• Journal of Appropriate Technology
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• v.6 no.1
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• pp.56-64
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• 2020

Recently, major manufacturers are focusing their efforts on securing global competitiveness through smart factory, but developing countries have many difficulties in applying smart factory due to financial and technical conditions. This study is a preliminary study on the development of an ICT-based power monitoring system applicable to developing countries. The questionnaire surveyed and analyzed workers' perceptions of smart factory in a garment manufacturing factory in developing countries, Indonesia. Before and after the installation of the power monitoring system, the survey was conducted for 126 local managers and workers, and the correlation was analyzed using SPSS. As a result of analysis, factory workers in developing countries such as Indonesia are also positively aware of the necessity of introducing smart factory technology, and it is expected that the introduction of these technologies will affect job satisfaction and improve the factory environment. In addition, the result of the survey conducted after the installation of the power monitoring system increased the job satisfaction score by 5.5% compared to before the installation, and the scores on the perception of the necessity of the power monitoring system and the positive effect of the application of the system on the factory environment were increased 13% and 5.9%, respectively. It was also confirmed that managers rather than workers and female rather than male showed positive perception for the introduction of smart factory technology. The result of this study is expected to be an important reference in the direction of development of appropriate smart factory technology applicable to developing countries and the introduction of smart factory by manufacturers operating factories in developing countries.

• Journal of the Korean Society of Safety
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• v.37 no.5
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• pp.22-32
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• 2022

The domestic smart factory is being built and spread rapidly, mainly by mid-sized companies and large enterprises according to the government's active introduction and support policy. But these factories only promote production system and efficiency, so harmfulness and risk factors are not considered. Therefore, to derive harmful risk factors in terms of industrial safety for 12,983 government-supported smart factory workplaces from 2014 to 2019, industrial accident status analysis compared workplaces with automation facilities and government-supported workplaces with automation facilities. Also, to reduce risks associated with domestic smart factory processes, twenty government-supported workplaces with automation facilities underwent analysis, evaluating risks through a status survey using the process evaluation table. In addition, the status survey considered region, size, industry, construction level, and accident rate; the difference in risk according to the structure of the process was confirmed. Based on the smart factory process evaluation results, statistical analysis confirmed that serial, parallel, and hybrid structures pose different risk levels and that the risks of mixed structures are greater. Finally, safety control system application was presented for risk assessment and reduction in the smart factory process, reflecting the results of disaster analysis and actual condition investigation.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.203-208
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• 2024

The convergence of deep learning and smart factory is drawing a lot of attentions from not only industrial but also academic circles. The objective of this article is to quantitatively review on deep learning and smart factory from 2010 to 2023. This research analyzed the 138 articles, extracted from the Core Collection of Web of Science, in terms of four dimensions such as the main trend in article publications, the main trend in article citations, the distribution of article publications by research area, and the keywords representing the main contents of published articles. The quantitative review results reveal the following four points: First, the article publications drastically grew from 2019 to 2022 in its annual trend. Second, the article citations have rapidly grown since 2018. Third, Engineering, Computer Science, and Telecommunications are the top 3 research areas composing the 138 articles. Fourth, it is the top 10 keywords such as 'deep', 'learning', 'smart', 'detection', factory', 'data', 'system', 'manufacturing', 'neural', and 'network' that represent the main contents of the 138 articles published from 2010 to 2023 in deep learning and smart factory. These findings revealed by this quantitative review will be significantly useful for deepening and widening relevant future research on deep learning and smart factory.

• 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.