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Identifying convergence fields and technologies for industrial safety: LDA-based network analysis

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  • Song, Bomi
  • Suh, Yongyoon

Abstract

As industrial systems expand and complex systems are developed, it is no longer effective to minimize hazards and risks for industrial safety using the technological solutions limited to a single industry. Thus, to resolve complicated problems, safety technology has been developed by promoting technology innovation and convergence. In this respect, this study aims at monitoring major safety fields and technologies through patent analysis to identify the trends in technology development that prevent the risks of various industrial systems. Patent information is effectively used for analyzing technology descriptions, which include the purpose and newness of technology. Using this patent information, we propose the major safety fields and related technology keywords using the following two techniques: (1) latent Dirichlet allocation (LDA), which aims to extract the latent topics and main keywords contained in documents, and (2) network analysis, which is useful for monitoring change patterns and relations. Further, the convergence trajectories of safety technology are identified to provide insights about the technology trends in safety fields. The results are expected to enable safety managers and engineers to effectively find relevant technology trends for reducing hazardous factors.

Suggested Citation

  • Song, Bomi & Suh, Yongyoon, 2019. "Identifying convergence fields and technologies for industrial safety: LDA-based network analysis," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 115-126.
    • Handle: RePEc:eee:tefoso:v:138:y:2019:i:c:p:115-126
    DOI: 10.1016/j.techfore.2018.08.013
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    References listed on IDEAS

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    1. Loet Leydesdorff & Duncan Kushnir & Ismael Rafols, 2014. "Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC)," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1583-1599, March.
    2. Song, Chie Hoon & Elvers, David & Leker, Jens, 2017. "Anticipation of converging technology areas — A refined approach for the identification of attractive fields of innovation," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 98-115.
    3. Geum, Youngjung & Kim, Moon-Soo & Lee, Sungjoo, 2016. "How industrial convergence happens: A taxonomical approach based on empirical evidences," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 112-120.
    4. Hyunseok Park & Janghyeok Yoon, 2014. "Assessing coreness and intermediarity of technology sectors using patent co-classification analysis: the case of Korean national R&D," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 853-890, February.
    5. Vito Albino & Umberto Berardi & Rosa Maria Dangelico, 2015. "Smart Cities: Definitions, Dimensions, Performance, and Initiatives," Journal of Urban Technology, Taylor & Francis Journals, vol. 22(1), pages 3-21, January.
    6. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.
    7. Sung, Tae Kyung, 2018. "Industry 4.0: A Korea perspective," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 40-45.
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    Cited by:

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