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Dominant Technology Identification of Wind Power Generation


Affiliations
1 School of Economics and Management, Harbin Engineering University, Harbin, China
2 Present address: No. 145, Nantong Street, Nangang District, Harbin City, Heilongjiang Province, China
 

Wind energy, the most commercialized prospect of renewable energy, is being developed and utilized on a large scale. Major institutions and universities have invested a lot of manpower, capital and technology in researching the wind power technology, which has made remarkable progress. In the era of green economy, the leading technology of wind power deserves more attention. Dominant technology represents the development direction of technology area. Identification of the dominant technology is of great significance for the technological choice and strategic layout of enterprises. In contrast to traditional technology identification methods, here we propose a visual analysis model based on patent citation relationship. First, the patent mutual citation data are input into the visualization software Gephi to identify the leading technology in the visual processing stage. The PageRank algorithm is used to cross contrast the technical value to build the leading technology recognition model. Second, we evaluate the technical value of each patent with five indices, including ‘number of patent cited’, ‘patent number of the same family’, ‘scope of patent coverage’, ‘claim number of each patent’, and ‘number of patent litigations’ to verify the accuracy of the visual model. Taking the database of wind power generation technology from European Patent Office as an example, we obtain 7421 patents from 1900 to 2017. The results of visual processing, evaluation of the index and PageRank judgment show that the visual model has a significant effect in the identification of the leading technology. The results also explain the applicability of the PageRank algorithm and the five indicators are the most scientific and reasonable for dominant technology identification.

Keywords

Leading Technology Identification, Patent Citation, Search Algorithms, Visual Analysis Model, Wind Power Generation.
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  • Li, J. et al., Current situation and prospect of wind power generation in China. China Sci. Technol. Invest., 2007, 11(1), 25–28.

  • Malone, E., Hultman, N. E., Anderson, K. L. and Romeiro, V., Stories about ourselves: how national narratives influence the diffusion of large-scale energy technologies. Energ. Res. Soc. Sci., 2017, 31(9), 70–76.

  • Suarez, F. F., Battles for technological dominance: an integrative framework. Res. Policy, 2004, 39(7), 271–286.

  • Sun, M., Gao, C., Jia, C., Ni, F. D. F. and Zhang, J., The selection and promotion of core technology to China’s energy goals. Energ. Proc., 2016, 104(3), 233–238.

  • Lee, H., Analysis and citation analysis of patents by social network analysis. Telecommun. Policy, 2007, 27(2), 293–312.

  • Noh, H., Song, Y. and Lee, S., Identifying emerging core technologies for the future: case study of patents published by leading telecommunication organizations. Telecommun. Policy, 2016, 40(10), 956–970.

  • Edsand, H., Identifying barriers to wind energy diffusion in Colombia: a function analysis of the technological innovation system and the wider context. Technol. Soc., 2017, 49(5), 1–15.

  • Bastian, M., Heymann, S. and Jacomy, M., Gephi: an open source software for exploring and manipulating networks. Int. AAAI Conf. Weblogs Soc. Media, 2009, 10(2), 361–362.

  • Zhang, J., Yang, G. C. and Liu, H. J., Review of the global patent statistics database (PATSTAT). Digit. Lib. Forum, 2015, 12(5), 63–65.

  • Narin, F., Patent bibliometrics. Scientometrics, 1994, 30(12), 147– 155.

  • Wang, S. and Wang, J., Evaluation of technological influence based on enterprise citation network. Sci. Res., 2011, 29(11), 396– 402.

  • Carpenter, M. P., Narin, F. and Woolf, P., Citation rates to technologically important patents. World Patent Inf., 1981, 4(2), 160–163.

  • Li, Y. A., Borders and distance in knowledge spillovers: dying over time or dying with age? – evidence from patent citations. Eur. Econ. Rev., 2014, 71(10), 152–172.

  • Albert, M. B., Avery, D., Narin, F. and Mcallister, P., Direct validation of citation counts as indicators of industrially important patents. Res. Policy, 1991, 20(3), 251–259.

  • Dietmar, H., Frederic, M. S. and Katrin, V., Citations, family size, opposition and the value of patent rights. Res. Policy, 2003, 32(8), 1343–1363.

  • Markus, R., Joachim, H. and Christopher, H., On sharks, trolls, and their patent prey – unrealistic damage awards and firms’ strategies of ‘being infringed’. Res. Policy, 2007, 36(1), 134–154.

  • Schettino, F., Sterlacchini, A. and Venturini, F., Inventive productivity and patent quality: evidence from Italian inventors. J. Policy Model., 2008, 35(6), 1043–1056.

  • Petruzzelli, A. M., Rotolo, D. and Albino, V., Determinants of patent citations in biotechnology: an analysis of patent influence across the industrial and organizational boundaries. Technol. Forecast. Soc. Change, 2015, 91(2), 208–221.

  • Lerner, J., The importance of patent scope: an empirical analysis. RAND J. Econ., 1994, 25(2), 319–333.

  • Tong, X. S. and Davidson, F. J., Measuring national technological performance with patent claims data. Res. Policy, 1994, 23(2), 133–141.

  • Elettra, A. and Rossella, A., An application of fuzzy methods to evaluate a patent under the chance of litigation. Exp. Syst. Appl., 2011, 38(10), 13143–13143.

  • Cremers, K., Settlement during patent litigation trials. An empirical analysis for Germany. J. Technol. Trans., 2009, 34(7), 182– 195.

  • Ma, R. and Wei, X., Research on core patent prediction from the perspective of technology subdivision. J. Infor. Sci., 2017, 12(5), 1279–1289.

  • Johan, N., Yuan, Z. and Xiao, Z., Innovation core, innovation semi-periphery and technology transfer: the case of wind energy patents. Energy Policy, 2018, 120(9), 213–227.

  • Huiming, Z., Yu, Z. and Dequn, Z., Selection of key technology policies for Chinese offshore wind power: a perspective on patent maps. Mar. Policy, 2018, 93(7), 47–53.

  • Bing, S. and Hongying, W., Comparative study on Chinese and global OLED industry based on patent data. IEEE Access, 2018, 12(6), 72381–72391.

  • Eduardo, D. and Wyse, J., A network-wide visualization of the implementation of the global strategy for plant conservation in Brazil. Rodriguésia, 2018, 4(10), 1613–16391.

  • Abdolreza, M. and Katja, R., Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling. Technol. Forecast. Soc. Change, 2016, 104(3), 16–29.

  • Chulhyun, K., Hakyeon, L. and Hyeonju, S., Identifying core technologies based on technological cross-impacts: an association rule mining (ARM) and analytic network process (ANP) approach. Exp. Syst. Appl., 2011, 38(10), 12559–12564.

  • Hyojeong, L. and Yongtae, P., Identification of technological knowledge intermediaries. Scientometrics, 2010, 84(3), 543–561.


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  • Dominant Technology Identification of Wind Power Generation

Abstract Views: 352  |  PDF Views: 119

Authors

Hongying Wang
School of Economics and Management, Harbin Engineering University, Harbin, China
Bing Sun
Present address: No. 145, Nantong Street, Nangang District, Harbin City, Heilongjiang Province, China

Abstract


Wind energy, the most commercialized prospect of renewable energy, is being developed and utilized on a large scale. Major institutions and universities have invested a lot of manpower, capital and technology in researching the wind power technology, which has made remarkable progress. In the era of green economy, the leading technology of wind power deserves more attention. Dominant technology represents the development direction of technology area. Identification of the dominant technology is of great significance for the technological choice and strategic layout of enterprises. In contrast to traditional technology identification methods, here we propose a visual analysis model based on patent citation relationship. First, the patent mutual citation data are input into the visualization software Gephi to identify the leading technology in the visual processing stage. The PageRank algorithm is used to cross contrast the technical value to build the leading technology recognition model. Second, we evaluate the technical value of each patent with five indices, including ‘number of patent cited’, ‘patent number of the same family’, ‘scope of patent coverage’, ‘claim number of each patent’, and ‘number of patent litigations’ to verify the accuracy of the visual model. Taking the database of wind power generation technology from European Patent Office as an example, we obtain 7421 patents from 1900 to 2017. The results of visual processing, evaluation of the index and PageRank judgment show that the visual model has a significant effect in the identification of the leading technology. The results also explain the applicability of the PageRank algorithm and the five indicators are the most scientific and reasonable for dominant technology identification.

Keywords


Leading Technology Identification, Patent Citation, Search Algorithms, Visual Analysis Model, Wind Power Generation.

References





DOI: https://doi.org/10.18520/cs%2Fv116%2Fi9%2F1525-1532