• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.203-212
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• 2019

Along with the recent fourth industrial revolution, the world's manufacturing powerhouses are pushing for national strategies to revive the sluggish manufacturing industry. Moon Jae-in, the government is in accordance with the trend, called 'advancement of science and technology is leading the fourth round of the Industrial Revolution' strategy. Intelligent information technology such as IoT, Cloud, Big Data, Mobile, and AI, which are key technologies that lead the fourth industrial revolution, is promoting the emergence of new industries such as robots and 3D printing and the smarting of existing major manufacturing industries. Advances in technologies such as smart factories have enabled IoT-based sensing technology to measure various data that could not be collected before, and data generated by each process has also exploded. Thus, this paper uses data generators to generate virtual data that can occur in smart factories, and uses them to analyze the cause of the defect in real time and to predict the replacement cycle of the facility.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.168-173
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• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.6
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• pp.370-376
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• 2016

Manufacturing data analysis and its applications are getting a huge popularity in various industries. In spite of the fast advancement in the big data analysis technology, however, the manufacturing quality data monitored from the automated inspection system sometimes is not reliable enough due to the complex patterns of product quality. In this study, thus, we aim to define the level of trusty of an automated quality inspection system and improve the reliability of the quality inspection data. By correlation analysis and feature selection, this paper presents a method of improving the inspection accuracy and efficiency in an SVM-based automatic product quality inspection system using thermal image data in an auto part manufacturing case. The proposed method is implemented in the sealer dispensing process of the automobile manufacturing and verified by the analysis of the optimal feature selection from the quality analysis results.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1175-1182
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• 2017

Recent trend in the manufacturing industry is focused on the convergence with IoT and Big Data, by emergence of the 4th Industrial Revolution. To realize a smart factory, the proposed system based on MTConnect technology collects and integrates various status information of machines from many production facilities including heterogeneous devices. Also it can distribute the acquisited status of heterogeneous manufacturing machines to the remote devices. As a key technology of a flexible automated production line, the proposed system can provide much possibility to manage important information such as error detection and processing state management in the unmanned automation line.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.55-59
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• 2018

Big-data, a revolutionary technology in the era of the 4th Industrial Revolution, provides services in various fields such as health, public sector, distribution, marketing, manufacturing, etc. It is very useful technology for marketing analysis and future design through accurate and quick data analysis. It is very likely to develop further. However, the biggest problem when using Big-data is privacy and privacy. When various data are analyzed using Big-data, the tendency of each user can be analyzed, and this information may be sensitive information of an individual and may invade privacy of an individual. Therefore, in this paper, we investigate the necessary measures for Personal private information infringement that may occur when using Personal private information in Big-data environment, and propose necessary Personal private information protection technologies to contribute to protection of Personal private information and privacy.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.10-16
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• 2021

As the implementation of smart factories spreads widely, the need for research to improve data efficiency is raised by prioritizing massive amounts of big data using IoT devices in terms of relevance and quality. The purpose of this study is to investigate whether prioritizing big data in management accounting decisions such as cost volatility estimation and recipe optimization can improve smart solution performance and decision-making effectiveness. Based on the survey answers of 84 decision makers at domestic small and medium-sized manufacturers who operate smart solutions such as ERP and MES that link manufacturing data in real time, empirical research was conducted. As a result, it was analyzed that setting prioritization of big data has a positive effect on decision-making in management accounting. became In addition, it was found that big data prioritization has a mediating effect that indirectly affects smart solution performance by using big data in management accounting decision making. Through the research results, it will be possible to contribute as a prior research to develop a scale to evaluate the correlation between big data in the process of business decision making.

• Journal of the Korea Safety Management & Science
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• v.12 no.3
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• pp.333-339
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• 2010

Data mining is the process of finding and analyzing data from a big database and summarizing it into useful information for a decision-making. A variety of data mining techniques have been being used for wide range of industries. One application of those is especially so for gathering meaningful information from process data in manufacturing factories for quality improvement. The purpose of this paper is to provide a methodology to improve manufacturing quality of fuel tanks which are auto-parts. The methodology is to analyse influential attributes and establish a model for optimal manufacturing condition of fuel tanks to improve the quality using decision tree, association rule, and feature selection.

• Journal of Navigation and Port Research
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• v.46 no.5
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• pp.409-417
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• 2022

The International Association of Lighthouse Authorities (IALA) utilizes a questionnaire to investigate the status of Aids to Navigation (AtoN) around the world. However, results of the IALA questionnaire have limited use because respondent understanding is inconsistent. In addition, there is uncertainty regarding the appropriateness of the questionnaire content. Furthermore, the overall response rate is low. Therefore, the status of AtoN is not clearly understood. AtoN data from around the world are generated hourly. Thus, big data solutions are required to effectively exploit the information. Digitization of analog data is an important component of building big data. Hence, the IALA has developed a Maritime Resource Name (MRN) scheme and an information exchange standard. Here, we used the AtoN information exchange standard and designed an S-201-based big data construction process that could collect and manage global AtoN information. In this study, construction of an IALA AtoN portal was proposed as the core of the construction of the AtoN big data. The process was divided into three stages. IALA AtoN portal is developed by IALA with the goal to provide various meaningful statistical analysis results based on AtoN data while managing AtoN information around the world based on S-201. If an AtoN portal capable of constructing S-201-based big data is developed, then a data collection and storage system that can gather basic S-201 AtoN data from the IALA and global AtoN management agencies could be achieved. Furthermore, insightful statistical analysis of AtoN status worldwide and changes in manufacturing technology will be possible.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.187-201
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• 2009

Semiconductor industry is based on equipment industry and timing industry. In particular, semiconductor process is very complex and as semiconductor-chip width tails and is becoming equipment gradually more as a high technology. Equipment operation is primarily engaged in semiconductor manufacturing (engineers and operator) of being conducted by, equipment errors have also been raised. Equipment operational data related to the error of korea occupational safety and health agency were based on data and production engineers involved in the operator's questionnaire was drawn through the error factor. Equipment operating in the error factor of 9 big item and 36 detail item detailed argument based on the errors down, and 9 big item the equipment during operation of the correlation error factor was conducted. Each of the significance level was correlated with the tabulation and analysis. Using the maximum correlation coefficient, the correlation between the error factors to derive the relationship between factors were analyzed. Facility operating with the analysis of error factors (big and detail item) derive a relationship between the model saw. The end of the operation of the facility in operation on the part of the two factors appeared as prevention. Safety aspects and ergonomics aspects of the approach should be guided to the conclusion.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.159-165
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• 2023

The entire industry is increasing the use of big data analysis using artificial intelligence technology due to the Fourth Industrial Revolution. The value of big data is increasing, and the same is true of the production technology. However, small and medium -sized manufacturers with small size are difficult to use for work due to lack of data management ability, and it is difficult to enter smart factories. Therefore, to help small and medium -sized manufacturing companies use big data, we will predict the gross production time through machine learning. In previous studies, machine learning was conducted as a time and quantity factor for production, and the excellence of the ExtraTree Algorithm was confirmed by predicting gross product time. In this study, the worker's proficiency factors were added to the time and quantity factors necessary for production, and the prediction rate of LightGBM Algorithm knowing was the highest. The results of the study will help to enhance the company's competitiveness and enhance the competitiveness of the company by identifying the possibility of data utilization of the MES system and supporting systematic production schedule management.