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Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis

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  • Chen-Yuan Liu

    (Tungnan University)

  • Jhen-Cheng Wang

    (Tungnan University)

Abstract

Patents contain much significant technical information which can serve as an indicator of technological and economical development. This study attempts to forecast the development of the biped robot walking technique in Japan by use of the patent data obtained from the Japan Patent Office. The study applies linear regression to the patent data using three S-curve models developed by Loglet Lab, Pearl, and Gompertz individually. Various parameters inherent to each model including the least sum of modulus error and the least mean of square error of the model are analyzed. The most appropriate model for measuring the inflection point, the growth and the saturation time of the technique is described. Based on the Gompertz model analysis, this study finds that the biped robot walking technique will continue to develop for several decades in Japan and the saturation period is estimated to be around the year 2079–2082. This finding can help related researchers and managers in the robot field to foresee the development trend of the biped robot walking technique in this century.

Suggested Citation

  • Chen-Yuan Liu & Jhen-Cheng Wang, 2010. "Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 21-36, January.
    • Handle: RePEc:spr:scient:v:82:y:2010:i:1:d:10.1007_s11192-009-0055-5
    DOI: 10.1007/s11192-009-0055-5
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    References listed on IDEAS

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    1. Chen-Yuan Liu & Shenq-Yih Luo, 2008. "Analysis of developing a specific technological field using the theme code of Japanese patent information," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(1), pages 51-65, April.
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    Cited by:

    1. Juan Sepúlveda & Adriana Paternina & Andrés Suarez, 2014. "Patent applications as source for measuring technological performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1385-1395, February.
    2. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    3. Li, Shuying & Garces, Edwin & Daim, Tugrul, 2019. "Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    4. Chang, Shu-Hao & Fan, Chin-Yuan, 2016. "Identification of the technology life cycle of telematics: A patent-based analytical perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 1-10.
    5. Hong Joo Lee & Hoyeon Oh, 2020. "A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development," Sustainability, MDPI, vol. 12(14), pages 1-15, July.
    6. Meizhen Zhang & Tao Lv & Xu Deng & Yuanxu Dai & Muhammad Sajid, 2019. "Diffusion of China’s coal-fired power generation technologies: historical evolution and development trends," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 7-23, January.
    7. Yang, Zaoli & Zhang, Weijian & Yuan, Fei & Islam, Nazrul, 2021. "Measuring topic network centrality for identifying technology and technological development in online communities," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    8. Lin, Deming & Liu, Wenbin & Guo, Yinxin & Meyer, Martin, 2021. "Using technological entropy to identify technology life cycle," Journal of Informetrics, Elsevier, vol. 15(2).
    9. Na Zhang & Chao Sun & Min Xu & Xuemei Wang & Jia Deng, 2023. "Catching Up of Latecomer Economies in ICT for Sustainable Development: An Analysis Based on Technology Life Cycle Using Patent Data," Sustainability, MDPI, vol. 15(11), pages 1-29, June.
    10. Qian Xu & Yabin Yu & Xiao Yu, 2022. "Analysis of the Technological Convergence in Smart Textiles," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    11. Woondong Yeo & Seonho Kim & Byoung-Youl Coh & Jaewoo Kang, 2013. "A quantitative approach to recommend promising technologies for SME innovation: a case study on knowledge arbitrage from LCD to solar cell," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 589-604, August.

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