• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.551-567
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• 2021

Purpose: The purpose of this paper is to present implications by analyzing research trends on smart factories by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on smart factories. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "SMART FACTORY" and "Smart Factory" as search terms, and the titles and Korean abstracts were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, 739 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; Smart factory research slowed down from 2005 to 2014, but until 2019, research increased rapidly. According to the analysis by fields, smart factories were studied in the order of engineering, social science, and complex science. There were many 'engineering' fields in the early stages of smart factories, and research was expanded to 'social science'. In particular, since 2015, it has been studied in various disciplines such as 'complex studies'. Overall, in keyword analysis, the keywords such as 'technology', 'data', and 'analysis' are most likely to appear, and it was analyzed that there were some differences by fields and years. Conclusion: Government support and expert support for smart factories should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to smart factories. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 1996.05a
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• pp.1-10
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• 1996

Factory-style plant production systems of various kinds are the final goal of greenhouse production systems. These systems facilitate planning for constant productivity per unit area and labor under various outside weather conditions, although energy consumption is intensive. Physical environmental control in combination with biological control can replace the use of agricultural chemicals such as insecticides, herbicides and hormones to regulate plants. In this way, closed systems which do not use such agricultural chemicals are ideal for environmental conservation for the future. Nutrient components in plants can be regulafied by physical environmental control including nutrient solution control in hydroponics. Therefore, specific contents of nutrients for particular plants can be listed on the container and be used as the basis of customer choice in the future. Plant production systems can be classified into three types based on the type of lighting: natural lighting, supplemental lighting and completely artificial lighting (Plant Factory). The amount of energy consumption increases in this order, although the degree of weather effects is in the reverse order. In the addition to lighting, factory-style plant production systems consist of mechanized and automated systems for transplanting, environmental control, hydroponics, transporting within the facility, and harvesting. Space farming and development of pharmaceutical in bio-reactors are other applications of these types of plant production systems. Various kinds of state-of-art factory-style plant production systems are discussed in the present paper. These systems are, in general, rather sophisticated and mechaized, and energy consumption is intensive. Factory-style plant production is the final goal of greenhouse production systems and the possibilities for the future are infinte but not clear.

• Korean Journal of Agricultural Science
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• v.40 no.4
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• pp.395-403
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• 2013

Plant factory has drawn attention in many countries in the world due to capability of environmental control not only for better yield and quality, but also for increase in functional and medicinal components of the products. In this paper, an experimental plant factory was constructed for various tests under different environmental conditions, and the operations were evaluated. A production room was constructed with adiabatic materials with dimensions of $6,900{\times}3,000{\times}2,500$ mm ($L{\times}W{\times}H$). Four sets of $2,890{\times}600{\times}2,320$ mm ($L{\times}W{\times}H$) production frame unit, each with 9 light-installed beds and an aeroponic fertigation system, resulting in 36 beds, were prepared. Accuracy and response were evaluated for each environmental control component with and without crops. Air temperature, humidity, $CO_2$ concentration, light intensity, frequency, and duty ratio, fertigation rate and scheduling were controllable from a main control computer through wireless communication devices. When the plant factory was operated without crop condition, the response times were 8 minutes for change in temperature from 20 to $15^{\circ}C$ and 20 minutes from 15 to $20^{\circ}C$; 7 minutes for change in humidity from 40 to 65%; and 4 minutes for change in $CO_2$ concentration from 450 to 1000 ppm. When operated for 24 hours with crop cultivation; average, maximum, and minimum values of temperatures were 20.06, 20.8, and $18.8^{\circ}C$; humidity were 66.72, 69.37, and 63.73%; $CO_2$ concentrations were 1017, 1168, and 911 ppm, respectively. Photosynthetic Photon Flux Density was increased as the distance from the light source decreased, but variability was greater at shorter distances. Results of the study would provide useful information for efficient application of the plant factory and to investigate the optimum environment for crop growth through various experiments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1467-1482
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• 2017

