J U L Y 2 0 2 1 • M A T E R I A L S E V A L U A T I O N 751 Mattar, R.A., and R. Kalai, 2018, “Development of a Wall-Sticking Drone for Non-destructive Ultrasonic and Corrosion Testing,” Drones, Vol. 2, No. 1, https://doi.org/10.3390/drones2010008 Meng, H., K. Ouyang, G. Bao, and S. Cai, 2018, “The Inspection Module of Robot for Detecting Large CFB Boiler Furnace Wear,” 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR), pp. 145–149, https://doi.org/10.1109/RCAR.2018.8621789 Meyendorf, N.G., L.J. Bond, J. Curtis-Beard, S. Heilmann, S. Pal, R. Schallert, H. Scholz, and C. Wunderlich, 2017, “NDE 4.0—NDE for the 21st Century—the Internet of Things and Cyber Physical Systems Will Revolutionize NDE,” Proceedings of the 15th Asia Pacific Conference for Non-Destructive Testing (APCNDT 2017), Singapore Miro, J.V., D. Hunt, N. Ulapane, and M. Behrens, 2018, “Towards Auto- matic Robotic NDT Dense Mapping for Pipeline Integrity Inspection,” Field and Service Robotics, pp. 319–333, https://doi.org/10.1007/978-3 -319-67361-5_21 Musyafa, A., and H. Adiyagsa, 2012, “Hazard and Operability Study in Boiler System of the Steam Power Plant,” IEESE International Journal of Science and Technology (IJSTE), Vol. 1, No. 3, pp. 1–10 Natesan, K., 2002, “High-Temperature Corrosion in Power-Generating Systems,” 7th Polish Corrosion Conference Korozja 2002, 17–21 June, Kraków, Poland Noble, W.S., 2006, “What Is a Support Vector Machine?” Nature Biotech- nology, Vol. 24, pp. 1565–1567, https://doi.org/10.1038/nbt1206-1565 ROS, 2021, “About ROS (Robot Operating System),” https://www.ros.org/about-ros/, accessed 1 June 2021 Schmidt, M., G. Fung, and R. Rosales, 2007, “Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches,” Machine Learning: ECML 2007, Lecture Notes in Computer Science, Vol. 4701, pp. 286–297, https://doi.org/10.1007/978-3-540-74958-5_28 Ullmann, I., P. Egerer, J. Schür, and M. Vossiek, 2020, “Automated Defect Detection for Non-Destructive Evaluation by Radar Imaging and Machine Learning,” 2020 German Microwave Conference (GeMiC), pp. 25–28 Wu, Z., C. Shen, and A. van den Hengel, 2019, “Wider or Deeper: Revis- iting the ResNet Model for Visual Recognition,” Pattern Recognition, Vol. 90, pp. 119–133, https://doi.org/10.1016/j.patcog.2019.01.006 Yu, Z., Y. Fu, L. Jiang, and F. Yang, 2021, “Detection of Circumferential Cracks in Heat Exchanger Tubes Using Pulsed Eddy Current Testing,” NDT & E International, Vol. 121, https://doi.org/10.1016/j.ndteint .2021.102444 Zhang, H., K. Dana, J. Shi, Z. Zhang, X. Wang, A. Tyagi, and A. Agrawal, 2018, “Context Encoding for Semantic Segmentation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7151–7160 Zhang, Y., Y. Gong, Y. Wang, and K. Han, 2020, “Analysis of Nondestruc- tive Testing Method for Transverse Crack of Water Wall of Supercritical Boiler,” IOP Conference Series: Earth and Environmental Science, Vol. 512, https://doi.org/10.1088/1755-1315/512/1/012149 Zhu, P., Y. Cheng, P. Banerjee, A. Tamburrino, and Y. Deng, 2019, “A Novel Machine Learning Model for Eddy Current Testing with Uncertainty,” NDT & E International, Vol. 101, pp. 104–112, https://doi.org/10.1016/j.ndteint.2018.09.010
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