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http://dx.doi.org/10.5392/JKCA.2021.21.10.048

Deep Learning-based Technology Valuation and Variables Estimation  

Sung, Tae-Eung (연세대학교(미래))
Kim, Min-Seung (연세대학교(미래))
Lee, Chan-Ho (연세대학교(미래))
Choi, Ji-Hye (연세대학교(미래))
Jang, Yong-Ju (연세대학교(미래))
Lee, Jeong-Hee (연세대학교(미래))
Publication Information
The Journal of the Korea Contents Association / v.21, no.10, 2021 , pp. 48-58 More about this Journal
Abstract
For securing technology and business competences of companies that is the engine of domestic industrial growth, government-supported policy programs for the creation of commercialization results in various forms such as 『Technology Transaction Market Vitalization』 and 『Technology Finance-based R&D Commercialization Support』 have been carried out since 2014. So far, various studies on technology valuation theories and evaluation variables have been formalized by experts from various fields, and have been utilized in the field of technology commercialization. However, Their practicality has been questioned due to the existing constraint that valuation results are assessed lower than the expectation in the evaluation sector. Even considering that the evaluation results may differ depending on factors such as the corporate situation and investment environment, it is necessary to establish a reference infrastructure to secure the objectivity and reliability of the technology valuation results. In this study, we investigate the evaluation infrastructure built by each institution and examine whether the latest artificial neural networks and deep learning technologies are applicable for performing predictive simulation of technology values based on principal variables, and predicting sales estimates and qualitative evaluation scores in order to embed onto the technology valuation system.
Keywords
Technology Valuation; Deep Learning; Artificial Intelligence; Technology Assessment System; Sales Prediction; Qualitative Evaluation Indicators;
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1 산업통상자원부.한국산업기술진흥원, 기술금융지원사업 종합성과분석 보고서, 2018.
2 한국과학기술정보연구원(KISTI), 기술가치평가시스템, http://www.starvalue.or.kr
3 성태응, 전승표, 김상국, 박현우, "웹기반 지능형 기술가치평가 시스템에 관한 연구," 지능정보연구, 제23 권, 제1호, pp.23-46, 2017.   DOI
4 M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, and prospects," Science, Vol.349, No.6245, pp.255-260, 2015.   DOI
5 E. J. Han and S. Y. Sohn, "Patent valuation based on text mining and survival analysis," J. Technol. Transf., Vol.40, pp.821-839, 2015.   DOI
6 C. Giri, S. Thomassey, J. Balkow, and X. Zeng, "Forecasting New Apparel Sales Using Deep Learning and Nonlinear Neural Network Regression," 2019 International Conference on Engineering, Science, and Industrial Applications (ICESI), pp.1-6, 2019.
7 V. C. Nitesh, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic minority over-sampling technique," J. Artific. Intell. Res., Vol.16, pp.321-357, 2002.   DOI
8 W. Jiang, Y. Hong, B. Zhou, X. He, and C. Cheng, "A GAN-Based Anomaly Detection Approach for Imbalanced Industrial Time Series," IEEE Access, Vol.7, pp.143608-143619, 2019.   DOI
9 박현우, 이종택, "초기단계 기술의 가치평가 방법론 적용 프레임워크," 기술혁신학회지, 제15권, 제2호, pp.242-261, 2012.
10 F. Zhou, S. Yang, H. Fujita, D. Chen, and C. Wen, "Deep learning fault diagnosis method based on global optimization GAN for unbalanced data," Knowledge-Based Systems, Vol.187, 104837, 2020.
11 M. Galar, A. Fernandez, E. Barrenechea, and F. Herrera, "EUSBoost: Enhancing ensembles for highly imbalance data-sets by evolutionary undersampling," Pattern Recogn, Vol.46, No.12, pp.3460-3471, 2013.   DOI
12 이준원, 윤점열, "기술력평가모형의 기술금융 활동 적합성 연구," 기술혁신학회지, 제20권, 제2호, pp.292-312, 2017.
13 국경완, 인공지능 기술 및 산업 분야별 적용 사례, 정보통신기획평가원, 2019.
14 정영임, 인공지능(AI) 부활의 동인과 국내외 기술개발 동향, 정보통신기획평가원, 2016.
15 K. Harsurinder, "A Systematic Review on Imbalanced Data Chanllenges in Machine Learning: Applications and Solutions," ACM Computing Surveys, Vol.52, No.4, Article 79, 2019.
16 산업통상자원부, 기술평가 실무가이드, 2021.
17 J. Cohen, Statistical Power Analysis for the Behavioral Sciences(2nd Ed.), Lawrence ErlBaum Associates, Inc., 1988.