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Trend Analysis of Factory Automation Using Topic Modeling.

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Titel: Trend Analysis of Factory Automation Using Topic Modeling.
Autoren: Cho, Insu, Ju, Yonghan
Quelle: Processes; Jul2025, Vol. 13 Issue 7, p1952, 20p
Schlagwörter: ARTIFICIAL intelligence, AUTOMATION, WIRELESS communications, INDUSTRIAL efficiency, COMPUTER vision, TREND analysis, DOCUMENT clustering, PATENT databases
Geografische Kategorien: SOUTH Korea
Abstract: Factory automation (FA) is a vital technology that enhances manufacturing efficiency, reduces defect rates, and maximizes productivity in response to evolving market demands. This study analyzes global research and development (R&D) trends in FA based on patent information from major manufacturing countries. It also proposes growth directions for FA technology in South Korea, applying latent Dirichlet allocation (LDA) to identify key technologies for the Korean market. Specifically, FA-related technology is classified into five topics, with documents less likely to belong to a single topic being reclassified and analyzed as hybrid topics. Furthermore, this study analyzes the growth rate of FA-related technologies and the current level of technological emergence through a four-quadrant analysis, providing valuable insights into global R&D trends. The results demonstrate that artificial intelligence-related patents are important for FA. Further R&D is necessary, as the development of wireless communication technology suitable for industrial environments has become crucial and is a competitive technology for FA in terms of infrastructure and maintenance. Visual processing technology, which enables accurate decision making using artificial intelligence in a precise and constantly changing operating environment through FA, requires more attention to secure international competitiveness in the Korean market. [ABSTRACT FROM AUTHOR]
Copyright of Processes is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22ARTIFICIAL+intelligence%22">ARTIFICIAL intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22AUTOMATION%22">AUTOMATION</searchLink><br /><searchLink fieldCode="DE" term="%22WIRELESS+communications%22">WIRELESS communications</searchLink><br /><searchLink fieldCode="DE" term="%22INDUSTRIAL+efficiency%22">INDUSTRIAL efficiency</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+vision%22">COMPUTER vision</searchLink><br /><searchLink fieldCode="DE" term="%22TREND+analysis%22">TREND analysis</searchLink><br /><searchLink fieldCode="DE" term="%22DOCUMENT+clustering%22">DOCUMENT clustering</searchLink><br /><searchLink fieldCode="DE" term="%22PATENT+databases%22">PATENT databases</searchLink>
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– Name: Abstract
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  Data: Factory automation (FA) is a vital technology that enhances manufacturing efficiency, reduces defect rates, and maximizes productivity in response to evolving market demands. This study analyzes global research and development (R&D) trends in FA based on patent information from major manufacturing countries. It also proposes growth directions for FA technology in South Korea, applying latent Dirichlet allocation (LDA) to identify key technologies for the Korean market. Specifically, FA-related technology is classified into five topics, with documents less likely to belong to a single topic being reclassified and analyzed as hybrid topics. Furthermore, this study analyzes the growth rate of FA-related technologies and the current level of technological emergence through a four-quadrant analysis, providing valuable insights into global R&D trends. The results demonstrate that artificial intelligence-related patents are important for FA. Further R&D is necessary, as the development of wireless communication technology suitable for industrial environments has become crucial and is a competitive technology for FA in terms of infrastructure and maintenance. Visual processing technology, which enables accurate decision making using artificial intelligence in a precise and constantly changing operating environment through FA, requires more attention to secure international competitiveness in the Korean market. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Processes is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/pr13071952
    Languages:
      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 1952
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      – SubjectFull: SOUTH Korea
        Type: general
      – SubjectFull: ARTIFICIAL intelligence
        Type: general
      – SubjectFull: AUTOMATION
        Type: general
      – SubjectFull: WIRELESS communications
        Type: general
      – SubjectFull: INDUSTRIAL efficiency
        Type: general
      – SubjectFull: COMPUTER vision
        Type: general
      – SubjectFull: TREND analysis
        Type: general
      – SubjectFull: DOCUMENT clustering
        Type: general
      – SubjectFull: PATENT databases
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      – TitleFull: Trend Analysis of Factory Automation Using Topic Modeling.
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            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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