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1,527 results on '"Mao, Jing"'

49. Estimates for eigenvalues of the Neumann and Steklov problems

Author

Du Feng, Mao Jing, Wang Qiaoling, Xia Changyu, and Zhao Yan

Subjects
neumann eigenvalue problem, steklov eigenvalue problem, biharmonic operator, eigenvalues, fourier transform, 35p15, 53c40, 58c40, Analysis, QA299.6-433
Abstract

We prove Li-Yau-Kröger-type bounds for Neumann-type eigenvalues of the biharmonic operator on bounded domains in a Euclidean space. We also prove sharp estimates for lower order eigenvalues of a biharmonic Steklov problem and of the Laplacian, which directly implies two sharp Reilly-type inequalities for the corresponding first nonzero eigenvalue.

Published
2023
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50. Research on Intelligent Inspection Method of Prestressed Bridge Engineering Based on Machine Learning

Author

Liang Peng, Zhang Liming, Zhuo Xiaoli, and Mao Jing

Subjects
finite element analysis, svm, resnet neural network, lstm, effective prestressing, intelligent detection, bridge engineering, 68m11, Mathematics, QA1-939
Abstract

As an important engineering structure for national and regional transportation infrastructure construction, bridges have important economic, social, and strategic significance. The research centers on the intelligent detection of prestressed bridge engineering, on the one hand, combined with the finite element analysis of the prestressed beam modal in the obtained area, based on LS-SVM to construct the intelligent detection method of effective prestressing of bridge engineering. On the other hand, the ResNet neural network is selected for feature extraction of bridge characteristic parameters, and LSTM is combined to complete the fusion of bridge spatiotemporal features to construct an intelligent detection model of bridge technical condition based on the ResNet-LSTM joint network. The detection performance of the two methods is evaluated through simulation and experimental tests on the dataset. The analysis shows that the maximum error for effective prestress detection of the LS-SVM model is 15.584%, which is 6.121% lower than that of the BP neural network model. The technical condition detection error of less than 0.1 is basically greater than 90% in both discontinuous and continuous time-span detection. It has been verified that the LS-SVM model has a better identification effect on effective prestressing, while the ResNet-LSTM model has a high accuracy prediction effect on the technical condition of the bridge.

Published
2024
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