참고문헌 (39)

1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/ 

2. Mater. Eval. J Aldrin 59 11 1313 2001 Aldrin, J., Achenbach, J., Andrew, G., P’an, C., Grills, B., Mullis, R., Spencer, F., Golis, M.: Case study for the implementation of an automated ultrasonic technique to detect fatigue cracks in aircraft weep holes. Mater. Eval. 59(11), 1313-1319 (2001) 

   상세보기

3. Annis, C.: Mil-hdbk-1823a, nondestructive evaluation system reliability assessment. Tech. Rep. (2009). http://www.statisticalengineering.com/mh1823/MIL-HDBK-1823A(2009).pdf 

4. ASTM: Standard practice for probability of detection analysis for hit/miss data. ASTM E2862-12. American Society for Testing and Materials, West Conshohocken (2012) 

5. ASTM: Standard practice for probability of detection analysis for â versus a data. ASTM E3023-15. American Society for Testing and Materials, West Conshohocken (2015) 

6. Bansal, M., Krizhevsky, A., Ogale, A.S.: Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst. CoRR (2018). arXiv:1812.03079 

7. 10.1109/ICASSP.1993.319163 Chen, C.H., Lee, G.G.: Neural networks for ultrasonic NDE signal classification using time-frequency analysis. In: 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp 493-496 (1993) 

   

8. F Chollet 2017 Deep Learning with Python 1 Chollet, F.: Deep Learning with Python, 1st edn. Manning Publications, Greenwich (2017) 

9. 10.1109/cvpr.2017.195 Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017). https://doi.org/10.1109/cvpr.2017.195 

   

10. Chollet, F., et al.: Keras (2015). https://keras.io 

11. Ultrasonics FC Cruz 73 1 2017 10.1016/j.ultras.2016.08.017 Cruz, F.C., Simas Filho, E.F., Albuquerque, M.C., Silva, I.C., Farias, C.T., Gouvea, L.L.: Efficient feature selection for neural network based detection of flaws in steel welded joints using ultrasound testing. Ultrasonics 73, 1-8 (2017). https://doi.org/10.1016/j.ultras.2016.08.017 

    상세보기

12. Constr. Build. Mater. S Dorafshan 186 1031 2018 10.1016/j.conbuildmat.2018.08.011 Dorafshan, S., Thomas, R.J., Maguire, M.: Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Constr. Build. Mater. 186, 1031-1045 (2018). https://doi.org/10.1016/j.conbuildmat.2018.08.011 

    상세보기

13. Russ. J. Nondestruct. Test. C Fei 42 3 190 2006 10.1134/s1061830906030077 Fei, C., Han, Z., Dong, J.: An ultrasonic flaw-classification system with wavelet-packet decomposition, a mutative scale chaotic genetic algorithm, and a support vector machine and its application to petroleum-transporting pipelines. Russ. J. Nondestruct. Test. 42(3), 190-197 (2006). https://doi.org/10.1134/s1061830906030077 

    상세보기

14. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors (2012). arXiv:1207.0580 

15. Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR (2015). arXiv:1502.03167 

16. J. Magn. Magn. Mater. S Kahrobaee 458 317 2018 10.1016/j.jmmm.2018.03.049 Kahrobaee, S., Haghighi, M.S., Akhlaghi, I.A.: Improving nondestructive characterization of dual phase steels using data fusion. J. Magn. Magn. Mater. 458, 317-326 (2018). https://doi.org/10.1016/j.jmmm.2018.03.049 

    상세보기

17. Insight T Koskinen 60 1 42 2018 10.1784/insi.2018.60.1.42 Koskinen, T., Virkkunen, I., Papula, S., Sarikka, T., Haapalainen, J.: Producing a pod curve with emulated signal response data. Insight 60(1), 42-48 (2018). https://doi.org/10.1784/insi.2018.60.1.42 

    상세보기

18. Commun. ACM A Krizhevsky 60 6 84 2017 10.1145/3065386 Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84-90 (2017). https://doi.org/10.1145/3065386 

    상세보기

19. Nat. Commun. S Lapuschkin 10 1096 2019 10.1038/s41467-019-08987-4 Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., Müller, K.R.: Unmasking clever hans predictors and assessing what machines really learn. Nat. Commun. 10, 1096 (2019). https://doi.org/10.1038/s41467-019-08987-4 

    상세보기

20. Comput. Methods Appl. Mech. Eng. S Liu 191 2831 2002 10.1016/S0045-7825(02)00221-9 Liu, S., Huang, J.H., Sung, J., Lee, C.: Detection of cracks using neural network and computational mechanics. Comput. Methods Appl. Mech. Eng. 191, 2831-2845 (2002) 

    상세보기

21. Marcus, G.: Deep learning: a critical appraisal. CoRR (2018). arXiv:1801.00631 

22. NDT & E Int. A Masnata 29 2 87 1996 10.1016/0963-8695(95)00053-4 Masnata, A., Sunser, M.: Neural network classification of flaws detected by ultrasonic means. NDT & E Int. 29(2), 87-93 (1996) 

    상세보기

23. Neurocomputing M Meng 257 128 2017 10.1016/j.neucom.2016.11.066 Meng, M., Chua, Y.J., Wouterson, E., Ong, C.P.K.: Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks. Neurocomputing 257, 128-135 (2017). https://doi.org/10.1016/j.neucom.2016.11.066 

