참고문헌 (22)

1. Adadi, A. and Berrada, M., Peeking inside the black-box: a survey on explainable artificial intelligence(XAI), IEEE Access, 2018, Vol. 6, pp. 52138-52160. 

   상세보기

2. Ahn, G., Yoo, J. H., Lee, S. H. and Kim, S. B., Explainable convolutional neural networks for multi-sensor data, Journal of the Korean Institute of Industrial Engineers, 2019, Vol. 45, No. 2. 

3. Babu, G.S., Zhao, P., and Li, X.L., Deep convoultional neural network based regression approach for estimation of remaining useful life, International Conference on Database Systems for Advanced Applications, 2016, pp. 214-228. 

4. Choi, Y. H. and Kim, H. S., Prognostics and health management for battery useful life prediction based on electrochemisty model: A tutorial, The Journal of Korean Institute of Communications and Information Sciences, 2017, pp. 939-949. 

5. Hancer, E., Xue, B., and Zhang, M., Differential evolution for filter feature selection based on information theory and feature ranking, Knowledge-based systems, 2018, Vol. 140, pp. 103-119. 

   상세보기

6. Hong, C.W., Lee, C., Lee, K., and Ko, M.S., Kim, D. E. and Hur, K., Remaining useful life prognosis for turbofan engine using explainable deep neural networks with dimensionality reduction, Sensors, 2020, Vol. 20, No. 22, pp. 6626. 

   상세보기

7. Hong, C.W. and Hur, K., Prognosis of the remaining useful life of a turbofan engine using deep neural network and pearson correlation coefficient, Journal of the KNST, 2021, Vol. 4, No. 1. pp. 078-083. 

8. Jung, C.Y. and Lee, H.J., A comparative study of explainable AI techniques for process analysis, Journal of the Institute of Electronics and Information Engineers, 2020, Vol. 57, No. 8, pp. 51-59. 

   상세보기

9. Jung, S.J. and Hur, J.W., Deep learning approaches to RUL prediction of lithium-ion batteries, Journal of the Korean Society of Manufacturing Process Engineers, 2020, vol. 19, no. 12, pp. 21-27. 

10. Ko, G.H., Lim, G.M., and Cho, H.M., Survey on feature attribution methods in explainable AI, Journal of KIISE, 2020, pp. 1181-1191. 

    상세보기

11. Kwon, H.G., Lee, D.K., and Shin, M.S., Dynamic forecasts of bankruptcy with recurrent neural network model, Journal of Intelligence and Information Systems, 2017, Vol. 23, No. 3, pp. 139-153. 

    원문보기 상세보기

12. Laguna, J.O., Olaya, A.G., and Borrajo, D., A dynamic sliding window approach for activity recognition, International conference on user modeling, adaptation, and personlization, 2011, pp. 219-230. 

13. Li, J., Li, X., and He, D., A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction, IEEE Access, 2019, Vol. 7, pp. 75464-75475. 

    상세보기

14. Wang, J. Wen, G., Yang, S., and Liu, Y., Remaining useful life estimation in prognostics using deep bidrectional lstm neural network, IEEE, In 2018 prognostics and system health management conference(PHM-chongqing), 2018, pp. 1037-1042. 

15. Marcilio, W.E. and Eler, D.M., From explanations to feature selection: accessing SHAP values as feature selection mechanism, IEEE, In 2020 33rd SIBGRAPI conference on Graphics, Patterns and Images(SIBGRAPI), 2020, pp. 340-347. 

16. Oh, J.Y., Lee, Y.G., and Kim, G.B., Improvement of solar power forecasting using interpretation of artificial intelligence, The transactions of the Korean Institute of Electrical Engineers, 2020, Vol. 69, No. 7, pp. 1111-1116. 

    상세보기

17. Seijo-Pardo, B., Porto-Diaz, I., Bolon-Canedo, V., and Alonso-Betanzos, A., Ensemble feature selection: homogeneous and heterogeneous approaches, Knowledge-Based Systems, 2017, Vol. 118, pp. 124-139. 

    상세보기

18. We, J.W., Choi, D.J., Lee, H.B., Lim, J.T., Lim, H.J., Bok, K.S., and Yoo, J.S., K-NN query optimization scheme based on machine learning using a dnn model, Journal of the Korea contents association, 2020, Vol. 20, No. 10, pp. 715-725. 

    원문보기 상세보기

19. Yu, S., Wu, Z., Zhu, X., and Pecht, M., A domain adaptive convolutional LSTM model for prognostic remaining useful life estimation under variant conditions, IEEE, In 2019 Prognostics and System Health Management Conference(PHM-Paris), pp. 130-137. 

20. Yun, Y.R., Kim, S.G., Cho, S.H., and Choi, J.H., Neural network based aircraft engine health management using C-mapss data, International Journal of Aerospace System Engineering, 2019, Vol. 13, No. 6, pp. 17-25. 

21. Zhang, A., Wang, H., Li, S., Cui, Y., Liu, Z., Yang, G., and Hu, J., Transfer learning with deep recurrent neural networks for remaining useful life estimation, Applied Sciences, 2018, Vol. 8, No. 12, pp. 2416. 

22. Zhang, X., Wu, G., Dong, Z., and Crawford, C., Embedded feature-selection support vector machine for driving pattern recognition, Journal of the Franklin Institute, 2015, Vol. 352, No. 2, pp. 669-685. 

    상세보기

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