최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국공간구조학회논문집 = Journal of the Korean Association for Spatial Structures, v.21 no.2, 2021년, pp.99 - 110
김지형 (고려대학교 건축사회환경공학과) , 장아름 (고려대학교 건축사회환경공학과) , 박민재 (고려대학교 건축사회환경공학부) , 주영규 (고려대학교 건축사회환경공학부)
This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental p...
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
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
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
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
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
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
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
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
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
Lee, J. (2020). Evaluation of concrete crack depth using UAV, thermal images, and artificial intelligence (Master's thesis). Korea University, Seoul, South Korea.
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
Zhang, C., & Ma, Y., "Ensemble Machine Learning: Methods and Applications", Springer Science & Business Media, 2012.
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
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J., "Practical Machine Learning Tools and Techniques", 2nd ed., Morgan Kaufmann, pp.578, 2005.
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
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
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
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
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
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
Molnar, C., "Interpretable machine learning", Lulu.com, 2020.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.