참고문헌 (22)

1. Omar, T., Nehdi, M. L., & Zayed, T., "Infrared thermography model for automated detection of delamination in RC bridge decks", Construction and Building Materials, Vol.168, pp.313~327, 2018, doi: 10.1016/j.conbuildmat.2018.02.126 

   상세보기

2. Sham, J., Chen, N., & Long, L. "Surface crack detection by flash thermography on concrete surface", Insight-Non-Destructive Testing and Condition Monitoring, Vol.50, No.5, pp.240~243, 2008, doi: 10.1784/insi.2008.50.5.240 

   상세보기

3. Mulaveesala, R., Dua, G., & Arora, V., "Applications of Infrared Thermography for Non-destructive Characterization of Concrete Structures", Advances in Structural Health Monitoring, 2019, doi: 10.5772/intechopen.83636 

4. Omar, T., & Nehdi, M. L., "Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography", Automation in Construction, Vol.83, pp.360~371, 2017, doi: 10.1016/j.autcon.2017.06.024 

   상세보기

5. Tong, X., Guo, J., Ling, Y., & Yin, Z. (2011). A new image-based method for concrete bridge bottom crack detection. Proceedings of the 2011 International Conference on Image Analysis and Signal Processing, China, pp.568~571, doi: 10.1109/IASP18108.2011 

   

6. Nguyen, H. N., Kam, T. Y., & Cheng, P. Y. (2012). A novel automatic concrete surface crack identification using isotropic undecimated wavelet transform. Proceedings of the 2012 International Symposium on Intelligent Signal Processing and Communications Systems, Taiwan, pp.766~771, doi: 10.1109/ISPACS20401.2012 

   

7. Zhu, J., & Song, J., "An Intelligent Classification Model for Surface Defects on Cement Concrete Bridges", Applied Sciences, Vol.10, No.3, pp.972, 2020, doi: 10.3390/app10030972 

   상세보기

8. Fisher, R. A., "Statistical Methods for Research Workers", Breakthroughs in Statistics, Springer, pp.66~70, 1992, doi: 10.1007/978-1-4612-4380-9_6 

9. Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep Sparse Rectifier Neural Networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, USA, pp.315~323, Retrieved from http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf 

10. Dumitru, C., & Maria, V., "Advantages and Disadvantages of Using Neural Networks for Predictions", Ovidius University Annals, Series Economic Sciences, Vol.13, No.1, pp.444~449, 2013 

11. Lee, J. (2020). Evaluation of concrete crack depth using UAV, thermal images, and artificial intelligence (Master's thesis). Korea University, Seoul, South Korea. 

12. Liaw, A., & Wiener, M., "Classification and Regression by randomForest", R News, Vol.2, No.3, pp.18~22, 2002, Retrieved from https://cogns.northwestern.edu/cbmg/LiawAndWiener2002.pdf 

13. Zhang, C., & Ma, Y., "Ensemble Machine Learning: Methods and Applications", Springer Science & Business Media, 2012. 

14. Friedman, J. H., "Greedy Function Approximation: A Gradient Boosting Machine", Vol.29, No.5, Annals of Statistics, pp.1189~1232, 2001, doi: 10.1214/aos/1013203451 

    상세보기

15. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J., "Practical Machine Learning Tools and Techniques", 2nd ed., Morgan Kaufmann, pp.578, 2005. 

16. Zhang, D., "A Coefficient of Determination for Generalized Linear Models", The American Statistician, Vol.71, No.4, pp.310~316, 2017, doi: 10.1080/00031305.2016.1256839 

    상세보기

17. De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F., "Mean Absolute Percentage Error for regression models", Neurocomputing, Vol.192, pp.38~48, 2016, doi: 10.1016/j.neucom.2015.12.114 

    상세보기

18. Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P., "Understanding variable importances in forests of randomized trees", Advances in Neural Information Processing Systems, Vol.26, 2013 

19. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... Duchesnay, E., "Scikit-learn: Machine learning in Python", Journal of Machine Learning Research, Vol.12, pp.2825~2830, 2011, Retrieved from https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf 

    상세보기

20. Altmann, A., Tolosi, L., Sander, O., & Lengauer, T., "Permutation importance: a corrected feature importance measure", Bioinformatics, Vol.26, No.10, pp.1340~1347, 2010, doi: 10.1093/bioinformatics/btq134 

    상세보기

21. Cutler, D. R., Edwards Jr., T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J., "Random Forests for Classification in Ecology", Ecology, Vol.88, No.11, pp.2783~2792, 2007, doi: 10.1890/07-0539.1 

    상세보기

22. Molnar, C., "Interpretable machine learning", Lulu.com, 2020. 

저자의 다른 논문 :

박민재 (4) 주영규 (52)