| 參考文獻 |
1. Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional
neural network. 2017 International Conference on Engineering and Technology (ICET),
2. Alves-Lima, D., Song, J., Li, X., Portieri, A., Shen, Y., Zeitler, J. A., & Lin, H. (2020).
Review of terahertz pulsed imaging for pharmaceutical film coating analysis. Sensors, 20(5),
1441.
3. Angst, U. M. (2018). Challenges and opportunities in corrosion of steel in concrete.
Materials and Structures, 51(1), 1-20.
4. Anzanello, M. J., & Fogliatto, F. S. (2011). Learning curve models and applications:
Literature review and research directions. International Journal of Industrial Ergonomics,
41(5), 573-583.
5. Arabasadi, Z., Khorasani, M., Akhlaghi, S., Fazilat, H., Gedde, U. W., Hedenqvist, M. S., &
Shiri, M. E. (2013). Prediction and optimization of fireproofing properties of intumescent
flame retardant coatings using artificial intelligence techniques. Fire Safety Journal, 61, 193-
199.
6. Bai, X., Wang, L., Luo, X., Mi, H., Chen, H., Liu, L., Ji, M., & Gao, Y. (2020). A Layer
Tracking Method for Ice Thickness Detection Based on GPR Mounted on the UAV. 2020
4th International Conference on Imaging, Signal Processing and Communications (ICISPC),
7. Belyadi, H., & Haghighat, A. (2021). Machine Learning Guide for Oil and Gas Using Python:
A Step-by-step Breakdown with Data, Algorithms, Codes, and Applications. Gulf
Professional Publishing.
8. Benoit, M., Bataillon, C., Gwinner, B., Miserque, F., Orazem, M. E., Sánchez-Sánchez, C.
M., Tribollet, B., & Vivier, V. (2016). Comparison of different methods for measuring the
passive film thickness on metals. Electrochimica Acta, 201, 340-347.
9. Botev, Z. I., Kroese, D. P., Rubinstein, R. Y., & L’Ecuyer, P. (2013). The cross-entropy
method for optimization. In Handbook of statistics (Vol. 31, pp. 35-59). Elsevier.
53
10. Brijain, M., Patel, R., Kushik, M., & Rana, K. (2014). A survey on decision tree algorithm
for classification.
11. Cai, Z., Deng, S., Liao, H., Zeng, C., & Montavon, G. (2014). The effect of spray distance
and scanning step on the coating thickness uniformity in cold spray process. Journal of
thermal spray technology, 23(3), 354-362.
12. Cai, Z., Liang, H., Quan, S., Deng, S., Zeng, C., & Zhang, F. (2015). Computer-aided robot
trajectory auto-generation strategy in thermal spraying. Journal of thermal spray technology,
24(7), 1235-1245.
13. Calders, T., & Verwer, S. (2010). Three naive bayes approaches for discrimination-free
classification. Data mining and knowledge discovery, 21(2), 277-292.
14. Camastra, F., & Vinciarelli, A. (2015). Machine learning for audio, image and video analysis:
theory and applications. Springer.
15. Celebi, M. E., & Aydin, K. (2016). Unsupervised learning algorithms. Springer.
16. Chidhambara, K., & Shankar, B. L. (2018). Optimization of robotic spray painting process
parameters using taguchi method. IOP Conference Series: Materials Science and
Engineering,
17. data_science_python.data_science_python. https://radimrehurek.com/data_science_python/
18. Dumoulin, V., & Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv
preprint arXiv:1603.07285.
19. Dwivedi, D., Lepková, K., & Becker, T. (2017). Carbon steel corrosion: a review of key
surface properties and characterization methods. RSC advances, 7(8), 4580-4610.
20. Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27(8), 861-
874.
21. Fisher, D. H., Pazzani, M. J., & Langley, P. (2014). Concept formation: Knowledge and
experience in unsupervised learning. Morgan Kaufmann.
22. García-Pedrajas, N., Del Castillo, J. A. R., & Cerruela-García, G. (2015). A proposal for
local $ k $ values for $ k $-nearest neighbor rule. IEEE transactions on neural networks and
learning systems, 28(2), 470-475.
23. Giurlani, W., Berretti, E., Innocenti, M., & Lavacchi, A. (2020). Measuring the thickness of
metal coatings: a review of the methods. Coatings, 10(12), 1211.
24. Goyal, T., Walia, R. S., & Sidhu, T. (2012). Study of coating thickness of cold spray process
using Taguchi method. Materials and Manufacturing Processes, 27(2), 185-192.
25. Hastie, T., Tibshirani, R., & Friedman, J. (2009). Overview of supervised learning. In The
elements of statistical learning (pp. 9-41). Springer.
26. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition.
Proceedings of the IEEE conference on computer vision and pattern recognition,
27. Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science,
349(6245), 261-266.
54
28. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M.,
& Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision
applications. arXiv preprint arXiv:1704.04861.
29. Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications
of support vector machine (SVM) learning in cancer genomics. Cancer genomics &
proteomics, 15(1), 41-51.
30. Itoh, Y., Hirohata, M., Hosoi, A., & Sugiura, Y. (2013). Anticorrosive performance of repair
painting as remedy for deterioration in metallised steel. Corrosion engineering, science and
technology, 48(7), 537-551.
31. Jadon, S. (2020). A survey of loss functions for semantic segmentation. 2020 IEEE
Conference on Computational Intelligence in Bioinformatics and Computational Biology
(CIBCB),
32. Jamali, S. S., & Mills, D. J. (2014). Steel surface preparation prior to painting and its impact
on protective performance of organic coating. Progress in Organic Coatings, 77(12), 2091-
2099.
33. Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent
architectures of deep convolutional neural networks. Artificial Intelligence Review, 53(8),
5455-5516.
34. Kim, W., Jo, J. H., Kim, D., Kim, D., & Hong, S. (2016). Development of Auto-spray system
to improve the quality of 3D Scanning Quality. Journal of the Korea Academia-Industrial
Cooperation Society, 17(4), 100-105.
35. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint
arXiv:1412.6980.
36. Kucukler, M., Xing, Z., & Gardner, L. (2020). Behaviour and design of stainless steel Isection
columns in fire. Journal of Constructional Steel Research, 165, 105890.
37. Li, Y. (2017). Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274.
38. Lyon, S. B., Bingham, R., & Mills, D. J. (2017). Advances in corrosion protection by organic
coatings: What we know and what we would like to know. Progress in Organic Coatings,
102, 2-7.
39. Mills, D. J., & Jamali, S. S. (2017). The best tests for anti-corrosive paints. And why: A
personal viewpoint. Progress in Organic Coatings, 102, 8-17.
40. Mohamed, I. S. (2017). Detection and tracking of pallets using a laser rangefinder and
machine learning techniques European Master on Advanced Robotics+(EMARO+),
University of Genova, Italy].
41. Mousavifard, S., Nouri, P. M., Attar, M., & Ramezanzadeh, B. (2013). The effects of zinc
aluminum phosphate (ZPA) and zinc aluminum polyphosphate (ZAPP) mixtures on
corrosion inhibition performance of epoxy/polyamide coating. Journal of Industrial and
Engineering Chemistry, 19(3), 1031-1039.
55
42. Naderi, R., & Attar, M. (2010). The role of zinc aluminum phosphate anticorrosive pigment
in protective performance and cathodic disbondment of epoxy coating. Corrosion Science,
52(4), 1291-1296.
43. Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011). Natural language
processing: an introduction. Journal of the American Medical Informatics Association, 18(5),
544-551.
44. Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons. b,
4, 51-62.
45. Nie, J.-G., Zhu, L., Tao, M.-X., & Tang, L. (2013). Shear strength of trapezoidal corrugated
steel webs. Journal of Constructional Steel Research, 85, 105-115.
46. Olisa, S. C., Khan, M. A., & Starr, A. (2021). Review of Current Guided Wave Ultrasonic
Testing (GWUT) Limitations and Future Directions. Sensors, 21(3), 811.
47. Oshiro, T. M., Perez, P. S., & Baranauskas, J. A. (2012). How many trees in a random forest?
International workshop on machine learning and data mining in pattern recognition,
48. Poursaee, A. (2016). Corrosion of steel in concrete structures. In Corrosion of steel in
concrete structures (pp. 19-33). Elsevier.
49. Pradhan, A. (2012). Support vector machine-a survey. International Journal of Emerging
Technology and Advanced Engineering, 2(8), 82-85.
50. Priyam, A., Abhijeeta, G., Rathee, A., & Srivastava, S. (2013). Comparative analysis of
decision tree classification algorithms. International Journal of current engineering and
technology, 3(2), 334-337.
51. RainbowPaint.https://www.rainbowpaint.com.tw/products/heavy-dutycoatings/epoxy/1076-
epoxy-zinc-phosphate-primer
52. Reis, A., Lopes, N., & Real, P. V. (2019). Ultimate shear strength of steel plate girders at
normal and fire conditions. Thin-Walled Structures, 137, 318-330.
53. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint
arXiv:1609.04747.
54. Russe, I.-S., Brock, D., Knop, K., Kleinebudde, P., & Zeitler, J. A. (2012). Validation of
terahertz coating thickness measurements using X-ray microtomography. Molecular
Pharmaceutics, 9(12), 3551-3559.
55. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). Mobilenetv2:
Inverted residuals and linear bottlenecks. Proceedings of the IEEE conference on computer
vision and pattern recognition,
56. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks,
61, 85-117.
57. Schwing, A. G., & Urtasun, R. (2015). Fully connected deep structured networks. arXiv
preprint arXiv:1503.02351.