    상세보기

24. 10.1016/j.ultras.2018.12.001 Munir, N., Kim, H.J., Park, J., Song, S.J., Kang, S.S.: Convolutional neural network for ultrasonic weldment flaw classification in noisy conditions. Ultrasonics (2018). https://doi.org/10.1016/j.ultras.2018.12.001 

    상세보기

25. J. Mech. Sci. Technol. N Munir 32 7 3073 2018 10.1007/s12206-018-0610-1 Munir, N., Kim, H.J., Song, S.J., Kang, S.S.: Investigation of deep neural network with drop out for ultrasonic flaw classification in weldments. J. Mech. Sci. Technol. 32(7), 3073-3080 (2018). https://doi.org/10.1007/s12206-018-0610-1 

    상세보기

26. 10.1007/s10921-010-0086-0 Sambath, S., Nagaraj, P., Selvakumar, N.: Automatic defect classification in ultrasonic NDT using artificial intelligence. J. Nondestruct. Eval. 30(1), 20-28 (2010). https://doi.org/10.1007/s10921-010-0086-0 

    상세보기

27. NDT & E Int. NJ Shipway 101 113 2019 10.1016/j.ndteint.2018.10.008 Shipway, N.J., Barden, T.J., Huthwaite, P., Lowe, M.J.S.: Automated defect detection for fluorescent penetrant inspection using random forest. NDT & E Int. 101, 113-123 (2019). https://doi.org/10.1016/j.ndteint.2018.10.008 

    상세보기

28. Ultrasonics LC Silva 102 106057 2020 10.1016/j.ultras.2019.106057 Silva, L.C., Simas Filho, E.F., Albuquerque, M.C., Silva, I.C., Farias, C.T.: Segmented analysis of time-of-flight diffraction ultrasound for flaw detection in welded steel plates using extreme learning machines. Ultrasonics 102, 106057 (2020). https://doi.org/10.1016/j.ultras.2019.106057 

    상세보기

29. Svahn, P.H., Virkkunen, I., Zettervall, T., Snögren, D.: The use of virtual flaws to increase flexibility of qualification. In: 12th European Conference on Non-Destructive Testing (ECNDT 2018), NDT.net, no. 8 in The e-Journal of Nondestructive Testing (2018) 

30. Constr. Build. Mater. Z Tong 169 69 2018 10.1016/j.conbuildmat.2018.02.081 Tong, Z., Gao, J., Zhang, H.: Innovative method for recognizing subgrade defects based on a convolutional neural network. Constr. Build. Mater. 169, 69-82 (2018). https://doi.org/10.1016/j.conbuildmat.2018.02.081 

    상세보기

31. Udpa, L., Ramuhalli, P.: Steam generator management program: Automated analysis of array probe eddy current data. Tech. Rep. 1018559, EPRI, Palo Alto, CA (2009) 

32. Virkkunen, I., Ylitalo, M.: Practical experiences in pod determination for airframe et inspection. In: International Symposium on NDT in Aerospace, 03-11-2016-05-11-2016 (2016) 

33. Virkkunen, I., Miettinen, K., Packalén, T.: Virtual flaws for nde training and qualification. In: 11th European Conference on Non-Destructive Testing (ECNDT 2014) (2014) 

34. Virkkunen, I., Rönneteg, U., Grybäck, T., Emilsson, G., Miettinen, K.: Feasibility study of using eflaws on qualification of nuclear spent fuel disposal canister inspection. http://www.12thnde.com. In: International Conference on Non Destructive Evaluation in Relation to Structural Integrity for Nuclear and Pressurized Components, 04-10-2016-06-10-2016 (2016) 

35. Virkkunen, I., Haapalainen, J., Papula, S., Sarikka, T., Kotamies, J., Hänninen, H.: Effect of feedback and variation on inspection reliability. In: 7th European-American Workshop on Reliability of NDE, German Society for Non-Destructive Testing (2017). https://www.ndt.net/article/reliability2017/papers/12.pdf 

36. KSMME Ent. J. W Yi 12 6 1150 1998 Yi, W., Is, Yun: The defect detection and non-destructive evaluation in weld zone of austenitic stainless steel 304 using neural network-ultrasonic wave. KSMME Ent. J. 12(6), 1150-1161 (1998) 

37. Zeiler, M.D.: Adadelta: an adaptive learning rate method (2012). arXiv:1212.5701 

38. IEEE Trans. Pattern Anal. Mach. Intell. X Zhang 38 10 1943 2016 10.1109/TPAMI.2015.2502579 Zhang, X., Zou, J., He, K., Sun, J.: Accelerating very deep convolutional networks for classification and detection. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 1943-1955 (2016). https://doi.org/10.1109/TPAMI.2015.2502579 

    상세보기

39. NDT & E Int. P Zhu 101 104 2019 10.1016/j.ndteint.2018.09.010 Zhu, P., Cheng, Y., Banerjee, P., Tamburrino, A., Deng, Y.: A novel machine learning model for eddy current testing with uncertainty. NDT & E Int. 101, 104-112 (2019). https://doi.org/10.1016/j.ndteint.2018.09.010 

    상세보기