한국전자통신학회논문지 (The Journal of the Korea institute of electronic communication sciences)

한국전자통신학회 (Korea Institute of Electronic Communication Science)
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딥러닝 모델을 이용한 전자 입찰에서의 예정가격 예측

Prediction of Budget Prices in Electronic Bidding using Deep Learning Model

  • 이은서 (전남대학교 전기 및 반도체공학과) ;
  • 박귀만 (전남대학교 전기 및 반도체공학과) ;
  • 이지은 (한양사이버대학교) ;
  • 배영철 (전남대학교 전기컴퓨터공학부)
  • 투고 : 2023.10.27
  • 심사 : 2023.12.27
  • 발행 : 2023.12.31
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초록

본 논문은 입찰사이트 전기넷과 OK EMS에서 입수한 입찰데이터로 DNBP(Deep learning Network to predict Budget Price) 모델을 통해 예정가격을 예측한다. 우리는 DNBP 모델을 활용하여 4개의 추첨예비가격을 예측을 하고, 이를 산술평균 한 뒤 예정가격 사정률을 계산하여, 실제 예정가격 사정률과 비교하여 모델의 성능을 평가한다. DNBP의 15개의 입력노드 중 일부 입력노드를 제거하여 모델을 학습시켰다. 예측 결과 예측 결과 입력노드가 6개(a, g, h, i, j, k) 일 때 DNBP의 RMSE가 0.75788% 로 가장 낮았다.

In this paper, we predicts the estimated price using the DNBP (Deep learning Network to predict Budget Price) model with bidding data obtained from the bidding websites, ElecNet and OK EMS. We use the DNBP model to predict four lottery preliminary price, calculate their arithmetic mean, and then estimate the expected budget price ratio. We evaluate the model's performance by comparing it with the actual expected budget price ratio. We train the DNBP model by removing some of the 15 input nodes. The prediction results showed the lowest RMSE of 0.75788% when the model had 6 input nodes (a, g, h, i, j, k).

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과제정보

본 과제(결과물)는 2023년도 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 지자체대학 협력기반 지역혁신 사업의 결과입니다.(2021RIS-002)

참고문헌

  1. M. Kang and M. Riew, "Effects of the methods for generating and selecting multiple preliminary prices on budget prices in bidding processes," Journal of the Korea Institute of Building Construction, vol. 15, no. 1, 2015, pp. 89-97. https://doi.org/10.5345/JKIBC.2015.15.1.089
  2. S. Eom and I. Joe, "Prediction of standard price based on multi-linear regression and its regularized models in multiple preliminary prices of the korea on-line e-procurement system (KONEPS)," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 10, 2022, pp. 1576-1585. https://doi.org/10.7840/kics.2022.47.10.1576
  3. D. Kim, "Bidding Equilibrium of the Qualification Review Auction in Public Procurement," The Korean Association Of Public Finance, vol. 13, no. 3, 2020, pp. 61-94.
  4. W. Lee and S.-W. Yi, "Analysis of Factors Affecting Successful Bid Price in Public Construction Technical Bidding", The Institute of Management and Economy Research., vol. 13, no. 1 2022. pp. 213-230. https://doi.org/10.32599/apjb.13.1.202203.213
  5. Lee, S. Woo, and S. Lee, "Analysis of the Influence of Design Score and Price Score for Design Build Bidding," Korean Journal of Construction Engineering and Management, vol. 24, no. 5, 2023, pp. 44-51. https://doi.org/10.6106/KJCEM.2023.24.5.044
  6. G. Bak and Y. Bae, "Performance comparison of machine learning in the various kind of prediction," J. of the Korea Institute of Electronic Communication Science, vol. 14, no. 1, 2019, pp. 169-178.
  7. Y. Han, "A study on motion prediction and subband coding of moving pictures using GRNN," J. of the Korea Institute of Electronic Communication Science, vol. 5, no. 3, 2010, pp. 256-261.
  8. G. Bak, S. Oh, G. Park, and Y. Bae, "Groundwater level prediction using ANFIS algorithm," J. of the Korea Institute of Electronic Communication Science, vol. 16, no. 6, 2021, pp. 1239-1248.
  9. J. Jang, Y. Lee, K. Lee, M. Kim, S. Oh, and S. Park, "The Improvement of predictability for very short range precipitation by blending of VDAPS and MAPLE using micro-genetic algorithm," The Journal of the Korea Institute of Intelligent Systems, vol. 29, no. 4, 2019, pp. 258-267. https://doi.org/10.5391/JKIIS.2019.29.4.258
  10. D. Hwang and Y. Bae, "The prediction of bidding price using deep learning in the electronic bidding," J. of the Korea Institute of Electronic Communication Science, vol. 15, no. 1 2020, pp. 147-152.