SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Bernardo Lemos(bernardolemos@dcc.ufmg.br)
2 Antônio Neves(antonioneto@dcc.ufmg.br)
3 Marcos Carvalho(marcoscarvalho@dcc.ufmg.br)
4 Sergio Canuto(sergio.canuto@ifg.edu.br)
5 Jorge Ribeiro(jwribeiro@isqbrasil.com.br)
6 Rodrigo Pires(rropires@isqbrasil.com.br)
7 Jussara Almeida(jussara@dcc.ufmg.br)
8 Marcos André Gonçalves(mgoncalv@dcc.ufmg.br)
9 Douglas Santos(douglas.santos@petrogalbrasil.com)
10 André Fonseca(andre.gama@galp.com)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Aldosari, H., Elfouly, R., and Ammar, R. (2020). Evaluation of machine learning-based regression techniques for prediction of oil and gas pipelines defect. In 2020 International Conference on Computational Science and Computational Intelligence (CSCI), pages 1452–1456.
2 Bender, Roman, Damien Féron, Douglas Mills, Stefan Ritter, Ralph Bäßler, Dirk Bettge, Iris De Graeve et al. "Corrosion challenges towards a sustainable society." Materials and corrosion 73, no. 11 (2022): 1730-1751.
3 Heuel, Janis, and Wolfgang Friederich. "Suppression of wind turbine noise from seismological data using nonlinear thresholding and denoising autoencoder." Journal of Seismology 26, no. 5 (2022): 913-934.
4 Kaji, Mohammadreza, Jamshid Parvizian, and Hans Wernher van de Venn. "Constructing a reliable health indicator for bearings using convolutional autoencoder and continuous wavelet transform." Applied Sciences 10, no. 24 (2020): 8948.
5 Keogh, Eamonn, Kaushik Chakrabarti, Michael Pazzani, and Sharad Mehrotra. "Dimensionality reduction for fast similarity search in large time series databases." Knowledge and information Systems 3, no. 3 (2001): 263-286.
6 Li, P., Pei, Y., & Li, J. (2023). A comprehensive survey on design and application of autoencoder in deep learning. Applied Soft Computing, 138, 110176.
7 May, Z., Alam, M. K., Nayan, N. A., Rahman, N. A. I. A., & Mahmud, M. S. (2021). Acoustic emission corrosion feature extraction and severity prediction using hybrid wavelet packet transform and linear support vector classifier. Plos one, 16(12), e0261040.
8 Multiphysics C (1998). Introduction to COMSOL Multiphysics®. COMSOL Multiphysics, Burlington, MA. Accessed February 9, 2018.
9 Shwartz-Ziv, R., & Armon, A. (2022). Tabular data: Deep learning is not all you need. Information Fusion, 81, 84-90.
10 Song, L., Cui, X., Han, X., Gao, Y., Liu, F., Yu, Y., & Yuan, Y. (2024). A Non-Metallic pipeline leak size recognition method based on CWT acoustic image transformation and CNN. Applied Acoustics, 225, 110180.
11 Sung, Y., Jeon, H. J., Kim, D., Kim, M. S., Choi, J., Jo, H. R., ... & Lim, H. G. (2024). Internal pipe corrosion assessment method in water distribution system using ultrasound and convolutional neural networks. npj Clean Water, 7(1), 63.
12 Xiao, R., Hu, Q., & Li, J. (2019). Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine. Measurement, 146, 479-489.
13 Xu, Z. D., Zhu, C., & Shao, L. W. (2021). Damage identification of pipeline based on ultrasonic guided wave and wavelet denoising. Journal of Pipeline Systems Engineering and Practice, 12(4), 04021051.
14 Zang, X., Xu, Z. D., Lu, H., Zhu, C., & Zhang, Z. (2023). Ultrasonic guided wave techniques and applications in pipeline defect detection: A review. International Journal of Pressure Vessels and Piping, 206, 105033.