• Journal of Digital Contents Society
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• v.19 no.4
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• pp.789-799
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• 2018

This article presents machine learning based approach on Big data to analyzing time series data for anomaly detection in such industrial complex system. Long Short-Term Memory (LSTM) network have been demonstrated to be improved version of RNN and have become a useful aid for many tasks. This LSTM based model learn the higher level temporal features as well as temporal pattern, then such predictor is used to prediction stage to estimate future data. The prediction error is the difference between predicted output made by predictor and actual in-coming values. An error-distribution estimation model is built using a Gaussian distribution to calculate the anomaly in the score of the observation. In this manner, we move from the concept of a single anomaly to the idea of the collective anomaly. This work can assist the monitoring and management of Smart Factory in minimizing failure and improving manufacturing quality.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.485-489
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• 2018

For the construction of smart factory that are part of the Fourth Industrial Revolution, data from the production environments and production machines should be collected, analyzed, and feedback should be given to predict when failures take place or parts should be replaced. For this purpose, a system that monitors the production environments and the status of the production machines are required. In this paper, the monitoring system for mobile devices and PC is implemented by collecting environmental data from production sites and sensor data of production machine using LoRa, a low-power wireless communication technology. On the mobile devices, production environments and vibration data can be displayed in real time. In PC monitoring program, sensor data can be displayed graphically to check standard deviation and data variation. The implemented system is used to collect data such as temperature, humidity, and atmospheric pressure of the production environment, and vibration data of production machines.

• The Journal of Bigdata
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• v.3 no.1
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• pp.95-103
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• 2018

In recent years, many people in the manufacturing field have been making efforts to increase efficiency while analyzing manufacturing data generated in the process according to the development of ICT technology. In this study, we propose a data mining based manufacturing process using decision tree algorithm (CHAID) as part of a smart factory. We used 432 sensor data from actual manufacturing plant collected for about 5 months to find out the variables that show a significant difference between the stable process period with low defect rate and the unstable process period with high defect rate. We set the range of the stable value of the variable to determine whether the selected final variable actually has an effect on the defect rate improvement. In addition, we measured the effect of the defect rate improvement by adjusting the process set-point so that the sensor did not deviate from the stable value range in the 14 day process. Through this, we expect to be able to provide empirical guidelines to improve the defect rate by utilizing and analyzing the process sensor data generated in the manufacturing industry.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.9-16
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• 2019

Companies that want to improve their manufacturing processes have recently introduced the smart factory, which is particularly noticeable. The ultimate goal is to maximize the area of the smart factory that performs the process of the production facility completely with minimal manual control and to minimize errors of reasoning. This research is a part of a project for unmanned production, management, packaging, and delivery management and the detection of the start point of rebars to perform the automatic calibration of the rollers through the tracking of the automated facilities of unmanned production. It must meet the requirement to accurately track the position from the start point to the end point. In order to improve the tracking performance, it is important to set the accurate start point. However, the probability of tracking errors is high depending on environments such as illumination and dust through the conventional time-based detection method. In this paper, we propose a starting point detection method using the average brightness change of high speed IR camera to reduce the errors according to the environments, As a result, its performance is improved by more than 15%.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.23-28
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• 2018

In this work, we developed a platform that can monitor status and manage events of factory workplaces by providing events and data collected from various types of multi-touch technology based sensors installed in the workplace. By using the image recognition technology, faces of the people in the factory workplace are recognized and the customized contents for each worker are provided, and security of contents is enhanced by the authenticating an individual worker through face recognition. Contents control function through gesture recognition is constructed, so that workers can easily search documents. Also, it is possible to provide contents for workers by implementing face recognition function in mobile devices. The result of this work can be used to improve workplace safety, convenience of workers, contents security and can be utilized as a base technology for future smart factory construction.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.229-235
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• 2013

This paper proposed the method to reduce and manage the amount of using power by using the Self-Learning of inference engine that evolves through learning increasingly smart ways for each spaces with in the Space-Based Energy Management System (SEMS, Space-based Energy Management System) that is defined as smallest unit space with constant size and similar characteristics by using the collectible Big Data from the various information networks and the informations of various sensors from the existing Energy Management System(EMS), mostly including such as the Energy Management Systems for the Factory (FEMS, Factory Energy Management System), the Energy Management Systems for Buildings (BEMS, Building Energy Management System), and Energy Management Systems for Residential (HEMS, Home Energy Management System), that is monitoring and controlling the power of systems through various sensors and administrators by measuring the temperature and illumination.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.1-9
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• 2020

A smart factory is a key industrial application introduced by the 4^th industrial revolution. The automatic guided vehicle (AGV) is one of the technology realizing smart factory, but the development cost is high due to its early stage of technology. Although developing a low-cost AGV requires a lot of data, it has limited data acquisition capability because of the limited storage and the AGV movement. Hence, we propose a development environment using Bluetooth to collect data and design a lane detector. The proposed lane detector shows a high lane detection ratio regardless of light variation and a shade.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.623-629
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• 2018

Smart Factory is a concept of automatic production system of machines by the fusion of ICT and manufacturing. As a base technology for realizing such a smart factory, there is an increasing interest in a low-power environmentally friendly LED lighting system, and researches on so-called optical ID related application technologies such as communication using a LED and position recognition are actively underway. In this paper, We have proposed a system that can reliably identify logistics location and additional information without being affected by electromagnetic interference such as high voltage, high current, and generator in the plant. Through the basic experiment, we confirmed the applicability of the color ID recognition rate from 98.8% to 93.8% according to the eight color variations in the short distance.

• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.79-94
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• 2022

Purpose - The purpose of this study is to examine firm-level attributes related to Korean manufacturing small and medium-sized enterprises' (SMEs') decisions to implement smart factories. Design/methodology/approach - This study uses the provided by the Ministry of SMEs and Startups of Korea and the Korea Federation of SMEs. Manufacturing SMEs' decisions to implement smart factories in 2018-2019 were analyzed using multinomial logit and ordered logit models. Findings - The findings of this study suggest that firms' decisions to implement smart factories were positively related to firm size, R&D intensity, international market scope, and transactional relationships with customers. However, smart factory implementation decisions were not related to firm age and CEO gender. Research implications or Originality - This study illuminates firm-level attributes that may drive organizational innovation in the era of Industry 4.0 and thus contributes to the innovation adoption literature. This study also contributes to growing research on smart factories by analyzing the actual, progressive decisions to implement smart factories, as opposed to perceived intentions to implement them.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.83-101
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• 2021

This study is to investigate, using AHP, what technologies should be applied first to Korean SMEs in order to respond to the 4th industrial revolution and change to a smart enterprise. To this end, technologies related to the 4th industrial revolution and smart factory are synthesized, and the classification criteria of Dae-Hoon Kim et al. (2019) are applied, but additional opinions of experts are collected and related technologies are converted to artificial intelligence (AI), Big Data, and Cloud Computing. As a base technology, mobile, Internet of Things (IoT), block chain as hyper-connected technology, unmanned transportation (autonomous driving), robot, 3D printing, drone as a convergence technology, smart manufacturing and logistics, smart healthcare, smart transportation and smart finance were classified as smart industrial technologies. As a result of confirming the priorities for technical use by AHP analysis and calculating the total weight, manufacturing companies have a high ranking in mobile, artificial intelligence (AI), big data, and robots, while service companies are in big data and robots, artificial intelligence (AI), and smart healthcare are ranked high, and in all companies, it is in the order of big data, artificial intelligence (AI), robot, and mobile. Through this study, it was clearly identified which technologies should be applied first in order to respond to the 4th industrial revolution and change to a smart company.