• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.81-87
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• 2020

This paper creates a standard node for acquiring sensor data from various industrial sensors (IoT/non-IoT) for the establishment of Smart Factory Digital Twin, and provides inter-compatible data by linking zones by group/process to secure data stability and to ensure the digital twin (Digital Twin) of Smart Factory. The process of the Zone Master platform contains interface specifications to define sensor objects and how sensor interactions between independent systems are performed and carries out individual policies for unique data exchange rules. The interface for execution control of the Zone Master Platform processor provides system management, declaration management for public-subscribe, object management for registering and communicating status information of sensor objects, ownership management for property ownership sharing, time management for data synchronization, and data distribution management for Route information on data exchange.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.3
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• pp.59-71
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• 2020

This study is empirically intended to look into the effects of smart factory technologies on quality and innovation performance in small and medium-sized Enterprises(SMEs). The research results are as follows. Device and application technologies for smart factory had a positive effect on the information quality and system quality, while platform technologies had an insignificant effect on the information quality and system quality, rejecting the effect of platform technologies for smart factory on information quality and system quality. Device technologies for smart factory had also a significant effect on innovative performance, while platform and application technologies had an insignificant effect on innovative performance, rejecting the effect of platform and application technologies for smart factory on innovative performance. The system quality had a significant effect on innovative performance, while the information quality had an insignificant effect on innovative performance. The quality played a partial mediating role in the effect of device technologies for smart factory on innovative performance. These results indicate that small and medium-sized venture firms should implement a high standard of information quality management(IQM) through interconnection as the kernel of a smart factory in the 4th revolutionary era, and that they can improve their corporate performance through the interlocking between components from manufacturing design to execution and analysis and the integrated management of systematic information collected from devices if necessary.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.177-182
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• 2017

As major ICT technologies such as IoT, cloud computing, and Big Data are being applied to manufacturing, smart factories are beginning to be built. The key of smart factory implementation is the ability to acquire and analyze data of the factory. Therefore, the need for a big data analysis platform is increasing. The purpose of this study is to construct a platform for big data analysis of manufacturing process and propose integrated method for analysis. The proposed platform is a RHadoop-based structure that integrates analysis tool R and Hadoop to distribute a large amount of datasets. It can store and analyze big data collected in the unit process and factory in the automation system directly in HBase, and it has overcome the limitations of RDB - based analysis. Such a platform should be developed in consideration of the unit process suitability for smart factories, and it is expected to be a guide to building IoT platforms for SMEs that intend to introduce smart factories into the manufacturing process.

• Fashion & Textile Research Journal
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• v.23 no.6
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• pp.799-809
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• 2021

This study aimed to grasp the perceptions and trends in fashion platforms and fashion smart factories using big data analysis. As a research method, big data analysis, fashion platform, and smart factory were identified through literature and prior studies, and text mining analysis and network analysis were performed after collecting text from the web environment between April 2019 and April 2021. After data purification with Textom, the words of fashion platform (1,0591 pieces) and fashion smart factory (9750 pieces) were used for analysis. Key words were derived, the frequency of appearance was calculated, and the results were visualized in word cloud and N-gram. The top 70 words by frequency of appearance were used to generate a matrix, structural equivalence analysis was performed, and the results were displayed using network visualization and dendrograms. The collected data revealed that smart factory had high social issues, but consumer interest and academic research were insufficient, and the amount and frequency of related words on the fashion platform were both high. As a result of structural equalization analysis, it was found that fashion platforms with strong connectivity between clusters are creating new competitiveness with service platforms that add sharing, manufacturing, and curation functions, and fashion smart factories can expect future value to grow together, according to digital technology innovation and platforms. This study can serve as a foundation for future research topics related to fashion platforms and smart factories.

• 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 Platform Technology
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• v.11 no.3
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• pp.76-82
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• 2023

Starting from the 4th industrial revolution, core technologies were applied to industries to build various smart environments. Smart factories in the manufacturing industry produce high-quality products by applying IIoT as a core technology that can collect and control a wide range of data for customized production. However, the network environment of the smart factory converted to open through IIoT was exposed to various security risks. In accordance with security breaches, IIoT has shown degradation in the quality of manufactured products and production processes due to network disturbance, use and maintenance of forged IIoT, and can cause reliability problems in business. Accordingly, in this study, a method for safe connection and utilization of IIoT was studied during the initial establishment of a smart factory. Specifically, a study was conducted to check the IIoT connection situation so that the practicality of the IIoT connected to the smart factory could be confirmed and the harmless environment established.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.1
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• pp.17-24
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• 2015

Smart factory requires 4 Zero factors including Zero Waiting-time, Zero Inventory, Zero Defect, Zero Down-time) that needs IT convergence for production resources of 4M1E(Man, Machine, Material, Method, Energy) in real time and event processing in all type of manufacturing enterprises. This paper will be explaining about core emerging production IT convergence technologies including cyber device security, 4M1E integration, real time event driven architecture, common platform of manufacturing standard applications, smart factory to-be model for small and medium manufacturing enterprises.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.4
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• pp.187-195
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• 2019

In the fourth industrial revolution era, various commercial smart platforms for smart system implementation are being developed and serviced. However, since most of the smart platforms have been developed for general purposes, they are difficult to apply / utilize because they cannot satisfy the requirements of real-time data management, data visualization and data storage of smart factory system. In this paper, we implemented an open source based smart manufacturing big data platform that can manage highly efficient / reliable data integration for the diagnosis diagnostic system of manufacturing robots.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.780-790
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• 2015

Studies for a plants factory is progressing for cultivating various plants by the needs of the times and industry around world. However most studies is carried out only in lab sized plants factory. It does not consider an economic feasibility. The study for a large scale plants factory is very required to get an economic gain. In this paper we has been studying a smart farm factory based on ICT using the hydroponics ginseng. The smart farm factory is to extend a concept of the general plants factory to full automated factory. The factory can collect the information about growing of plants and automate operating and management of factory like the existing plants factory. Also it is the total plants factory management system, which analyzes the collected information for optimized growth and development of plants and applies the result to the system back.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.143-150
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• 2020

Recently, many companies are interested in smart factory services. Because various smart factory services are provided by the combination of mobile devices, cloud computing, and IoT services. However, many workers turn away from these systems because most of them are not implemented from the worker's point of view. To solve this, we implemented a development tool that allows field workers to produce their own services so that workers can easily create smart factory services. Manufacturing data is collected in real time from sensors which are connected to manufacturing facilities and stored within smart factory platforms. Implemented development tools can produce services such as monitoring, processing, analysis, and control of manufacturing data in drag-and-drop. The implemented system is effective for small manufacturing companies because of their environment: making various services quickly according to the company's purpose. In addition, it is assumed that this also will help workers' improve operation skills on running smart factories and fostering smart factory capable personnel.