58. Shan, M., Cheng, Q., Zhong, Z., Liu, B., & Zhang, Y. (2020). Deep-learning-enhanced ice
thickness measurement using Raman scattering. Optics express, 28(1), 48-56.
56
59. Shang, H., Ma, K., Wei, Y., & Lu, Y. (2019). Experimental studies on shear resistance
performances for the shear key of H shape steel spatial grid roofs. Latin American Journal
of Solids and Structures, 16.
60. Sharma, S., Sharma, S., & Athaiya, A. (2017). Activation functions in neural networks.
towards data science, 6(12), 310-316.
61. Singh, A., Yadav, A., & Rana, A. (2013). K-means with Three different Distance Metrics.
International Journal of Computer Applications, 67(10).
62. Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia medica, 24(1),
12-18.
63. Standard, N. (2012). Surface preparation and protective coating. NORSOK M-501, Norway.
64. Sun, M., Song, Z., Jiang, X., Pan, J., & Pang, Y. (2017). Learning pooling for convolutional
neural network. Neurocomputing, 224, 96-104.
65. Szepesvári, C. (2010). Algorithms for reinforcement learning. Synthesis lectures on artificial
intelligence and machine learning, 4(1), 1-103.
66. Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A survey on deep transfer
learning. International conference on artificial neural networks,
67. Tayade, A., Thakur, M., Vathari, L., & Kuchiwale, S. (2020). Interactive Speech Recognition
Agent System using AI.
68. Thierry, D., Prosek, T., Le Bozec, N., & Diller, E. (2011). Corrosion protection and corrosion
mechanisms of continuous galvanised steel sheet with focus on new coating alloys. Proc.
Galvatech ‘11, 8th International Conference on Zinc and Zinc Alloy Coated Steel Sheet,
Genova, Italy,
69. Umadevi, S., & Marseline, K. J. (2017). A survey on data mining classification algorithms.
2017 International Conference on Signal Processing and Communication (ICSPC),
70. Wang, B., Ma, F., Ge, L., Ma, H., Wang, H., & Mohamed, M. A. (2020). Icing-EdgeNet: a
pruning lightweight edge intelligent method of discriminative driving channel for ice
thickness of transmission lines. IEEE Transactions on Instrumentation and Measurement,
70, 1-12.
71. Wang, H.-C., Zyuzin, A., & Mamishev, A. V. (2013). Measurement of coating thickness and
loading using concentric fringing electric field sensors. IEEE Sensors Journal, 14(1), 68-78.
72. Wang, Y.-B., Li, G.-Q., Chen, S.-W., & Sun, F.-F. (2012). Experimental and numerical study
on the behavior of axially compressed high strength steel columns with H-section.
Engineering Structures, 43, 149-159.
73. Wang, Y., Li, Y., Song, Y., & Rong, X. (2020). The influence of the activation function in a
convolution neural network model of facial expression recognition. Applied Sciences, 10(5),
1897.
74. Wang, M., Lu, S., Zhu, D., Lin, J., & Wang, Z. (2018, October). A high-speed and lowcomplexity
architecture for softmax function in deep learning. In 2018 IEEE Asia Pacific
Conference on Circuits and Systems (APCCAS) (pp. 223-226). IEEE.
57
75. Xu, C., He, L., Xiao, D., Ma, P., & Wang, Q. (2020). A novel high-frequency ultrasonic
approach for evaluation of homogeneity and measurement of sprayed coating thickness.
Coatings, 10(7), 676.
76. Yang, F., Zhang, W., Tao, L., & Ma, J. (2020). Transfer learning strategies for deep learningbased
PHM algorithms. Applied Sciences, 10(7), 2361.
77. Zakwan, F., Krishnamoorthy, R., Ibrahim, A., & Ismail, R. (2020). Finite Element Analysis
of Coated (Intumescent Coating Protection) Cellular Steel Beam (CSB) Expose to Fire. IOP
Conference Series: Materials Science and Engineering,
78. Zhang, D., Zhou, X., Leung, S. C., & Zheng, J. (2010). Vertical bagging decision trees model
for credit scoring. Expert Systems with Applications, 37(12), 7838-7843.
79. Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2017). Efficient kNN classification with
different numbers of nearest neighbors. IEEE transactions on neural networks and learning
systems, 29(5), 1774-1785.
80. Zhao, Z., Liu, G., & Li, D. (2019). Deposition Thickness Detection Method based on Dust
Distribution Law of Ventilation Dust Removal Pipeline. 2019 2nd International Conference
on Safety Produce Informatization (IICSPI),
81. Zheng, H., Chen, Z., & Xu, J. (2016). Bond behavior of H-shaped steel embedded in recycled
aggregate concrete under push-out loads. International Journal of Steel Structures, 16(2),
347-360.
82. 公共工程委員會. 防蝕塗裝.
https://pcces.pcc.gov.tw/CSInew/Default.aspx?FunID=Fun_14&SearchText=09971 |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
至系統瀏覽論文 (2025-6-14以後開放)
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit