Technology), which has been proved through many years of research, I have studied the factors that influence the acceptance of smart factory technology. As a result of research, performance expectancy, social influence, and facilitating conditions of UTAUT model had a positive(+) effect on behavior intention. Their relationship of influence was in the order of performance expectancy (β = .459)> facilitating conditions (β = .212)> social influence (β = .210). However, it was found that the effort expectancy did not affect the behavior intention, and the impact of the newly perceived risk on the behavior intention to use was not confirmed. The main reason is that the acceptance of smart factory technology is not a matter of personal interest but a matter of organizational choice. Trust, on the other hand, was found to be partially mediated between performance expectancy, facilitating conditions, social influence and behavior intention. For many years, many researchers have validated the UTAUT, which has been validated through various empirical studies. It is academically meaningful to begin the study of factors affecting the acceptance of smart factory technology in terms of the UTAUT. In practice, it is necessary to provide SME employees with more information related to the introduction of smart factories, to provide advanced services related to the establishment of smart factories, and to establish a standardized model for each industry.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.601-612
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• 2019

As the $4^{th}$ industrial revolution is accelerating, IT convergence application technologies are attracting attention in various fields. In the manufacturing industry, Smart Factory technology, which is blended with IT technology, has been developed to solve the problem casued by the decrease of the labor force, and a monitoring server is required to remotely control the equipment or to inquire about the operation status of the factory. In this paper, we designed and implemented RESTful API for data sharing between factory equipment and monitoring server in Smart Factory. In order to verify the designed API, a testbed was operated for an actual plastics manufacturing plant. As a result, it was confirmed that the testbed can be operated normally in actual operating environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1323-1333
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• 2016

According to spreading of smart devices and development of communication technology, the security issues come to the fore in the modern factory. Especially, the smart facpry should be considered the risk management plan how to identify and evaluate, control the risks. In this paper, we suggest the minimum security requirements applying SFRPN(Smart Factory Risk Priority Number) model to domestic smart factory on the basis of the results inspecting factories.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.783-788
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• 2021

Smart manufacturing is defined as the fully ICT-based manufacturing process which digitized, optimized, and automized the of manufacturing system in smart factory which includes product planning, design, production, quality, stock, procure. In this paper, we introduce the development of domestic standardization of smart factory and manufacturing data which are generated in operation of smart factory. We focus on general standardization of smart factory/ICT-based manufacturing system and data transactions related issues since the range of standardization is too wide. Based on these standardization review, we discuss the several concerns for utilization of manufacturing data.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.269-279
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• 2024

The study explores the evolving landscape of overseas expansion strategies by Korean corporations, focusing on recent geopolitical tensions, the COVID-19 pandemic, and disruptions in global supply chains. It emphasizes the challenges faced by industries producing high-value products and delves into the concept of "Friend-Shoring" policies in the United States, leading major Korean companies to invest in local semiconductor, battery, and automotive factories. Recognizing the potential fragmentation of Korea's manufacturing sector, the paper introduces the "Mother Factory" strategy as a policy initiative, inspired by Japan's model, to establish core production facilities domestically. The discussion unfolds by examining the cases of major companies in Japan and the United States, highlighting the need for Korea to adopt a mother factory strategy to mitigate risks associated with friend-shoring policies. Inspired by Intel's "Copy Exactly" approach, the paper proposes a Korean mother factory model integrating smart factory technology and digital twin systems. This strategic shift aims to enhance responsiveness to geopolitical challenges and fortify the competitiveness of Korean high-tech industries. Finally, the paper proposes a Korean Mother Factory based on smart factory concepts. The suggested model integrates smart factory technology and digital twin frameworks to enhance responsiveness and fortify competitiveness. In conclusion, the paper advocates for the adoption of a comprehensive Korean Mother Factory model to address contemporary challenges, foster advanced manufacturing, and ensure the sustainability and competitiveness of Korean high-tech industries in the global landscape. The proposed strategy aligns with the evolving dynamics of the manufacturing sector and emphasizes technological advancements, collaboration, and strategic realignment.

• Journal of Institute of Convergence Technology
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• v.7 no.2
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• pp.21-23
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• 2017

Smart Factories could be regarded as a result of the integration of various key technologies of the fourth industrial revolutions. In smart factory, the IoT (Internet of things) is applied to capture the data generated by the production facility, store and analyze data generated in real time using Big Data technology. In addition, 3D printers are used to print expensive and complex parts, industrial robots supply materials and parts to the production site, store finished products in warehouses. In this paper, we introduced the definition of smart factory and change of job market. Also, we summarize several national policies to support enhancing transformation process of smart factory.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.206-211
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• 2022

Recently, the world is facing many difficulties in all areas as a result of price instability and the Russian-Ukraine war. Personally, as many jobs disappear, household income is declining sharply. In particular, the possibility of such bankruptcies is relatively increasing in small and medium-sized enterprises (SMEs), and it is highly likely that this will continue for the time being. In this situation, countries around the world are continuously making efforts to improve corporate productivity. In Korea, the smart factory support project implemented by the Smart Manufacturing Innovation Promotion Team has been continuously promoted for the past several years, providing opportunities for SMEs in need to improve productivity and change the corporate structure. In this study, the current status of smart factory promotion at home and abroad was examined, and problems and improvement measures were studied for the insufficient efficiency in the smart factory promotion system implemented in Korea.

• Journal of the Korean Society of Systems Engineering
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• v.13 no.2
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• pp.57-64
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• 2017

A new paradigm based on distributed manufacturing services is emerging. This paradigm shift can be realized by smart functions and smart technologies such as Cyber Physical System (CPS), Artificial Intelligence (AI), and Cloud Computing. Most architectures define stack levels from Level 0 (equipment) to Level 4 (business area) and specify the services to be provided between them. Because of their a rough technical specification, there is a limitation on how to actually utilize a technology to actually implement a smart factory service with this architecture. In this paper, we propose a smart factory architecture that can be utilized directly from the perspective of a smart service system by making the use of System Engineering Process and System Modeling Language (SysML).

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.367-382
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• 2022

This study examined the impact of smart factory's quality factors on the intention to accept Advance Smart Factory(ASF) by mediating Perceived Value(PV) and Smart Factor Performance(SFP) of domestic SMEs that had introduced smart factor. As a result, First, Information Quality(IQ) and Service Quality(SQ) had a significant positive impact on Perceived Value(PV). Second, System Quality(SysQ), IQ, and SQ had a significant positive impact on SFP. Third, PV had a significant positive impact on intention to ASF, and PV and SFP had a significant positive impact on the intention to accept ASP. Also, PV had a mediating effect between SysQ, IS and Perfromance, and PV and Performance had a moderation between SQ and ASF. These results could provide one implication to SMEs that are taking into account to improve their smart factor to be advanced.