Recently, Interests on The Fourth Industrial Revolution has been increased. In the manufacturing sector, the introduction of Smart Factory, which automates and intelligent all stages of manufacturing based on Cyber Physical System (CPS) technology, is spreading. The complexity and uncertainty of smart factories are likely to cause unexpected problems, which can lead to manufacturing process interruptions, malfunctions, and leakage of important information to the enterprise. It is emphasized that there is a need to perform systematic management by analyzing the threats to the Smart Factory. Therefore, this paper systematically identifies the threats using the STRIDE threat modeling technique using the data flow diagram of the overall production process procedure of Smart Factory. Then, using the Attack Tree, we analyze the risks and ultimately derive a checklist. The checklist provides quantitative data that can be used for future safety verification and security guideline production of Smart Factory.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.138-144
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• 2021

In the electronic component assembly business, when product defects occur, it is important to track incoming raw material defects or work defects, and it is important to improve suppliers or work sites according to the results. The core task of the smart factory is to build an integrated data hub to process storage, management, and analysis in real time, and to manage cluster processes, energy, environment, and safety. In order to improve reliability through accurate analysis and collection of production data by real-time monitoring of production site management for electronic parts-related small and medium-sized enterprises (SMEs), the establishment of a smart factory is essential. This paper was developed to be utilized in the construction by defining the system configuration method, smart factory-related technology and application cases, considering the characteristics of SMEs related to electronic components that want to introduce a smart factory.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.16-21
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• 2019

Small and medium-sized Korean companies are currently changing their industrial structure faster than in the past due to various environmental factors (such as securing competitiveness and developing excellent products). In particular, the importance of collecting and utilizing data produced in smart factory environments is increasing as diverse devices related to artificial intelligence are put into manufacturing sites. This paper proposes an artificial intelligence-based smart factory model to improve the process of products produced at the manufacturing site with the recent smart factory. The proposed model aims to ensure the increasingly competitive manufacturing environment and minimize production costs. The proposed model is managed by considering not only information on products produced at the site of smart factory based on artificial intelligence, but also labour force consumed in the production of products, working hours and operating plant machinery. In addition, data produced in the proposed model can be linked with similar companies and share information, enabling strategic cooperation between enterprises in manufacturing site operations.

• 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 the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.115-123
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• 2021

Manufacturing execution system is a factory information system that handles production-related quality data as well as executes production plans of process unit for all resources in the production process on site. As the 4th industrial revolution, which maximizes an automation and connectivity with artificial intelligence, has become a hot topic, manufacturers are showing interest in building a smart factories, but enormous construction costs and unstandardized production processes are obstacles to smart factory construction. Therefore, this paper designs and implements a manufacturing execution system for building a smart factory in a deterioration factory. we propose a Web-based manufacturing execution system aiming at a smart factory at the basic level for steel wire processing. The proposed system will smoothly support interworking with the existing ERP system using REST APIs, and will consider extensibility so that it can be used in various devices and browsers. We will show practicality by implementing the proposed WoT-based manufacturing execution system.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.4
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• pp.183-189
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• 2018

The focus of this paper is to design and control a three fingered hand system with eight axes for smart factory with an flexible controller, and to keep a useful big database for dynamic manipulation based on the experimental results. The weight of the hand module is only 1.2 kg, but flexible motion and powerful grasping are possible. To achieve such a flexible motion control of a robotic hand, we have developed a robust and precise fingered hand with a control system incorporating image recognition system in which we deal with the problems of not only accuracy and range of motion but also the flexibility of hand. The fingers are arranged so as to grasp both circular and prismatic objects. In order to achieve the light mechanism, we reduced the number of joints and fingers as much as possible. In this study, it was used three fingers with eight axes which is the optimal number to achieve a robust grasping diverse shape parts for smart factory.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.6
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• pp.115-122
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• 2010

The main purpose of centralized monitoring system is to manage factory electrical installation efficiently by on-line data acquisition and supervisory control. The existing centralized system is only able to be managed by operator whenever electrical installation's faults are detected. Therefore, it may be possible for propagating the installation's faults when operator make the unexpected mistakes. To overcome the unexpected mistakes, in this paper, the author presents a centralized monitoring system for factory electrical installation using active database. by using active database production rule, stated system can minimize unexpected mistake and can operate centralized monitoring system efficiently. Test results on the five factory electrical installations show that performance is efficient and robust.