References
- Diederik, P., & Ba, J. L. (2015). Adam: Amethod for stochastic optimization, In The 3rd international conference on learning representations (ICLR), San Diego (pp. 1–15). https://arxiv.org/abs/1412.6980v9
- Fujiyama, K. (2012). The Frontier of Technology Development in Remaining Life Assessment for High Temperature Components. Journal of the Society of Materials Science, Japan, 61(11), 919–36. https://doi.org/10.2472/jsms.61.919
- Fujiyama, K., & Harada, K. (2015). Akihiro Ogawa and Hirohisa Kimachi, “EBSD Analysis of Grain Strain Distribution for Creep Damaged SUS304HTB. Journal of the Society of Materials Science, Japan, 64(2), 88–93. https://doi.org/10.2472/jsms.64.88
- Fujiyama, K., Mizutani, Y., Taniguchi, Y., & Kimachi, H. (2013). EBSD Analysis of Creep Damage Process in SUS304HTB Steel. Journal of the Society of Materials Science, Japan, 62(5), 305–310. https://doi.org/10.2472/jsms.62.305
- Fujiyama, K., Ogawa, A., Harada, K., & Kimachi, H. (2015). EBSD Analysis of Grain Strain Distribution for Creep Damaged Modified 9Cr Steel. Journal of the Society of Materials Science, Japan, 64(2), 94–99. https://doi.org/10.2472/jsms.64.94
- Fukuda, M. (2014). Advanced USC Technology Development. Journal of Smart Processing, 3(2), 78–85. https://doi.org/10.7791/jspmee.3.78
- Hara, K., Saito, D., & Shouno, H., “Analysis of Function of Rectified Linear Unit Used in Deep Learning”, 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney. https://doi.org/10.1109/IJCNN.2015.7280578
- Harada, K., “Evaluation of grain strain distribution in creep and creep-fatigue damaged SUS304HTB steel through EBSD observation”, Master Thesis (2015), Meijo University.
- Imaging solution, available from https://imagingsolution.blog.fc2.com/blog-entry-20.html, (10 December, 2019).
- Kurashige, Y., “Investigation on the applicability of machine learning and statistical analysis of creep and creep-fatigue damage evaluation for heat resistant steel”, Master Thesis (2020), Meijo University.
- Kurashige, Y., & Fujiyama, K. (2019). Development of creep damage AI evaluation system for austenitic stainless steel. Transactions of the Japan Society of Mechanical Engineers (In Japanese), 85(878), 1–9. https://doi.org/10.1299/transjsme.18-00436
- Kurashige, Y., & Fujiyama, K. (2020). Application of Machine Learning and Statistical Analysis to Creep and Creep-Fatigue Damage Evaluation for Austenitic Stainless Steel. Journal of the Society of Materials Science, Japan, 69(9), 666–671. https://doi.org/10.2472/jsms.69.666
- Kuroda, M., Kamaya, M., Mori, T., & Izaki, T. (2013). Detection of Fatigue Damage in Stainless Steel by EBSD Analysis (Analysis Focused on Grain Boundaries). Transactions of the Japan Society of Mechanical Engineers, Series A, 79(807), 1690–1694. https://doi.org/10.1299/kikaia.79.1690
- Nakamura, T., Kataoka, O., Yoshimura, H., Hirata, H., & Fujiyama, K. (2021). EBSD Evaluation of Creep Damage Process for Fine Grained Heat Affected Zone of Serviced Mod.9Cr-1Mo Steel. Journal of the Society of Materials Science, Japan, 70(2), 169–176. https://doi.org/10.2472/jsms.70.169
- Nomura, K., Kubushiro, K., Sakakibara, Y., Takahashi, S., & Yoshizawa, H. (2012). Effect of Grain Size on Plastic Strain Analysis by EBSD for Austenitic Stainless Steels with Tensile Strain at 650°C. Journal of the Society of Materials Science, Japan, 61(4), 371–376. https://doi.org/10.2472/jsms.61.371
- Nomura, Y., & Shigemura, K. (2019). Development of Real-Time Screening System for Structural Surface Damage Using Object Detection and Generative Model Based on Deep Learning. Journal of the Society of Materials Science, Japan, 68(3), 250–257. https://doi.org/10.2472/jsms.68.250
- Ogata, T. (2012). Damage Evaluation Method for High-Temperature Components in Thermal Power Plants. Transactions of the Japan Society of Mechanical Engineers, Series A, 78(789), 694–707. https://doi.org/10.1299/kikaia.78.694
- Oinuma, S., Takaku, R., Nakatani, Y., & Takeyama, M. (2021). Effect of Microstructure and Deformation on Hardness Distribution of Wrought Precipitation Strengthened Ni-Based Alloy after Creep Damage. Journal of the Society of Materials Science, Japan, 70(3), 258–263. https://doi.org/10.2472/jsms.70.258
- Pieter-Tjerk De, B., Kroese, D. P., Mannor, S., & Rubinstein, R. Y. (2005). A Tutorial on the Cross-Entropy Method. In Manufactured in The Netherlands, Annals of Operations Research (Vol. 134, 2005, pp. 19–67). Springer Science + Business Media, Inc. https://doi.org/10.1007/s10479-005-5724-z
- Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M.-L., Chen, S.-C., & Iyengar, S. S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys, 51(5), 1–36. https://doi.org/10.1145/3234150.
- Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. WIREs Data Mining and Knowledge Discovery, 8(4), 1–18. https://doi.org/10.1002/widm.1249
- Santurkar, S., Tsipras, D., Ilyas, A., & Madry, A. (2018). How does batch normalization help optimization? In The 32nd conference on Neural Information Processing System (NeurIPS 2018), Montreal. https://arxiv.org/abs/1805.11604v5
- Shigemura, K., & Nomura, Y. (2020). A Two-Step Screening System for Surface Crack Using Object Detection and Recognition Technique Based on Deep Learning. Journal of the Society of Materials Science, Japan, 69(3), 218–225. https://doi.org/10.2472/jsms.69.218
- Toshikazu, F., Ryohei, F., Daisuke, O., & Masashi, S. (2017). Outlook for big data and machine learning. Journal of Information Processing and Management, 60(8), 543–554. https://doi.org/10.1241/johokanri.60.543
- Werbos, P. J. (1990). Backpropagation Through Time: What It Does and How to Do It. Proceedings of the IEEE, 78(10), 1550–1560. https://doi.org/10.1109/5.58337
- Yoda, R., Kamaya, M., Kimura, H., Ohtani, T., & Fujiyama, K. (2017). Round Robin Test Using EBSD for Creep Damage Evaluation. Journal of the Society of Materials Science, Japan, 66(2), 130–137. https://doi.org/10.2472/jsms.66.130
- Zhang, S., & Fukutomi, H. (2021). Creep Rupture Behavior for Welded Pipe of Ni-Based Alloy Using Full Thickness Specimen and Application of Nondestructive Damage Detection. Journal of the Society of Materials Science, Japan, 70(2), 184–190. https://doi.org/10.2472/jsms.70.184