Ensemble modeling for sustainable technology transfer

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

doi:10.3390/su10072278

Ensemble modeling for sustainable technology transfer

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

School of Industrial Management Engineering

Research output: Contribution to journal › Article › peer-review

42 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 language	English
Article number	2278
Journal	Sustainability (Switzerland)
Volume	10
Issue number	7
DOIs	https://doi.org/10.3390/su10072278
Publication status	Published - 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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/su10072278

Cite this

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title = "Ensemble modeling for sustainable technology transfer",

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.",

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author = "Junseok Lee and Kang, {Ji Ho} and Sunghae Jun and Hyunwoong Lim and Dongsik Jang and Sangsung Park",

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: {\textcopyright} 2018 by the authors.",

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AU - Jang, Dongsik

AU - Park, Sangsung

N1 - 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.

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N2 - 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.

AB - 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.

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Ensemble modeling for sustainable technology transfer

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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