Тема: Разработка системы для сбора, хранения и анализа данных электродуговой сварки промышленных труб

1. Hou W. Deep features based on a DCNN model for classifyingimbalanced weld flaw types. / W. Hou, Y. Wei, Y. Jin, C. Zhu. // Measurement. – 2018. – 09. – Vol. 131.
2. Imani S. Matrix Profile XIII: Time Series Snippets: A New Primitivefor Time Series Data Mining. / S. Imani, F. Madrid, W. Ding, S.E. Crouter, E.J. Keogh. // 2018 IEEE International Conference on Big Knowledge, ICBK 2018, Singapore, November 17-18, 2018. – IEEE Computer Society, 2018. – P. 382–389.
3. Keogh E.J., Lin J., Fu A.W. HOT SAX: Efficiently Finding the MostUnusual Time Series Subsequence. // Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 27-30 November 2005, Houston, Texas, USA. – IEEE Computer Society, 2005. – P. 226–233.
4. Kramer M.A. Nonlinear principal component analysis using autoassociative neural networks. // Aiche
Journal. – 1991. – Vol. 37. – P. 233–243.
5. Mirapeix J. Real-time arc-welding defect detection and classificationwith Principal Component Analysis and Artificial Neural Networks. / J. Mirapeix, P.B. Garcia-Allende, A. Cobo, O.M. Conde, J. López-Higuera. // NDT & E International. – 2007. – June. – Vol. 40. – P. 315–323.
6. Mueen A. Exact Discovery of Time Series Motifs. / A. Mueen, E.J. Keogh, Q. Zhu, S. Cash, M.B. Westover. // Proceedings of the SIAM International Conference on Data Mining, SDM 2009, April 30 - May 2, 2009, Sparks, Nevada, USA. – SIAM, 2009. – P. 473–484.
7. Shin S. Real-Time Detection of Weld Defects for AutomatedWelding Process Base on Deep Neural Network. / S. Shin, C. Jin, J. Yu, S. Rhee. // Metals. – 2020. – March. – Vol. 10. – P. 389.
8. Willmott C., Matsuura K. Advantages of the Mean Absolute Error(MAE) over the Root Mean Square Error (RMSE) in Assessing AverageModel Performance. // Climate Research. – 2005. – 12. – Vol. 30. – P. 79.
9. Yeh C.M. Matrix Profile I: All Pairs Similarity Joins for Time Series:A Unifying View That Includes Motifs, Discords and Shapelets. / C.M. Yeh,
Y. Zhu, L. Ulanova, N. Begum, Y. Ding, et al. // IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain. – IEEE Computer Society, 2016. – P. 1317–1322.
10. Zhao D. Welding quality evaluation of resistance spot weldingbased on a hybrid approach. / D. Zhao, M. Ivanov, Y. Wang, W. Du. // J. Intell. Manuf. – 2021. – Vol. 32. – No. 7.–P. 1819–1832.
11. Zhu Y. Matrix Profile II: Exploiting a Novel Algorithm and GPUsto Break the One Hundred Million Barrier for Time Series Motifs and Joins. / Y. Zhu, Z. Zimmerman, N.S. Senobari, C.M. Yeh, G.J. Funning, et al. // IEEE 16th International Conference on Data Mining, ICDM 2016, December 12-15, 2016, Barcelona, Spain. – IEEE Computer Society, 2016. – P. 739–748.
12. Библиотека Pandas. [Электронный ресурс] URL: https://pandas.pydata.org/, (дата обращения: 25.04.2022 г.).
13. Библиотека SQLAlchemy. [Электронный ресурс] URL: https://www.sqlalchemy.org/, (дата обращения: 27.04.2022 г.).
14. Библиотека gRPC. [Электронный ресурс] URL: https://grpc.io/, (дата обращения: 13.04.2022 г.).
15. ГОСТ 2601-84. Сварка металлов. Термины и определения основных понятий – М.: Изд-во стандартов, 1997. – 11 с.
16. ГОСТ 30242-97. Дефекты соединений при сварке металлов плавлением. Классификация, обозначение и определения – М.: Изд-во стандартов, 2001. – 11 с.
17. Документация для СУБД PostgreSQL. [Электронный ресурс] URL: https://postgrespro.ru/docs/postgresql/11/index, (дата обращения: 26.04.2022 г.).
18. Банников Е.А. Сварка. – АСТ, 2014. – P. 304.
19. Пакет Matplotlib. [Электронный ресурс] URL: https://matplotlib.org/, (дата обращения: 25.04.2022 г.).
20. Ультразвуковой контроль Научно-Производственного Центра «ЭХО+». [Электронный ресурс] URL: https://echoplus.ru/, (дата обращения: 25.03.2022 г.). 

Купить