Abstract
In order to improve the intelligent management and information scheduling ability of bridge long-term health monitoring, the real-time data monitoring and automatic collection design of bridge long-term health monitoring are carried out with big data analysis method. A classification method of bridge long-term health monitoring data based on fuzzy correlation feature detection and grid area clustering is proposed. The information fusion and fuzzy chromatography analysis method are used to realize the information fusion of the real-time data of bridge long-term health monitoring, and the adaptive feature extraction of related data is carried out. Excavate the positive correlation characteristic quantity of bridge long-term health monitoring real-time monitoring data flow, carry on the fuzzy clustering and information prediction of bridge long-term health monitoring data flow, and improve the accuracy of bridge long-term health monitoring real-time data monitoring. The simulation results show that the intelligent classification of bridge long-term health monitoring based on this method has high accuracy and low error rate, which improves the real-time performance of bridge monitoring.
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References
Liu, Y., Yang, H., Cai, S., Zhang, C.: Single image super-resolution reconstruction method based on improved convolutional neural network. J. Comput. Appl. 39(5), 1440–1447 (2019)
Xu, R., Zhang, J.G., Huang, K.Q.: Image super-resolution using two-channel convolutional neural networks. J. Image Graphic 21(5), 556–564 (2016)
Megha, G., Yashpal, L., Vivek, L.: Analytical relation & comparison of PSNR and SSIM on babbon image and human eye perception using Matlab. Int. J. Adv. Res. Eng. Appl. Sci. 4(5), 108–119 (2015)
Li, Y.F., Fu, R.D., Jin, W., et al.: Image super-resolution using multi-channel convolution. J. Image Graphics 22(12), 1690–1700 (2017)
Dong, X.W., Yao, S.M., Wang, Y.W., et al.: Virtual sample image set based multi manifold discriminant learning algorithm. Appl. Res. Comput. 35(6), 1871–1878 (2018)
Zhu, Y., Zhu, X., Wang, J.: Time series motif discovery algorithm based on subsequence full join and maximum clique. J. Comput. Appl. 39(2), 414–420 (2019)
Wang, Z., Huang, M., et al.: Integrated algorithm based on density peaks and density-based clustering. J. Comput. Appl. 39(2), 398–402 (2019)
Gui, J., Sun, Z.N., Jia, W., et al.: Discriminant sparse neighborhood preserving embedding for face recognition. Pattern Recogn. 45(8), 2884–2893 (2012)
Dong, B., Chen, D., Jing, W.: Salient region detection method based on symmetric region filtering. Comput. Eng. 45(5), 216–221 (2019)
Zhou, S.B., Xu, W.X.: A novel clustering algorithm based on relative density and decision graph. Control Decis. 33(11), 1921–1930 (2018)
Tu, B., Chuai, R., Xu, H.: Outlier detection based on K-mean distance outlier factor for gait signal. Inf. Control 48(1), 16–21 (2019)
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Two heights “project of xichang university (LGLZ201824): settlement characteristics analysis and deformation prediction research of xigeda high-rise building with soil layer in xichang.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Hong, X., Yu, M. (2019). Research on the Large Data Intelligent Classification Method for Long-Term Health Monitoring of Bridge. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-030-36405-2_1
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DOI: https://doi.org/10.1007/978-3-030-36405-2_1
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