Outlier Detection and Correction for Time Series Data of Tunnel Boring Machine
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6480-2_21
Reference11 articles.
1. Rehman, H., Ali, W., Naji, A.M., et al.: Review of rock-mass rating and tunneling quality index systems for tunnel design: development, refinement, application and limitation. Appl. Sci. 8(8), 1250 (2018)
2. Mazaira, A., Konicek, P.: Intense rockburst impacts in deep underground construction and their prevention. Can. Geotech J. 52(10), 1426–1439 (2015)
3. Shi, M., Zhang, L., Sun, W., et al.: A fuzzy c-means algorithm guided by attribute correlations and its application in the big data analysis of tunnel boring machine. Knowl.-Based Syst. 182, 104859 (2019)
4. Pang, Y., Shi, M., Zhang, L., et al.: A multivariate time series segmentation algorithm for analyzing the operating statuses of tunnel boring machines. Knowl.-Based Syst. 242, 108362 (2022)
5. Qin, C., Shi, G., Tao, J., et al.: An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine. Mech. Syst. Signal Process. 175, 109148 (2022)
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