Ensemble modeling for sustainable technology transfer

Junseok Lee, Ji Ho Kang, Sunghae Jun, Hyunwoong Lim, Dongsik Jang, Sangsung Park

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

These days, technological advances are being made through technological conversion. Following this trend, companies need to adapt and secure their own sustainable technological strategies. Technology transfer is one such strategy. This method is especially effective in coping with recent technological developments. In addition, universities and research institutes are able to secure new research opportunities through technology transfer. The aim of our study is to provide a technology transfer prediction model for the sustainable growth of companies. In the proposed method, we first collected patent data from a Korean patent information service provider. Next, we used latent Dirichlet allocation, which is a topic modeling method used to identify the technical field of the collected patents. Quantitative indicators on the patent data were also extracted. Finally, we used the variables that we obtained to create a technology transfer prediction model using the AdaBoost algorithm. The model was found to have sufficient classification performance. It is expected that the proposed model will enable universities and research institutes to secure new technology development opportunities more efficiently. In addition, companies using this model can maintain sustainable growth in line, coping with the changing pace of society.

Original languageEnglish
Article number2278
JournalSustainability (Switzerland)
Volume10
Issue number7
DOIs
Publication statusPublished - 2018 Jul 2

Bibliographical note

Funding Information:
This research received no external funding. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01059742). This research was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of ICT & Future Planning (NRF-2017R1A2B1010208). Lastly, this research was supported by the BK 21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University).

Funding Information:
Acknowledgments: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01059742). This research was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of ICT & Future Planning (NRF-2017R1A2B1010208). Lastly, this research was supported by the BK 21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University).

Publisher Copyright:
© 2018 by the authors.

Keywords

  • Ensemble model
  • Latent Dirichlet allocation
  • Prediction model
  • Technology topic
  • Technology transfer

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Management, Monitoring, Policy and Law

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