Deep Learning-Based Diagnosing Structural Behavior in Dam Safety Monitoring System.

ABSTRACT: Collecting a myriad of prototype data through various types of monitoring sensors plays a virtual important role in many aspects of dam safety such as real-time grasp of safety state, exposure of hidden dangers, and inspection design and construction. However, the current methods of prediction are weak in the long-term sequence of nodes with missing and abnormal error value. Moreover, the limitation caused by the apparatus, environmental factors, and network transmission can lead to the deviation and inconsistency of diagnosis and evaluation of local region. In this paper, we consider the correlation of data on nodes in the entire monitoring network. To avoid the deviation caused by noise and missing value in the single-node data sequence, we calculate the correlation between the multiple

SUBMITTER: Wang L 

PROVIDER: S-EPMC7915524 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature