• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.522-523
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• 2022

In this paper, we design a gateway protocol architecture for building an IoT-based smart factory. A smart factory system consists of several terminal devices, gateways and servers, and connects them through a wireless network. The terminal device collects information from various sensors and transmits the information to the server through the gateway. The terminal device controls the actuator by receiving a control signal according to a control algorithm of the server or by manual operation. Therefore, the gateway system connects Wi-SUN, a wireless smart utility network, and the Internet, serves as a Wi-SUN coordinator, and is equipped with Modbus-TCP to interwork with SCADA.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.59-71
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• 2020

The paper reports on a systematic literature review results concerning the smart factory research in Korea. 144 papers were identified from the articles published in Korean journals listed in the Korean citation index by keyword search related to smart factory. Bibliometric analyses were conducted by way of co-occurrence and network analysis using the VOSViewer. Automation, intelligence, and bigdata were identifed as three critical clusters of research while, operating systems, international policy and cases, concept analysis as other three clusters of research. Internet of Things turned out to be a key technology of smart factory linking all of these areas. Servitization studies were small in numbers but seemed to have a lot of potential. Security researches seemed to be lacking connections with other areas of studies. Results of this study can be used as a milestone for identifying future research issues in smart factories.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.41-44
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• 2021

In this paper, we propose an efficient reference-based compression artifact reduction network for 360-degree images in an equi-rectangular projection (ERP) domain. In our insight, conventional image restoration methods cannot be applied straightforwardly to 360-degree images due to the spherical distortion. To address this problem, we propose an adaptive disparity estimator using a deformable convolution to exploit correlation among 360-degree images. With the help of the proposed convolution, the disparity estimator establishes the spatial correspondence successfully between the ERPs and extract matched textures to be used for image restoration. The experimental results demonstrate that the proposed algorithm provides reliable high-quality textures from the reference and improves the quality of the restored image as compared to the state-of-the-art single image restoration methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.471-477
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• 2021

In the field of display, a smart factory means more efficient display manufacturing using AI/BIG DATA technology not only for job automation, but also for existing process management, moving facilities, process abnormalities, and defect classification. In the past, when defects appeared in the display manufacturing process, the classification of defects and coping with process abnormalities were different, a lot of time was consumed for this. However, in the field of display manufacturing, advanced process equipment must be used, and it can be said that the competitiveness of the display manufacturing industry is to quickly identify the cause of defects and increase the yield. In this paper, we will summarize the cases in which smart factory AI/BIG DATA technology is applied to domestic display manufacturing, and analyze what advantages can be derived compared to existing methods. This information can be used as prior knowledge for improved smart factory development in the field of display manufacturing using AI/BIG DATA.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.153-158
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• 2017

Due to the rapid development of information and communication, we live in the smart age of information society. Smart Factory, which integrates Information and communication technology, is being hailed as the technology of the $4^{th}$ industrial revolution. As a result of entering the information society, the factory has made a lot of progress in automation. In this thesis, we used kinetization to research and implement the leisure system of the factory through motion. Kinect provides users with various convenience because they can take advantage of the user's actions and control the system through information. This study is expected to produce a smart factory with video processing, and it is expected to affect the efficiency of the production by providing various conveniences for people working in factories.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.751-756
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• 2018

In smart factories and industrial IoT, all facilities in a factory are monitored over the Internet, thereby facility can reduce the downtime and increase the availiability by preventive maintenance before it breaks down. The abnormal conditions of the major facilities in the plant are caused by abnormal temperature rise, vibration, and variations in noise. Consequently, it is critical to develop a very small smart device that is easily installed in a small space to enable real-time monitoring of the vibration status of the facility. In this study, smart devices were developed for smart factory fault prediction and robustness management using ultra small micro-controllers with WiFi capabilities and MEMS acceleration sensors.

• Journal of the Korea Safety Management & Science
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• v.23 no.3
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• pp.47-55
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• 2021

Developed countries that have experienced decline in productivity due to the economic crisis in the past have come to recognize the smart factory as an important means to strengthen the competitiveness of the manufacturing industry due to the increase in labor costs, the avoidance of the manufacturing industry, and the resolution of the shortage of skilled manpower. The necessity of nurturing manpower for self-maintenance was felt through identifying factors for successful smart factory introduction by companies and providing smart factory education. Therefore, the effects of educational satisfaction and operational competency on self-efficacy as a parameter and self-efficacy as a parameter were analyzed using research models and hypotheses to determine whether there was an effect between job satisfaction as a dependent variable. As a result of the analysis, it was found that the mediating effect of self-efficacy and self-efficacy on job satisfaction was found to have significant effects on operational competency and self-efficacy as parameters, as well as educational satisfaction and operational competency. The implication of this study is that continuous education and innovation activities are important in order to increase the business performance of companies, and through this, the manufacturing competitiveness of SMEs can be improved.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.148-154
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• 2021

We propose the AI Smart Factory Model for integrated management of production processes in this paper .It is an integrated platform system for the production of food packaging containers, consisting of a platform system for the main producer, one or more production partner platform systems, and one or more raw material partner platform systems while each subsystem of the three systems consists of an integrated storage server platform that can be expanded infinitely with flexible systems that can extend client PCs and main servers according to size and integrated management of overall raw materials and production-related information. The hardware collects production site information in real time by using various equipment such as PLCs, on-site PCs, barcode printers, and wireless APs at the production site. MES and e-SCM data are stored in the cloud database server to ensure security and high availability of data, and accumulated as big data. It was built based on the project focused on dissemination and diffusion of the smart factory construction, advancement, and easy maintenance system promoted by the Ministry of SMEs and Startups to enhance the competitiveness of small and medium-sized enterprises (SMEs) manufacturing sites while we plan to propose this model in the paper to state funding projects for SMEs.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.561-573
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• 2019

This study proposes a new approach to implement smart factory based on robot monitoring system for a small and medium sized enterprise. The simulator includes the forward kinematics and inverse kinematics analyzes of articulated robots. We also modeled the dynamics of the robot and made it possible to simulate it in the program. We studied the interface between the user and the monitoring programming system using the GUI environment of Windows OS, and it is configured to select the appropriate trajectory planning and control method. The reliability of simulator is illustrated by simulation test.